Last updated on 2020-02-19 10:48:59 CET.
| Flavor | Version | Tinstall | Tcheck | Ttotal | Status | Flags |
|---|---|---|---|---|---|---|
| r-devel-linux-x86_64-debian-clang | 1.0.1 | 11.37 | 84.47 | 95.84 | ERROR | |
| r-devel-linux-x86_64-debian-gcc | 1.0.1 | 8.81 | 66.43 | 75.24 | ERROR | |
| r-devel-linux-x86_64-fedora-clang | 1.0.1 | 114.10 | ERROR | |||
| r-devel-linux-x86_64-fedora-gcc | 1.0.1 | 113.17 | ERROR | |||
| r-devel-windows-ix86+x86_64 | 1.0.1 | 22.00 | 119.00 | 141.00 | OK | |
| r-devel-windows-ix86+x86_64-gcc8 | 1.0.1 | 30.00 | 173.00 | 203.00 | OK | |
| r-patched-linux-x86_64 | 1.0.1 | 8.84 | 76.52 | 85.36 | OK | |
| r-patched-solaris-x86 | 1.0.1 | 176.50 | OK | |||
| r-release-linux-x86_64 | 1.0.1 | 8.52 | 76.01 | 84.53 | OK | |
| r-release-windows-ix86+x86_64 | 1.0.1 | 20.00 | 113.00 | 133.00 | OK | |
| r-release-osx-x86_64 | 1.0.1 | OK | ||||
| r-oldrel-windows-ix86+x86_64 | 1.0.1 | 15.00 | 119.00 | 134.00 | OK | |
| r-oldrel-osx-x86_64 | 1.0.1 | OK |
Version: 1.0.1
Check: examples
Result: ERROR
Running examples in 'MiRKAT-Ex.R' failed
The error most likely occurred in:
> base::assign(".ptime", proc.time(), pos = "CheckExEnv")
> ### Name: KRV
> ### Title: Kernel RV Coefficient Test
> ### Aliases: KRV
>
> ### ** Examples
>
> library(MASS)
> library(GUniFrac)
> data(throat.tree)
> data(throat.otu.tab)
> data(throat.meta)
> attach(throat.meta)
>
> set.seed(123)
> n = nrow(throat.otu.tab)
> Male = (Sex == "Male")**2
> Smoker =(SmokingStatus == "Smoker") **2
> anti = (AntibioticUsePast3Months_TimeFromAntibioticUsage != "None")^2
> cova = cbind(Male, anti)
>
> otu.tab.rff <- Rarefy(throat.otu.tab)$otu.tab.rff
> unifracs <- GUniFrac(otu.tab.rff, throat.tree, alpha=c(0, 0.5, 1))$unifracs
>
> D.weighted = unifracs[,,"d_1"]
> D.unweighted = unifracs[,,"d_UW"]
> D.BC= as.matrix(vegdist(otu.tab.rff , method="bray"))
>
> K.weighted = D2K(D.weighted)
> K.unweighted = D2K(D.unweighted)
> K.BC = D2K(D.BC)
>
> rho = 0.2
> Va = matrix(rep(rho, (2*n)^2), 2*n, 2*n)+diag(1-rho, 2*n)
> G = mvrnorm(n, rep(0, 2*n), Va)
>
> #############################################################
>
> KRV(kernel.otu = K.weighted, y = G, X = cova, kernel.y = "Gaussian")
----------- FAILURE REPORT --------------
--- failure: the condition has length > 1 ---
--- srcref ---
:
--- package (from environment) ---
MiRKAT
--- call from context ---
KRV(kernel.otu = K.weighted, y = G, X = cova, kernel.y = "Gaussian")
--- call from argument ---
if ((class(kernel.otu) != "matrix")) {
stop("Please provide a kernel matrix for microbiome data")
}
--- R stacktrace ---
where 1: KRV(kernel.otu = K.weighted, y = G, X = cova, kernel.y = "Gaussian")
--- value of length: 2 type: logical ---
[1] FALSE TRUE
--- function from context ---
function (kernel.otu, y = NULL, X = NULL, kernel.y)
{
if ((class(kernel.otu) != "matrix")) {
stop("Please provide a kernel matrix for microbiome data")
}
if (is.matrix(kernel.y)) {
n = nrow(kernel.otu)
if (!is.null(X)) {
warning("Covariates can't be adjusted for in this case, and hence argument \"X\" will be ignored.")
}
if (!is.null(y)) {
warning("No need to provide phenotype matrix when a phenotype kernel is provided, and hence argument \"y\" will be ignored.")
}
if (ncol(kernel.otu) != n | nrow(kernel.y) != n | ncol(kernel.y) !=
n) {
stop("kernel matrices need to be n x n, where n is the sample size \n ")
}
}
if (!is.matrix(kernel.y)) {
if (!(kernel.y %in% c("Gaussian", "linear"))) {
stop("Please choose kernel.y = \"Gaussian\" or \"linear\", or enter a kernel matrix for \"kernel.y\"")
}
if (is.null(y)) {
stop("Please enter a phenotype matrix for argument \"y\" or enter a kernel matrix for argument \"kernel.y\" ")
}
n = NROW(y)
if (nrow(kernel.otu) != n | ncol(kernel.otu) != n) {
stop("kernel matrix needs to be n x n, where n is the sample size \n ")
}
if (any(is.na(y))) {
ids = which(is.na(y))
stop(paste("subjects", ids, "has missing response, please remove before proceed \n"))
}
if (!is.null(X)) {
if (any(is.na(X))) {
stop("NAs in covariates X, please impute or remove subjects which has missing covariates values")
}
if (NROW(X) != NROW(y))
stop("Dimensions of X and y don't match.")
}
}
K = kernel.otu
kern_g = function(zz) {
n = nrow(zz)
D = matrix(NA, nrow = n, ncol = n)
for (i in 1:n) {
for (j in 1:n) {
D[i, j] = sum((zz[i, ] - zz[j, ])^2)
}
}
temp = c(D)
D1 = temp[temp > 0]
scl = median(D1)
K = matrix(NA, nrow = n, ncol = n)
for (i in 1:n) {
for (j in 1:n) {
K[i, j] = exp(-sum((zz[i, ] - zz[j, ])^2)/scl)
}
}
return(K)
}
n = nrow(K)
I.n = diag(1, n)
I.1 = rep(1, n)
if (is.matrix(kernel.y)) {
L = kernel.y
}
else {
if (!is.null(X)) {
Px = X %*% solve(t(X) %*% X) %*% t(X)
err.Y = (I.n - Px) %*% y
}
else {
err.Y = y
}
if (kernel.y == "Gaussian") {
L = kern_g(err.Y)
}
else {
if (kernel.y == "linear") {
L = err.Y %*% t(err.Y)
}
}
}
H = I.n - I.1 %*% t(I.1)/n
K = H %*% K %*% H
L = H %*% L %*% H
A = K/tr(K %*% K)
W = L/tr(L %*% L)
Fstar = tr(A %*% W)
mean.krv = tr(A) * tr(W)/(n - 1)
T = tr(A)
T2 = tr(A %*% A)
S2 = sum(diag(A)^2)
Ts = tr(W)
T2s = tr(W %*% W)
S2s = sum(diag(W)^2)
temp1 = 2 * ((n - 1) * T2 - T^2) * ((n - 1) * T2s - Ts^2)/(n -
1)^2/(n + 1)/(n - 2)
temp21 = n * (n + 1) * S2 - (n - 1) * (T^2 + 2 * T2)
temp22 = n * (n + 1) * S2s - (n - 1) * (Ts^2 + 2 * T2s)
temp23 = (n + 1) * n * (n - 1) * (n - 2) * (n - 3)
temp2 = temp21 * temp22/temp23
variance.krv = temp1 + temp2
T3 = tr(A %*% A %*% A)
S3 = sum(diag(A)^3)
U = sum(A^3)
R = t(diag(A)) %*% diag(A %*% A)
B = t(diag(A)) %*% A %*% diag(A)
T3s = tr(W %*% W %*% W)
S3s = sum(diag(W)^3)
Us = sum(W^3)
Rs = t(diag(W)) %*% diag(W %*% W)
Bs = t(diag(W)) %*% W %*% diag(W)
t1 = n^2 * (n + 1) * (n^2 + 15 * n - 4) * S3 * S3s
t2 = 4 * (n^4 - 8 * n^3 + 19 * n^2 - 4 * n - 16) * U * Us
t3 = 24 * (n^2 - n - 4) * (U * Bs + B * Us)
t4 = 6 * (n^4 - 8 * n^3 + 21 * n^2 - 6 * n - 24) * B * Bs
t5 = 12 * (n^4 - n^3 - 8 * n^2 + 36 * n - 48) * R * Rs
t6 = 12 * (n^3 - 2 * n^2 + 9 * n - 12) * (T * S2 * Rs + R *
Ts * S2s)
t7 = 3 * (n^4 - 4 * n^3 - 2 * n^2 + 9 * n - 12) * T * Ts *
S2 * S2s
t81 = (n^3 - 3 * n^2 - 2 * n + 8) * (R * Us + U * Rs)
t82 = (n^3 - 2 * n^2 - 3 * n + 12) * (R * Bs + B * Rs)
t8 = 24 * (t81 + t82)
t9 = 12 * (n^2 - n + 4) * (T * S2 * Us + U * Ts * S2s)
t10 = 6 * (2 * n^3 - 7 * n^2 - 3 * n + 12) * (T * S2 * Bs +
B * Ts * S2s)
t11 = -2 * n * (n - 1) * (n^2 - n + 4) * ((2 * U + 3 * B) *
S3s + (2 * Us + 3 * Bs) * S3)
t12 = -3 * n * (n - 1)^2 * (n + 4) * ((T * S2 + 4 * R) *
S3s + (Ts * S2s + 4 * Rs) * S3)
t13 = 2 * n * (n - 1) * (n - 2) * ((T^3 + 6 * T * T2 + 8 *
T3) * S3s + (Ts^3 + 6 * Ts * T2s + 8 * T3s) * S3)
t14 = T^3 * ((n^3 - 9 * n^2 + 23 * n - 14) * Ts^3 + 6 * (n -
4) * Ts * T2s + 8 * T3s)
t15 = 6 * T * T2 * ((n - 4) * Ts^3 + (n^3 - 9 * n^2 + 24 *
n - 14) * Ts * T2s + 4 * (n - 3) * T3s)
t16 = 8 * T3 * (Ts^3 + 3 * (n - 3) * Ts * T2s + (n^3 - 9 *
n^2 + 26 * n - 22) * T3s)
t17 = -16 * (T^3 * Us + U * Ts^3) - 6 * (T * T2 * Us + U *
Ts * T2s) * (2 * n^2 - 10 * n + 16)
t18 = -8 * (T3 * Us + U * T3s) * (3 * n^2 - 15 * n + 16) -
(T^3 * Bs + B * Ts^3) * (6 * n^2 - 30 * n + 24)
t19 = -6 * (T * T2 * Bs + B * Ts * T2s) * (4 * n^2 - 20 *
n + 24) - 8 * (T3 * Bs + B * T3s) * (3 * n^2 - 15 * n +
24)
t201 = 24 * (T^3 * Rs + R * Ts^3) + 6 * (T * T2 * Rs + R *
Ts * T2s) * (2 * n^2 - 10 * n + 24)
t202 = 8 * (T3 * Rs + R * T3s) * (3 * n^2 - 15 * n + 24) +
(3 * n^2 - 15 * n + 6) * (T^3 * Ts * S2s + T * S2 * Ts^3)
t203 = 6 * (T * T2 * Ts * S2s + Ts * T2s * T * S2) * (n^2 -
5 * n + 6) + 48 * (T3 * Ts * S2s + T3s * T * S2)
t20 = -(n - 2) * (t201 + t202 + t203)
temp31 = t1 + t2 + t3 + t4 + t5 + t6 + t7 + t8 + t9 + t10 +
t11 + t12 + t13 + t14 + t15 + t16 + t17 + t18 + t19 +
t20
temp32 = n * (n - 1) * (n - 2) * (n - 3) * (n - 4) * (n -
5)
mom3 = temp31/temp32
skewness.krv = (mom3 - 3 * mean.krv * variance.krv - mean.krv^3)/variance.krv^1.5
m1 = mean.krv
m2 = variance.krv
m3 = skewness.krv
shape = 4/m3^2
scale = sqrt(m2) * m3/2
location = m1 - 2 * sqrt(m2)/m3
PIIIpars = list(shape, location, scale)
pv = 1 - ppearsonIII(Fstar, params = PIIIpars)
return(pv)
}
<bytecode: 0x6a37268>
<environment: namespace:MiRKAT>
--- function search by body ---
Function KRV in namespace MiRKAT has this body.
----------- END OF FAILURE REPORT --------------
Error in if ((class(kernel.otu) != "matrix")) { :
the condition has length > 1
Calls: KRV
Execution halted
Flavor: r-devel-linux-x86_64-debian-clang
Version: 1.0.1
Check: re-building of vignette outputs
Result: WARN
Error(s) in re-building vignettes:
...
--- re-building 'MiRKAT.Rnw' using Sweave
This is vegan 2.5-6
----------- FAILURE REPORT --------------
--- failure: the condition has length > 1 ---
--- srcref ---
:
--- package (from environment) ---
MiRKAT
--- call from context ---
MiRKAT(y = Smoker, Ks = K.weighted, X = cbind(Male, anti), out_type = "D",
method = "davies")
--- call from argument ---
if (class(Ks) == "matrix") {
Ks = list(Ks)
}
--- R stacktrace ---
where 1: MiRKAT(y = Smoker, Ks = K.weighted, X = cbind(Male, anti), out_type = "D",
method = "davies")
where 2: eval(expr, .GlobalEnv)
where 3: eval(expr, .GlobalEnv)
where 4: withVisible(eval(expr, .GlobalEnv))
where 5: doTryCatch(return(expr), name, parentenv, handler)
where 6: tryCatchOne(expr, names, parentenv, handlers[[1L]])
where 7: tryCatchList(expr, classes, parentenv, handlers)
where 8: tryCatch(expr, error = function(e) {
call <- conditionCall(e)
if (!is.null(call)) {
if (identical(call[[1L]], quote(doTryCatch)))
call <- sys.call(-4L)
dcall <- deparse(call)[1L]
prefix <- paste("Error in", dcall, ": ")
LONG <- 75L
sm <- strsplit(conditionMessage(e), "\n")[[1L]]
w <- 14L + nchar(dcall, type = "w") + nchar(sm[1L], type = "w")
if (is.na(w))
w <- 14L + nchar(dcall, type = "b") + nchar(sm[1L],
type = "b")
if (w > LONG)
prefix <- paste0(prefix, "\n ")
}
else prefix <- "Error : "
msg <- paste0(prefix, conditionMessage(e), "\n")
.Internal(seterrmessage(msg[1L]))
if (!silent && isTRUE(getOption("show.error.messages"))) {
cat(msg, file = outFile)
.Internal(printDeferredWarnings())
}
invisible(structure(msg, class = "try-error", condition = e))
})
where 9: try(withVisible(eval(expr, .GlobalEnv)), silent = TRUE)
where 10: evalFunc(ce, options)
where 11: tryCatchList(expr, classes, parentenv, handlers)
where 12: tryCatch(evalFunc(ce, options), finally = {
cat("\n")
sink()
})
where 13: driver$runcode(drobj, chunk, chunkopts)
where 14: utils::Sweave(...)
where 15: engine$weave(file, quiet = quiet, encoding = enc)
where 16: doTryCatch(return(expr), name, parentenv, handler)
where 17: tryCatchOne(expr, names, parentenv, handlers[[1L]])
where 18: tryCatchList(expr, classes, parentenv, handlers)
where 19: 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)
}
outputs <- c(outputs, output)
}, error = function(e) {
thisOK <<- FALSE
fails <<- c(fails, file)
message(gettextf("Error: processing vignette '%s' failed with diagnostics:\n%s",
file, conditionMessage(e)))
})
where 20: tools:::buildVignettes(dir = "/home/hornik/tmp/R.check/r-devel-clang/Work/PKGS/MiRKAT.Rcheck/vign_test/MiRKAT",
ser_elibs = "/tmp/RtmpSOT4TH/file2fda79e2c763.rds")
--- value of length: 2 type: logical ---
[1] TRUE FALSE
--- function from context ---
function (y, X = NULL, Ks, out_type = "C", nperm = 999, method = "davies")
{
n = length(y)
if (any(is.na(y))) {
ids = which(is.na(y))
stop(paste("subjects", ids, "has missing response, please remove before proceed \n"))
}
if (is.null(X) == FALSE) {
if (NROW(X) != length(y))
stop("Dimensions of X and y don't match.")
}
if (class(Ks) == "matrix") {
Ks = list(Ks)
}
if (class(Ks) == "list") {
if ((any(lapply(Ks, "nrow") != n)) | (any(lapply(Ks,
"ncol") != n))) {
stop("distance matrix need to be n x n, where n is the sample size \n ")
}
if (class(Ks) != "list") {
stop("Distance needs to be a list of n x n matrices or a single n x n matrix \n")
}
}
if (!is.null(X)) {
if (any(is.na(X))) {
stop("NAs in covariates X, please impute or remove subjects which has missing covariates values")
}
}
if (method == "moment" & n < 100 & out_type == "C") {
warning("Continuous outcome: sample size < 100, p-value using moment matching can be inaccurate at tails, davies or permutation is recommended")
}
if (method == "moment" & n < 200 & out_type == "D") {
warning("Continuous outcome: sample size < 200, p-value using moment matching can be inaccurate at tails, davies or permutation is recommended")
}
if (!(out_type %in% c("C", "D"))) {
stop("Currently only continuous and Binary outcome are supported. Please choose out_type = \"C\" or \"D\" ")
}
if (out_type == "C") {
re = MiRKAT_continuous(y, X = X, Ks = Ks, method = method,
nperm = nperm)
}
if (out_type == "D") {
re = MiRKAT_binary(y, X = X, Ks = Ks, method = method,
nperm = nperm)
}
return(re)
}
<bytecode: 0x68e8400>
<environment: namespace:MiRKAT>
--- function search by body ---
Function MiRKAT in namespace MiRKAT has this body.
----------- END OF FAILURE REPORT --------------
Error: processing vignette 'MiRKAT.Rnw' failed with diagnostics:
chunk 5 (label = data5)
Error in if (class(Ks) == "matrix") { : the condition has length > 1
--- failed re-building 'MiRKAT.Rnw'
SUMMARY: processing the following file failed:
'MiRKAT.Rnw'
Error: Vignette re-building failed.
Execution halted
Flavor: r-devel-linux-x86_64-debian-clang
Version: 1.0.1
Check: examples
Result: ERROR
Running examples in ‘MiRKAT-Ex.R’ failed
The error most likely occurred in:
> base::assign(".ptime", proc.time(), pos = "CheckExEnv")
> ### Name: KRV
> ### Title: Kernel RV Coefficient Test
> ### Aliases: KRV
>
> ### ** Examples
>
> library(MASS)
> library(GUniFrac)
> data(throat.tree)
> data(throat.otu.tab)
> data(throat.meta)
> attach(throat.meta)
>
> set.seed(123)
> n = nrow(throat.otu.tab)
> Male = (Sex == "Male")**2
> Smoker =(SmokingStatus == "Smoker") **2
> anti = (AntibioticUsePast3Months_TimeFromAntibioticUsage != "None")^2
> cova = cbind(Male, anti)
>
> otu.tab.rff <- Rarefy(throat.otu.tab)$otu.tab.rff
> unifracs <- GUniFrac(otu.tab.rff, throat.tree, alpha=c(0, 0.5, 1))$unifracs
>
> D.weighted = unifracs[,,"d_1"]
> D.unweighted = unifracs[,,"d_UW"]
> D.BC= as.matrix(vegdist(otu.tab.rff , method="bray"))
>
> K.weighted = D2K(D.weighted)
> K.unweighted = D2K(D.unweighted)
> K.BC = D2K(D.BC)
>
> rho = 0.2
> Va = matrix(rep(rho, (2*n)^2), 2*n, 2*n)+diag(1-rho, 2*n)
> G = mvrnorm(n, rep(0, 2*n), Va)
>
> #############################################################
>
> KRV(kernel.otu = K.weighted, y = G, X = cova, kernel.y = "Gaussian")
----------- FAILURE REPORT --------------
--- failure: the condition has length > 1 ---
--- srcref ---
:
--- package (from environment) ---
MiRKAT
--- call from context ---
KRV(kernel.otu = K.weighted, y = G, X = cova, kernel.y = "Gaussian")
--- call from argument ---
if ((class(kernel.otu) != "matrix")) {
stop("Please provide a kernel matrix for microbiome data")
}
--- R stacktrace ---
where 1: KRV(kernel.otu = K.weighted, y = G, X = cova, kernel.y = "Gaussian")
--- value of length: 2 type: logical ---
[1] FALSE TRUE
--- function from context ---
function (kernel.otu, y = NULL, X = NULL, kernel.y)
{
if ((class(kernel.otu) != "matrix")) {
stop("Please provide a kernel matrix for microbiome data")
}
if (is.matrix(kernel.y)) {
n = nrow(kernel.otu)
if (!is.null(X)) {
warning("Covariates can't be adjusted for in this case, and hence argument \"X\" will be ignored.")
}
if (!is.null(y)) {
warning("No need to provide phenotype matrix when a phenotype kernel is provided, and hence argument \"y\" will be ignored.")
}
if (ncol(kernel.otu) != n | nrow(kernel.y) != n | ncol(kernel.y) !=
n) {
stop("kernel matrices need to be n x n, where n is the sample size \n ")
}
}
if (!is.matrix(kernel.y)) {
if (!(kernel.y %in% c("Gaussian", "linear"))) {
stop("Please choose kernel.y = \"Gaussian\" or \"linear\", or enter a kernel matrix for \"kernel.y\"")
}
if (is.null(y)) {
stop("Please enter a phenotype matrix for argument \"y\" or enter a kernel matrix for argument \"kernel.y\" ")
}
n = NROW(y)
if (nrow(kernel.otu) != n | ncol(kernel.otu) != n) {
stop("kernel matrix needs to be n x n, where n is the sample size \n ")
}
if (any(is.na(y))) {
ids = which(is.na(y))
stop(paste("subjects", ids, "has missing response, please remove before proceed \n"))
}
if (!is.null(X)) {
if (any(is.na(X))) {
stop("NAs in covariates X, please impute or remove subjects which has missing covariates values")
}
if (NROW(X) != NROW(y))
stop("Dimensions of X and y don't match.")
}
}
K = kernel.otu
kern_g = function(zz) {
n = nrow(zz)
D = matrix(NA, nrow = n, ncol = n)
for (i in 1:n) {
for (j in 1:n) {
D[i, j] = sum((zz[i, ] - zz[j, ])^2)
}
}
temp = c(D)
D1 = temp[temp > 0]
scl = median(D1)
K = matrix(NA, nrow = n, ncol = n)
for (i in 1:n) {
for (j in 1:n) {
K[i, j] = exp(-sum((zz[i, ] - zz[j, ])^2)/scl)
}
}
return(K)
}
n = nrow(K)
I.n = diag(1, n)
I.1 = rep(1, n)
if (is.matrix(kernel.y)) {
L = kernel.y
}
else {
if (!is.null(X)) {
Px = X %*% solve(t(X) %*% X) %*% t(X)
err.Y = (I.n - Px) %*% y
}
else {
err.Y = y
}
if (kernel.y == "Gaussian") {
L = kern_g(err.Y)
}
else {
if (kernel.y == "linear") {
L = err.Y %*% t(err.Y)
}
}
}
H = I.n - I.1 %*% t(I.1)/n
K = H %*% K %*% H
L = H %*% L %*% H
A = K/tr(K %*% K)
W = L/tr(L %*% L)
Fstar = tr(A %*% W)
mean.krv = tr(A) * tr(W)/(n - 1)
T = tr(A)
T2 = tr(A %*% A)
S2 = sum(diag(A)^2)
Ts = tr(W)
T2s = tr(W %*% W)
S2s = sum(diag(W)^2)
temp1 = 2 * ((n - 1) * T2 - T^2) * ((n - 1) * T2s - Ts^2)/(n -
1)^2/(n + 1)/(n - 2)
temp21 = n * (n + 1) * S2 - (n - 1) * (T^2 + 2 * T2)
temp22 = n * (n + 1) * S2s - (n - 1) * (Ts^2 + 2 * T2s)
temp23 = (n + 1) * n * (n - 1) * (n - 2) * (n - 3)
temp2 = temp21 * temp22/temp23
variance.krv = temp1 + temp2
T3 = tr(A %*% A %*% A)
S3 = sum(diag(A)^3)
U = sum(A^3)
R = t(diag(A)) %*% diag(A %*% A)
B = t(diag(A)) %*% A %*% diag(A)
T3s = tr(W %*% W %*% W)
S3s = sum(diag(W)^3)
Us = sum(W^3)
Rs = t(diag(W)) %*% diag(W %*% W)
Bs = t(diag(W)) %*% W %*% diag(W)
t1 = n^2 * (n + 1) * (n^2 + 15 * n - 4) * S3 * S3s
t2 = 4 * (n^4 - 8 * n^3 + 19 * n^2 - 4 * n - 16) * U * Us
t3 = 24 * (n^2 - n - 4) * (U * Bs + B * Us)
t4 = 6 * (n^4 - 8 * n^3 + 21 * n^2 - 6 * n - 24) * B * Bs
t5 = 12 * (n^4 - n^3 - 8 * n^2 + 36 * n - 48) * R * Rs
t6 = 12 * (n^3 - 2 * n^2 + 9 * n - 12) * (T * S2 * Rs + R *
Ts * S2s)
t7 = 3 * (n^4 - 4 * n^3 - 2 * n^2 + 9 * n - 12) * T * Ts *
S2 * S2s
t81 = (n^3 - 3 * n^2 - 2 * n + 8) * (R * Us + U * Rs)
t82 = (n^3 - 2 * n^2 - 3 * n + 12) * (R * Bs + B * Rs)
t8 = 24 * (t81 + t82)
t9 = 12 * (n^2 - n + 4) * (T * S2 * Us + U * Ts * S2s)
t10 = 6 * (2 * n^3 - 7 * n^2 - 3 * n + 12) * (T * S2 * Bs +
B * Ts * S2s)
t11 = -2 * n * (n - 1) * (n^2 - n + 4) * ((2 * U + 3 * B) *
S3s + (2 * Us + 3 * Bs) * S3)
t12 = -3 * n * (n - 1)^2 * (n + 4) * ((T * S2 + 4 * R) *
S3s + (Ts * S2s + 4 * Rs) * S3)
t13 = 2 * n * (n - 1) * (n - 2) * ((T^3 + 6 * T * T2 + 8 *
T3) * S3s + (Ts^3 + 6 * Ts * T2s + 8 * T3s) * S3)
t14 = T^3 * ((n^3 - 9 * n^2 + 23 * n - 14) * Ts^3 + 6 * (n -
4) * Ts * T2s + 8 * T3s)
t15 = 6 * T * T2 * ((n - 4) * Ts^3 + (n^3 - 9 * n^2 + 24 *
n - 14) * Ts * T2s + 4 * (n - 3) * T3s)
t16 = 8 * T3 * (Ts^3 + 3 * (n - 3) * Ts * T2s + (n^3 - 9 *
n^2 + 26 * n - 22) * T3s)
t17 = -16 * (T^3 * Us + U * Ts^3) - 6 * (T * T2 * Us + U *
Ts * T2s) * (2 * n^2 - 10 * n + 16)
t18 = -8 * (T3 * Us + U * T3s) * (3 * n^2 - 15 * n + 16) -
(T^3 * Bs + B * Ts^3) * (6 * n^2 - 30 * n + 24)
t19 = -6 * (T * T2 * Bs + B * Ts * T2s) * (4 * n^2 - 20 *
n + 24) - 8 * (T3 * Bs + B * T3s) * (3 * n^2 - 15 * n +
24)
t201 = 24 * (T^3 * Rs + R * Ts^3) + 6 * (T * T2 * Rs + R *
Ts * T2s) * (2 * n^2 - 10 * n + 24)
t202 = 8 * (T3 * Rs + R * T3s) * (3 * n^2 - 15 * n + 24) +
(3 * n^2 - 15 * n + 6) * (T^3 * Ts * S2s + T * S2 * Ts^3)
t203 = 6 * (T * T2 * Ts * S2s + Ts * T2s * T * S2) * (n^2 -
5 * n + 6) + 48 * (T3 * Ts * S2s + T3s * T * S2)
t20 = -(n - 2) * (t201 + t202 + t203)
temp31 = t1 + t2 + t3 + t4 + t5 + t6 + t7 + t8 + t9 + t10 +
t11 + t12 + t13 + t14 + t15 + t16 + t17 + t18 + t19 +
t20
temp32 = n * (n - 1) * (n - 2) * (n - 3) * (n - 4) * (n -
5)
mom3 = temp31/temp32
skewness.krv = (mom3 - 3 * mean.krv * variance.krv - mean.krv^3)/variance.krv^1.5
m1 = mean.krv
m2 = variance.krv
m3 = skewness.krv
shape = 4/m3^2
scale = sqrt(m2) * m3/2
location = m1 - 2 * sqrt(m2)/m3
PIIIpars = list(shape, location, scale)
pv = 1 - ppearsonIII(Fstar, params = PIIIpars)
return(pv)
}
<bytecode: 0x563c4b3afdc0>
<environment: namespace:MiRKAT>
--- function search by body ---
Function KRV in namespace MiRKAT has this body.
----------- END OF FAILURE REPORT --------------
Error in if ((class(kernel.otu) != "matrix")) { :
the condition has length > 1
Calls: KRV
Execution halted
Flavor: r-devel-linux-x86_64-debian-gcc
Version: 1.0.1
Check: re-building of vignette outputs
Result: WARN
Error(s) in re-building vignettes:
...
--- re-building ‘MiRKAT.Rnw’ using Sweave
This is vegan 2.5-6
----------- FAILURE REPORT --------------
--- failure: the condition has length > 1 ---
--- srcref ---
:
--- package (from environment) ---
MiRKAT
--- call from context ---
MiRKAT(y = Smoker, Ks = K.weighted, X = cbind(Male, anti), out_type = "D",
method = "davies")
--- call from argument ---
if (class(Ks) == "matrix") {
Ks = list(Ks)
}
--- R stacktrace ---
where 1: MiRKAT(y = Smoker, Ks = K.weighted, X = cbind(Male, anti), out_type = "D",
method = "davies")
where 2: eval(expr, .GlobalEnv)
where 3: eval(expr, .GlobalEnv)
where 4: withVisible(eval(expr, .GlobalEnv))
where 5: doTryCatch(return(expr), name, parentenv, handler)
where 6: tryCatchOne(expr, names, parentenv, handlers[[1L]])
where 7: tryCatchList(expr, classes, parentenv, handlers)
where 8: tryCatch(expr, error = function(e) {
call <- conditionCall(e)
if (!is.null(call)) {
if (identical(call[[1L]], quote(doTryCatch)))
call <- sys.call(-4L)
dcall <- deparse(call)[1L]
prefix <- paste("Error in", dcall, ": ")
LONG <- 75L
sm <- strsplit(conditionMessage(e), "\n")[[1L]]
w <- 14L + nchar(dcall, type = "w") + nchar(sm[1L], type = "w")
if (is.na(w))
w <- 14L + nchar(dcall, type = "b") + nchar(sm[1L],
type = "b")
if (w > LONG)
prefix <- paste0(prefix, "\n ")
}
else prefix <- "Error : "
msg <- paste0(prefix, conditionMessage(e), "\n")
.Internal(seterrmessage(msg[1L]))
if (!silent && isTRUE(getOption("show.error.messages"))) {
cat(msg, file = outFile)
.Internal(printDeferredWarnings())
}
invisible(structure(msg, class = "try-error", condition = e))
})
where 9: try(withVisible(eval(expr, .GlobalEnv)), silent = TRUE)
where 10: evalFunc(ce, options)
where 11: tryCatchList(expr, classes, parentenv, handlers)
where 12: tryCatch(evalFunc(ce, options), finally = {
cat("\n")
sink()
})
where 13: driver$runcode(drobj, chunk, chunkopts)
where 14: utils::Sweave(...)
where 15: engine$weave(file, quiet = quiet, encoding = enc)
where 16: doTryCatch(return(expr), name, parentenv, handler)
where 17: tryCatchOne(expr, names, parentenv, handlers[[1L]])
where 18: tryCatchList(expr, classes, parentenv, handlers)
where 19: tryCatch({
engine$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)
}
outputs <- c(outputs, output)
}, error = function(e) {
thisOK <<- FALSE
fails <<- c(fails, file)
message(gettextf("Error: processing vignette '%s' failed with diagnostics:\n%s",
file, conditionMessage(e)))
})
where 20: tools:::buildVignettes(dir = "/home/hornik/tmp/R.check/r-devel-gcc/Work/PKGS/MiRKAT.Rcheck/vign_test/MiRKAT",
ser_elibs = "/home/hornik/tmp/scratch/RtmpskUzjb/filea6c33cd2524.rds")
--- value of length: 2 type: logical ---
[1] TRUE FALSE
--- function from context ---
function (y, X = NULL, Ks, out_type = "C", nperm = 999, method = "davies")
{
n = length(y)
if (any(is.na(y))) {
ids = which(is.na(y))
stop(paste("subjects", ids, "has missing response, please remove before proceed \n"))
}
if (is.null(X) == FALSE) {
if (NROW(X) != length(y))
stop("Dimensions of X and y don't match.")
}
if (class(Ks) == "matrix") {
Ks = list(Ks)
}
if (class(Ks) == "list") {
if ((any(lapply(Ks, "nrow") != n)) | (any(lapply(Ks,
"ncol") != n))) {
stop("distance matrix need to be n x n, where n is the sample size \n ")
}
if (class(Ks) != "list") {
stop("Distance needs to be a list of n x n matrices or a single n x n matrix \n")
}
}
if (!is.null(X)) {
if (any(is.na(X))) {
stop("NAs in covariates X, please impute or remove subjects which has missing covariates values")
}
}
if (method == "moment" & n < 100 & out_type == "C") {
warning("Continuous outcome: sample size < 100, p-value using moment matching can be inaccurate at tails, davies or permutation is recommended")
}
if (method == "moment" & n < 200 & out_type == "D") {
warning("Continuous outcome: sample size < 200, p-value using moment matching can be inaccurate at tails, davies or permutation is recommended")
}
if (!(out_type %in% c("C", "D"))) {
stop("Currently only continuous and Binary outcome are supported. Please choose out_type = \"C\" or \"D\" ")
}
if (out_type == "C") {
re = MiRKAT_continuous(y, X = X, Ks = Ks, method = method,
nperm = nperm)
}
if (out_type == "D") {
re = MiRKAT_binary(y, X = X, Ks = Ks, method = method,
nperm = nperm)
}
return(re)
}
<bytecode: 0x55fc1e6e0e40>
<environment: namespace:MiRKAT>
--- function search by body ---
Function MiRKAT in namespace MiRKAT has this body.
----------- END OF FAILURE REPORT --------------
Error: processing vignette 'MiRKAT.Rnw' failed with diagnostics:
chunk 5 (label = data5)
Error in if (class(Ks) == "matrix") { : the condition has length > 1
--- failed re-building ‘MiRKAT.Rnw’
SUMMARY: processing the following file failed:
‘MiRKAT.Rnw’
Error: Vignette re-building failed.
Execution halted
Flavor: r-devel-linux-x86_64-debian-gcc
Version: 1.0.1
Check: examples
Result: ERROR
Running examples in ‘MiRKAT-Ex.R’ failed
The error most likely occurred in:
> ### Name: KRV
> ### Title: Kernel RV Coefficient Test
> ### Aliases: KRV
>
> ### ** Examples
>
> library(MASS)
> library(GUniFrac)
> data(throat.tree)
> data(throat.otu.tab)
> data(throat.meta)
> attach(throat.meta)
>
> set.seed(123)
> n = nrow(throat.otu.tab)
> Male = (Sex == "Male")**2
> Smoker =(SmokingStatus == "Smoker") **2
> anti = (AntibioticUsePast3Months_TimeFromAntibioticUsage != "None")^2
> cova = cbind(Male, anti)
>
> otu.tab.rff <- Rarefy(throat.otu.tab)$otu.tab.rff
> unifracs <- GUniFrac(otu.tab.rff, throat.tree, alpha=c(0, 0.5, 1))$unifracs
>
> D.weighted = unifracs[,,"d_1"]
> D.unweighted = unifracs[,,"d_UW"]
> D.BC= as.matrix(vegdist(otu.tab.rff , method="bray"))
>
> K.weighted = D2K(D.weighted)
> K.unweighted = D2K(D.unweighted)
> K.BC = D2K(D.BC)
>
> rho = 0.2
> Va = matrix(rep(rho, (2*n)^2), 2*n, 2*n)+diag(1-rho, 2*n)
> G = mvrnorm(n, rep(0, 2*n), Va)
>
> #############################################################
>
> KRV(kernel.otu = K.weighted, y = G, X = cova, kernel.y = "Gaussian")
----------- FAILURE REPORT --------------
--- failure: the condition has length > 1 ---
--- srcref ---
:
--- package (from environment) ---
MiRKAT
--- call from context ---
KRV(kernel.otu = K.weighted, y = G, X = cova, kernel.y = "Gaussian")
--- call from argument ---
if ((class(kernel.otu) != "matrix")) {
stop("Please provide a kernel matrix for microbiome data")
}
--- R stacktrace ---
where 1: KRV(kernel.otu = K.weighted, y = G, X = cova, kernel.y = "Gaussian")
--- value of length: 2 type: logical ---
[1] FALSE TRUE
--- function from context ---
function (kernel.otu, y = NULL, X = NULL, kernel.y)
{
if ((class(kernel.otu) != "matrix")) {
stop("Please provide a kernel matrix for microbiome data")
}
if (is.matrix(kernel.y)) {
n = nrow(kernel.otu)
if (!is.null(X)) {
warning("Covariates can't be adjusted for in this case, and hence argument \"X\" will be ignored.")
}
if (!is.null(y)) {
warning("No need to provide phenotype matrix when a phenotype kernel is provided, and hence argument \"y\" will be ignored.")
}
if (ncol(kernel.otu) != n | nrow(kernel.y) != n | ncol(kernel.y) !=
n) {
stop("kernel matrices need to be n x n, where n is the sample size \n ")
}
}
if (!is.matrix(kernel.y)) {
if (!(kernel.y %in% c("Gaussian", "linear"))) {
stop("Please choose kernel.y = \"Gaussian\" or \"linear\", or enter a kernel matrix for \"kernel.y\"")
}
if (is.null(y)) {
stop("Please enter a phenotype matrix for argument \"y\" or enter a kernel matrix for argument \"kernel.y\" ")
}
n = NROW(y)
if (nrow(kernel.otu) != n | ncol(kernel.otu) != n) {
stop("kernel matrix needs to be n x n, where n is the sample size \n ")
}
if (any(is.na(y))) {
ids = which(is.na(y))
stop(paste("subjects", ids, "has missing response, please remove before proceed \n"))
}
if (!is.null(X)) {
if (any(is.na(X))) {
stop("NAs in covariates X, please impute or remove subjects which has missing covariates values")
}
if (NROW(X) != NROW(y))
stop("Dimensions of X and y don't match.")
}
}
K = kernel.otu
kern_g = function(zz) {
n = nrow(zz)
D = matrix(NA, nrow = n, ncol = n)
for (i in 1:n) {
for (j in 1:n) {
D[i, j] = sum((zz[i, ] - zz[j, ])^2)
}
}
temp = c(D)
D1 = temp[temp > 0]
scl = median(D1)
K = matrix(NA, nrow = n, ncol = n)
for (i in 1:n) {
for (j in 1:n) {
K[i, j] = exp(-sum((zz[i, ] - zz[j, ])^2)/scl)
}
}
return(K)
}
n = nrow(K)
I.n = diag(1, n)
I.1 = rep(1, n)
if (is.matrix(kernel.y)) {
L = kernel.y
}
else {
if (!is.null(X)) {
Px = X %*% solve(t(X) %*% X) %*% t(X)
err.Y = (I.n - Px) %*% y
}
else {
err.Y = y
}
if (kernel.y == "Gaussian") {
L = kern_g(err.Y)
}
else {
if (kernel.y == "linear") {
L = err.Y %*% t(err.Y)
}
}
}
H = I.n - I.1 %*% t(I.1)/n
K = H %*% K %*% H
L = H %*% L %*% H
A = K/tr(K %*% K)
W = L/tr(L %*% L)
Fstar = tr(A %*% W)
mean.krv = tr(A) * tr(W)/(n - 1)
T = tr(A)
T2 = tr(A %*% A)
S2 = sum(diag(A)^2)
Ts = tr(W)
T2s = tr(W %*% W)
S2s = sum(diag(W)^2)
temp1 = 2 * ((n - 1) * T2 - T^2) * ((n - 1) * T2s - Ts^2)/(n -
1)^2/(n + 1)/(n - 2)
temp21 = n * (n + 1) * S2 - (n - 1) * (T^2 + 2 * T2)
temp22 = n * (n + 1) * S2s - (n - 1) * (Ts^2 + 2 * T2s)
temp23 = (n + 1) * n * (n - 1) * (n - 2) * (n - 3)
temp2 = temp21 * temp22/temp23
variance.krv = temp1 + temp2
T3 = tr(A %*% A %*% A)
S3 = sum(diag(A)^3)
U = sum(A^3)
R = t(diag(A)) %*% diag(A %*% A)
B = t(diag(A)) %*% A %*% diag(A)
T3s = tr(W %*% W %*% W)
S3s = sum(diag(W)^3)
Us = sum(W^3)
Rs = t(diag(W)) %*% diag(W %*% W)
Bs = t(diag(W)) %*% W %*% diag(W)
t1 = n^2 * (n + 1) * (n^2 + 15 * n - 4) * S3 * S3s
t2 = 4 * (n^4 - 8 * n^3 + 19 * n^2 - 4 * n - 16) * U * Us
t3 = 24 * (n^2 - n - 4) * (U * Bs + B * Us)
t4 = 6 * (n^4 - 8 * n^3 + 21 * n^2 - 6 * n - 24) * B * Bs
t5 = 12 * (n^4 - n^3 - 8 * n^2 + 36 * n - 48) * R * Rs
t6 = 12 * (n^3 - 2 * n^2 + 9 * n - 12) * (T * S2 * Rs + R *
Ts * S2s)
t7 = 3 * (n^4 - 4 * n^3 - 2 * n^2 + 9 * n - 12) * T * Ts *
S2 * S2s
t81 = (n^3 - 3 * n^2 - 2 * n + 8) * (R * Us + U * Rs)
t82 = (n^3 - 2 * n^2 - 3 * n + 12) * (R * Bs + B * Rs)
t8 = 24 * (t81 + t82)
t9 = 12 * (n^2 - n + 4) * (T * S2 * Us + U * Ts * S2s)
t10 = 6 * (2 * n^3 - 7 * n^2 - 3 * n + 12) * (T * S2 * Bs +
B * Ts * S2s)
t11 = -2 * n * (n - 1) * (n^2 - n + 4) * ((2 * U + 3 * B) *
S3s + (2 * Us + 3 * Bs) * S3)
t12 = -3 * n * (n - 1)^2 * (n + 4) * ((T * S2 + 4 * R) *
S3s + (Ts * S2s + 4 * Rs) * S3)
t13 = 2 * n * (n - 1) * (n - 2) * ((T^3 + 6 * T * T2 + 8 *
T3) * S3s + (Ts^3 + 6 * Ts * T2s + 8 * T3s) * S3)
t14 = T^3 * ((n^3 - 9 * n^2 + 23 * n - 14) * Ts^3 + 6 * (n -
4) * Ts * T2s + 8 * T3s)
t15 = 6 * T * T2 * ((n - 4) * Ts^3 + (n^3 - 9 * n^2 + 24 *
n - 14) * Ts * T2s + 4 * (n - 3) * T3s)
t16 = 8 * T3 * (Ts^3 + 3 * (n - 3) * Ts * T2s + (n^3 - 9 *
n^2 + 26 * n - 22) * T3s)
t17 = -16 * (T^3 * Us + U * Ts^3) - 6 * (T * T2 * Us + U *
Ts * T2s) * (2 * n^2 - 10 * n + 16)
t18 = -8 * (T3 * Us + U * T3s) * (3 * n^2 - 15 * n + 16) -
(T^3 * Bs + B * Ts^3) * (6 * n^2 - 30 * n + 24)
t19 = -6 * (T * T2 * Bs + B * Ts * T2s) * (4 * n^2 - 20 *
n + 24) - 8 * (T3 * Bs + B * T3s) * (3 * n^2 - 15 * n +
24)
t201 = 24 * (T^3 * Rs + R * Ts^3) + 6 * (T * T2 * Rs + R *
Ts * T2s) * (2 * n^2 - 10 * n + 24)
t202 = 8 * (T3 * Rs + R * T3s) * (3 * n^2 - 15 * n + 24) +
(3 * n^2 - 15 * n + 6) * (T^3 * Ts * S2s + T * S2 * Ts^3)
t203 = 6 * (T * T2 * Ts * S2s + Ts * T2s * T * S2) * (n^2 -
5 * n + 6) + 48 * (T3 * Ts * S2s + T3s * T * S2)
t20 = -(n - 2) * (t201 + t202 + t203)
temp31 = t1 + t2 + t3 + t4 + t5 + t6 + t7 + t8 + t9 + t10 +
t11 + t12 + t13 + t14 + t15 + t16 + t17 + t18 + t19 +
t20
temp32 = n * (n - 1) * (n - 2) * (n - 3) * (n - 4) * (n -
5)
mom3 = temp31/temp32
skewness.krv = (mom3 - 3 * mean.krv * variance.krv - mean.krv^3)/variance.krv^1.5
m1 = mean.krv
m2 = variance.krv
m3 = skewness.krv
shape = 4/m3^2
scale = sqrt(m2) * m3/2
location = m1 - 2 * sqrt(m2)/m3
PIIIpars = list(shape, location, scale)
pv = 1 - ppearsonIII(Fstar, params = PIIIpars)
return(pv)
}
<bytecode: 0x6b8a998>
<environment: namespace:MiRKAT>
--- function search by body ---
Function KRV in namespace MiRKAT has this body.
----------- END OF FAILURE REPORT --------------
Error in if ((class(kernel.otu) != "matrix")) { :
the condition has length > 1
Calls: KRV
Execution halted
Flavor: r-devel-linux-x86_64-fedora-clang
Version: 1.0.1
Check: re-building of vignette outputs
Result: WARN
Error(s) in re-building vignettes:
--- re-building ‘MiRKAT.Rnw’ using Sweave
This is vegan 2.5-6
----------- FAILURE REPORT --------------
--- failure: the condition has length > 1 ---
--- srcref ---
:
--- package (from environment) ---
MiRKAT
--- call from context ---
MiRKAT(y = Smoker, Ks = K.weighted, X = cbind(Male, anti), out_type = "D",
method = "davies")
--- call from argument ---
if (class(Ks) == "matrix") {
Ks = list(Ks)
}
--- R stacktrace ---
where 1: MiRKAT(y = Smoker, Ks = K.weighted, X = cbind(Male, anti), out_type = "D",
method = "davies")
where 2: eval(expr, .GlobalEnv)
where 3: eval(expr, .GlobalEnv)
where 4: withVisible(eval(expr, .GlobalEnv))
where 5: doTryCatch(return(expr), name, parentenv, handler)
where 6: tryCatchOne(expr, names, parentenv, handlers[[1L]])
where 7: tryCatchList(expr, classes, parentenv, handlers)
where 8: tryCatch(expr, error = function(e) {
call <- conditionCall(e)
if (!is.null(call)) {
if (identical(call[[1L]], quote(doTryCatch)))
call <- sys.call(-4L)
dcall <- deparse(call)[1L]
prefix <- paste("Error in", dcall, ": ")
LONG <- 75L
sm <- strsplit(conditionMessage(e), "\n")[[1L]]
w <- 14L + nchar(dcall, type = "w") + nchar(sm[1L], type = "w")
if (is.na(w))
w <- 14L + nchar(dcall, type = "b") + nchar(sm[1L],
type = "b")
if (w > LONG)
prefix <- paste0(prefix, "\n ")
}
else prefix <- "Error : "
msg <- paste0(prefix, conditionMessage(e), "\n")
.Internal(seterrmessage(msg[1L]))
if (!silent && isTRUE(getOption("show.error.messages"))) {
cat(msg, file = outFile)
.Internal(printDeferredWarnings())
}
invisible(structure(msg, class = "try-error", condition = e))
})
where 9: try(withVisible(eval(expr, .GlobalEnv)), silent = TRUE)
where 10: evalFunc(ce, options)
where 11: tryCatchList(expr, classes, parentenv, handlers)
where 12: tryCatch(evalFunc(ce, options), finally = {
cat("\n")
sink()
})
where 13: driver$runcode(drobj, chunk, chunkopts)
where 14: utils::Sweave(...)
where 15: engine$weave(file, quiet = quiet, encoding = enc)
where 16: doTryCatch(return(expr), name, parentenv, handler)
where 17: tryCatchOne(expr, names, parentenv, handlers[[1L]])
where 18: tryCatchList(expr, classes, parentenv, handlers)
where 19: 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)
}
outputs <- c(outputs, output)
}, error = function(e) {
thisOK <<- FALSE
fails <<- c(fails, file)
message(gettextf("Error: processing vignette '%s' failed with diagnostics:\n%s",
file, conditionMessage(e)))
})
where 20: tools:::buildVignettes(dir = "/data/gannet/ripley/R/packages/tests-clang/MiRKAT.Rcheck/vign_test/MiRKAT",
ser_elibs = "/tmp/RtmpgYUPve/filebcf83281160e.rds")
--- value of length: 2 type: logical ---
[1] TRUE FALSE
--- function from context ---
function (y, X = NULL, Ks, out_type = "C", nperm = 999, method = "davies")
{
n = length(y)
if (any(is.na(y))) {
ids = which(is.na(y))
stop(paste("subjects", ids, "has missing response, please remove before proceed \n"))
}
if (is.null(X) == FALSE) {
if (NROW(X) != length(y))
stop("Dimensions of X and y don't match.")
}
if (class(Ks) == "matrix") {
Ks = list(Ks)
}
if (class(Ks) == "list") {
if ((any(lapply(Ks, "nrow") != n)) | (any(lapply(Ks,
"ncol") != n))) {
stop("distance matrix need to be n x n, where n is the sample size \n ")
}
if (class(Ks) != "list") {
stop("Distance needs to be a list of n x n matrices or a single n x n matrix \n")
}
}
if (!is.null(X)) {
if (any(is.na(X))) {
stop("NAs in covariates X, please impute or remove subjects which has missing covariates values")
}
}
if (method == "moment" & n < 100 & out_type == "C") {
warning("Continuous outcome: sample size < 100, p-value using moment matching can be inaccurate at tails, davies or permutation is recommended")
}
if (method == "moment" & n < 200 & out_type == "D") {
warning("Continuous outcome: sample size < 200, p-value using moment matching can be inaccurate at tails, davies or permutation is recommended")
}
if (!(out_type %in% c("C", "D"))) {
stop("Currently only continuous and Binary outcome are supported. Please choose out_type = \"C\" or \"D\" ")
}
if (out_type == "C") {
re = MiRKAT_continuous(y, X = X, Ks = Ks, method = method,
nperm = nperm)
}
if (out_type == "D") {
re = MiRKAT_binary(y, X = X, Ks = Ks, method = method,
nperm = nperm)
}
return(re)
}
<bytecode: 0x7df75e0>
<environment: namespace:MiRKAT>
--- function search by body ---
Function MiRKAT in namespace MiRKAT has this body.
----------- END OF FAILURE REPORT --------------
Error: processing vignette 'MiRKAT.Rnw' failed with diagnostics:
chunk 5 (label = data5)
Error in if (class(Ks) == "matrix") { : the condition has length > 1
--- failed re-building ‘MiRKAT.Rnw’
SUMMARY: processing the following file failed:
‘MiRKAT.Rnw’
Error: Vignette re-building failed.
Execution halted
Flavor: r-devel-linux-x86_64-fedora-clang
Version: 1.0.1
Check: examples
Result: ERROR
Running examples in ‘MiRKAT-Ex.R’ failed
The error most likely occurred in:
> ### Name: KRV
> ### Title: Kernel RV Coefficient Test
> ### Aliases: KRV
>
> ### ** Examples
>
> library(MASS)
> library(GUniFrac)
> data(throat.tree)
> data(throat.otu.tab)
> data(throat.meta)
> attach(throat.meta)
>
> set.seed(123)
> n = nrow(throat.otu.tab)
> Male = (Sex == "Male")**2
> Smoker =(SmokingStatus == "Smoker") **2
> anti = (AntibioticUsePast3Months_TimeFromAntibioticUsage != "None")^2
> cova = cbind(Male, anti)
>
> otu.tab.rff <- Rarefy(throat.otu.tab)$otu.tab.rff
> unifracs <- GUniFrac(otu.tab.rff, throat.tree, alpha=c(0, 0.5, 1))$unifracs
>
> D.weighted = unifracs[,,"d_1"]
> D.unweighted = unifracs[,,"d_UW"]
> D.BC= as.matrix(vegdist(otu.tab.rff , method="bray"))
>
> K.weighted = D2K(D.weighted)
> K.unweighted = D2K(D.unweighted)
> K.BC = D2K(D.BC)
>
> rho = 0.2
> Va = matrix(rep(rho, (2*n)^2), 2*n, 2*n)+diag(1-rho, 2*n)
> G = mvrnorm(n, rep(0, 2*n), Va)
>
> #############################################################
>
> KRV(kernel.otu = K.weighted, y = G, X = cova, kernel.y = "Gaussian")
----------- FAILURE REPORT --------------
--- failure: the condition has length > 1 ---
--- srcref ---
:
--- package (from environment) ---
MiRKAT
--- call from context ---
KRV(kernel.otu = K.weighted, y = G, X = cova, kernel.y = "Gaussian")
--- call from argument ---
if ((class(kernel.otu) != "matrix")) {
stop("Please provide a kernel matrix for microbiome data")
}
--- R stacktrace ---
where 1: KRV(kernel.otu = K.weighted, y = G, X = cova, kernel.y = "Gaussian")
--- value of length: 2 type: logical ---
[1] FALSE TRUE
--- function from context ---
function (kernel.otu, y = NULL, X = NULL, kernel.y)
{
if ((class(kernel.otu) != "matrix")) {
stop("Please provide a kernel matrix for microbiome data")
}
if (is.matrix(kernel.y)) {
n = nrow(kernel.otu)
if (!is.null(X)) {
warning("Covariates can't be adjusted for in this case, and hence argument \"X\" will be ignored.")
}
if (!is.null(y)) {
warning("No need to provide phenotype matrix when a phenotype kernel is provided, and hence argument \"y\" will be ignored.")
}
if (ncol(kernel.otu) != n | nrow(kernel.y) != n | ncol(kernel.y) !=
n) {
stop("kernel matrices need to be n x n, where n is the sample size \n ")
}
}
if (!is.matrix(kernel.y)) {
if (!(kernel.y %in% c("Gaussian", "linear"))) {
stop("Please choose kernel.y = \"Gaussian\" or \"linear\", or enter a kernel matrix for \"kernel.y\"")
}
if (is.null(y)) {
stop("Please enter a phenotype matrix for argument \"y\" or enter a kernel matrix for argument \"kernel.y\" ")
}
n = NROW(y)
if (nrow(kernel.otu) != n | ncol(kernel.otu) != n) {
stop("kernel matrix needs to be n x n, where n is the sample size \n ")
}
if (any(is.na(y))) {
ids = which(is.na(y))
stop(paste("subjects", ids, "has missing response, please remove before proceed \n"))
}
if (!is.null(X)) {
if (any(is.na(X))) {
stop("NAs in covariates X, please impute or remove subjects which has missing covariates values")
}
if (NROW(X) != NROW(y))
stop("Dimensions of X and y don't match.")
}
}
K = kernel.otu
kern_g = function(zz) {
n = nrow(zz)
D = matrix(NA, nrow = n, ncol = n)
for (i in 1:n) {
for (j in 1:n) {
D[i, j] = sum((zz[i, ] - zz[j, ])^2)
}
}
temp = c(D)
D1 = temp[temp > 0]
scl = median(D1)
K = matrix(NA, nrow = n, ncol = n)
for (i in 1:n) {
for (j in 1:n) {
K[i, j] = exp(-sum((zz[i, ] - zz[j, ])^2)/scl)
}
}
return(K)
}
n = nrow(K)
I.n = diag(1, n)
I.1 = rep(1, n)
if (is.matrix(kernel.y)) {
L = kernel.y
}
else {
if (!is.null(X)) {
Px = X %*% solve(t(X) %*% X) %*% t(X)
err.Y = (I.n - Px) %*% y
}
else {
err.Y = y
}
if (kernel.y == "Gaussian") {
L = kern_g(err.Y)
}
else {
if (kernel.y == "linear") {
L = err.Y %*% t(err.Y)
}
}
}
H = I.n - I.1 %*% t(I.1)/n
K = H %*% K %*% H
L = H %*% L %*% H
A = K/tr(K %*% K)
W = L/tr(L %*% L)
Fstar = tr(A %*% W)
mean.krv = tr(A) * tr(W)/(n - 1)
T = tr(A)
T2 = tr(A %*% A)
S2 = sum(diag(A)^2)
Ts = tr(W)
T2s = tr(W %*% W)
S2s = sum(diag(W)^2)
temp1 = 2 * ((n - 1) * T2 - T^2) * ((n - 1) * T2s - Ts^2)/(n -
1)^2/(n + 1)/(n - 2)
temp21 = n * (n + 1) * S2 - (n - 1) * (T^2 + 2 * T2)
temp22 = n * (n + 1) * S2s - (n - 1) * (Ts^2 + 2 * T2s)
temp23 = (n + 1) * n * (n - 1) * (n - 2) * (n - 3)
temp2 = temp21 * temp22/temp23
variance.krv = temp1 + temp2
T3 = tr(A %*% A %*% A)
S3 = sum(diag(A)^3)
U = sum(A^3)
R = t(diag(A)) %*% diag(A %*% A)
B = t(diag(A)) %*% A %*% diag(A)
T3s = tr(W %*% W %*% W)
S3s = sum(diag(W)^3)
Us = sum(W^3)
Rs = t(diag(W)) %*% diag(W %*% W)
Bs = t(diag(W)) %*% W %*% diag(W)
t1 = n^2 * (n + 1) * (n^2 + 15 * n - 4) * S3 * S3s
t2 = 4 * (n^4 - 8 * n^3 + 19 * n^2 - 4 * n - 16) * U * Us
t3 = 24 * (n^2 - n - 4) * (U * Bs + B * Us)
t4 = 6 * (n^4 - 8 * n^3 + 21 * n^2 - 6 * n - 24) * B * Bs
t5 = 12 * (n^4 - n^3 - 8 * n^2 + 36 * n - 48) * R * Rs
t6 = 12 * (n^3 - 2 * n^2 + 9 * n - 12) * (T * S2 * Rs + R *
Ts * S2s)
t7 = 3 * (n^4 - 4 * n^3 - 2 * n^2 + 9 * n - 12) * T * Ts *
S2 * S2s
t81 = (n^3 - 3 * n^2 - 2 * n + 8) * (R * Us + U * Rs)
t82 = (n^3 - 2 * n^2 - 3 * n + 12) * (R * Bs + B * Rs)
t8 = 24 * (t81 + t82)
t9 = 12 * (n^2 - n + 4) * (T * S2 * Us + U * Ts * S2s)
t10 = 6 * (2 * n^3 - 7 * n^2 - 3 * n + 12) * (T * S2 * Bs +
B * Ts * S2s)
t11 = -2 * n * (n - 1) * (n^2 - n + 4) * ((2 * U + 3 * B) *
S3s + (2 * Us + 3 * Bs) * S3)
t12 = -3 * n * (n - 1)^2 * (n + 4) * ((T * S2 + 4 * R) *
S3s + (Ts * S2s + 4 * Rs) * S3)
t13 = 2 * n * (n - 1) * (n - 2) * ((T^3 + 6 * T * T2 + 8 *
T3) * S3s + (Ts^3 + 6 * Ts * T2s + 8 * T3s) * S3)
t14 = T^3 * ((n^3 - 9 * n^2 + 23 * n - 14) * Ts^3 + 6 * (n -
4) * Ts * T2s + 8 * T3s)
t15 = 6 * T * T2 * ((n - 4) * Ts^3 + (n^3 - 9 * n^2 + 24 *
n - 14) * Ts * T2s + 4 * (n - 3) * T3s)
t16 = 8 * T3 * (Ts^3 + 3 * (n - 3) * Ts * T2s + (n^3 - 9 *
n^2 + 26 * n - 22) * T3s)
t17 = -16 * (T^3 * Us + U * Ts^3) - 6 * (T * T2 * Us + U *
Ts * T2s) * (2 * n^2 - 10 * n + 16)
t18 = -8 * (T3 * Us + U * T3s) * (3 * n^2 - 15 * n + 16) -
(T^3 * Bs + B * Ts^3) * (6 * n^2 - 30 * n + 24)
t19 = -6 * (T * T2 * Bs + B * Ts * T2s) * (4 * n^2 - 20 *
n + 24) - 8 * (T3 * Bs + B * T3s) * (3 * n^2 - 15 * n +
24)
t201 = 24 * (T^3 * Rs + R * Ts^3) + 6 * (T * T2 * Rs + R *
Ts * T2s) * (2 * n^2 - 10 * n + 24)
t202 = 8 * (T3 * Rs + R * T3s) * (3 * n^2 - 15 * n + 24) +
(3 * n^2 - 15 * n + 6) * (T^3 * Ts * S2s + T * S2 * Ts^3)
t203 = 6 * (T * T2 * Ts * S2s + Ts * T2s * T * S2) * (n^2 -
5 * n + 6) + 48 * (T3 * Ts * S2s + T3s * T * S2)
t20 = -(n - 2) * (t201 + t202 + t203)
temp31 = t1 + t2 + t3 + t4 + t5 + t6 + t7 + t8 + t9 + t10 +
t11 + t12 + t13 + t14 + t15 + t16 + t17 + t18 + t19 +
t20
temp32 = n * (n - 1) * (n - 2) * (n - 3) * (n - 4) * (n -
5)
mom3 = temp31/temp32
skewness.krv = (mom3 - 3 * mean.krv * variance.krv - mean.krv^3)/variance.krv^1.5
m1 = mean.krv
m2 = variance.krv
m3 = skewness.krv
shape = 4/m3^2
scale = sqrt(m2) * m3/2
location = m1 - 2 * sqrt(m2)/m3
PIIIpars = list(shape, location, scale)
pv = 1 - ppearsonIII(Fstar, params = PIIIpars)
return(pv)
}
<bytecode: 0x6c33e78>
<environment: namespace:MiRKAT>
--- function search by body ---
Function KRV in namespace MiRKAT has this body.
----------- END OF FAILURE REPORT --------------
Error in if ((class(kernel.otu) != "matrix")) { :
the condition has length > 1
Calls: KRV
Execution halted
Flavor: r-devel-linux-x86_64-fedora-gcc
Version: 1.0.1
Check: re-building of vignette outputs
Result: WARN
Error(s) in re-building vignettes:
--- re-building ‘MiRKAT.Rnw’ using Sweave
This is vegan 2.5-6
----------- FAILURE REPORT --------------
--- failure: the condition has length > 1 ---
--- srcref ---
:
--- package (from environment) ---
MiRKAT
--- call from context ---
MiRKAT(y = Smoker, Ks = K.weighted, X = cbind(Male, anti), out_type = "D",
method = "davies")
--- call from argument ---
if (class(Ks) == "matrix") {
Ks = list(Ks)
}
--- R stacktrace ---
where 1: MiRKAT(y = Smoker, Ks = K.weighted, X = cbind(Male, anti), out_type = "D",
method = "davies")
where 2: eval(expr, .GlobalEnv)
where 3: eval(expr, .GlobalEnv)
where 4: withVisible(eval(expr, .GlobalEnv))
where 5: doTryCatch(return(expr), name, parentenv, handler)
where 6: tryCatchOne(expr, names, parentenv, handlers[[1L]])
where 7: tryCatchList(expr, classes, parentenv, handlers)
where 8: tryCatch(expr, error = function(e) {
call <- conditionCall(e)
if (!is.null(call)) {
if (identical(call[[1L]], quote(doTryCatch)))
call <- sys.call(-4L)
dcall <- deparse(call)[1L]
prefix <- paste("Error in", dcall, ": ")
LONG <- 75L
sm <- strsplit(conditionMessage(e), "\n")[[1L]]
w <- 14L + nchar(dcall, type = "w") + nchar(sm[1L], type = "w")
if (is.na(w))
w <- 14L + nchar(dcall, type = "b") + nchar(sm[1L],
type = "b")
if (w > LONG)
prefix <- paste0(prefix, "\n ")
}
else prefix <- "Error : "
msg <- paste0(prefix, conditionMessage(e), "\n")
.Internal(seterrmessage(msg[1L]))
if (!silent && isTRUE(getOption("show.error.messages"))) {
cat(msg, file = outFile)
.Internal(printDeferredWarnings())
}
invisible(structure(msg, class = "try-error", condition = e))
})
where 9: try(withVisible(eval(expr, .GlobalEnv)), silent = TRUE)
where 10: evalFunc(ce, options)
where 11: tryCatchList(expr, classes, parentenv, handlers)
where 12: tryCatch(evalFunc(ce, options), finally = {
cat("\n")
sink()
})
where 13: driver$runcode(drobj, chunk, chunkopts)
where 14: utils::Sweave(...)
where 15: engine$weave(file, quiet = quiet, encoding = enc)
where 16: doTryCatch(return(expr), name, parentenv, handler)
where 17: tryCatchOne(expr, names, parentenv, handlers[[1L]])
where 18: tryCatchList(expr, classes, parentenv, handlers)
where 19: tryCatch({
engine$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)
}
outputs <- c(outputs, output)
}, error = function(e) {
thisOK <<- FALSE
fails <<- c(fails, file)
message(gettextf("Error: processing vignette '%s' failed with diagnostics:\n%s",
file, conditionMessage(e)))
})
where 20: tools:::buildVignettes(dir = "/data/gannet/ripley/R/packages/tests-devel/MiRKAT.Rcheck/vign_test/MiRKAT",
ser_elibs = "/tmp/RtmpnpUFDZ/file9bf21276aefc.rds")
--- value of length: 2 type: logical ---
[1] TRUE FALSE
--- function from context ---
function (y, X = NULL, Ks, out_type = "C", nperm = 999, method = "davies")
{
n = length(y)
if (any(is.na(y))) {
ids = which(is.na(y))
stop(paste("subjects", ids, "has missing response, please remove before proceed \n"))
}
if (is.null(X) == FALSE) {
if (NROW(X) != length(y))
stop("Dimensions of X and y don't match.")
}
if (class(Ks) == "matrix") {
Ks = list(Ks)
}
if (class(Ks) == "list") {
if ((any(lapply(Ks, "nrow") != n)) | (any(lapply(Ks,
"ncol") != n))) {
stop("distance matrix need to be n x n, where n is the sample size \n ")
}
if (class(Ks) != "list") {
stop("Distance needs to be a list of n x n matrices or a single n x n matrix \n")
}
}
if (!is.null(X)) {
if (any(is.na(X))) {
stop("NAs in covariates X, please impute or remove subjects which has missing covariates values")
}
}
if (method == "moment" & n < 100 & out_type == "C") {
warning("Continuous outcome: sample size < 100, p-value using moment matching can be inaccurate at tails, davies or permutation is recommended")
}
if (method == "moment" & n < 200 & out_type == "D") {
warning("Continuous outcome: sample size < 200, p-value using moment matching can be inaccurate at tails, davies or permutation is recommended")
}
if (!(out_type %in% c("C", "D"))) {
stop("Currently only continuous and Binary outcome are supported. Please choose out_type = \"C\" or \"D\" ")
}
if (out_type == "C") {
re = MiRKAT_continuous(y, X = X, Ks = Ks, method = method,
nperm = nperm)
}
if (out_type == "D") {
re = MiRKAT_binary(y, X = X, Ks = Ks, method = method,
nperm = nperm)
}
return(re)
}
<bytecode: 0x6f8ea28>
<environment: namespace:MiRKAT>
--- function search by body ---
Function MiRKAT in namespace MiRKAT has this body.
----------- END OF FAILURE REPORT --------------
Error: processing vignette 'MiRKAT.Rnw' failed with diagnostics:
chunk 5 (label = data5)
Error in if (class(Ks) == "matrix") { : the condition has length > 1
--- failed re-building ‘MiRKAT.Rnw’
SUMMARY: processing the following file failed:
‘MiRKAT.Rnw’
Error: Vignette re-building failed.
Execution halted
Flavor: r-devel-linux-x86_64-fedora-gcc