Last updated on 2020-02-19 10:48:47 CET.
| Flavor | Version | Tinstall | Tcheck | Ttotal | Status | Flags |
|---|---|---|---|---|---|---|
| r-devel-linux-x86_64-debian-clang | 0.9.6 | 42.63 | 168.52 | 211.15 | WARN | |
| r-devel-linux-x86_64-debian-gcc | 0.9.6 | 23.09 | 122.46 | 145.55 | WARN | |
| r-devel-linux-x86_64-fedora-clang | 0.9.6 | 258.50 | WARN | |||
| r-devel-linux-x86_64-fedora-gcc | 0.9.6 | 237.69 | WARN | |||
| r-devel-windows-ix86+x86_64 | 0.9.6 | 71.00 | 219.00 | 290.00 | NOTE | |
| r-devel-windows-ix86+x86_64-gcc8 | 0.9.6 | 114.00 | 290.00 | 404.00 | NOTE | |
| r-patched-linux-x86_64 | 0.9.6 | 29.43 | 195.74 | 225.17 | OK | |
| r-patched-solaris-x86 | 0.9.6 | 344.30 | NOTE | |||
| r-release-linux-x86_64 | 0.9.6 | 27.87 | 195.85 | 223.72 | OK | |
| r-release-windows-ix86+x86_64 | 0.9.6 | 69.00 | 208.00 | 277.00 | OK | |
| r-release-osx-x86_64 | 0.9.6 | NOTE | ||||
| r-oldrel-windows-ix86+x86_64 | 0.9.6 | 61.00 | 174.00 | 235.00 | OK | |
| r-oldrel-osx-x86_64 | 0.9.6 | NOTE |
Version: 0.9.6
Check: package vignettes
Result: NOTE
Package vignette with placeholder title 'Vignette Title':
'Optimize_parameter_vector.Rmd'
Flavors: r-devel-linux-x86_64-debian-clang, r-devel-linux-x86_64-debian-gcc, r-devel-windows-ix86+x86_64, r-devel-windows-ix86+x86_64-gcc8
Version: 0.9.6
Check: re-building of vignette outputs
Result: WARN
Error(s) in re-building vignettes:
...
--- re-building 'Intro_to_BSL.Rmd' using rmarkdown
----------- FAILURE REPORT --------------
--- failure: the condition has length > 1 ---
--- srcref ---
:
--- package (from environment) ---
BreedingSchemeLanguage
--- call from context ---
kin.blup4.4(kbDat, geno = "phenoGID", pheno = "pValue", fixed = c("loc",
"year"), K = K, reduce = mt1ObsPerGID, R = kbDat$error)
--- call from argument ---
if (class(K) == "dist") {
K <- as.matrix(K)
}
--- R stacktrace ---
where 1: kin.blup4.4(kbDat, geno = "phenoGID", pheno = "pValue", fixed = c("loc",
"year"), K = K, reduce = mt1ObsPerGID, R = kbDat$error)
where 2: FUN(X[[i]], ...)
where 3: lapply(sims, predictValue.func, popID = popID, trainingPopID = trainingPopID,
locations = locations, years = years, sharingInfo = sharingInfo)
where 4: eval(substitute(expr), data, enclos = parent.frame())
where 5: eval(substitute(expr), data, enclos = parent.frame())
where 6: with.default(sEnv, {
if (exists("totalCost")) {
costs$popID <- ifelse(is.null(popID), max(budgetRec$popID),
popID)
totalCost <- totalCost + costs$predCost
}
if (!onlyCost) {
if (nCore > 1) {
snowfall::sfInit(parallel = T, cpus = nCore)
sims <- snowfall::sfLapply(sims, predictValue.func,
popID = popID, trainingPopID = trainingPopID,
locations = locations, years = years, sharingInfo = sharingInfo)
snowfall::sfStop()
}
else {
sims <- lapply(sims, predictValue.func, popID = popID,
trainingPopID = trainingPopID, locations = locations,
years = years, sharingInfo = sharingInfo)
}
}
})
where 7: with(sEnv, {
if (exists("totalCost")) {
costs$popID <- ifelse(is.null(popID), max(budgetRec$popID),
popID)
totalCost <- totalCost + costs$predCost
}
if (!onlyCost) {
if (nCore > 1) {
snowfall::sfInit(parallel = T, cpus = nCore)
sims <- snowfall::sfLapply(sims, predictValue.func,
popID = popID, trainingPopID = trainingPopID,
locations = locations, years = years, sharingInfo = sharingInfo)
snowfall::sfStop()
}
else {
sims <- lapply(sims, predictValue.func, popID = popID,
trainingPopID = trainingPopID, locations = locations,
years = years, sharingInfo = sharingInfo)
}
}
})
where 8: predictValue(sharingInfo = "pedigree")
where 9: eval(expr, envir, enclos)
where 10: eval(expr, envir, enclos)
where 11: withVisible(eval(expr, envir, enclos))
where 12: withCallingHandlers(withVisible(eval(expr, envir, enclos)), warning = wHandler,
error = eHandler, message = mHandler)
where 13: handle(ev <- withCallingHandlers(withVisible(eval(expr, envir,
enclos)), warning = wHandler, error = eHandler, message = mHandler))
where 14: timing_fn(handle(ev <- withCallingHandlers(withVisible(eval(expr,
envir, enclos)), warning = wHandler, error = eHandler, message = mHandler)))
where 15: evaluate_call(expr, parsed$src[[i]], envir = envir, enclos = enclos,
debug = debug, last = i == length(out), use_try = stop_on_error !=
2L, keep_warning = keep_warning, keep_message = keep_message,
output_handler = output_handler, include_timing = include_timing)
where 16: evaluate::evaluate(...)
where 17: evaluate(code, envir = env, new_device = FALSE, keep_warning = !isFALSE(options$warning),
keep_message = !isFALSE(options$message), stop_on_error = if (options$error &&
options$include) 0L else 2L, output_handler = knit_handlers(options$render,
options))
where 18: in_dir(input_dir(), evaluate(code, envir = env, new_device = FALSE,
keep_warning = !isFALSE(options$warning), keep_message = !isFALSE(options$message),
stop_on_error = if (options$error && options$include) 0L else 2L,
output_handler = knit_handlers(options$render, options)))
where 19: block_exec(params)
where 20: call_block(x)
where 21: process_group.block(group)
where 22: process_group(group)
where 23: withCallingHandlers(if (tangle) process_tangle(group) else process_group(group),
error = function(e) {
setwd(wd)
cat(res, sep = "\n", file = output %n% "")
message("Quitting from lines ", paste(current_lines(i),
collapse = "-"), " (", knit_concord$get("infile"),
") ")
})
where 24: process_file(text, output)
where 25: knitr::knit(knit_input, knit_output, envir = envir, quiet = quiet)
where 26: rmarkdown::render(file, encoding = encoding, quiet = quiet, envir = globalenv(),
output_dir = getwd(), ...)
where 27: vweave_rmarkdown(...)
where 28: engine$weave(file, quiet = quiet, encoding = enc)
where 29: doTryCatch(return(expr), name, parentenv, handler)
where 30: tryCatchOne(expr, names, parentenv, handlers[[1L]])
where 31: tryCatchList(expr, classes, parentenv, handlers)
where 32: tryCatch({
engine$weave(file, quiet = quiet, encoding = enc)
setwd(startdir)
output <- find_vignette_product(name, by = "weave", engine = engine)
if (!have.makefile && vignette_is_tex(output)) {
texi2pdf(file = output, clean = FALSE, quiet = quiet)
output <- find_vignette_product(name, by = "texi2pdf",
engine = engine)
}
}, error = function(e) {
OK <<- FALSE
message(gettextf("Error: processing vignette '%s' failed with diagnostics:\n%s",
file, conditionMessage(e)))
})
where 33: tools:::.buildOneVignette("Intro_to_BSL.Rmd", "/home/hornik/tmp/R.check/r-devel-clang/Work/PKGS/BreedingSchemeLanguage.Rcheck/vign_test/BreedingSchemeLanguage",
TRUE, FALSE, "Intro_to_BSL", "UTF-8", "/tmp/RtmpdZlm9p/file5e70645f0644.rds")
--- value of length: 2 type: logical ---
[1] FALSE FALSE
--- function from context ---
function (data, geno, pheno, GAUSS = FALSE, K = NULL, fixed = NULL,
covariate = NULL, PEV = FALSE, n.core = 1, theta.seq = NULL,
reduce = FALSE, R = NULL)
{
make.full <- function(X) {
svd.X <- svd(X)
r <- max(which(svd.X$d > 1e-08))
return(as.matrix(svd.X$u[, 1:r]))
}
names <- colnames(data)
ypos <- match(pheno, names)
if (is.na(ypos)) {
stop("Phenotype name does not appear in data.")
}
else {
y <- data[, ypos]
}
if (!is.null(R) & (length(R) != length(y))) {
stop("Length of R does not equal length of y")
}
not.miss <- which(!is.na(y))
if (length(not.miss) < length(y)) {
data <- data[not.miss, ]
y <- y[not.miss]
if (!is.null(R)) {
R <- R[not.miss]
}
}
n <- length(y)
X <- matrix(1, n, 1)
if (!is.null(fixed)) {
p <- length(fixed)
for (i in 1:p) {
xpos <- match(fixed[i], names)
xx <- factor(data[, xpos])
if (length(unique(xx)) > 1) {
X <- cbind(X, stats::model.matrix(~x - 1, data.frame(x = xx)))
}
}
}
if (!is.null(covariate)) {
p <- length(covariate)
for (i in 1:p) {
xpos <- match(covariate[i], names)
X <- cbind(X, data[, xpos])
}
}
gid.pos <- match(geno, names)
if (is.na(gid.pos)) {
stop("Genotype name does not appear in data.")
}
not.miss.gid <- as.character(unique(data[, gid.pos]))
if (is.null(K)) {
if (reduce) {
print("reduce=TRUE is not valid for independent genotypes. Proceeding without reduction.")
}
gid <- not.miss.gid
v <- length(gid)
Z <- matrix(0, n, v)
colnames(Z) <- gid
Z[cbind(1:n, match(data[, gid.pos], gid))] <- 1
X2 <- make.full(X)
ans <- rrBLUP::mixed.solve(y = y, X = X2, Z = Z, SE = PEV)
resid <- y - X2 %*% ans$beta - Z %*% ans$u
if (PEV) {
return(list(Vg = ans$Vu, Ve = ans$Ve, g = ans$u,
PEV = ans$u.SE^2, resid = resid))
}
else {
return(list(Vg = ans$Vu, Ve = ans$Ve, g = ans$u,
resid = resid))
}
}
else {
if (class(K) == "dist") {
K <- as.matrix(K)
}
gid <- rownames(K)
ix.pheno <- match(not.miss.gid, gid)
miss.pheno.gid <- which(is.na(ix.pheno))
if (length(miss.pheno.gid) > 0) {
stop(paste("The following lines have phenotypes but no genotypes:",
paste(not.miss.gid[miss.pheno.gid], collapse = " ")))
}
miss.gid <- setdiff(gid, not.miss.gid)
ix <- c(ix.pheno, match(miss.gid, gid))
K <- K[ix, ix]
v <- length(not.miss.gid)
Z <- matrix(0, n, v)
Z[cbind(1:n, match(data[, gid.pos], not.miss.gid))] <- 1
if (!is.null(R)) {
sqrt.R <- sqrt(R)
X2 <- X/sqrt.R
y2 <- y/sqrt.R
Z2 <- Z/sqrt.R
}
else {
X2 <- X
y2 <- y
Z2 <- Z
}
if ((n > v) & (reduce)) {
w <- sqrt(diag(crossprod(Z2)))
X2 <- make.full(crossprod(Z2, X2)/w)
y2 <- crossprod(Z2, y2)/w
Z2 <- cbind(diag(w), matrix(0, v, nrow(K) - v))
reduced <- TRUE
}
else {
X2 <- make.full(X2)
Z2 <- cbind(Z2, matrix(0, n, nrow(K) - v))
reduced <- FALSE
}
rm(X, Z, y)
if (!GAUSS) {
ans <- rrBLUP::mixed.solve(y = y2, X = X2, Z = Z2,
K = K, SE = PEV)
ix <- match(gid, rownames(ans$u))
if (reduced) {
resid <- NULL
}
else {
resid <- y2 - X2 %*% ans$beta - Z2 %*% ans$u
}
if (PEV) {
return(list(Vg = ans$Vu, Ve = ans$Ve, g = ans$u[ix],
PEV = ans$u.SE[ix]^2, resid = resid))
}
else {
return(list(Vg = ans$Vu, Ve = ans$Ve, g = ans$u[ix],
resid = resid))
}
}
else {
if (is.null(theta.seq)) {
theta <- setdiff(seq(0, max(K), length.out = 11),
0)
}
else {
theta <- theta.seq
}
n.profile <- length(theta)
ms.fun <- function(theta) {
soln <- list()
n.t <- length(theta)
for (i in 1:n.t) {
soln[[i]] <- rrBLUP::mixed.solve(y = y2, X = X2,
Z = Z2, K = exp(-(K/theta[i])^2), SE = PEV)
}
return(soln)
}
if (n.core > 1) {
it <- split(theta, factor(cut(theta, n.core,
labels = FALSE)))
soln <- unlist(snowfall::sfLapply(it, ms.fun,
mc.cores = n.core), recursive = FALSE)
}
else {
soln <- ms.fun(theta)
}
LL <- rep(0, n.profile)
for (i in 1:n.profile) {
LL[i] <- soln[[i]]$LL
}
ans <- soln[[which.max(LL)]]
profile <- cbind(theta, LL)
ix <- match(gid, rownames(ans$u))
if (reduced) {
resid <- NULL
}
else {
resid <- y2 - X2 %*% ans$beta - Z2 %*% ans$u
}
if (PEV) {
return(list(Vg = ans$Vu, Ve = ans$Ve, profile = profile,
g = ans$u[ix], PEV = ans$u.SE[ix]^2, resid = resid))
}
else {
return(list(Vg = ans$Vu, Ve = ans$Ve, profile = profile,
g = ans$u[ix], resid = resid))
}
}
}
}
<bytecode: 0x10730ef0>
<environment: 0x14d82d10>
--- function search by body ---
----------- END OF FAILURE REPORT --------------
Quitting from lines 85-89 (Intro_to_BSL.Rmd)
Error: processing vignette 'Intro_to_BSL.Rmd' failed with diagnostics:
the condition has length > 1
--- failed re-building 'Intro_to_BSL.Rmd'
--- re-building 'Load_existing_data.Rmd' using rmarkdown
Loading required package: snowfall
Loading required package: snow
Loading required package: Rcpp
----------- FAILURE REPORT --------------
--- failure: the condition has length > 1 ---
--- srcref ---
:
--- package (from environment) ---
BreedingSchemeLanguage
--- call from context ---
kin.blup4.4(kbDat, geno = "phenoGID", pheno = "pValue", fixed = c("loc",
"year"), K = K, reduce = mt1ObsPerGID, R = kbDat$error)
--- call from argument ---
if (class(K) == "dist") {
K <- as.matrix(K)
}
--- R stacktrace ---
where 1: kin.blup4.4(kbDat, geno = "phenoGID", pheno = "pValue", fixed = c("loc",
"year"), K = K, reduce = mt1ObsPerGID, R = kbDat$error)
where 2: FUN(X[[i]], ...)
where 3: lapply(sims, predictValue.func, popID = popID, trainingPopID = trainingPopID,
locations = locations, years = years, sharingInfo = sharingInfo)
where 4: eval(substitute(expr), data, enclos = parent.frame())
where 5: eval(substitute(expr), data, enclos = parent.frame())
where 6: with.default(sEnv, {
if (exists("totalCost")) {
costs$popID <- ifelse(is.null(popID), max(budgetRec$popID),
popID)
totalCost <- totalCost + costs$predCost
}
if (!onlyCost) {
if (nCore > 1) {
snowfall::sfInit(parallel = T, cpus = nCore)
sims <- snowfall::sfLapply(sims, predictValue.func,
popID = popID, trainingPopID = trainingPopID,
locations = locations, years = years, sharingInfo = sharingInfo)
snowfall::sfStop()
}
else {
sims <- lapply(sims, predictValue.func, popID = popID,
trainingPopID = trainingPopID, locations = locations,
years = years, sharingInfo = sharingInfo)
}
}
})
where 7: with(sEnv, {
if (exists("totalCost")) {
costs$popID <- ifelse(is.null(popID), max(budgetRec$popID),
popID)
totalCost <- totalCost + costs$predCost
}
if (!onlyCost) {
if (nCore > 1) {
snowfall::sfInit(parallel = T, cpus = nCore)
sims <- snowfall::sfLapply(sims, predictValue.func,
popID = popID, trainingPopID = trainingPopID,
locations = locations, years = years, sharingInfo = sharingInfo)
snowfall::sfStop()
}
else {
sims <- lapply(sims, predictValue.func, popID = popID,
trainingPopID = trainingPopID, locations = locations,
years = years, sharingInfo = sharingInfo)
}
}
})
where 8: predictValue()
where 9: eval(expr, envir, enclos)
where 10: eval(expr, envir, enclos)
where 11: withVisible(eval(expr, envir, enclos))
where 12: withCallingHandlers(withVisible(eval(expr, envir, enclos)), warning = wHandler,
error = eHandler, message = mHandler)
where 13: handle(ev <- withCallingHandlers(withVisible(eval(expr, envir,
enclos)), warning = wHandler, error = eHandler, message = mHandler))
where 14: timing_fn(handle(ev <- withCallingHandlers(withVisible(eval(expr,
envir, enclos)), warning = wHandler, error = eHandler, message = mHandler)))
where 15: evaluate_call(expr, parsed$src[[i]], envir = envir, enclos = enclos,
debug = debug, last = i == length(out), use_try = stop_on_error !=
2L, keep_warning = keep_warning, keep_message = keep_message,
output_handler = output_handler, include_timing = include_timing)
where 16: evaluate::evaluate(...)
where 17: evaluate(code, envir = env, new_device = FALSE, keep_warning = !isFALSE(options$warning),
keep_message = !isFALSE(options$message), stop_on_error = if (options$error &&
options$include) 0L else 2L, output_handler = knit_handlers(options$render,
options))
where 18: in_dir(input_dir(), evaluate(code, envir = env, new_device = FALSE,
keep_warning = !isFALSE(options$warning), keep_message = !isFALSE(options$message),
stop_on_error = if (options$error && options$include) 0L else 2L,
output_handler = knit_handlers(options$render, options)))
where 19: block_exec(params)
where 20: call_block(x)
where 21: process_group.block(group)
where 22: process_group(group)
where 23: withCallingHandlers(if (tangle) process_tangle(group) else process_group(group),
error = function(e) {
setwd(wd)
cat(res, sep = "\n", file = output %n% "")
message("Quitting from lines ", paste(current_lines(i),
collapse = "-"), " (", knit_concord$get("infile"),
") ")
})
where 24: process_file(text, output)
where 25: knitr::knit(knit_input, knit_output, envir = envir, quiet = quiet)
where 26: rmarkdown::render(file, encoding = encoding, quiet = quiet, envir = globalenv(),
output_dir = getwd(), ...)
where 27: vweave_rmarkdown(...)
where 28: engine$weave(file, quiet = quiet, encoding = enc)
where 29: doTryCatch(return(expr), name, parentenv, handler)
where 30: tryCatchOne(expr, names, parentenv, handlers[[1L]])
where 31: tryCatchList(expr, classes, parentenv, handlers)
where 32: tryCatch({
engine$weave(file, quiet = quiet, encoding = enc)
setwd(startdir)
output <- find_vignette_product(name, by = "weave", engine = engine)
if (!have.makefile && vignette_is_tex(output)) {
texi2pdf(file = output, clean = FALSE, quiet = quiet)
output <- find_vignette_product(name, by = "texi2pdf",
engine = engine)
}
}, error = function(e) {
OK <<- FALSE
message(gettextf("Error: processing vignette '%s' failed with diagnostics:\n%s",
file, conditionMessage(e)))
})
where 33: tools:::.buildOneVignette("Load_existing_data.Rmd", "/home/hornik/tmp/R.check/r-devel-clang/Work/PKGS/BreedingSchemeLanguage.Rcheck/vign_test/BreedingSchemeLanguage",
TRUE, FALSE, "Load_existing_data", "UTF-8", "/tmp/RtmpdZlm9p/file5e707ff2f988.rds")
--- value of length: 2 type: logical ---
[1] FALSE FALSE
--- function from context ---
function (data, geno, pheno, GAUSS = FALSE, K = NULL, fixed = NULL,
covariate = NULL, PEV = FALSE, n.core = 1, theta.seq = NULL,
reduce = FALSE, R = NULL)
{
make.full <- function(X) {
svd.X <- svd(X)
r <- max(which(svd.X$d > 1e-08))
return(as.matrix(svd.X$u[, 1:r]))
}
names <- colnames(data)
ypos <- match(pheno, names)
if (is.na(ypos)) {
stop("Phenotype name does not appear in data.")
}
else {
y <- data[, ypos]
}
if (!is.null(R) & (length(R) != length(y))) {
stop("Length of R does not equal length of y")
}
not.miss <- which(!is.na(y))
if (length(not.miss) < length(y)) {
data <- data[not.miss, ]
y <- y[not.miss]
if (!is.null(R)) {
R <- R[not.miss]
}
}
n <- length(y)
X <- matrix(1, n, 1)
if (!is.null(fixed)) {
p <- length(fixed)
for (i in 1:p) {
xpos <- match(fixed[i], names)
xx <- factor(data[, xpos])
if (length(unique(xx)) > 1) {
X <- cbind(X, stats::model.matrix(~x - 1, data.frame(x = xx)))
}
}
}
if (!is.null(covariate)) {
p <- length(covariate)
for (i in 1:p) {
xpos <- match(covariate[i], names)
X <- cbind(X, data[, xpos])
}
}
gid.pos <- match(geno, names)
if (is.na(gid.pos)) {
stop("Genotype name does not appear in data.")
}
not.miss.gid <- as.character(unique(data[, gid.pos]))
if (is.null(K)) {
if (reduce) {
print("reduce=TRUE is not valid for independent genotypes. Proceeding without reduction.")
}
gid <- not.miss.gid
v <- length(gid)
Z <- matrix(0, n, v)
colnames(Z) <- gid
Z[cbind(1:n, match(data[, gid.pos], gid))] <- 1
X2 <- make.full(X)
ans <- rrBLUP::mixed.solve(y = y, X = X2, Z = Z, SE = PEV)
resid <- y - X2 %*% ans$beta - Z %*% ans$u
if (PEV) {
return(list(Vg = ans$Vu, Ve = ans$Ve, g = ans$u,
PEV = ans$u.SE^2, resid = resid))
}
else {
return(list(Vg = ans$Vu, Ve = ans$Ve, g = ans$u,
resid = resid))
}
}
else {
if (class(K) == "dist") {
K <- as.matrix(K)
}
gid <- rownames(K)
ix.pheno <- match(not.miss.gid, gid)
miss.pheno.gid <- which(is.na(ix.pheno))
if (length(miss.pheno.gid) > 0) {
stop(paste("The following lines have phenotypes but no genotypes:",
paste(not.miss.gid[miss.pheno.gid], collapse = " ")))
}
miss.gid <- setdiff(gid, not.miss.gid)
ix <- c(ix.pheno, match(miss.gid, gid))
K <- K[ix, ix]
v <- length(not.miss.gid)
Z <- matrix(0, n, v)
Z[cbind(1:n, match(data[, gid.pos], not.miss.gid))] <- 1
if (!is.null(R)) {
sqrt.R <- sqrt(R)
X2 <- X/sqrt.R
y2 <- y/sqrt.R
Z2 <- Z/sqrt.R
}
else {
X2 <- X
y2 <- y
Z2 <- Z
}
if ((n > v) & (reduce)) {
w <- sqrt(diag(crossprod(Z2)))
X2 <- make.full(crossprod(Z2, X2)/w)
y2 <- crossprod(Z2, y2)/w
Z2 <- cbind(diag(w), matrix(0, v, nrow(K) - v))
reduced <- TRUE
}
else {
X2 <- make.full(X2)
Z2 <- cbind(Z2, matrix(0, n, nrow(K) - v))
reduced <- FALSE
}
rm(X, Z, y)
if (!GAUSS) {
ans <- rrBLUP::mixed.solve(y = y2, X = X2, Z = Z2,
K = K, SE = PEV)
ix <- match(gid, rownames(ans$u))
if (reduced) {
resid <- NULL
}
else {
resid <- y2 - X2 %*% ans$beta - Z2 %*% ans$u
}
if (PEV) {
return(list(Vg = ans$Vu, Ve = ans$Ve, g = ans$u[ix],
PEV = ans$u.SE[ix]^2, resid = resid))
}
else {
return(list(Vg = ans$Vu, Ve = ans$Ve, g = ans$u[ix],
resid = resid))
}
}
else {
if (is.null(theta.seq)) {
theta <- setdiff(seq(0, max(K), length.out = 11),
0)
}
else {
theta <- theta.seq
}
n.profile <- length(theta)
ms.fun <- function(theta) {
soln <- list()
n.t <- length(theta)
for (i in 1:n.t) {
soln[[i]] <- rrBLUP::mixed.solve(y = y2, X = X2,
Z = Z2, K = exp(-(K/theta[i])^2), SE = PEV)
}
return(soln)
}
if (n.core > 1) {
it <- split(theta, factor(cut(theta, n.core,
labels = FALSE)))
soln <- unlist(snowfall::sfLapply(it, ms.fun,
mc.cores = n.core), recursive = FALSE)
}
else {
soln <- ms.fun(theta)
}
LL <- rep(0, n.profile)
for (i in 1:n.profile) {
LL[i] <- soln[[i]]$LL
}
ans <- soln[[which.max(LL)]]
profile <- cbind(theta, LL)
ix <- match(gid, rownames(ans$u))
if (reduced) {
resid <- NULL
}
else {
resid <- y2 - X2 %*% ans$beta - Z2 %*% ans$u
}
if (PEV) {
return(list(Vg = ans$Vu, Ve = ans$Ve, profile = profile,
g = ans$u[ix], PEV = ans$u.SE[ix]^2, resid = resid))
}
else {
return(list(Vg = ans$Vu, Ve = ans$Ve, profile = profile,
g = ans$u[ix], resid = resid))
}
}
}
}
<bytecode: 0x5f7def8>
<environment: 0x5f54170>
--- function search by body ---
----------- END OF FAILURE REPORT --------------
Quitting from lines 65-70 (Load_existing_data.Rmd)
Error: processing vignette 'Load_existing_data.Rmd' failed with diagnostics:
the condition has length > 1
--- failed re-building 'Load_existing_data.Rmd'
--- re-building 'Optimize_parameter_vector.Rmd' using rmarkdown
Loading required package: snowfall
Loading required package: snow
Loading required package: Rcpp
----------- FAILURE REPORT --------------
--- failure: the condition has length > 1 ---
--- srcref ---
:
--- package (from environment) ---
BreedingSchemeLanguage
--- call from context ---
kin.blup4.4(kbDat, geno = "phenoGID", pheno = "pValue", fixed = c("loc",
"year"), K = K, reduce = mt1ObsPerGID, R = kbDat$error)
--- call from argument ---
if (class(K) == "dist") {
K <- as.matrix(K)
}
--- R stacktrace ---
where 1: kin.blup4.4(kbDat, geno = "phenoGID", pheno = "pValue", fixed = c("loc",
"year"), K = K, reduce = mt1ObsPerGID, R = kbDat$error)
where 2: FUN(X[[i]], ...)
where 3: lapply(sims, predictValue.func, popID = popID, trainingPopID = trainingPopID,
locations = locations, years = years, sharingInfo = sharingInfo)
where 4: eval(substitute(expr), data, enclos = parent.frame())
where 5: eval(substitute(expr), data, enclos = parent.frame())
where 6: with.default(sEnv, {
if (exists("totalCost")) {
costs$popID <- ifelse(is.null(popID), max(budgetRec$popID),
popID)
totalCost <- totalCost + costs$predCost
}
if (!onlyCost) {
if (nCore > 1) {
snowfall::sfInit(parallel = T, cpus = nCore)
sims <- snowfall::sfLapply(sims, predictValue.func,
popID = popID, trainingPopID = trainingPopID,
locations = locations, years = years, sharingInfo = sharingInfo)
snowfall::sfStop()
}
else {
sims <- lapply(sims, predictValue.func, popID = popID,
trainingPopID = trainingPopID, locations = locations,
years = years, sharingInfo = sharingInfo)
}
}
})
where 7: with(sEnv, {
if (exists("totalCost")) {
costs$popID <- ifelse(is.null(popID), max(budgetRec$popID),
popID)
totalCost <- totalCost + costs$predCost
}
if (!onlyCost) {
if (nCore > 1) {
snowfall::sfInit(parallel = T, cpus = nCore)
sims <- snowfall::sfLapply(sims, predictValue.func,
popID = popID, trainingPopID = trainingPopID,
locations = locations, years = years, sharingInfo = sharingInfo)
snowfall::sfStop()
}
else {
sims <- lapply(sims, predictValue.func, popID = popID,
trainingPopID = trainingPopID, locations = locations,
years = years, sharingInfo = sharingInfo)
}
}
})
where 8: predictValue()
where 9: eval(ei, envir)
where 10: eval(ei, envir)
where 11: withVisible(eval(ei, envir))
where 12: source(schemeFileName, local = TRUE)
where 13: testParameterOptimality(schemeFileName = schemeScriptPath, parmList = parmList,
objectiveFunc = objFunc, budget = allowableBudget)
where 14: eval(expr, envir, enclos)
where 15: eval(expr, envir, enclos)
where 16: withVisible(eval(expr, envir, enclos))
where 17: withCallingHandlers(withVisible(eval(expr, envir, enclos)), warning = wHandler,
error = eHandler, message = mHandler)
where 18: handle(ev <- withCallingHandlers(withVisible(eval(expr, envir,
enclos)), warning = wHandler, error = eHandler, message = mHandler))
where 19: timing_fn(handle(ev <- withCallingHandlers(withVisible(eval(expr,
envir, enclos)), warning = wHandler, error = eHandler, message = mHandler)))
where 20: evaluate_call(expr, parsed$src[[i]], envir = envir, enclos = enclos,
debug = debug, last = i == length(out), use_try = stop_on_error !=
2L, keep_warning = keep_warning, keep_message = keep_message,
output_handler = output_handler, include_timing = include_timing)
where 21: evaluate::evaluate(...)
where 22: evaluate(code, envir = env, new_device = FALSE, keep_warning = !isFALSE(options$warning),
keep_message = !isFALSE(options$message), stop_on_error = if (options$error &&
options$include) 0L else 2L, output_handler = knit_handlers(options$render,
options))
where 23: in_dir(input_dir(), evaluate(code, envir = env, new_device = FALSE,
keep_warning = !isFALSE(options$warning), keep_message = !isFALSE(options$message),
stop_on_error = if (options$error && options$include) 0L else 2L,
output_handler = knit_handlers(options$render, options)))
where 24: block_exec(params)
where 25: call_block(x)
where 26: process_group.block(group)
where 27: process_group(group)
where 28: withCallingHandlers(if (tangle) process_tangle(group) else process_group(group),
error = function(e) {
setwd(wd)
cat(res, sep = "\n", file = output %n% "")
message("Quitting from lines ", paste(current_lines(i),
collapse = "-"), " (", knit_concord$get("infile"),
") ")
})
where 29: process_file(text, output)
where 30: knitr::knit(knit_input, knit_output, envir = envir, quiet = quiet)
where 31: rmarkdown::render(file, encoding = encoding, quiet = quiet, envir = globalenv(),
output_dir = getwd(), ...)
where 32: vweave_rmarkdown(...)
where 33: engine$weave(file, quiet = quiet, encoding = enc)
where 34: doTryCatch(return(expr), name, parentenv, handler)
where 35: tryCatchOne(expr, names, parentenv, handlers[[1L]])
where 36: tryCatchList(expr, classes, parentenv, handlers)
where 37: tryCatch({
engine$weave(file, quiet = quiet, encoding = enc)
setwd(startdir)
output <- find_vignette_product(name, by = "weave", engine = engine)
if (!have.makefile && vignette_is_tex(output)) {
texi2pdf(file = output, clean = FALSE, quiet = quiet)
output <- find_vignette_product(name, by = "texi2pdf",
engine = engine)
}
}, error = function(e) {
OK <<- FALSE
message(gettextf("Error: processing vignette '%s' failed with diagnostics:\n%s",
file, conditionMessage(e)))
})
where 38: tools:::.buildOneVignette("Optimize_parameter_vector.Rmd", "/home/hornik/tmp/R.check/r-devel-clang/Work/PKGS/BreedingSchemeLanguage.Rcheck/vign_test/BreedingSchemeLanguage",
TRUE, FALSE, "Optimize_parameter_vector", "UTF-8", "/tmp/RtmpdZlm9p/file5e701e182148.rds")
--- value of length: 2 type: logical ---
[1] FALSE FALSE
--- function from context ---
function (data, geno, pheno, GAUSS = FALSE, K = NULL, fixed = NULL,
covariate = NULL, PEV = FALSE, n.core = 1, theta.seq = NULL,
reduce = FALSE, R = NULL)
{
make.full <- function(X) {
svd.X <- svd(X)
r <- max(which(svd.X$d > 1e-08))
return(as.matrix(svd.X$u[, 1:r]))
}
names <- colnames(data)
ypos <- match(pheno, names)
if (is.na(ypos)) {
stop("Phenotype name does not appear in data.")
}
else {
y <- data[, ypos]
}
if (!is.null(R) & (length(R) != length(y))) {
stop("Length of R does not equal length of y")
}
not.miss <- which(!is.na(y))
if (length(not.miss) < length(y)) {
data <- data[not.miss, ]
y <- y[not.miss]
if (!is.null(R)) {
R <- R[not.miss]
}
}
n <- length(y)
X <- matrix(1, n, 1)
if (!is.null(fixed)) {
p <- length(fixed)
for (i in 1:p) {
xpos <- match(fixed[i], names)
xx <- factor(data[, xpos])
if (length(unique(xx)) > 1) {
X <- cbind(X, stats::model.matrix(~x - 1, data.frame(x = xx)))
}
}
}
if (!is.null(covariate)) {
p <- length(covariate)
for (i in 1:p) {
xpos <- match(covariate[i], names)
X <- cbind(X, data[, xpos])
}
}
gid.pos <- match(geno, names)
if (is.na(gid.pos)) {
stop("Genotype name does not appear in data.")
}
not.miss.gid <- as.character(unique(data[, gid.pos]))
if (is.null(K)) {
if (reduce) {
print("reduce=TRUE is not valid for independent genotypes. Proceeding without reduction.")
}
gid <- not.miss.gid
v <- length(gid)
Z <- matrix(0, n, v)
colnames(Z) <- gid
Z[cbind(1:n, match(data[, gid.pos], gid))] <- 1
X2 <- make.full(X)
ans <- rrBLUP::mixed.solve(y = y, X = X2, Z = Z, SE = PEV)
resid <- y - X2 %*% ans$beta - Z %*% ans$u
if (PEV) {
return(list(Vg = ans$Vu, Ve = ans$Ve, g = ans$u,
PEV = ans$u.SE^2, resid = resid))
}
else {
return(list(Vg = ans$Vu, Ve = ans$Ve, g = ans$u,
resid = resid))
}
}
else {
if (class(K) == "dist") {
K <- as.matrix(K)
}
gid <- rownames(K)
ix.pheno <- match(not.miss.gid, gid)
miss.pheno.gid <- which(is.na(ix.pheno))
if (length(miss.pheno.gid) > 0) {
stop(paste("The following lines have phenotypes but no genotypes:",
paste(not.miss.gid[miss.pheno.gid], collapse = " ")))
}
miss.gid <- setdiff(gid, not.miss.gid)
ix <- c(ix.pheno, match(miss.gid, gid))
K <- K[ix, ix]
v <- length(not.miss.gid)
Z <- matrix(0, n, v)
Z[cbind(1:n, match(data[, gid.pos], not.miss.gid))] <- 1
if (!is.null(R)) {
sqrt.R <- sqrt(R)
X2 <- X/sqrt.R
y2 <- y/sqrt.R
Z2 <- Z/sqrt.R
}
else {
X2 <- X
y2 <- y
Z2 <- Z
}
if ((n > v) & (reduce)) {
w <- sqrt(diag(crossprod(Z2)))
X2 <- make.full(crossprod(Z2, X2)/w)
y2 <- crossprod(Z2, y2)/w
Z2 <- cbind(diag(w), matrix(0, v, nrow(K) - v))
reduced <- TRUE
}
else {
X2 <- make.full(X2)
Z2 <- cbind(Z2, matrix(0, n, nrow(K) - v))
reduced <- FALSE
}
rm(X, Z, y)
if (!GAUSS) {
ans <- rrBLUP::mixed.solve(y = y2, X = X2, Z = Z2,
K = K, SE = PEV)
ix <- match(gid, rownames(ans$u))
if (reduced) {
resid <- NULL
}
else {
resid <- y2 - X2 %*% ans$beta - Z2 %*% ans$u
}
if (PEV) {
return(list(Vg = ans$Vu, Ve = ans$Ve, g = ans$u[ix],
PEV = ans$u.SE[ix]^2, resid = resid))
}
else {
return(list(Vg = ans$Vu, Ve = ans$Ve, g = ans$u[ix],
resid = resid))
}
}
else {
if (is.null(theta.seq)) {
theta <- setdiff(seq(0, max(K), length.out = 11),
0)
}
else {
theta <- theta.seq
}
n.profile <- length(theta)
ms.fun <- function(theta) {
soln <- list()
n.t <- length(theta)
for (i in 1:n.t) {
soln[[i]] <- rrBLUP::mixed.solve(y = y2, X = X2,
Z = Z2, K = exp(-(K/theta[i])^2), SE = PEV)
}
return(soln)
}
if (n.core > 1) {
it <- split(theta, factor(cut(theta, n.core,
labels = FALSE)))
soln <- unlist(snowfall::sfLapply(it, ms.fun,
mc.cores = n.core), recursive = FALSE)
}
else {
soln <- ms.fun(theta)
}
LL <- rep(0, n.profile)
for (i in 1:n.profile) {
LL[i] <- soln[[i]]$LL
}
ans <- soln[[which.max(LL)]]
profile <- cbind(theta, LL)
ix <- match(gid, rownames(ans$u))
if (reduced) {
resid <- NULL
}
else {
resid <- y2 - X2 %*% ans$beta - Z2 %*% ans$u
}
if (PEV) {
return(list(Vg = ans$Vu, Ve = ans$Ve, profile = profile,
g = ans$u[ix], PEV = ans$u.SE[ix]^2, resid = resid))
}
else {
return(list(Vg = ans$Vu, Ve = ans$Ve, profile = profile,
g = ans$u[ix], resid = resid))
}
}
}
}
<bytecode: 0x63bfd20>
<environment: 0xcd1b528>
--- function search by body ---
----------- END OF FAILURE REPORT --------------
Quitting from lines 68-106 (Optimize_parameter_vector.Rmd)
Error: processing vignette 'Optimize_parameter_vector.Rmd' failed with diagnostics:
the condition has length > 1
--- failed re-building 'Optimize_parameter_vector.Rmd'
--- re-building 'Within_family_selection.Rmd' using rmarkdown
Loading required package: snowfall
Loading required package: snow
Loading required package: Rcpp
--- finished re-building 'Within_family_selection.Rmd'
SUMMARY: processing the following files failed:
'Intro_to_BSL.Rmd' 'Load_existing_data.Rmd'
'Optimize_parameter_vector.Rmd'
Error: Vignette re-building failed.
Execution halted
Flavor: r-devel-linux-x86_64-debian-clang
Version: 0.9.6
Check: re-building of vignette outputs
Result: WARN
Error(s) in re-building vignettes:
...
--- re-building ‘Intro_to_BSL.Rmd’ using rmarkdown
----------- FAILURE REPORT --------------
--- failure: the condition has length > 1 ---
--- srcref ---
:
--- package (from environment) ---
BreedingSchemeLanguage
--- call from context ---
kin.blup4.4(kbDat, geno = "phenoGID", pheno = "pValue", fixed = c("loc",
"year"), K = K, reduce = mt1ObsPerGID, R = kbDat$error)
--- call from argument ---
if (class(K) == "dist") {
K <- as.matrix(K)
}
--- R stacktrace ---
where 1: kin.blup4.4(kbDat, geno = "phenoGID", pheno = "pValue", fixed = c("loc",
"year"), K = K, reduce = mt1ObsPerGID, R = kbDat$error)
where 2: FUN(X[[i]], ...)
where 3: lapply(sims, predictValue.func, popID = popID, trainingPopID = trainingPopID,
locations = locations, years = years, sharingInfo = sharingInfo)
where 4: eval(substitute(expr), data, enclos = parent.frame())
where 5: eval(substitute(expr), data, enclos = parent.frame())
where 6: with.default(sEnv, {
if (exists("totalCost")) {
costs$popID <- ifelse(is.null(popID), max(budgetRec$popID),
popID)
totalCost <- totalCost + costs$predCost
}
if (!onlyCost) {
if (nCore > 1) {
snowfall::sfInit(parallel = T, cpus = nCore)
sims <- snowfall::sfLapply(sims, predictValue.func,
popID = popID, trainingPopID = trainingPopID,
locations = locations, years = years, sharingInfo = sharingInfo)
snowfall::sfStop()
}
else {
sims <- lapply(sims, predictValue.func, popID = popID,
trainingPopID = trainingPopID, locations = locations,
years = years, sharingInfo = sharingInfo)
}
}
})
where 7: with(sEnv, {
if (exists("totalCost")) {
costs$popID <- ifelse(is.null(popID), max(budgetRec$popID),
popID)
totalCost <- totalCost + costs$predCost
}
if (!onlyCost) {
if (nCore > 1) {
snowfall::sfInit(parallel = T, cpus = nCore)
sims <- snowfall::sfLapply(sims, predictValue.func,
popID = popID, trainingPopID = trainingPopID,
locations = locations, years = years, sharingInfo = sharingInfo)
snowfall::sfStop()
}
else {
sims <- lapply(sims, predictValue.func, popID = popID,
trainingPopID = trainingPopID, locations = locations,
years = years, sharingInfo = sharingInfo)
}
}
})
where 8: predictValue(sharingInfo = "pedigree")
where 9: eval(expr, envir, enclos)
where 10: eval(expr, envir, enclos)
where 11: withVisible(eval(expr, envir, enclos))
where 12: withCallingHandlers(withVisible(eval(expr, envir, enclos)), warning = wHandler,
error = eHandler, message = mHandler)
where 13: handle(ev <- withCallingHandlers(withVisible(eval(expr, envir,
enclos)), warning = wHandler, error = eHandler, message = mHandler))
where 14: timing_fn(handle(ev <- withCallingHandlers(withVisible(eval(expr,
envir, enclos)), warning = wHandler, error = eHandler, message = mHandler)))
where 15: evaluate_call(expr, parsed$src[[i]], envir = envir, enclos = enclos,
debug = debug, last = i == length(out), use_try = stop_on_error !=
2L, keep_warning = keep_warning, keep_message = keep_message,
output_handler = output_handler, include_timing = include_timing)
where 16: evaluate::evaluate(...)
where 17: evaluate(code, envir = env, new_device = FALSE, keep_warning = !isFALSE(options$warning),
keep_message = !isFALSE(options$message), stop_on_error = if (options$error &&
options$include) 0L else 2L, output_handler = knit_handlers(options$render,
options))
where 18: in_dir(input_dir(), evaluate(code, envir = env, new_device = FALSE,
keep_warning = !isFALSE(options$warning), keep_message = !isFALSE(options$message),
stop_on_error = if (options$error && options$include) 0L else 2L,
output_handler = knit_handlers(options$render, options)))
where 19: block_exec(params)
where 20: call_block(x)
where 21: process_group.block(group)
where 22: process_group(group)
where 23: withCallingHandlers(if (tangle) process_tangle(group) else process_group(group),
error = function(e) {
setwd(wd)
cat(res, sep = "\n", file = output %n% "")
message("Quitting from lines ", paste(current_lines(i),
collapse = "-"), " (", knit_concord$get("infile"),
") ")
})
where 24: process_file(text, output)
where 25: knitr::knit(knit_input, knit_output, envir = envir, quiet = quiet)
where 26: rmarkdown::render(file, encoding = encoding, quiet = quiet, envir = globalenv(),
output_dir = getwd(), ...)
where 27: vweave_rmarkdown(...)
where 28: engine$weave(file, quiet = quiet, encoding = enc)
where 29: doTryCatch(return(expr), name, parentenv, handler)
where 30: tryCatchOne(expr, names, parentenv, handlers[[1L]])
where 31: tryCatchList(expr, classes, parentenv, handlers)
where 32: tryCatch({
engine$weave(file, quiet = quiet, encoding = enc)
setwd(startdir)
output <- find_vignette_product(name, by = "weave", engine = engine)
if (!have.makefile && vignette_is_tex(output)) {
texi2pdf(file = output, clean = FALSE, quiet = quiet)
output <- find_vignette_product(name, by = "texi2pdf",
engine = engine)
}
}, error = function(e) {
OK <<- FALSE
message(gettextf("Error: processing vignette '%s' failed with diagnostics:\n%s",
file, conditionMessage(e)))
})
where 33: tools:::.buildOneVignette("Intro_to_BSL.Rmd", "/home/hornik/tmp/R.check/r-devel-gcc/Work/PKGS/BreedingSchemeLanguage.Rcheck/vign_test/BreedingSchemeLanguage",
TRUE, FALSE, "Intro_to_BSL", "UTF-8", "/home/hornik/tmp/scratch/RtmpLb7D9f/file21b5a257b32.rds")
--- value of length: 2 type: logical ---
[1] FALSE FALSE
--- function from context ---
function (data, geno, pheno, GAUSS = FALSE, K = NULL, fixed = NULL,
covariate = NULL, PEV = FALSE, n.core = 1, theta.seq = NULL,
reduce = FALSE, R = NULL)
{
make.full <- function(X) {
svd.X <- svd(X)
r <- max(which(svd.X$d > 1e-08))
return(as.matrix(svd.X$u[, 1:r]))
}
names <- colnames(data)
ypos <- match(pheno, names)
if (is.na(ypos)) {
stop("Phenotype name does not appear in data.")
}
else {
y <- data[, ypos]
}
if (!is.null(R) & (length(R) != length(y))) {
stop("Length of R does not equal length of y")
}
not.miss <- which(!is.na(y))
if (length(not.miss) < length(y)) {
data <- data[not.miss, ]
y <- y[not.miss]
if (!is.null(R)) {
R <- R[not.miss]
}
}
n <- length(y)
X <- matrix(1, n, 1)
if (!is.null(fixed)) {
p <- length(fixed)
for (i in 1:p) {
xpos <- match(fixed[i], names)
xx <- factor(data[, xpos])
if (length(unique(xx)) > 1) {
X <- cbind(X, stats::model.matrix(~x - 1, data.frame(x = xx)))
}
}
}
if (!is.null(covariate)) {
p <- length(covariate)
for (i in 1:p) {
xpos <- match(covariate[i], names)
X <- cbind(X, data[, xpos])
}
}
gid.pos <- match(geno, names)
if (is.na(gid.pos)) {
stop("Genotype name does not appear in data.")
}
not.miss.gid <- as.character(unique(data[, gid.pos]))
if (is.null(K)) {
if (reduce) {
print("reduce=TRUE is not valid for independent genotypes. Proceeding without reduction.")
}
gid <- not.miss.gid
v <- length(gid)
Z <- matrix(0, n, v)
colnames(Z) <- gid
Z[cbind(1:n, match(data[, gid.pos], gid))] <- 1
X2 <- make.full(X)
ans <- rrBLUP::mixed.solve(y = y, X = X2, Z = Z, SE = PEV)
resid <- y - X2 %*% ans$beta - Z %*% ans$u
if (PEV) {
return(list(Vg = ans$Vu, Ve = ans$Ve, g = ans$u,
PEV = ans$u.SE^2, resid = resid))
}
else {
return(list(Vg = ans$Vu, Ve = ans$Ve, g = ans$u,
resid = resid))
}
}
else {
if (class(K) == "dist") {
K <- as.matrix(K)
}
gid <- rownames(K)
ix.pheno <- match(not.miss.gid, gid)
miss.pheno.gid <- which(is.na(ix.pheno))
if (length(miss.pheno.gid) > 0) {
stop(paste("The following lines have phenotypes but no genotypes:",
paste(not.miss.gid[miss.pheno.gid], collapse = " ")))
}
miss.gid <- setdiff(gid, not.miss.gid)
ix <- c(ix.pheno, match(miss.gid, gid))
K <- K[ix, ix]
v <- length(not.miss.gid)
Z <- matrix(0, n, v)
Z[cbind(1:n, match(data[, gid.pos], not.miss.gid))] <- 1
if (!is.null(R)) {
sqrt.R <- sqrt(R)
X2 <- X/sqrt.R
y2 <- y/sqrt.R
Z2 <- Z/sqrt.R
}
else {
X2 <- X
y2 <- y
Z2 <- Z
}
if ((n > v) & (reduce)) {
w <- sqrt(diag(crossprod(Z2)))
X2 <- make.full(crossprod(Z2, X2)/w)
y2 <- crossprod(Z2, y2)/w
Z2 <- cbind(diag(w), matrix(0, v, nrow(K) - v))
reduced <- TRUE
}
else {
X2 <- make.full(X2)
Z2 <- cbind(Z2, matrix(0, n, nrow(K) - v))
reduced <- FALSE
}
rm(X, Z, y)
if (!GAUSS) {
ans <- rrBLUP::mixed.solve(y = y2, X = X2, Z = Z2,
K = K, SE = PEV)
ix <- match(gid, rownames(ans$u))
if (reduced) {
resid <- NULL
}
else {
resid <- y2 - X2 %*% ans$beta - Z2 %*% ans$u
}
if (PEV) {
return(list(Vg = ans$Vu, Ve = ans$Ve, g = ans$u[ix],
PEV = ans$u.SE[ix]^2, resid = resid))
}
else {
return(list(Vg = ans$Vu, Ve = ans$Ve, g = ans$u[ix],
resid = resid))
}
}
else {
if (is.null(theta.seq)) {
theta <- setdiff(seq(0, max(K), length.out = 11),
0)
}
else {
theta <- theta.seq
}
n.profile <- length(theta)
ms.fun <- function(theta) {
soln <- list()
n.t <- length(theta)
for (i in 1:n.t) {
soln[[i]] <- rrBLUP::mixed.solve(y = y2, X = X2,
Z = Z2, K = exp(-(K/theta[i])^2), SE = PEV)
}
return(soln)
}
if (n.core > 1) {
it <- split(theta, factor(cut(theta, n.core,
labels = FALSE)))
soln <- unlist(snowfall::sfLapply(it, ms.fun,
mc.cores = n.core), recursive = FALSE)
}
else {
soln <- ms.fun(theta)
}
LL <- rep(0, n.profile)
for (i in 1:n.profile) {
LL[i] <- soln[[i]]$LL
}
ans <- soln[[which.max(LL)]]
profile <- cbind(theta, LL)
ix <- match(gid, rownames(ans$u))
if (reduced) {
resid <- NULL
}
else {
resid <- y2 - X2 %*% ans$beta - Z2 %*% ans$u
}
if (PEV) {
return(list(Vg = ans$Vu, Ve = ans$Ve, profile = profile,
g = ans$u[ix], PEV = ans$u.SE[ix]^2, resid = resid))
}
else {
return(list(Vg = ans$Vu, Ve = ans$Ve, profile = profile,
g = ans$u[ix], resid = resid))
}
}
}
}
<bytecode: 0x55ba66d4ebf8>
<environment: 0x55ba6a929eb8>
--- function search by body ---
----------- END OF FAILURE REPORT --------------
Quitting from lines 85-89 (Intro_to_BSL.Rmd)
Error: processing vignette 'Intro_to_BSL.Rmd' failed with diagnostics:
the condition has length > 1
--- failed re-building ‘Intro_to_BSL.Rmd’
--- re-building ‘Load_existing_data.Rmd’ using rmarkdown
Loading required package: snowfall
Loading required package: snow
Loading required package: Rcpp
----------- FAILURE REPORT --------------
--- failure: the condition has length > 1 ---
--- srcref ---
:
--- package (from environment) ---
BreedingSchemeLanguage
--- call from context ---
kin.blup4.4(kbDat, geno = "phenoGID", pheno = "pValue", fixed = c("loc",
"year"), K = K, reduce = mt1ObsPerGID, R = kbDat$error)
--- call from argument ---
if (class(K) == "dist") {
K <- as.matrix(K)
}
--- R stacktrace ---
where 1: kin.blup4.4(kbDat, geno = "phenoGID", pheno = "pValue", fixed = c("loc",
"year"), K = K, reduce = mt1ObsPerGID, R = kbDat$error)
where 2: FUN(X[[i]], ...)
where 3: lapply(sims, predictValue.func, popID = popID, trainingPopID = trainingPopID,
locations = locations, years = years, sharingInfo = sharingInfo)
where 4: eval(substitute(expr), data, enclos = parent.frame())
where 5: eval(substitute(expr), data, enclos = parent.frame())
where 6: with.default(sEnv, {
if (exists("totalCost")) {
costs$popID <- ifelse(is.null(popID), max(budgetRec$popID),
popID)
totalCost <- totalCost + costs$predCost
}
if (!onlyCost) {
if (nCore > 1) {
snowfall::sfInit(parallel = T, cpus = nCore)
sims <- snowfall::sfLapply(sims, predictValue.func,
popID = popID, trainingPopID = trainingPopID,
locations = locations, years = years, sharingInfo = sharingInfo)
snowfall::sfStop()
}
else {
sims <- lapply(sims, predictValue.func, popID = popID,
trainingPopID = trainingPopID, locations = locations,
years = years, sharingInfo = sharingInfo)
}
}
})
where 7: with(sEnv, {
if (exists("totalCost")) {
costs$popID <- ifelse(is.null(popID), max(budgetRec$popID),
popID)
totalCost <- totalCost + costs$predCost
}
if (!onlyCost) {
if (nCore > 1) {
snowfall::sfInit(parallel = T, cpus = nCore)
sims <- snowfall::sfLapply(sims, predictValue.func,
popID = popID, trainingPopID = trainingPopID,
locations = locations, years = years, sharingInfo = sharingInfo)
snowfall::sfStop()
}
else {
sims <- lapply(sims, predictValue.func, popID = popID,
trainingPopID = trainingPopID, locations = locations,
years = years, sharingInfo = sharingInfo)
}
}
})
where 8: predictValue()
where 9: eval(expr, envir, enclos)
where 10: eval(expr, envir, enclos)
where 11: withVisible(eval(expr, envir, enclos))
where 12: withCallingHandlers(withVisible(eval(expr, envir, enclos)), warning = wHandler,
error = eHandler, message = mHandler)
where 13: handle(ev <- withCallingHandlers(withVisible(eval(expr, envir,
enclos)), warning = wHandler, error = eHandler, message = mHandler))
where 14: timing_fn(handle(ev <- withCallingHandlers(withVisible(eval(expr,
envir, enclos)), warning = wHandler, error = eHandler, message = mHandler)))
where 15: evaluate_call(expr, parsed$src[[i]], envir = envir, enclos = enclos,
debug = debug, last = i == length(out), use_try = stop_on_error !=
2L, keep_warning = keep_warning, keep_message = keep_message,
output_handler = output_handler, include_timing = include_timing)
where 16: evaluate::evaluate(...)
where 17: evaluate(code, envir = env, new_device = FALSE, keep_warning = !isFALSE(options$warning),
keep_message = !isFALSE(options$message), stop_on_error = if (options$error &&
options$include) 0L else 2L, output_handler = knit_handlers(options$render,
options))
where 18: in_dir(input_dir(), evaluate(code, envir = env, new_device = FALSE,
keep_warning = !isFALSE(options$warning), keep_message = !isFALSE(options$message),
stop_on_error = if (options$error && options$include) 0L else 2L,
output_handler = knit_handlers(options$render, options)))
where 19: block_exec(params)
where 20: call_block(x)
where 21: process_group.block(group)
where 22: process_group(group)
where 23: withCallingHandlers(if (tangle) process_tangle(group) else process_group(group),
error = function(e) {
setwd(wd)
cat(res, sep = "\n", file = output %n% "")
message("Quitting from lines ", paste(current_lines(i),
collapse = "-"), " (", knit_concord$get("infile"),
") ")
})
where 24: process_file(text, output)
where 25: knitr::knit(knit_input, knit_output, envir = envir, quiet = quiet)
where 26: rmarkdown::render(file, encoding = encoding, quiet = quiet, envir = globalenv(),
output_dir = getwd(), ...)
where 27: vweave_rmarkdown(...)
where 28: engine$weave(file, quiet = quiet, encoding = enc)
where 29: doTryCatch(return(expr), name, parentenv, handler)
where 30: tryCatchOne(expr, names, parentenv, handlers[[1L]])
where 31: tryCatchList(expr, classes, parentenv, handlers)
where 32: tryCatch({
engine$weave(file, quiet = quiet, encoding = enc)
setwd(startdir)
output <- find_vignette_product(name, by = "weave", engine = engine)
if (!have.makefile && vignette_is_tex(output)) {
texi2pdf(file = output, clean = FALSE, quiet = quiet)
output <- find_vignette_product(name, by = "texi2pdf",
engine = engine)
}
}, error = function(e) {
OK <<- FALSE
message(gettextf("Error: processing vignette '%s' failed with diagnostics:\n%s",
file, conditionMessage(e)))
})
where 33: tools:::.buildOneVignette("Load_existing_data.Rmd", "/home/hornik/tmp/R.check/r-devel-gcc/Work/PKGS/BreedingSchemeLanguage.Rcheck/vign_test/BreedingSchemeLanguage",
TRUE, FALSE, "Load_existing_data", "UTF-8", "/home/hornik/tmp/scratch/RtmpLb7D9f/file21b57488d210.rds")
--- value of length: 2 type: logical ---
[1] FALSE FALSE
--- function from context ---
function (data, geno, pheno, GAUSS = FALSE, K = NULL, fixed = NULL,
covariate = NULL, PEV = FALSE, n.core = 1, theta.seq = NULL,
reduce = FALSE, R = NULL)
{
make.full <- function(X) {
svd.X <- svd(X)
r <- max(which(svd.X$d > 1e-08))
return(as.matrix(svd.X$u[, 1:r]))
}
names <- colnames(data)
ypos <- match(pheno, names)
if (is.na(ypos)) {
stop("Phenotype name does not appear in data.")
}
else {
y <- data[, ypos]
}
if (!is.null(R) & (length(R) != length(y))) {
stop("Length of R does not equal length of y")
}
not.miss <- which(!is.na(y))
if (length(not.miss) < length(y)) {
data <- data[not.miss, ]
y <- y[not.miss]
if (!is.null(R)) {
R <- R[not.miss]
}
}
n <- length(y)
X <- matrix(1, n, 1)
if (!is.null(fixed)) {
p <- length(fixed)
for (i in 1:p) {
xpos <- match(fixed[i], names)
xx <- factor(data[, xpos])
if (length(unique(xx)) > 1) {
X <- cbind(X, stats::model.matrix(~x - 1, data.frame(x = xx)))
}
}
}
if (!is.null(covariate)) {
p <- length(covariate)
for (i in 1:p) {
xpos <- match(covariate[i], names)
X <- cbind(X, data[, xpos])
}
}
gid.pos <- match(geno, names)
if (is.na(gid.pos)) {
stop("Genotype name does not appear in data.")
}
not.miss.gid <- as.character(unique(data[, gid.pos]))
if (is.null(K)) {
if (reduce) {
print("reduce=TRUE is not valid for independent genotypes. Proceeding without reduction.")
}
gid <- not.miss.gid
v <- length(gid)
Z <- matrix(0, n, v)
colnames(Z) <- gid
Z[cbind(1:n, match(data[, gid.pos], gid))] <- 1
X2 <- make.full(X)
ans <- rrBLUP::mixed.solve(y = y, X = X2, Z = Z, SE = PEV)
resid <- y - X2 %*% ans$beta - Z %*% ans$u
if (PEV) {
return(list(Vg = ans$Vu, Ve = ans$Ve, g = ans$u,
PEV = ans$u.SE^2, resid = resid))
}
else {
return(list(Vg = ans$Vu, Ve = ans$Ve, g = ans$u,
resid = resid))
}
}
else {
if (class(K) == "dist") {
K <- as.matrix(K)
}
gid <- rownames(K)
ix.pheno <- match(not.miss.gid, gid)
miss.pheno.gid <- which(is.na(ix.pheno))
if (length(miss.pheno.gid) > 0) {
stop(paste("The following lines have phenotypes but no genotypes:",
paste(not.miss.gid[miss.pheno.gid], collapse = " ")))
}
miss.gid <- setdiff(gid, not.miss.gid)
ix <- c(ix.pheno, match(miss.gid, gid))
K <- K[ix, ix]
v <- length(not.miss.gid)
Z <- matrix(0, n, v)
Z[cbind(1:n, match(data[, gid.pos], not.miss.gid))] <- 1
if (!is.null(R)) {
sqrt.R <- sqrt(R)
X2 <- X/sqrt.R
y2 <- y/sqrt.R
Z2 <- Z/sqrt.R
}
else {
X2 <- X
y2 <- y
Z2 <- Z
}
if ((n > v) & (reduce)) {
w <- sqrt(diag(crossprod(Z2)))
X2 <- make.full(crossprod(Z2, X2)/w)
y2 <- crossprod(Z2, y2)/w
Z2 <- cbind(diag(w), matrix(0, v, nrow(K) - v))
reduced <- TRUE
}
else {
X2 <- make.full(X2)
Z2 <- cbind(Z2, matrix(0, n, nrow(K) - v))
reduced <- FALSE
}
rm(X, Z, y)
if (!GAUSS) {
ans <- rrBLUP::mixed.solve(y = y2, X = X2, Z = Z2,
K = K, SE = PEV)
ix <- match(gid, rownames(ans$u))
if (reduced) {
resid <- NULL
}
else {
resid <- y2 - X2 %*% ans$beta - Z2 %*% ans$u
}
if (PEV) {
return(list(Vg = ans$Vu, Ve = ans$Ve, g = ans$u[ix],
PEV = ans$u.SE[ix]^2, resid = resid))
}
else {
return(list(Vg = ans$Vu, Ve = ans$Ve, g = ans$u[ix],
resid = resid))
}
}
else {
if (is.null(theta.seq)) {
theta <- setdiff(seq(0, max(K), length.out = 11),
0)
}
else {
theta <- theta.seq
}
n.profile <- length(theta)
ms.fun <- function(theta) {
soln <- list()
n.t <- length(theta)
for (i in 1:n.t) {
soln[[i]] <- rrBLUP::mixed.solve(y = y2, X = X2,
Z = Z2, K = exp(-(K/theta[i])^2), SE = PEV)
}
return(soln)
}
if (n.core > 1) {
it <- split(theta, factor(cut(theta, n.core,
labels = FALSE)))
soln <- unlist(snowfall::sfLapply(it, ms.fun,
mc.cores = n.core), recursive = FALSE)
}
else {
soln <- ms.fun(theta)
}
LL <- rep(0, n.profile)
for (i in 1:n.profile) {
LL[i] <- soln[[i]]$LL
}
ans <- soln[[which.max(LL)]]
profile <- cbind(theta, LL)
ix <- match(gid, rownames(ans$u))
if (reduced) {
resid <- NULL
}
else {
resid <- y2 - X2 %*% ans$beta - Z2 %*% ans$u
}
if (PEV) {
return(list(Vg = ans$Vu, Ve = ans$Ve, profile = profile,
g = ans$u[ix], PEV = ans$u.SE[ix]^2, resid = resid))
}
else {
return(list(Vg = ans$Vu, Ve = ans$Ve, profile = profile,
g = ans$u[ix], resid = resid))
}
}
}
}
<bytecode: 0x55b9f2910c60>
<environment: 0x55b9f28e2f20>
--- function search by body ---
----------- END OF FAILURE REPORT --------------
Quitting from lines 65-70 (Load_existing_data.Rmd)
Error: processing vignette 'Load_existing_data.Rmd' failed with diagnostics:
the condition has length > 1
--- failed re-building ‘Load_existing_data.Rmd’
--- re-building ‘Optimize_parameter_vector.Rmd’ using rmarkdown
Loading required package: snowfall
Loading required package: snow
Loading required package: Rcpp
----------- FAILURE REPORT --------------
--- failure: the condition has length > 1 ---
--- srcref ---
:
--- package (from environment) ---
BreedingSchemeLanguage
--- call from context ---
kin.blup4.4(kbDat, geno = "phenoGID", pheno = "pValue", fixed = c("loc",
"year"), K = K, reduce = mt1ObsPerGID, R = kbDat$error)
--- call from argument ---
if (class(K) == "dist") {
K <- as.matrix(K)
}
--- R stacktrace ---
where 1: kin.blup4.4(kbDat, geno = "phenoGID", pheno = "pValue", fixed = c("loc",
"year"), K = K, reduce = mt1ObsPerGID, R = kbDat$error)
where 2: FUN(X[[i]], ...)
where 3: lapply(sims, predictValue.func, popID = popID, trainingPopID = trainingPopID,
locations = locations, years = years, sharingInfo = sharingInfo)
where 4: eval(substitute(expr), data, enclos = parent.frame())
where 5: eval(substitute(expr), data, enclos = parent.frame())
where 6: with.default(sEnv, {
if (exists("totalCost")) {
costs$popID <- ifelse(is.null(popID), max(budgetRec$popID),
popID)
totalCost <- totalCost + costs$predCost
}
if (!onlyCost) {
if (nCore > 1) {
snowfall::sfInit(parallel = T, cpus = nCore)
sims <- snowfall::sfLapply(sims, predictValue.func,
popID = popID, trainingPopID = trainingPopID,
locations = locations, years = years, sharingInfo = sharingInfo)
snowfall::sfStop()
}
else {
sims <- lapply(sims, predictValue.func, popID = popID,
trainingPopID = trainingPopID, locations = locations,
years = years, sharingInfo = sharingInfo)
}
}
})
where 7: with(sEnv, {
if (exists("totalCost")) {
costs$popID <- ifelse(is.null(popID), max(budgetRec$popID),
popID)
totalCost <- totalCost + costs$predCost
}
if (!onlyCost) {
if (nCore > 1) {
snowfall::sfInit(parallel = T, cpus = nCore)
sims <- snowfall::sfLapply(sims, predictValue.func,
popID = popID, trainingPopID = trainingPopID,
locations = locations, years = years, sharingInfo = sharingInfo)
snowfall::sfStop()
}
else {
sims <- lapply(sims, predictValue.func, popID = popID,
trainingPopID = trainingPopID, locations = locations,
years = years, sharingInfo = sharingInfo)
}
}
})
where 8: predictValue()
where 9: eval(ei, envir)
where 10: eval(ei, envir)
where 11: withVisible(eval(ei, envir))
where 12: source(schemeFileName, local = TRUE)
where 13: testParameterOptimality(schemeFileName = schemeScriptPath, parmList = parmList,
objectiveFunc = objFunc, budget = allowableBudget)
where 14: eval(expr, envir, enclos)
where 15: eval(expr, envir, enclos)
where 16: withVisible(eval(expr, envir, enclos))
where 17: withCallingHandlers(withVisible(eval(expr, envir, enclos)), warning = wHandler,
error = eHandler, message = mHandler)
where 18: handle(ev <- withCallingHandlers(withVisible(eval(expr, envir,
enclos)), warning = wHandler, error = eHandler, message = mHandler))
where 19: timing_fn(handle(ev <- withCallingHandlers(withVisible(eval(expr,
envir, enclos)), warning = wHandler, error = eHandler, message = mHandler)))
where 20: evaluate_call(expr, parsed$src[[i]], envir = envir, enclos = enclos,
debug = debug, last = i == length(out), use_try = stop_on_error !=
2L, keep_warning = keep_warning, keep_message = keep_message,
output_handler = output_handler, include_timing = include_timing)
where 21: evaluate::evaluate(...)
where 22: evaluate(code, envir = env, new_device = FALSE, keep_warning = !isFALSE(options$warning),
keep_message = !isFALSE(options$message), stop_on_error = if (options$error &&
options$include) 0L else 2L, output_handler = knit_handlers(options$render,
options))
where 23: in_dir(input_dir(), evaluate(code, envir = env, new_device = FALSE,
keep_warning = !isFALSE(options$warning), keep_message = !isFALSE(options$message),
stop_on_error = if (options$error && options$include) 0L else 2L,
output_handler = knit_handlers(options$render, options)))
where 24: block_exec(params)
where 25: call_block(x)
where 26: process_group.block(group)
where 27: process_group(group)
where 28: withCallingHandlers(if (tangle) process_tangle(group) else process_group(group),
error = function(e) {
setwd(wd)
cat(res, sep = "\n", file = output %n% "")
message("Quitting from lines ", paste(current_lines(i),
collapse = "-"), " (", knit_concord$get("infile"),
") ")
})
where 29: process_file(text, output)
where 30: knitr::knit(knit_input, knit_output, envir = envir, quiet = quiet)
where 31: rmarkdown::render(file, encoding = encoding, quiet = quiet, envir = globalenv(),
output_dir = getwd(), ...)
where 32: vweave_rmarkdown(...)
where 33: engine$weave(file, quiet = quiet, encoding = enc)
where 34: doTryCatch(return(expr), name, parentenv, handler)
where 35: tryCatchOne(expr, names, parentenv, handlers[[1L]])
where 36: tryCatchList(expr, classes, parentenv, handlers)
where 37: tryCatch({
engine$weave(file, quiet = quiet, encoding = enc)
setwd(startdir)
output <- find_vignette_product(name, by = "weave", engine = engine)
if (!have.makefile && vignette_is_tex(output)) {
texi2pdf(file = output, clean = FALSE, quiet = quiet)
output <- find_vignette_product(name, by = "texi2pdf",
engine = engine)
}
}, error = function(e) {
OK <<- FALSE
message(gettextf("Error: processing vignette '%s' failed with diagnostics:\n%s",
file, conditionMessage(e)))
})
where 38: tools:::.buildOneVignette("Optimize_parameter_vector.Rmd", "/home/hornik/tmp/R.check/r-devel-gcc/Work/PKGS/BreedingSchemeLanguage.Rcheck/vign_test/BreedingSchemeLanguage",
TRUE, FALSE, "Optimize_parameter_vector", "UTF-8", "/home/hornik/tmp/scratch/RtmpLb7D9f/file21b579077297.rds")
--- value of length: 2 type: logical ---
[1] FALSE FALSE
--- function from context ---
function (data, geno, pheno, GAUSS = FALSE, K = NULL, fixed = NULL,
covariate = NULL, PEV = FALSE, n.core = 1, theta.seq = NULL,
reduce = FALSE, R = NULL)
{
make.full <- function(X) {
svd.X <- svd(X)
r <- max(which(svd.X$d > 1e-08))
return(as.matrix(svd.X$u[, 1:r]))
}
names <- colnames(data)
ypos <- match(pheno, names)
if (is.na(ypos)) {
stop("Phenotype name does not appear in data.")
}
else {
y <- data[, ypos]
}
if (!is.null(R) & (length(R) != length(y))) {
stop("Length of R does not equal length of y")
}
not.miss <- which(!is.na(y))
if (length(not.miss) < length(y)) {
data <- data[not.miss, ]
y <- y[not.miss]
if (!is.null(R)) {
R <- R[not.miss]
}
}
n <- length(y)
X <- matrix(1, n, 1)
if (!is.null(fixed)) {
p <- length(fixed)
for (i in 1:p) {
xpos <- match(fixed[i], names)
xx <- factor(data[, xpos])
if (length(unique(xx)) > 1) {
X <- cbind(X, stats::model.matrix(~x - 1, data.frame(x = xx)))
}
}
}
if (!is.null(covariate)) {
p <- length(covariate)
for (i in 1:p) {
xpos <- match(covariate[i], names)
X <- cbind(X, data[, xpos])
}
}
gid.pos <- match(geno, names)
if (is.na(gid.pos)) {
stop("Genotype name does not appear in data.")
}
not.miss.gid <- as.character(unique(data[, gid.pos]))
if (is.null(K)) {
if (reduce) {
print("reduce=TRUE is not valid for independent genotypes. Proceeding without reduction.")
}
gid <- not.miss.gid
v <- length(gid)
Z <- matrix(0, n, v)
colnames(Z) <- gid
Z[cbind(1:n, match(data[, gid.pos], gid))] <- 1
X2 <- make.full(X)
ans <- rrBLUP::mixed.solve(y = y, X = X2, Z = Z, SE = PEV)
resid <- y - X2 %*% ans$beta - Z %*% ans$u
if (PEV) {
return(list(Vg = ans$Vu, Ve = ans$Ve, g = ans$u,
PEV = ans$u.SE^2, resid = resid))
}
else {
return(list(Vg = ans$Vu, Ve = ans$Ve, g = ans$u,
resid = resid))
}
}
else {
if (class(K) == "dist") {
K <- as.matrix(K)
}
gid <- rownames(K)
ix.pheno <- match(not.miss.gid, gid)
miss.pheno.gid <- which(is.na(ix.pheno))
if (length(miss.pheno.gid) > 0) {
stop(paste("The following lines have phenotypes but no genotypes:",
paste(not.miss.gid[miss.pheno.gid], collapse = " ")))
}
miss.gid <- setdiff(gid, not.miss.gid)
ix <- c(ix.pheno, match(miss.gid, gid))
K <- K[ix, ix]
v <- length(not.miss.gid)
Z <- matrix(0, n, v)
Z[cbind(1:n, match(data[, gid.pos], not.miss.gid))] <- 1
if (!is.null(R)) {
sqrt.R <- sqrt(R)
X2 <- X/sqrt.R
y2 <- y/sqrt.R
Z2 <- Z/sqrt.R
}
else {
X2 <- X
y2 <- y
Z2 <- Z
}
if ((n > v) & (reduce)) {
w <- sqrt(diag(crossprod(Z2)))
X2 <- make.full(crossprod(Z2, X2)/w)
y2 <- crossprod(Z2, y2)/w
Z2 <- cbind(diag(w), matrix(0, v, nrow(K) - v))
reduced <- TRUE
}
else {
X2 <- make.full(X2)
Z2 <- cbind(Z2, matrix(0, n, nrow(K) - v))
reduced <- FALSE
}
rm(X, Z, y)
if (!GAUSS) {
ans <- rrBLUP::mixed.solve(y = y2, X = X2, Z = Z2,
K = K, SE = PEV)
ix <- match(gid, rownames(ans$u))
if (reduced) {
resid <- NULL
}
else {
resid <- y2 - X2 %*% ans$beta - Z2 %*% ans$u
}
if (PEV) {
return(list(Vg = ans$Vu, Ve = ans$Ve, g = ans$u[ix],
PEV = ans$u.SE[ix]^2, resid = resid))
}
else {
return(list(Vg = ans$Vu, Ve = ans$Ve, g = ans$u[ix],
resid = resid))
}
}
else {
if (is.null(theta.seq)) {
theta <- setdiff(seq(0, max(K), length.out = 11),
0)
}
else {
theta <- theta.seq
}
n.profile <- length(theta)
ms.fun <- function(theta) {
soln <- list()
n.t <- length(theta)
for (i in 1:n.t) {
soln[[i]] <- rrBLUP::mixed.solve(y = y2, X = X2,
Z = Z2, K = exp(-(K/theta[i])^2), SE = PEV)
}
return(soln)
}
if (n.core > 1) {
it <- split(theta, factor(cut(theta, n.core,
labels = FALSE)))
soln <- unlist(snowfall::sfLapply(it, ms.fun,
mc.cores = n.core), recursive = FALSE)
}
else {
soln <- ms.fun(theta)
}
LL <- rep(0, n.profile)
for (i in 1:n.profile) {
LL[i] <- soln[[i]]$LL
}
ans <- soln[[which.max(LL)]]
profile <- cbind(theta, LL)
ix <- match(gid, rownames(ans$u))
if (reduced) {
resid <- NULL
}
else {
resid <- y2 - X2 %*% ans$beta - Z2 %*% ans$u
}
if (PEV) {
return(list(Vg = ans$Vu, Ve = ans$Ve, profile = profile,
g = ans$u[ix], PEV = ans$u.SE[ix]^2, resid = resid))
}
else {
return(list(Vg = ans$Vu, Ve = ans$Ve, profile = profile,
g = ans$u[ix], resid = resid))
}
}
}
}
<bytecode: 0x5582af0a9e58>
<environment: 0x5582b0957c08>
--- function search by body ---
----------- END OF FAILURE REPORT --------------
Quitting from lines 68-106 (Optimize_parameter_vector.Rmd)
Error: processing vignette 'Optimize_parameter_vector.Rmd' failed with diagnostics:
the condition has length > 1
--- failed re-building ‘Optimize_parameter_vector.Rmd’
--- re-building ‘Within_family_selection.Rmd’ using rmarkdown
Loading required package: snowfall
Loading required package: snow
Loading required package: Rcpp
--- finished re-building ‘Within_family_selection.Rmd’
SUMMARY: processing the following files failed:
‘Intro_to_BSL.Rmd’ ‘Load_existing_data.Rmd’
‘Optimize_parameter_vector.Rmd’
Error: Vignette re-building failed.
Execution halted
Flavor: r-devel-linux-x86_64-debian-gcc
Version: 0.9.6
Check: dependencies in R code
Result: NOTE
Namespace in Imports field not imported from: ‘lme4’
All declared Imports should be used.
Flavors: r-devel-linux-x86_64-fedora-clang, r-devel-linux-x86_64-fedora-gcc, r-patched-solaris-x86, r-release-osx-x86_64, r-oldrel-osx-x86_64
Version: 0.9.6
Check: re-building of vignette outputs
Result: WARN
Error(s) in re-building vignettes:
--- re-building ‘Intro_to_BSL.Rmd’ using rmarkdown
----------- FAILURE REPORT --------------
--- failure: the condition has length > 1 ---
--- srcref ---
:
--- package (from environment) ---
BreedingSchemeLanguage
--- call from context ---
kin.blup4.4(kbDat, geno = "phenoGID", pheno = "pValue", fixed = c("loc",
"year"), K = K, reduce = mt1ObsPerGID, R = kbDat$error)
--- call from argument ---
if (class(K) == "dist") {
K <- as.matrix(K)
}
--- R stacktrace ---
where 1: kin.blup4.4(kbDat, geno = "phenoGID", pheno = "pValue", fixed = c("loc",
"year"), K = K, reduce = mt1ObsPerGID, R = kbDat$error)
where 2: FUN(X[[i]], ...)
where 3: lapply(sims, predictValue.func, popID = popID, trainingPopID = trainingPopID,
locations = locations, years = years, sharingInfo = sharingInfo)
where 4: eval(substitute(expr), data, enclos = parent.frame())
where 5: eval(substitute(expr), data, enclos = parent.frame())
where 6: with.default(sEnv, {
if (exists("totalCost")) {
costs$popID <- ifelse(is.null(popID), max(budgetRec$popID),
popID)
totalCost <- totalCost + costs$predCost
}
if (!onlyCost) {
if (nCore > 1) {
snowfall::sfInit(parallel = T, cpus = nCore)
sims <- snowfall::sfLapply(sims, predictValue.func,
popID = popID, trainingPopID = trainingPopID,
locations = locations, years = years, sharingInfo = sharingInfo)
snowfall::sfStop()
}
else {
sims <- lapply(sims, predictValue.func, popID = popID,
trainingPopID = trainingPopID, locations = locations,
years = years, sharingInfo = sharingInfo)
}
}
})
where 7: with(sEnv, {
if (exists("totalCost")) {
costs$popID <- ifelse(is.null(popID), max(budgetRec$popID),
popID)
totalCost <- totalCost + costs$predCost
}
if (!onlyCost) {
if (nCore > 1) {
snowfall::sfInit(parallel = T, cpus = nCore)
sims <- snowfall::sfLapply(sims, predictValue.func,
popID = popID, trainingPopID = trainingPopID,
locations = locations, years = years, sharingInfo = sharingInfo)
snowfall::sfStop()
}
else {
sims <- lapply(sims, predictValue.func, popID = popID,
trainingPopID = trainingPopID, locations = locations,
years = years, sharingInfo = sharingInfo)
}
}
})
where 8: predictValue(sharingInfo = "pedigree")
where 9: eval(expr, envir, enclos)
where 10: eval(expr, envir, enclos)
where 11: withVisible(eval(expr, envir, enclos))
where 12: withCallingHandlers(withVisible(eval(expr, envir, enclos)), warning = wHandler,
error = eHandler, message = mHandler)
where 13: handle(ev <- withCallingHandlers(withVisible(eval(expr, envir,
enclos)), warning = wHandler, error = eHandler, message = mHandler))
where 14: timing_fn(handle(ev <- withCallingHandlers(withVisible(eval(expr,
envir, enclos)), warning = wHandler, error = eHandler, message = mHandler)))
where 15: evaluate_call(expr, parsed$src[[i]], envir = envir, enclos = enclos,
debug = debug, last = i == length(out), use_try = stop_on_error !=
2L, keep_warning = keep_warning, keep_message = keep_message,
output_handler = output_handler, include_timing = include_timing)
where 16: evaluate::evaluate(...)
where 17: evaluate(code, envir = env, new_device = FALSE, keep_warning = !isFALSE(options$warning),
keep_message = !isFALSE(options$message), stop_on_error = if (options$error &&
options$include) 0L else 2L, output_handler = knit_handlers(options$render,
options))
where 18: in_dir(input_dir(), evaluate(code, envir = env, new_device = FALSE,
keep_warning = !isFALSE(options$warning), keep_message = !isFALSE(options$message),
stop_on_error = if (options$error && options$include) 0L else 2L,
output_handler = knit_handlers(options$render, options)))
where 19: block_exec(params)
where 20: call_block(x)
where 21: process_group.block(group)
where 22: process_group(group)
where 23: withCallingHandlers(if (tangle) process_tangle(group) else process_group(group),
error = function(e) {
setwd(wd)
cat(res, sep = "\n", file = output %n% "")
message("Quitting from lines ", paste(current_lines(i),
collapse = "-"), " (", knit_concord$get("infile"),
") ")
})
where 24: process_file(text, output)
where 25: knitr::knit(knit_input, knit_output, envir = envir, quiet = quiet)
where 26: rmarkdown::render(file, encoding = encoding, quiet = quiet, envir = globalenv(),
output_dir = getwd(), ...)
where 27: vweave_rmarkdown(...)
where 28: engine$weave(file, quiet = quiet, encoding = enc)
where 29: doTryCatch(return(expr), name, parentenv, handler)
where 30: tryCatchOne(expr, names, parentenv, handlers[[1L]])
where 31: tryCatchList(expr, classes, parentenv, handlers)
where 32: tryCatch({
engine$weave(file, quiet = quiet, encoding = enc)
setwd(startdir)
output <- find_vignette_product(name, by = "weave", engine = engine)
if (!have.makefile && vignette_is_tex(output)) {
texi2pdf(file = output, clean = FALSE, quiet = quiet)
output <- find_vignette_product(name, by = "texi2pdf",
engine = engine)
}
}, error = function(e) {
OK <<- FALSE
message(gettextf("Error: processing vignette '%s' failed with diagnostics:\n%s",
file, conditionMessage(e)))
})
where 33: tools:::.buildOneVignette("Intro_to_BSL.Rmd", "/data/gannet/ripley/R/packages/tests-clang/BreedingSchemeLanguage.Rcheck/vign_test/BreedingSchemeLanguage",
TRUE, FALSE, "Intro_to_BSL", "UTF-8", "/tmp/Rtmpc3wIiu/working_dir/Rtmp5OSybw/file3c4823ac8454.rds")
--- value of length: 2 type: logical ---
[1] FALSE FALSE
--- function from context ---
function (data, geno, pheno, GAUSS = FALSE, K = NULL, fixed = NULL,
covariate = NULL, PEV = FALSE, n.core = 1, theta.seq = NULL,
reduce = FALSE, R = NULL)
{
make.full <- function(X) {
svd.X <- svd(X)
r <- max(which(svd.X$d > 1e-08))
return(as.matrix(svd.X$u[, 1:r]))
}
names <- colnames(data)
ypos <- match(pheno, names)
if (is.na(ypos)) {
stop("Phenotype name does not appear in data.")
}
else {
y <- data[, ypos]
}
if (!is.null(R) & (length(R) != length(y))) {
stop("Length of R does not equal length of y")
}
not.miss <- which(!is.na(y))
if (length(not.miss) < length(y)) {
data <- data[not.miss, ]
y <- y[not.miss]
if (!is.null(R)) {
R <- R[not.miss]
}
}
n <- length(y)
X <- matrix(1, n, 1)
if (!is.null(fixed)) {
p <- length(fixed)
for (i in 1:p) {
xpos <- match(fixed[i], names)
xx <- factor(data[, xpos])
if (length(unique(xx)) > 1) {
X <- cbind(X, stats::model.matrix(~x - 1, data.frame(x = xx)))
}
}
}
if (!is.null(covariate)) {
p <- length(covariate)
for (i in 1:p) {
xpos <- match(covariate[i], names)
X <- cbind(X, data[, xpos])
}
}
gid.pos <- match(geno, names)
if (is.na(gid.pos)) {
stop("Genotype name does not appear in data.")
}
not.miss.gid <- as.character(unique(data[, gid.pos]))
if (is.null(K)) {
if (reduce) {
print("reduce=TRUE is not valid for independent genotypes. Proceeding without reduction.")
}
gid <- not.miss.gid
v <- length(gid)
Z <- matrix(0, n, v)
colnames(Z) <- gid
Z[cbind(1:n, match(data[, gid.pos], gid))] <- 1
X2 <- make.full(X)
ans <- rrBLUP::mixed.solve(y = y, X = X2, Z = Z, SE = PEV)
resid <- y - X2 %*% ans$beta - Z %*% ans$u
if (PEV) {
return(list(Vg = ans$Vu, Ve = ans$Ve, g = ans$u,
PEV = ans$u.SE^2, resid = resid))
}
else {
return(list(Vg = ans$Vu, Ve = ans$Ve, g = ans$u,
resid = resid))
}
}
else {
if (class(K) == "dist") {
K <- as.matrix(K)
}
gid <- rownames(K)
ix.pheno <- match(not.miss.gid, gid)
miss.pheno.gid <- which(is.na(ix.pheno))
if (length(miss.pheno.gid) > 0) {
stop(paste("The following lines have phenotypes but no genotypes:",
paste(not.miss.gid[miss.pheno.gid], collapse = " ")))
}
miss.gid <- setdiff(gid, not.miss.gid)
ix <- c(ix.pheno, match(miss.gid, gid))
K <- K[ix, ix]
v <- length(not.miss.gid)
Z <- matrix(0, n, v)
Z[cbind(1:n, match(data[, gid.pos], not.miss.gid))] <- 1
if (!is.null(R)) {
sqrt.R <- sqrt(R)
X2 <- X/sqrt.R
y2 <- y/sqrt.R
Z2 <- Z/sqrt.R
}
else {
X2 <- X
y2 <- y
Z2 <- Z
}
if ((n > v) & (reduce)) {
w <- sqrt(diag(crossprod(Z2)))
X2 <- make.full(crossprod(Z2, X2)/w)
y2 <- crossprod(Z2, y2)/w
Z2 <- cbind(diag(w), matrix(0, v, nrow(K) - v))
reduced <- TRUE
}
else {
X2 <- make.full(X2)
Z2 <- cbind(Z2, matrix(0, n, nrow(K) - v))
reduced <- FALSE
}
rm(X, Z, y)
if (!GAUSS) {
ans <- rrBLUP::mixed.solve(y = y2, X = X2, Z = Z2,
K = K, SE = PEV)
ix <- match(gid, rownames(ans$u))
if (reduced) {
resid <- NULL
}
else {
resid <- y2 - X2 %*% ans$beta - Z2 %*% ans$u
}
if (PEV) {
return(list(Vg = ans$Vu, Ve = ans$Ve, g = ans$u[ix],
PEV = ans$u.SE[ix]^2, resid = resid))
}
else {
return(list(Vg = ans$Vu, Ve = ans$Ve, g = ans$u[ix],
resid = resid))
}
}
else {
if (is.null(theta.seq)) {
theta <- setdiff(seq(0, max(K), length.out = 11),
0)
}
else {
theta <- theta.seq
}
n.profile <- length(theta)
ms.fun <- function(theta) {
soln <- list()
n.t <- length(theta)
for (i in 1:n.t) {
soln[[i]] <- rrBLUP::mixed.solve(y = y2, X = X2,
Z = Z2, K = exp(-(K/theta[i])^2), SE = PEV)
}
return(soln)
}
if (n.core > 1) {
it <- split(theta, factor(cut(theta, n.core,
labels = FALSE)))
soln <- unlist(snowfall::sfLapply(it, ms.fun,
mc.cores = n.core), recursive = FALSE)
}
else {
soln <- ms.fun(theta)
}
LL <- rep(0, n.profile)
for (i in 1:n.profile) {
LL[i] <- soln[[i]]$LL
}
ans <- soln[[which.max(LL)]]
profile <- cbind(theta, LL)
ix <- match(gid, rownames(ans$u))
if (reduced) {
resid <- NULL
}
else {
resid <- y2 - X2 %*% ans$beta - Z2 %*% ans$u
}
if (PEV) {
return(list(Vg = ans$Vu, Ve = ans$Ve, profile = profile,
g = ans$u[ix], PEV = ans$u.SE[ix]^2, resid = resid))
}
else {
return(list(Vg = ans$Vu, Ve = ans$Ve, profile = profile,
g = ans$u[ix], resid = resid))
}
}
}
}
<bytecode: 0x14876488>
<environment: 0x129fcab8>
--- function search by body ---
----------- END OF FAILURE REPORT --------------
Quitting from lines 85-89 (Intro_to_BSL.Rmd)
Error: processing vignette 'Intro_to_BSL.Rmd' failed with diagnostics:
the condition has length > 1
--- failed re-building ‘Intro_to_BSL.Rmd’
--- re-building ‘Load_existing_data.Rmd’ using rmarkdown
Loading required package: snowfall
Loading required package: snow
Loading required package: Rcpp
----------- FAILURE REPORT --------------
--- failure: the condition has length > 1 ---
--- srcref ---
:
--- package (from environment) ---
BreedingSchemeLanguage
--- call from context ---
kin.blup4.4(kbDat, geno = "phenoGID", pheno = "pValue", fixed = c("loc",
"year"), K = K, reduce = mt1ObsPerGID, R = kbDat$error)
--- call from argument ---
if (class(K) == "dist") {
K <- as.matrix(K)
}
--- R stacktrace ---
where 1: kin.blup4.4(kbDat, geno = "phenoGID", pheno = "pValue", fixed = c("loc",
"year"), K = K, reduce = mt1ObsPerGID, R = kbDat$error)
where 2: FUN(X[[i]], ...)
where 3: lapply(sims, predictValue.func, popID = popID, trainingPopID = trainingPopID,
locations = locations, years = years, sharingInfo = sharingInfo)
where 4: eval(substitute(expr), data, enclos = parent.frame())
where 5: eval(substitute(expr), data, enclos = parent.frame())
where 6: with.default(sEnv, {
if (exists("totalCost")) {
costs$popID <- ifelse(is.null(popID), max(budgetRec$popID),
popID)
totalCost <- totalCost + costs$predCost
}
if (!onlyCost) {
if (nCore > 1) {
snowfall::sfInit(parallel = T, cpus = nCore)
sims <- snowfall::sfLapply(sims, predictValue.func,
popID = popID, trainingPopID = trainingPopID,
locations = locations, years = years, sharingInfo = sharingInfo)
snowfall::sfStop()
}
else {
sims <- lapply(sims, predictValue.func, popID = popID,
trainingPopID = trainingPopID, locations = locations,
years = years, sharingInfo = sharingInfo)
}
}
})
where 7: with(sEnv, {
if (exists("totalCost")) {
costs$popID <- ifelse(is.null(popID), max(budgetRec$popID),
popID)
totalCost <- totalCost + costs$predCost
}
if (!onlyCost) {
if (nCore > 1) {
snowfall::sfInit(parallel = T, cpus = nCore)
sims <- snowfall::sfLapply(sims, predictValue.func,
popID = popID, trainingPopID = trainingPopID,
locations = locations, years = years, sharingInfo = sharingInfo)
snowfall::sfStop()
}
else {
sims <- lapply(sims, predictValue.func, popID = popID,
trainingPopID = trainingPopID, locations = locations,
years = years, sharingInfo = sharingInfo)
}
}
})
where 8: predictValue()
where 9: eval(expr, envir, enclos)
where 10: eval(expr, envir, enclos)
where 11: withVisible(eval(expr, envir, enclos))
where 12: withCallingHandlers(withVisible(eval(expr, envir, enclos)), warning = wHandler,
error = eHandler, message = mHandler)
where 13: handle(ev <- withCallingHandlers(withVisible(eval(expr, envir,
enclos)), warning = wHandler, error = eHandler, message = mHandler))
where 14: timing_fn(handle(ev <- withCallingHandlers(withVisible(eval(expr,
envir, enclos)), warning = wHandler, error = eHandler, message = mHandler)))
where 15: evaluate_call(expr, parsed$src[[i]], envir = envir, enclos = enclos,
debug = debug, last = i == length(out), use_try = stop_on_error !=
2L, keep_warning = keep_warning, keep_message = keep_message,
output_handler = output_handler, include_timing = include_timing)
where 16: evaluate::evaluate(...)
where 17: evaluate(code, envir = env, new_device = FALSE, keep_warning = !isFALSE(options$warning),
keep_message = !isFALSE(options$message), stop_on_error = if (options$error &&
options$include) 0L else 2L, output_handler = knit_handlers(options$render,
options))
where 18: in_dir(input_dir(), evaluate(code, envir = env, new_device = FALSE,
keep_warning = !isFALSE(options$warning), keep_message = !isFALSE(options$message),
stop_on_error = if (options$error && options$include) 0L else 2L,
output_handler = knit_handlers(options$render, options)))
where 19: block_exec(params)
where 20: call_block(x)
where 21: process_group.block(group)
where 22: process_group(group)
where 23: withCallingHandlers(if (tangle) process_tangle(group) else process_group(group),
error = function(e) {
setwd(wd)
cat(res, sep = "\n", file = output %n% "")
message("Quitting from lines ", paste(current_lines(i),
collapse = "-"), " (", knit_concord$get("infile"),
") ")
})
where 24: process_file(text, output)
where 25: knitr::knit(knit_input, knit_output, envir = envir, quiet = quiet)
where 26: rmarkdown::render(file, encoding = encoding, quiet = quiet, envir = globalenv(),
output_dir = getwd(), ...)
where 27: vweave_rmarkdown(...)
where 28: engine$weave(file, quiet = quiet, encoding = enc)
where 29: doTryCatch(return(expr), name, parentenv, handler)
where 30: tryCatchOne(expr, names, parentenv, handlers[[1L]])
where 31: tryCatchList(expr, classes, parentenv, handlers)
where 32: tryCatch({
engine$weave(file, quiet = quiet, encoding = enc)
setwd(startdir)
output <- find_vignette_product(name, by = "weave", engine = engine)
if (!have.makefile && vignette_is_tex(output)) {
texi2pdf(file = output, clean = FALSE, quiet = quiet)
output <- find_vignette_product(name, by = "texi2pdf",
engine = engine)
}
}, error = function(e) {
OK <<- FALSE
message(gettextf("Error: processing vignette '%s' failed with diagnostics:\n%s",
file, conditionMessage(e)))
})
where 33: tools:::.buildOneVignette("Load_existing_data.Rmd", "/data/gannet/ripley/R/packages/tests-clang/BreedingSchemeLanguage.Rcheck/vign_test/BreedingSchemeLanguage",
TRUE, FALSE, "Load_existing_data", "UTF-8", "/tmp/Rtmpc3wIiu/working_dir/Rtmp5OSybw/file3c485891636d.rds")
--- value of length: 2 type: logical ---
[1] FALSE FALSE
--- function from context ---
function (data, geno, pheno, GAUSS = FALSE, K = NULL, fixed = NULL,
covariate = NULL, PEV = FALSE, n.core = 1, theta.seq = NULL,
reduce = FALSE, R = NULL)
{
make.full <- function(X) {
svd.X <- svd(X)
r <- max(which(svd.X$d > 1e-08))
return(as.matrix(svd.X$u[, 1:r]))
}
names <- colnames(data)
ypos <- match(pheno, names)
if (is.na(ypos)) {
stop("Phenotype name does not appear in data.")
}
else {
y <- data[, ypos]
}
if (!is.null(R) & (length(R) != length(y))) {
stop("Length of R does not equal length of y")
}
not.miss <- which(!is.na(y))
if (length(not.miss) < length(y)) {
data <- data[not.miss, ]
y <- y[not.miss]
if (!is.null(R)) {
R <- R[not.miss]
}
}
n <- length(y)
X <- matrix(1, n, 1)
if (!is.null(fixed)) {
p <- length(fixed)
for (i in 1:p) {
xpos <- match(fixed[i], names)
xx <- factor(data[, xpos])
if (length(unique(xx)) > 1) {
X <- cbind(X, stats::model.matrix(~x - 1, data.frame(x = xx)))
}
}
}
if (!is.null(covariate)) {
p <- length(covariate)
for (i in 1:p) {
xpos <- match(covariate[i], names)
X <- cbind(X, data[, xpos])
}
}
gid.pos <- match(geno, names)
if (is.na(gid.pos)) {
stop("Genotype name does not appear in data.")
}
not.miss.gid <- as.character(unique(data[, gid.pos]))
if (is.null(K)) {
if (reduce) {
print("reduce=TRUE is not valid for independent genotypes. Proceeding without reduction.")
}
gid <- not.miss.gid
v <- length(gid)
Z <- matrix(0, n, v)
colnames(Z) <- gid
Z[cbind(1:n, match(data[, gid.pos], gid))] <- 1
X2 <- make.full(X)
ans <- rrBLUP::mixed.solve(y = y, X = X2, Z = Z, SE = PEV)
resid <- y - X2 %*% ans$beta - Z %*% ans$u
if (PEV) {
return(list(Vg = ans$Vu, Ve = ans$Ve, g = ans$u,
PEV = ans$u.SE^2, resid = resid))
}
else {
return(list(Vg = ans$Vu, Ve = ans$Ve, g = ans$u,
resid = resid))
}
}
else {
if (class(K) == "dist") {
K <- as.matrix(K)
}
gid <- rownames(K)
ix.pheno <- match(not.miss.gid, gid)
miss.pheno.gid <- which(is.na(ix.pheno))
if (length(miss.pheno.gid) > 0) {
stop(paste("The following lines have phenotypes but no genotypes:",
paste(not.miss.gid[miss.pheno.gid], collapse = " ")))
}
miss.gid <- setdiff(gid, not.miss.gid)
ix <- c(ix.pheno, match(miss.gid, gid))
K <- K[ix, ix]
v <- length(not.miss.gid)
Z <- matrix(0, n, v)
Z[cbind(1:n, match(data[, gid.pos], not.miss.gid))] <- 1
if (!is.null(R)) {
sqrt.R <- sqrt(R)
X2 <- X/sqrt.R
y2 <- y/sqrt.R
Z2 <- Z/sqrt.R
}
else {
X2 <- X
y2 <- y
Z2 <- Z
}
if ((n > v) & (reduce)) {
w <- sqrt(diag(crossprod(Z2)))
X2 <- make.full(crossprod(Z2, X2)/w)
y2 <- crossprod(Z2, y2)/w
Z2 <- cbind(diag(w), matrix(0, v, nrow(K) - v))
reduced <- TRUE
}
else {
X2 <- make.full(X2)
Z2 <- cbind(Z2, matrix(0, n, nrow(K) - v))
reduced <- FALSE
}
rm(X, Z, y)
if (!GAUSS) {
ans <- rrBLUP::mixed.solve(y = y2, X = X2, Z = Z2,
K = K, SE = PEV)
ix <- match(gid, rownames(ans$u))
if (reduced) {
resid <- NULL
}
else {
resid <- y2 - X2 %*% ans$beta - Z2 %*% ans$u
}
if (PEV) {
return(list(Vg = ans$Vu, Ve = ans$Ve, g = ans$u[ix],
PEV = ans$u.SE[ix]^2, resid = resid))
}
else {
return(list(Vg = ans$Vu, Ve = ans$Ve, g = ans$u[ix],
resid = resid))
}
}
else {
if (is.null(theta.seq)) {
theta <- setdiff(seq(0, max(K), length.out = 11),
0)
}
else {
theta <- theta.seq
}
n.profile <- length(theta)
ms.fun <- function(theta) {
soln <- list()
n.t <- length(theta)
for (i in 1:n.t) {
soln[[i]] <- rrBLUP::mixed.solve(y = y2, X = X2,
Z = Z2, K = exp(-(K/theta[i])^2), SE = PEV)
}
return(soln)
}
if (n.core > 1) {
it <- split(theta, factor(cut(theta, n.core,
labels = FALSE)))
soln <- unlist(snowfall::sfLapply(it, ms.fun,
mc.cores = n.core), recursive = FALSE)
}
else {
soln <- ms.fun(theta)
}
LL <- rep(0, n.profile)
for (i in 1:n.profile) {
LL[i] <- soln[[i]]$LL
}
ans <- soln[[which.max(LL)]]
profile <- cbind(theta, LL)
ix <- match(gid, rownames(ans$u))
if (reduced) {
resid <- NULL
}
else {
resid <- y2 - X2 %*% ans$beta - Z2 %*% ans$u
}
if (PEV) {
return(list(Vg = ans$Vu, Ve = ans$Ve, profile = profile,
g = ans$u[ix], PEV = ans$u.SE[ix]^2, resid = resid))
}
else {
return(list(Vg = ans$Vu, Ve = ans$Ve, profile = profile,
g = ans$u[ix], resid = resid))
}
}
}
}
<bytecode: 0x75d4a18>
<environment: 0x75ab308>
--- function search by body ---
----------- END OF FAILURE REPORT --------------
Quitting from lines 65-70 (Load_existing_data.Rmd)
Error: processing vignette 'Load_existing_data.Rmd' failed with diagnostics:
the condition has length > 1
--- failed re-building ‘Load_existing_data.Rmd’
--- re-building ‘Optimize_parameter_vector.Rmd’ using rmarkdown
Loading required package: snowfall
Loading required package: snow
Loading required package: Rcpp
----------- FAILURE REPORT --------------
--- failure: the condition has length > 1 ---
--- srcref ---
:
--- package (from environment) ---
BreedingSchemeLanguage
--- call from context ---
kin.blup4.4(kbDat, geno = "phenoGID", pheno = "pValue", fixed = c("loc",
"year"), K = K, reduce = mt1ObsPerGID, R = kbDat$error)
--- call from argument ---
if (class(K) == "dist") {
K <- as.matrix(K)
}
--- R stacktrace ---
where 1: kin.blup4.4(kbDat, geno = "phenoGID", pheno = "pValue", fixed = c("loc",
"year"), K = K, reduce = mt1ObsPerGID, R = kbDat$error)
where 2: FUN(X[[i]], ...)
where 3: lapply(sims, predictValue.func, popID = popID, trainingPopID = trainingPopID,
locations = locations, years = years, sharingInfo = sharingInfo)
where 4: eval(substitute(expr), data, enclos = parent.frame())
where 5: eval(substitute(expr), data, enclos = parent.frame())
where 6: with.default(sEnv, {
if (exists("totalCost")) {
costs$popID <- ifelse(is.null(popID), max(budgetRec$popID),
popID)
totalCost <- totalCost + costs$predCost
}
if (!onlyCost) {
if (nCore > 1) {
snowfall::sfInit(parallel = T, cpus = nCore)
sims <- snowfall::sfLapply(sims, predictValue.func,
popID = popID, trainingPopID = trainingPopID,
locations = locations, years = years, sharingInfo = sharingInfo)
snowfall::sfStop()
}
else {
sims <- lapply(sims, predictValue.func, popID = popID,
trainingPopID = trainingPopID, locations = locations,
years = years, sharingInfo = sharingInfo)
}
}
})
where 7: with(sEnv, {
if (exists("totalCost")) {
costs$popID <- ifelse(is.null(popID), max(budgetRec$popID),
popID)
totalCost <- totalCost + costs$predCost
}
if (!onlyCost) {
if (nCore > 1) {
snowfall::sfInit(parallel = T, cpus = nCore)
sims <- snowfall::sfLapply(sims, predictValue.func,
popID = popID, trainingPopID = trainingPopID,
locations = locations, years = years, sharingInfo = sharingInfo)
snowfall::sfStop()
}
else {
sims <- lapply(sims, predictValue.func, popID = popID,
trainingPopID = trainingPopID, locations = locations,
years = years, sharingInfo = sharingInfo)
}
}
})
where 8: predictValue()
where 9: eval(ei, envir)
where 10: eval(ei, envir)
where 11: withVisible(eval(ei, envir))
where 12: source(schemeFileName, local = TRUE)
where 13: testParameterOptimality(schemeFileName = schemeScriptPath, parmList = parmList,
objectiveFunc = objFunc, budget = allowableBudget)
where 14: eval(expr, envir, enclos)
where 15: eval(expr, envir, enclos)
where 16: withVisible(eval(expr, envir, enclos))
where 17: withCallingHandlers(withVisible(eval(expr, envir, enclos)), warning = wHandler,
error = eHandler, message = mHandler)
where 18: handle(ev <- withCallingHandlers(withVisible(eval(expr, envir,
enclos)), warning = wHandler, error = eHandler, message = mHandler))
where 19: timing_fn(handle(ev <- withCallingHandlers(withVisible(eval(expr,
envir, enclos)), warning = wHandler, error = eHandler, message = mHandler)))
where 20: evaluate_call(expr, parsed$src[[i]], envir = envir, enclos = enclos,
debug = debug, last = i == length(out), use_try = stop_on_error !=
2L, keep_warning = keep_warning, keep_message = keep_message,
output_handler = output_handler, include_timing = include_timing)
where 21: evaluate::evaluate(...)
where 22: evaluate(code, envir = env, new_device = FALSE, keep_warning = !isFALSE(options$warning),
keep_message = !isFALSE(options$message), stop_on_error = if (options$error &&
options$include) 0L else 2L, output_handler = knit_handlers(options$render,
options))
where 23: in_dir(input_dir(), evaluate(code, envir = env, new_device = FALSE,
keep_warning = !isFALSE(options$warning), keep_message = !isFALSE(options$message),
stop_on_error = if (options$error && options$include) 0L else 2L,
output_handler = knit_handlers(options$render, options)))
where 24: block_exec(params)
where 25: call_block(x)
where 26: process_group.block(group)
where 27: process_group(group)
where 28: withCallingHandlers(if (tangle) process_tangle(group) else process_group(group),
error = function(e) {
setwd(wd)
cat(res, sep = "\n", file = output %n% "")
message("Quitting from lines ", paste(current_lines(i),
collapse = "-"), " (", knit_concord$get("infile"),
") ")
})
where 29: process_file(text, output)
where 30: knitr::knit(knit_input, knit_output, envir = envir, quiet = quiet)
where 31: rmarkdown::render(file, encoding = encoding, quiet = quiet, envir = globalenv(),
output_dir = getwd(), ...)
where 32: vweave_rmarkdown(...)
where 33: engine$weave(file, quiet = quiet, encoding = enc)
where 34: doTryCatch(return(expr), name, parentenv, handler)
where 35: tryCatchOne(expr, names, parentenv, handlers[[1L]])
where 36: tryCatchList(expr, classes, parentenv, handlers)
where 37: tryCatch({
engine$weave(file, quiet = quiet, encoding = enc)
setwd(startdir)
output <- find_vignette_product(name, by = "weave", engine = engine)
if (!have.makefile && vignette_is_tex(output)) {
texi2pdf(file = output, clean = FALSE, quiet = quiet)
output <- find_vignette_product(name, by = "texi2pdf",
engine = engine)
}
}, error = function(e) {
OK <<- FALSE
message(gettextf("Error: processing vignette '%s' failed with diagnostics:\n%s",
file, conditionMessage(e)))
})
where 38: tools:::.buildOneVignette("Optimize_parameter_vector.Rmd", "/data/gannet/ripley/R/packages/tests-clang/BreedingSchemeLanguage.Rcheck/vign_test/BreedingSchemeLanguage",
TRUE, FALSE, "Optimize_parameter_vector", "UTF-8", "/tmp/Rtmpc3wIiu/working_dir/Rtmp5OSybw/file3c483b463751.rds")
--- value of length: 2 type: logical ---
[1] FALSE FALSE
--- function from context ---
function (data, geno, pheno, GAUSS = FALSE, K = NULL, fixed = NULL,
covariate = NULL, PEV = FALSE, n.core = 1, theta.seq = NULL,
reduce = FALSE, R = NULL)
{
make.full <- function(X) {
svd.X <- svd(X)
r <- max(which(svd.X$d > 1e-08))
return(as.matrix(svd.X$u[, 1:r]))
}
names <- colnames(data)
ypos <- match(pheno, names)
if (is.na(ypos)) {
stop("Phenotype name does not appear in data.")
}
else {
y <- data[, ypos]
}
if (!is.null(R) & (length(R) != length(y))) {
stop("Length of R does not equal length of y")
}
not.miss <- which(!is.na(y))
if (length(not.miss) < length(y)) {
data <- data[not.miss, ]
y <- y[not.miss]
if (!is.null(R)) {
R <- R[not.miss]
}
}
n <- length(y)
X <- matrix(1, n, 1)
if (!is.null(fixed)) {
p <- length(fixed)
for (i in 1:p) {
xpos <- match(fixed[i], names)
xx <- factor(data[, xpos])
if (length(unique(xx)) > 1) {
X <- cbind(X, stats::model.matrix(~x - 1, data.frame(x = xx)))
}
}
}
if (!is.null(covariate)) {
p <- length(covariate)
for (i in 1:p) {
xpos <- match(covariate[i], names)
X <- cbind(X, data[, xpos])
}
}
gid.pos <- match(geno, names)
if (is.na(gid.pos)) {
stop("Genotype name does not appear in data.")
}
not.miss.gid <- as.character(unique(data[, gid.pos]))
if (is.null(K)) {
if (reduce) {
print("reduce=TRUE is not valid for independent genotypes. Proceeding without reduction.")
}
gid <- not.miss.gid
v <- length(gid)
Z <- matrix(0, n, v)
colnames(Z) <- gid
Z[cbind(1:n, match(data[, gid.pos], gid))] <- 1
X2 <- make.full(X)
ans <- rrBLUP::mixed.solve(y = y, X = X2, Z = Z, SE = PEV)
resid <- y - X2 %*% ans$beta - Z %*% ans$u
if (PEV) {
return(list(Vg = ans$Vu, Ve = ans$Ve, g = ans$u,
PEV = ans$u.SE^2, resid = resid))
}
else {
return(list(Vg = ans$Vu, Ve = ans$Ve, g = ans$u,
resid = resid))
}
}
else {
if (class(K) == "dist") {
K <- as.matrix(K)
}
gid <- rownames(K)
ix.pheno <- match(not.miss.gid, gid)
miss.pheno.gid <- which(is.na(ix.pheno))
if (length(miss.pheno.gid) > 0) {
stop(paste("The following lines have phenotypes but no genotypes:",
paste(not.miss.gid[miss.pheno.gid], collapse = " ")))
}
miss.gid <- setdiff(gid, not.miss.gid)
ix <- c(ix.pheno, match(miss.gid, gid))
K <- K[ix, ix]
v <- length(not.miss.gid)
Z <- matrix(0, n, v)
Z[cbind(1:n, match(data[, gid.pos], not.miss.gid))] <- 1
if (!is.null(R)) {
sqrt.R <- sqrt(R)
X2 <- X/sqrt.R
y2 <- y/sqrt.R
Z2 <- Z/sqrt.R
}
else {
X2 <- X
y2 <- y
Z2 <- Z
}
if ((n > v) & (reduce)) {
w <- sqrt(diag(crossprod(Z2)))
X2 <- make.full(crossprod(Z2, X2)/w)
y2 <- crossprod(Z2, y2)/w
Z2 <- cbind(diag(w), matrix(0, v, nrow(K) - v))
reduced <- TRUE
}
else {
X2 <- make.full(X2)
Z2 <- cbind(Z2, matrix(0, n, nrow(K) - v))
reduced <- FALSE
}
rm(X, Z, y)
if (!GAUSS) {
ans <- rrBLUP::mixed.solve(y = y2, X = X2, Z = Z2,
K = K, SE = PEV)
ix <- match(gid, rownames(ans$u))
if (reduced) {
resid <- NULL
}
else {
resid <- y2 - X2 %*% ans$beta - Z2 %*% ans$u
}
if (PEV) {
return(list(Vg = ans$Vu, Ve = ans$Ve, g = ans$u[ix],
PEV = ans$u.SE[ix]^2, resid = resid))
}
else {
return(list(Vg = ans$Vu, Ve = ans$Ve, g = ans$u[ix],
resid = resid))
}
}
else {
if (is.null(theta.seq)) {
theta <- setdiff(seq(0, max(K), length.out = 11),
0)
}
else {
theta <- theta.seq
}
n.profile <- length(theta)
ms.fun <- function(theta) {
soln <- list()
n.t <- length(theta)
for (i in 1:n.t) {
soln[[i]] <- rrBLUP::mixed.solve(y = y2, X = X2,
Z = Z2, K = exp(-(K/theta[i])^2), SE = PEV)
}
return(soln)
}
if (n.core > 1) {
it <- split(theta, factor(cut(theta, n.core,
labels = FALSE)))
soln <- unlist(snowfall::sfLapply(it, ms.fun,
mc.cores = n.core), recursive = FALSE)
}
else {
soln <- ms.fun(theta)
}
LL <- rep(0, n.profile)
for (i in 1:n.profile) {
LL[i] <- soln[[i]]$LL
}
ans <- soln[[which.max(LL)]]
profile <- cbind(theta, LL)
ix <- match(gid, rownames(ans$u))
if (reduced) {
resid <- NULL
}
else {
resid <- y2 - X2 %*% ans$beta - Z2 %*% ans$u
}
if (PEV) {
return(list(Vg = ans$Vu, Ve = ans$Ve, profile = profile,
g = ans$u[ix], PEV = ans$u.SE[ix]^2, resid = resid))
}
else {
return(list(Vg = ans$Vu, Ve = ans$Ve, profile = profile,
g = ans$u[ix], resid = resid))
}
}
}
}
<bytecode: 0x55733f8>
<environment: 0xb992460>
--- function search by body ---
----------- END OF FAILURE REPORT --------------
Quitting from lines 68-106 (Optimize_parameter_vector.Rmd)
Error: processing vignette 'Optimize_parameter_vector.Rmd' failed with diagnostics:
the condition has length > 1
--- failed re-building ‘Optimize_parameter_vector.Rmd’
--- re-building ‘Within_family_selection.Rmd’ using rmarkdown
Loading required package: snowfall
Loading required package: snow
Loading required package: Rcpp
--- finished re-building ‘Within_family_selection.Rmd’
SUMMARY: processing the following files failed:
‘Intro_to_BSL.Rmd’ ‘Load_existing_data.Rmd’
‘Optimize_parameter_vector.Rmd’
Error: Vignette re-building failed.
Execution halted
Flavor: r-devel-linux-x86_64-fedora-clang
Version: 0.9.6
Check: re-building of vignette outputs
Result: WARN
Error(s) in re-building vignettes:
--- re-building ‘Intro_to_BSL.Rmd’ using rmarkdown
----------- FAILURE REPORT --------------
--- failure: the condition has length > 1 ---
--- srcref ---
:
--- package (from environment) ---
BreedingSchemeLanguage
--- call from context ---
kin.blup4.4(kbDat, geno = "phenoGID", pheno = "pValue", fixed = c("loc",
"year"), K = K, reduce = mt1ObsPerGID, R = kbDat$error)
--- call from argument ---
if (class(K) == "dist") {
K <- as.matrix(K)
}
--- R stacktrace ---
where 1: kin.blup4.4(kbDat, geno = "phenoGID", pheno = "pValue", fixed = c("loc",
"year"), K = K, reduce = mt1ObsPerGID, R = kbDat$error)
where 2: FUN(X[[i]], ...)
where 3: lapply(sims, predictValue.func, popID = popID, trainingPopID = trainingPopID,
locations = locations, years = years, sharingInfo = sharingInfo)
where 4: eval(substitute(expr), data, enclos = parent.frame())
where 5: eval(substitute(expr), data, enclos = parent.frame())
where 6: with.default(sEnv, {
if (exists("totalCost")) {
costs$popID <- ifelse(is.null(popID), max(budgetRec$popID),
popID)
totalCost <- totalCost + costs$predCost
}
if (!onlyCost) {
if (nCore > 1) {
snowfall::sfInit(parallel = T, cpus = nCore)
sims <- snowfall::sfLapply(sims, predictValue.func,
popID = popID, trainingPopID = trainingPopID,
locations = locations, years = years, sharingInfo = sharingInfo)
snowfall::sfStop()
}
else {
sims <- lapply(sims, predictValue.func, popID = popID,
trainingPopID = trainingPopID, locations = locations,
years = years, sharingInfo = sharingInfo)
}
}
})
where 7: with(sEnv, {
if (exists("totalCost")) {
costs$popID <- ifelse(is.null(popID), max(budgetRec$popID),
popID)
totalCost <- totalCost + costs$predCost
}
if (!onlyCost) {
if (nCore > 1) {
snowfall::sfInit(parallel = T, cpus = nCore)
sims <- snowfall::sfLapply(sims, predictValue.func,
popID = popID, trainingPopID = trainingPopID,
locations = locations, years = years, sharingInfo = sharingInfo)
snowfall::sfStop()
}
else {
sims <- lapply(sims, predictValue.func, popID = popID,
trainingPopID = trainingPopID, locations = locations,
years = years, sharingInfo = sharingInfo)
}
}
})
where 8: predictValue(sharingInfo = "pedigree")
where 9: eval(expr, envir, enclos)
where 10: eval(expr, envir, enclos)
where 11: withVisible(eval(expr, envir, enclos))
where 12: withCallingHandlers(withVisible(eval(expr, envir, enclos)), warning = wHandler,
error = eHandler, message = mHandler)
where 13: handle(ev <- withCallingHandlers(withVisible(eval(expr, envir,
enclos)), warning = wHandler, error = eHandler, message = mHandler))
where 14: timing_fn(handle(ev <- withCallingHandlers(withVisible(eval(expr,
envir, enclos)), warning = wHandler, error = eHandler, message = mHandler)))
where 15: evaluate_call(expr, parsed$src[[i]], envir = envir, enclos = enclos,
debug = debug, last = i == length(out), use_try = stop_on_error !=
2L, keep_warning = keep_warning, keep_message = keep_message,
output_handler = output_handler, include_timing = include_timing)
where 16: evaluate::evaluate(...)
where 17: evaluate(code, envir = env, new_device = FALSE, keep_warning = !isFALSE(options$warning),
keep_message = !isFALSE(options$message), stop_on_error = if (options$error &&
options$include) 0L else 2L, output_handler = knit_handlers(options$render,
options))
where 18: in_dir(input_dir(), evaluate(code, envir = env, new_device = FALSE,
keep_warning = !isFALSE(options$warning), keep_message = !isFALSE(options$message),
stop_on_error = if (options$error && options$include) 0L else 2L,
output_handler = knit_handlers(options$render, options)))
where 19: block_exec(params)
where 20: call_block(x)
where 21: process_group.block(group)
where 22: process_group(group)
where 23: withCallingHandlers(if (tangle) process_tangle(group) else process_group(group),
error = function(e) {
setwd(wd)
cat(res, sep = "\n", file = output %n% "")
message("Quitting from lines ", paste(current_lines(i),
collapse = "-"), " (", knit_concord$get("infile"),
") ")
})
where 24: process_file(text, output)
where 25: knitr::knit(knit_input, knit_output, envir = envir, quiet = quiet)
where 26: rmarkdown::render(file, encoding = encoding, quiet = quiet, envir = globalenv(),
output_dir = getwd(), ...)
where 27: vweave_rmarkdown(...)
where 28: engine$weave(file, quiet = quiet, encoding = enc)
where 29: doTryCatch(return(expr), name, parentenv, handler)
where 30: tryCatchOne(expr, names, parentenv, handlers[[1L]])
where 31: tryCatchList(expr, classes, parentenv, handlers)
where 32: tryCatch({
engine$weave(file, quiet = quiet, encoding = enc)
setwd(startdir)
output <- find_vignette_product(name, by = "weave", engine = engine)
if (!have.makefile && vignette_is_tex(output)) {
texi2pdf(file = output, clean = FALSE, quiet = quiet)
output <- find_vignette_product(name, by = "texi2pdf",
engine = engine)
}
}, error = function(e) {
OK <<- FALSE
message(gettextf("Error: processing vignette '%s' failed with diagnostics:\n%s",
file, conditionMessage(e)))
})
where 33: tools:::.buildOneVignette("Intro_to_BSL.Rmd", "/data/gannet/ripley/R/packages/tests-devel/BreedingSchemeLanguage.Rcheck/vign_test/BreedingSchemeLanguage",
TRUE, FALSE, "Intro_to_BSL", "UTF-8", "/tmp/RtmpQLz0y2/working_dir/RtmpB4KaJW/file154173d3256a.rds")
--- value of length: 2 type: logical ---
[1] FALSE FALSE
--- function from context ---
function (data, geno, pheno, GAUSS = FALSE, K = NULL, fixed = NULL,
covariate = NULL, PEV = FALSE, n.core = 1, theta.seq = NULL,
reduce = FALSE, R = NULL)
{
make.full <- function(X) {
svd.X <- svd(X)
r <- max(which(svd.X$d > 1e-08))
return(as.matrix(svd.X$u[, 1:r]))
}
names <- colnames(data)
ypos <- match(pheno, names)
if (is.na(ypos)) {
stop("Phenotype name does not appear in data.")
}
else {
y <- data[, ypos]
}
if (!is.null(R) & (length(R) != length(y))) {
stop("Length of R does not equal length of y")
}
not.miss <- which(!is.na(y))
if (length(not.miss) < length(y)) {
data <- data[not.miss, ]
y <- y[not.miss]
if (!is.null(R)) {
R <- R[not.miss]
}
}
n <- length(y)
X <- matrix(1, n, 1)
if (!is.null(fixed)) {
p <- length(fixed)
for (i in 1:p) {
xpos <- match(fixed[i], names)
xx <- factor(data[, xpos])
if (length(unique(xx)) > 1) {
X <- cbind(X, stats::model.matrix(~x - 1, data.frame(x = xx)))
}
}
}
if (!is.null(covariate)) {
p <- length(covariate)
for (i in 1:p) {
xpos <- match(covariate[i], names)
X <- cbind(X, data[, xpos])
}
}
gid.pos <- match(geno, names)
if (is.na(gid.pos)) {
stop("Genotype name does not appear in data.")
}
not.miss.gid <- as.character(unique(data[, gid.pos]))
if (is.null(K)) {
if (reduce) {
print("reduce=TRUE is not valid for independent genotypes. Proceeding without reduction.")
}
gid <- not.miss.gid
v <- length(gid)
Z <- matrix(0, n, v)
colnames(Z) <- gid
Z[cbind(1:n, match(data[, gid.pos], gid))] <- 1
X2 <- make.full(X)
ans <- rrBLUP::mixed.solve(y = y, X = X2, Z = Z, SE = PEV)
resid <- y - X2 %*% ans$beta - Z %*% ans$u
if (PEV) {
return(list(Vg = ans$Vu, Ve = ans$Ve, g = ans$u,
PEV = ans$u.SE^2, resid = resid))
}
else {
return(list(Vg = ans$Vu, Ve = ans$Ve, g = ans$u,
resid = resid))
}
}
else {
if (class(K) == "dist") {
K <- as.matrix(K)
}
gid <- rownames(K)
ix.pheno <- match(not.miss.gid, gid)
miss.pheno.gid <- which(is.na(ix.pheno))
if (length(miss.pheno.gid) > 0) {
stop(paste("The following lines have phenotypes but no genotypes:",
paste(not.miss.gid[miss.pheno.gid], collapse = " ")))
}
miss.gid <- setdiff(gid, not.miss.gid)
ix <- c(ix.pheno, match(miss.gid, gid))
K <- K[ix, ix]
v <- length(not.miss.gid)
Z <- matrix(0, n, v)
Z[cbind(1:n, match(data[, gid.pos], not.miss.gid))] <- 1
if (!is.null(R)) {
sqrt.R <- sqrt(R)
X2 <- X/sqrt.R
y2 <- y/sqrt.R
Z2 <- Z/sqrt.R
}
else {
X2 <- X
y2 <- y
Z2 <- Z
}
if ((n > v) & (reduce)) {
w <- sqrt(diag(crossprod(Z2)))
X2 <- make.full(crossprod(Z2, X2)/w)
y2 <- crossprod(Z2, y2)/w
Z2 <- cbind(diag(w), matrix(0, v, nrow(K) - v))
reduced <- TRUE
}
else {
X2 <- make.full(X2)
Z2 <- cbind(Z2, matrix(0, n, nrow(K) - v))
reduced <- FALSE
}
rm(X, Z, y)
if (!GAUSS) {
ans <- rrBLUP::mixed.solve(y = y2, X = X2, Z = Z2,
K = K, SE = PEV)
ix <- match(gid, rownames(ans$u))
if (reduced) {
resid <- NULL
}
else {
resid <- y2 - X2 %*% ans$beta - Z2 %*% ans$u
}
if (PEV) {
return(list(Vg = ans$Vu, Ve = ans$Ve, g = ans$u[ix],
PEV = ans$u.SE[ix]^2, resid = resid))
}
else {
return(list(Vg = ans$Vu, Ve = ans$Ve, g = ans$u[ix],
resid = resid))
}
}
else {
if (is.null(theta.seq)) {
theta <- setdiff(seq(0, max(K), length.out = 11),
0)
}
else {
theta <- theta.seq
}
n.profile <- length(theta)
ms.fun <- function(theta) {
soln <- list()
n.t <- length(theta)
for (i in 1:n.t) {
soln[[i]] <- rrBLUP::mixed.solve(y = y2, X = X2,
Z = Z2, K = exp(-(K/theta[i])^2), SE = PEV)
}
return(soln)
}
if (n.core > 1) {
it <- split(theta, factor(cut(theta, n.core,
labels = FALSE)))
soln <- unlist(snowfall::sfLapply(it, ms.fun,
mc.cores = n.core), recursive = FALSE)
}
else {
soln <- ms.fun(theta)
}
LL <- rep(0, n.profile)
for (i in 1:n.profile) {
LL[i] <- soln[[i]]$LL
}
ans <- soln[[which.max(LL)]]
profile <- cbind(theta, LL)
ix <- match(gid, rownames(ans$u))
if (reduced) {
resid <- NULL
}
else {
resid <- y2 - X2 %*% ans$beta - Z2 %*% ans$u
}
if (PEV) {
return(list(Vg = ans$Vu, Ve = ans$Ve, profile = profile,
g = ans$u[ix], PEV = ans$u.SE[ix]^2, resid = resid))
}
else {
return(list(Vg = ans$Vu, Ve = ans$Ve, profile = profile,
g = ans$u[ix], resid = resid))
}
}
}
}
<bytecode: 0x16578d90>
<environment: 0x161940c0>
--- function search by body ---
----------- END OF FAILURE REPORT --------------
Quitting from lines 85-89 (Intro_to_BSL.Rmd)
Error: processing vignette 'Intro_to_BSL.Rmd' failed with diagnostics:
the condition has length > 1
--- failed re-building ‘Intro_to_BSL.Rmd’
--- re-building ‘Load_existing_data.Rmd’ using rmarkdown
Loading required package: snowfall
Loading required package: snow
Loading required package: Rcpp
----------- FAILURE REPORT --------------
--- failure: the condition has length > 1 ---
--- srcref ---
:
--- package (from environment) ---
BreedingSchemeLanguage
--- call from context ---
kin.blup4.4(kbDat, geno = "phenoGID", pheno = "pValue", fixed = c("loc",
"year"), K = K, reduce = mt1ObsPerGID, R = kbDat$error)
--- call from argument ---
if (class(K) == "dist") {
K <- as.matrix(K)
}
--- R stacktrace ---
where 1: kin.blup4.4(kbDat, geno = "phenoGID", pheno = "pValue", fixed = c("loc",
"year"), K = K, reduce = mt1ObsPerGID, R = kbDat$error)
where 2: FUN(X[[i]], ...)
where 3: lapply(sims, predictValue.func, popID = popID, trainingPopID = trainingPopID,
locations = locations, years = years, sharingInfo = sharingInfo)
where 4: eval(substitute(expr), data, enclos = parent.frame())
where 5: eval(substitute(expr), data, enclos = parent.frame())
where 6: with.default(sEnv, {
if (exists("totalCost")) {
costs$popID <- ifelse(is.null(popID), max(budgetRec$popID),
popID)
totalCost <- totalCost + costs$predCost
}
if (!onlyCost) {
if (nCore > 1) {
snowfall::sfInit(parallel = T, cpus = nCore)
sims <- snowfall::sfLapply(sims, predictValue.func,
popID = popID, trainingPopID = trainingPopID,
locations = locations, years = years, sharingInfo = sharingInfo)
snowfall::sfStop()
}
else {
sims <- lapply(sims, predictValue.func, popID = popID,
trainingPopID = trainingPopID, locations = locations,
years = years, sharingInfo = sharingInfo)
}
}
})
where 7: with(sEnv, {
if (exists("totalCost")) {
costs$popID <- ifelse(is.null(popID), max(budgetRec$popID),
popID)
totalCost <- totalCost + costs$predCost
}
if (!onlyCost) {
if (nCore > 1) {
snowfall::sfInit(parallel = T, cpus = nCore)
sims <- snowfall::sfLapply(sims, predictValue.func,
popID = popID, trainingPopID = trainingPopID,
locations = locations, years = years, sharingInfo = sharingInfo)
snowfall::sfStop()
}
else {
sims <- lapply(sims, predictValue.func, popID = popID,
trainingPopID = trainingPopID, locations = locations,
years = years, sharingInfo = sharingInfo)
}
}
})
where 8: predictValue()
where 9: eval(expr, envir, enclos)
where 10: eval(expr, envir, enclos)
where 11: withVisible(eval(expr, envir, enclos))
where 12: withCallingHandlers(withVisible(eval(expr, envir, enclos)), warning = wHandler,
error = eHandler, message = mHandler)
where 13: handle(ev <- withCallingHandlers(withVisible(eval(expr, envir,
enclos)), warning = wHandler, error = eHandler, message = mHandler))
where 14: timing_fn(handle(ev <- withCallingHandlers(withVisible(eval(expr,
envir, enclos)), warning = wHandler, error = eHandler, message = mHandler)))
where 15: evaluate_call(expr, parsed$src[[i]], envir = envir, enclos = enclos,
debug = debug, last = i == length(out), use_try = stop_on_error !=
2L, keep_warning = keep_warning, keep_message = keep_message,
output_handler = output_handler, include_timing = include_timing)
where 16: evaluate::evaluate(...)
where 17: evaluate(code, envir = env, new_device = FALSE, keep_warning = !isFALSE(options$warning),
keep_message = !isFALSE(options$message), stop_on_error = if (options$error &&
options$include) 0L else 2L, output_handler = knit_handlers(options$render,
options))
where 18: in_dir(input_dir(), evaluate(code, envir = env, new_device = FALSE,
keep_warning = !isFALSE(options$warning), keep_message = !isFALSE(options$message),
stop_on_error = if (options$error && options$include) 0L else 2L,
output_handler = knit_handlers(options$render, options)))
where 19: block_exec(params)
where 20: call_block(x)
where 21: process_group.block(group)
where 22: process_group(group)
where 23: withCallingHandlers(if (tangle) process_tangle(group) else process_group(group),
error = function(e) {
setwd(wd)
cat(res, sep = "\n", file = output %n% "")
message("Quitting from lines ", paste(current_lines(i),
collapse = "-"), " (", knit_concord$get("infile"),
") ")
})
where 24: process_file(text, output)
where 25: knitr::knit(knit_input, knit_output, envir = envir, quiet = quiet)
where 26: rmarkdown::render(file, encoding = encoding, quiet = quiet, envir = globalenv(),
output_dir = getwd(), ...)
where 27: vweave_rmarkdown(...)
where 28: engine$weave(file, quiet = quiet, encoding = enc)
where 29: doTryCatch(return(expr), name, parentenv, handler)
where 30: tryCatchOne(expr, names, parentenv, handlers[[1L]])
where 31: tryCatchList(expr, classes, parentenv, handlers)
where 32: tryCatch({
engine$weave(file, quiet = quiet, encoding = enc)
setwd(startdir)
output <- find_vignette_product(name, by = "weave", engine = engine)
if (!have.makefile && vignette_is_tex(output)) {
texi2pdf(file = output, clean = FALSE, quiet = quiet)
output <- find_vignette_product(name, by = "texi2pdf",
engine = engine)
}
}, error = function(e) {
OK <<- FALSE
message(gettextf("Error: processing vignette '%s' failed with diagnostics:\n%s",
file, conditionMessage(e)))
})
where 33: tools:::.buildOneVignette("Load_existing_data.Rmd", "/data/gannet/ripley/R/packages/tests-devel/BreedingSchemeLanguage.Rcheck/vign_test/BreedingSchemeLanguage",
TRUE, FALSE, "Load_existing_data", "UTF-8", "/tmp/RtmpQLz0y2/working_dir/RtmpB4KaJW/file15417a8bf7c.rds")
--- value of length: 2 type: logical ---
[1] FALSE FALSE
--- function from context ---
function (data, geno, pheno, GAUSS = FALSE, K = NULL, fixed = NULL,
covariate = NULL, PEV = FALSE, n.core = 1, theta.seq = NULL,
reduce = FALSE, R = NULL)
{
make.full <- function(X) {
svd.X <- svd(X)
r <- max(which(svd.X$d > 1e-08))
return(as.matrix(svd.X$u[, 1:r]))
}
names <- colnames(data)
ypos <- match(pheno, names)
if (is.na(ypos)) {
stop("Phenotype name does not appear in data.")
}
else {
y <- data[, ypos]
}
if (!is.null(R) & (length(R) != length(y))) {
stop("Length of R does not equal length of y")
}
not.miss <- which(!is.na(y))
if (length(not.miss) < length(y)) {
data <- data[not.miss, ]
y <- y[not.miss]
if (!is.null(R)) {
R <- R[not.miss]
}
}
n <- length(y)
X <- matrix(1, n, 1)
if (!is.null(fixed)) {
p <- length(fixed)
for (i in 1:p) {
xpos <- match(fixed[i], names)
xx <- factor(data[, xpos])
if (length(unique(xx)) > 1) {
X <- cbind(X, stats::model.matrix(~x - 1, data.frame(x = xx)))
}
}
}
if (!is.null(covariate)) {
p <- length(covariate)
for (i in 1:p) {
xpos <- match(covariate[i], names)
X <- cbind(X, data[, xpos])
}
}
gid.pos <- match(geno, names)
if (is.na(gid.pos)) {
stop("Genotype name does not appear in data.")
}
not.miss.gid <- as.character(unique(data[, gid.pos]))
if (is.null(K)) {
if (reduce) {
print("reduce=TRUE is not valid for independent genotypes. Proceeding without reduction.")
}
gid <- not.miss.gid
v <- length(gid)
Z <- matrix(0, n, v)
colnames(Z) <- gid
Z[cbind(1:n, match(data[, gid.pos], gid))] <- 1
X2 <- make.full(X)
ans <- rrBLUP::mixed.solve(y = y, X = X2, Z = Z, SE = PEV)
resid <- y - X2 %*% ans$beta - Z %*% ans$u
if (PEV) {
return(list(Vg = ans$Vu, Ve = ans$Ve, g = ans$u,
PEV = ans$u.SE^2, resid = resid))
}
else {
return(list(Vg = ans$Vu, Ve = ans$Ve, g = ans$u,
resid = resid))
}
}
else {
if (class(K) == "dist") {
K <- as.matrix(K)
}
gid <- rownames(K)
ix.pheno <- match(not.miss.gid, gid)
miss.pheno.gid <- which(is.na(ix.pheno))
if (length(miss.pheno.gid) > 0) {
stop(paste("The following lines have phenotypes but no genotypes:",
paste(not.miss.gid[miss.pheno.gid], collapse = " ")))
}
miss.gid <- setdiff(gid, not.miss.gid)
ix <- c(ix.pheno, match(miss.gid, gid))
K <- K[ix, ix]
v <- length(not.miss.gid)
Z <- matrix(0, n, v)
Z[cbind(1:n, match(data[, gid.pos], not.miss.gid))] <- 1
if (!is.null(R)) {
sqrt.R <- sqrt(R)
X2 <- X/sqrt.R
y2 <- y/sqrt.R
Z2 <- Z/sqrt.R
}
else {
X2 <- X
y2 <- y
Z2 <- Z
}
if ((n > v) & (reduce)) {
w <- sqrt(diag(crossprod(Z2)))
X2 <- make.full(crossprod(Z2, X2)/w)
y2 <- crossprod(Z2, y2)/w
Z2 <- cbind(diag(w), matrix(0, v, nrow(K) - v))
reduced <- TRUE
}
else {
X2 <- make.full(X2)
Z2 <- cbind(Z2, matrix(0, n, nrow(K) - v))
reduced <- FALSE
}
rm(X, Z, y)
if (!GAUSS) {
ans <- rrBLUP::mixed.solve(y = y2, X = X2, Z = Z2,
K = K, SE = PEV)
ix <- match(gid, rownames(ans$u))
if (reduced) {
resid <- NULL
}
else {
resid <- y2 - X2 %*% ans$beta - Z2 %*% ans$u
}
if (PEV) {
return(list(Vg = ans$Vu, Ve = ans$Ve, g = ans$u[ix],
PEV = ans$u.SE[ix]^2, resid = resid))
}
else {
return(list(Vg = ans$Vu, Ve = ans$Ve, g = ans$u[ix],
resid = resid))
}
}
else {
if (is.null(theta.seq)) {
theta <- setdiff(seq(0, max(K), length.out = 11),
0)
}
else {
theta <- theta.seq
}
n.profile <- length(theta)
ms.fun <- function(theta) {
soln <- list()
n.t <- length(theta)
for (i in 1:n.t) {
soln[[i]] <- rrBLUP::mixed.solve(y = y2, X = X2,
Z = Z2, K = exp(-(K/theta[i])^2), SE = PEV)
}
return(soln)
}
if (n.core > 1) {
it <- split(theta, factor(cut(theta, n.core,
labels = FALSE)))
soln <- unlist(snowfall::sfLapply(it, ms.fun,
mc.cores = n.core), recursive = FALSE)
}
else {
soln <- ms.fun(theta)
}
LL <- rep(0, n.profile)
for (i in 1:n.profile) {
LL[i] <- soln[[i]]$LL
}
ans <- soln[[which.max(LL)]]
profile <- cbind(theta, LL)
ix <- match(gid, rownames(ans$u))
if (reduced) {
resid <- NULL
}
else {
resid <- y2 - X2 %*% ans$beta - Z2 %*% ans$u
}
if (PEV) {
return(list(Vg = ans$Vu, Ve = ans$Ve, profile = profile,
g = ans$u[ix], PEV = ans$u.SE[ix]^2, resid = resid))
}
else {
return(list(Vg = ans$Vu, Ve = ans$Ve, profile = profile,
g = ans$u[ix], resid = resid))
}
}
}
}
<bytecode: 0x69beda8>
<environment: 0x696ac28>
--- function search by body ---
----------- END OF FAILURE REPORT --------------
Quitting from lines 65-70 (Load_existing_data.Rmd)
Error: processing vignette 'Load_existing_data.Rmd' failed with diagnostics:
the condition has length > 1
--- failed re-building ‘Load_existing_data.Rmd’
--- re-building ‘Optimize_parameter_vector.Rmd’ using rmarkdown
Loading required package: snowfall
Loading required package: snow
Loading required package: Rcpp
----------- FAILURE REPORT --------------
--- failure: the condition has length > 1 ---
--- srcref ---
:
--- package (from environment) ---
BreedingSchemeLanguage
--- call from context ---
kin.blup4.4(kbDat, geno = "phenoGID", pheno = "pValue", fixed = c("loc",
"year"), K = K, reduce = mt1ObsPerGID, R = kbDat$error)
--- call from argument ---
if (class(K) == "dist") {
K <- as.matrix(K)
}
--- R stacktrace ---
where 1: kin.blup4.4(kbDat, geno = "phenoGID", pheno = "pValue", fixed = c("loc",
"year"), K = K, reduce = mt1ObsPerGID, R = kbDat$error)
where 2: FUN(X[[i]], ...)
where 3: lapply(sims, predictValue.func, popID = popID, trainingPopID = trainingPopID,
locations = locations, years = years, sharingInfo = sharingInfo)
where 4: eval(substitute(expr), data, enclos = parent.frame())
where 5: eval(substitute(expr), data, enclos = parent.frame())
where 6: with.default(sEnv, {
if (exists("totalCost")) {
costs$popID <- ifelse(is.null(popID), max(budgetRec$popID),
popID)
totalCost <- totalCost + costs$predCost
}
if (!onlyCost) {
if (nCore > 1) {
snowfall::sfInit(parallel = T, cpus = nCore)
sims <- snowfall::sfLapply(sims, predictValue.func,
popID = popID, trainingPopID = trainingPopID,
locations = locations, years = years, sharingInfo = sharingInfo)
snowfall::sfStop()
}
else {
sims <- lapply(sims, predictValue.func, popID = popID,
trainingPopID = trainingPopID, locations = locations,
years = years, sharingInfo = sharingInfo)
}
}
})
where 7: with(sEnv, {
if (exists("totalCost")) {
costs$popID <- ifelse(is.null(popID), max(budgetRec$popID),
popID)
totalCost <- totalCost + costs$predCost
}
if (!onlyCost) {
if (nCore > 1) {
snowfall::sfInit(parallel = T, cpus = nCore)
sims <- snowfall::sfLapply(sims, predictValue.func,
popID = popID, trainingPopID = trainingPopID,
locations = locations, years = years, sharingInfo = sharingInfo)
snowfall::sfStop()
}
else {
sims <- lapply(sims, predictValue.func, popID = popID,
trainingPopID = trainingPopID, locations = locations,
years = years, sharingInfo = sharingInfo)
}
}
})
where 8: predictValue()
where 9: eval(ei, envir)
where 10: eval(ei, envir)
where 11: withVisible(eval(ei, envir))
where 12: source(schemeFileName, local = TRUE)
where 13: testParameterOptimality(schemeFileName = schemeScriptPath, parmList = parmList,
objectiveFunc = objFunc, budget = allowableBudget)
where 14: eval(expr, envir, enclos)
where 15: eval(expr, envir, enclos)
where 16: withVisible(eval(expr, envir, enclos))
where 17: withCallingHandlers(withVisible(eval(expr, envir, enclos)), warning = wHandler,
error = eHandler, message = mHandler)
where 18: handle(ev <- withCallingHandlers(withVisible(eval(expr, envir,
enclos)), warning = wHandler, error = eHandler, message = mHandler))
where 19: timing_fn(handle(ev <- withCallingHandlers(withVisible(eval(expr,
envir, enclos)), warning = wHandler, error = eHandler, message = mHandler)))
where 20: evaluate_call(expr, parsed$src[[i]], envir = envir, enclos = enclos,
debug = debug, last = i == length(out), use_try = stop_on_error !=
2L, keep_warning = keep_warning, keep_message = keep_message,
output_handler = output_handler, include_timing = include_timing)
where 21: evaluate::evaluate(...)
where 22: evaluate(code, envir = env, new_device = FALSE, keep_warning = !isFALSE(options$warning),
keep_message = !isFALSE(options$message), stop_on_error = if (options$error &&
options$include) 0L else 2L, output_handler = knit_handlers(options$render,
options))
where 23: in_dir(input_dir(), evaluate(code, envir = env, new_device = FALSE,
keep_warning = !isFALSE(options$warning), keep_message = !isFALSE(options$message),
stop_on_error = if (options$error && options$include) 0L else 2L,
output_handler = knit_handlers(options$render, options)))
where 24: block_exec(params)
where 25: call_block(x)
where 26: process_group.block(group)
where 27: process_group(group)
where 28: withCallingHandlers(if (tangle) process_tangle(group) else process_group(group),
error = function(e) {
setwd(wd)
cat(res, sep = "\n", file = output %n% "")
message("Quitting from lines ", paste(current_lines(i),
collapse = "-"), " (", knit_concord$get("infile"),
") ")
})
where 29: process_file(text, output)
where 30: knitr::knit(knit_input, knit_output, envir = envir, quiet = quiet)
where 31: rmarkdown::render(file, encoding = encoding, quiet = quiet, envir = globalenv(),
output_dir = getwd(), ...)
where 32: vweave_rmarkdown(...)
where 33: engine$weave(file, quiet = quiet, encoding = enc)
where 34: doTryCatch(return(expr), name, parentenv, handler)
where 35: tryCatchOne(expr, names, parentenv, handlers[[1L]])
where 36: tryCatchList(expr, classes, parentenv, handlers)
where 37: tryCatch({
engine$weave(file, quiet = quiet, encoding = enc)
setwd(startdir)
output <- find_vignette_product(name, by = "weave", engine = engine)
if (!have.makefile && vignette_is_tex(output)) {
texi2pdf(file = output, clean = FALSE, quiet = quiet)
output <- find_vignette_product(name, by = "texi2pdf",
engine = engine)
}
}, error = function(e) {
OK <<- FALSE
message(gettextf("Error: processing vignette '%s' failed with diagnostics:\n%s",
file, conditionMessage(e)))
})
where 38: tools:::.buildOneVignette("Optimize_parameter_vector.Rmd", "/data/gannet/ripley/R/packages/tests-devel/BreedingSchemeLanguage.Rcheck/vign_test/BreedingSchemeLanguage",
TRUE, FALSE, "Optimize_parameter_vector", "UTF-8", "/tmp/RtmpQLz0y2/working_dir/RtmpB4KaJW/file15414b2433c1.rds")
--- value of length: 2 type: logical ---
[1] FALSE FALSE
--- function from context ---
function (data, geno, pheno, GAUSS = FALSE, K = NULL, fixed = NULL,
covariate = NULL, PEV = FALSE, n.core = 1, theta.seq = NULL,
reduce = FALSE, R = NULL)
{
make.full <- function(X) {
svd.X <- svd(X)
r <- max(which(svd.X$d > 1e-08))
return(as.matrix(svd.X$u[, 1:r]))
}
names <- colnames(data)
ypos <- match(pheno, names)
if (is.na(ypos)) {
stop("Phenotype name does not appear in data.")
}
else {
y <- data[, ypos]
}
if (!is.null(R) & (length(R) != length(y))) {
stop("Length of R does not equal length of y")
}
not.miss <- which(!is.na(y))
if (length(not.miss) < length(y)) {
data <- data[not.miss, ]
y <- y[not.miss]
if (!is.null(R)) {
R <- R[not.miss]
}
}
n <- length(y)
X <- matrix(1, n, 1)
if (!is.null(fixed)) {
p <- length(fixed)
for (i in 1:p) {
xpos <- match(fixed[i], names)
xx <- factor(data[, xpos])
if (length(unique(xx)) > 1) {
X <- cbind(X, stats::model.matrix(~x - 1, data.frame(x = xx)))
}
}
}
if (!is.null(covariate)) {
p <- length(covariate)
for (i in 1:p) {
xpos <- match(covariate[i], names)
X <- cbind(X, data[, xpos])
}
}
gid.pos <- match(geno, names)
if (is.na(gid.pos)) {
stop("Genotype name does not appear in data.")
}
not.miss.gid <- as.character(unique(data[, gid.pos]))
if (is.null(K)) {
if (reduce) {
print("reduce=TRUE is not valid for independent genotypes. Proceeding without reduction.")
}
gid <- not.miss.gid
v <- length(gid)
Z <- matrix(0, n, v)
colnames(Z) <- gid
Z[cbind(1:n, match(data[, gid.pos], gid))] <- 1
X2 <- make.full(X)
ans <- rrBLUP::mixed.solve(y = y, X = X2, Z = Z, SE = PEV)
resid <- y - X2 %*% ans$beta - Z %*% ans$u
if (PEV) {
return(list(Vg = ans$Vu, Ve = ans$Ve, g = ans$u,
PEV = ans$u.SE^2, resid = resid))
}
else {
return(list(Vg = ans$Vu, Ve = ans$Ve, g = ans$u,
resid = resid))
}
}
else {
if (class(K) == "dist") {
K <- as.matrix(K)
}
gid <- rownames(K)
ix.pheno <- match(not.miss.gid, gid)
miss.pheno.gid <- which(is.na(ix.pheno))
if (length(miss.pheno.gid) > 0) {
stop(paste("The following lines have phenotypes but no genotypes:",
paste(not.miss.gid[miss.pheno.gid], collapse = " ")))
}
miss.gid <- setdiff(gid, not.miss.gid)
ix <- c(ix.pheno, match(miss.gid, gid))
K <- K[ix, ix]
v <- length(not.miss.gid)
Z <- matrix(0, n, v)
Z[cbind(1:n, match(data[, gid.pos], not.miss.gid))] <- 1
if (!is.null(R)) {
sqrt.R <- sqrt(R)
X2 <- X/sqrt.R
y2 <- y/sqrt.R
Z2 <- Z/sqrt.R
}
else {
X2 <- X
y2 <- y
Z2 <- Z
}
if ((n > v) & (reduce)) {
w <- sqrt(diag(crossprod(Z2)))
X2 <- make.full(crossprod(Z2, X2)/w)
y2 <- crossprod(Z2, y2)/w
Z2 <- cbind(diag(w), matrix(0, v, nrow(K) - v))
reduced <- TRUE
}
else {
X2 <- make.full(X2)
Z2 <- cbind(Z2, matrix(0, n, nrow(K) - v))
reduced <- FALSE
}
rm(X, Z, y)
if (!GAUSS) {
ans <- rrBLUP::mixed.solve(y = y2, X = X2, Z = Z2,
K = K, SE = PEV)
ix <- match(gid, rownames(ans$u))
if (reduced) {
resid <- NULL
}
else {
resid <- y2 - X2 %*% ans$beta - Z2 %*% ans$u
}
if (PEV) {
return(list(Vg = ans$Vu, Ve = ans$Ve, g = ans$u[ix],
PEV = ans$u.SE[ix]^2, resid = resid))
}
else {
return(list(Vg = ans$Vu, Ve = ans$Ve, g = ans$u[ix],
resid = resid))
}
}
else {
if (is.null(theta.seq)) {
theta <- setdiff(seq(0, max(K), length.out = 11),
0)
}
else {
theta <- theta.seq
}
n.profile <- length(theta)
ms.fun <- function(theta) {
soln <- list()
n.t <- length(theta)
for (i in 1:n.t) {
soln[[i]] <- rrBLUP::mixed.solve(y = y2, X = X2,
Z = Z2, K = exp(-(K/theta[i])^2), SE = PEV)
}
return(soln)
}
if (n.core > 1) {
it <- split(theta, factor(cut(theta, n.core,
labels = FALSE)))
soln <- unlist(snowfall::sfLapply(it, ms.fun,
mc.cores = n.core), recursive = FALSE)
}
else {
soln <- ms.fun(theta)
}
LL <- rep(0, n.profile)
for (i in 1:n.profile) {
LL[i] <- soln[[i]]$LL
}
ans <- soln[[which.max(LL)]]
profile <- cbind(theta, LL)
ix <- match(gid, rownames(ans$u))
if (reduced) {
resid <- NULL
}
else {
resid <- y2 - X2 %*% ans$beta - Z2 %*% ans$u
}
if (PEV) {
return(list(Vg = ans$Vu, Ve = ans$Ve, profile = profile,
g = ans$u[ix], PEV = ans$u.SE[ix]^2, resid = resid))
}
else {
return(list(Vg = ans$Vu, Ve = ans$Ve, profile = profile,
g = ans$u[ix], resid = resid))
}
}
}
}
<bytecode: 0x518c128>
<environment: 0x811f738>
--- function search by body ---
----------- END OF FAILURE REPORT --------------
Quitting from lines 68-106 (Optimize_parameter_vector.Rmd)
Error: processing vignette 'Optimize_parameter_vector.Rmd' failed with diagnostics:
the condition has length > 1
--- failed re-building ‘Optimize_parameter_vector.Rmd’
--- re-building ‘Within_family_selection.Rmd’ using rmarkdown
Loading required package: snowfall
Loading required package: snow
Loading required package: Rcpp
--- finished re-building ‘Within_family_selection.Rmd’
SUMMARY: processing the following files failed:
‘Intro_to_BSL.Rmd’ ‘Load_existing_data.Rmd’
‘Optimize_parameter_vector.Rmd’
Error: Vignette re-building failed.
Execution halted
Flavor: r-devel-linux-x86_64-fedora-gcc