Last updated on 2020-02-19 10:49:02 CET.
| Flavor | Version | Tinstall | Tcheck | Ttotal | Status | Flags |
|---|---|---|---|---|---|---|
| r-devel-linux-x86_64-debian-clang | 1.0.0 | 3.74 | 18.97 | 22.71 | ERROR | |
| r-devel-linux-x86_64-debian-gcc | 1.0.0 | 2.38 | 15.09 | 17.47 | ERROR | |
| r-devel-linux-x86_64-fedora-clang | 1.0.0 | 28.75 | ERROR | |||
| r-devel-linux-x86_64-fedora-gcc | 1.0.0 | 27.11 | ERROR | |||
| r-devel-windows-ix86+x86_64 | 1.0.0 | 11.00 | 44.00 | 55.00 | OK | |
| r-devel-windows-ix86+x86_64-gcc8 | 1.0.0 | 16.00 | 48.00 | 64.00 | OK | |
| r-patched-linux-x86_64 | 1.0.0 | 2.28 | 19.73 | 22.01 | OK | |
| r-patched-solaris-x86 | 1.0.0 | 42.70 | OK | |||
| r-release-linux-x86_64 | 1.0.0 | 2.20 | 19.75 | 21.95 | OK | |
| r-release-windows-ix86+x86_64 | 1.0.0 | 9.00 | 44.00 | 53.00 | OK | |
| r-release-osx-x86_64 | 1.0.0 | OK | ||||
| r-oldrel-windows-ix86+x86_64 | 1.0.0 | 9.00 | 46.00 | 55.00 | OK | |
| r-oldrel-osx-x86_64 | 1.0.0 | OK |
Version: 1.0.0
Check: examples
Result: ERROR
Running examples in 'pawls-Ex.R' failed
The error most likely occurred in:
> base::assign(".ptime", proc.time(), pos = "CheckExEnv")
> ### Name: pawls
> ### Title: Penalized adaptive weighted least squares regression
> ### Aliases: pawls
>
> ### ** Examples
>
> ## generate data
> library("mvtnorm")
> set.seed(123) # for reproducibility
> n = 100 # number of observations
> p = 8 # number of variables
> beta = c(1, 2, 3, 0, 0, 0, 0, 0) # coefficients
> sigma <- 0.5 # controls signal-to-noise ratio
> epsilon <- 0.1 # contamination level
> Sigma <- 0.5^t(sapply(1:p, function(i, j) abs(i-j), 1:p))
> x <- rmvnorm(n, sigma=Sigma) # predictor matrix
> e <- rnorm(n) # error terms
> i <- 1:ceiling(epsilon*n) # observations to be contaminated
> e[i] <- e[i] + 5 # vertical outliers
> y <- c(x %*% beta + sigma * e) # response
> x[i,] <- x[i,] + 5 # bad leverage points
>
> ## fit pawls model over a find grid of tuning parameters
> pawls(x,y)
----------- FAILURE REPORT --------------
--- failure: the condition has length > 1 ---
--- srcref ---
:
--- package (from environment) ---
pawls
--- call from context ---
pawls(x, y)
--- call from argument ---
if (class(x) != "matrix") {
tmp <- try(x <- as.matrix(x), silent = TRUE)
if (class(tmp)[1] == "try-error")
stop("x must be a matrix or able to be coerced to a matrix")
}
--- R stacktrace ---
where 1: pawls(x, y)
--- value of length: 2 type: logical ---
[1] FALSE TRUE
--- function from context ---
function (x, y, nlambda1 = 100, nlambda2 = 50, lambda1 = NULL,
lambda2 = NULL, lambda1.min = 0.05, lambda2.min = 0.001,
beta0 = NULL, w0 = NULL, initial = c("uniform", "PAWLS"),
delta = 1e-06, maxIter = 1000, intercept = TRUE, standardize = TRUE,
search = c("grid", "cross"))
{
n = length(y)
p = dim(x)[2]
if (class(x) != "matrix") {
tmp <- try(x <- as.matrix(x), silent = TRUE)
if (class(tmp)[1] == "try-error")
stop("x must be a matrix or able to be coerced to a matrix")
}
if (class(y) != "numeric") {
tmp <- try(y <- as.numeric(y), silent = TRUE)
if (class(tmp)[1] == "try-error")
stop("y must numeric or able to be coerced to numeric")
}
if (any(is.na(y)) | any(is.na(x)))
stop("Missing data (NA's) detected.Take actions to eliminate missing data before passing \n X and y to pawls.")
initial <- match.arg(initial)
search <- match.arg(search)
penalty2 <- "L1"
penalty1 <- "L1"
criterion <- "BIC"
startBeta <- NULL
startW <- NULL
if (!is.null(lambda1))
nlambda1 <- length(lambda1)
if (!is.null(lambda2))
nlambda2 <- length(lambda2)
if (initial == "PAWLS") {
init = pawls(x, y, lambda1.min = 0.05, lambda2.min = 0.001,
intercept = intercept, search = "grid")
beta0 = SetBeta0(init$beta)
w0 = ifelse(init$w == 1, 0.99, init$w)
}
else if (initial == "uniform") {
if (is.null(beta0)) {
beta0 = rep(1, p)
if (intercept)
beta0 = c(1, beta0)
}
if (is.null(w0))
w0 = rep(0.99, n)
beta0 = SetBeta0(beta0)
w0 = ifelse(w0 == 1, 0.99, w0)
}
if (intercept) {
x = AddIntercept(x)
}
std = 0
scale = 0
if (standardize) {
std <- .Call("Standardize", x, y)
XX <- std[[1]]
yy <- std[[2]]
scale <- std[[3]]
}
else {
XX = x
yy = y
}
if (is.null(lambda1) || is.null(lambda2)) {
lambda = setup_parameter(x = XX, y = yy, nlambda1 = nlambda1,
nlambda2 = nlambda2, lambda1.min = lambda1.min, lambda2.min = lambda2.min,
beta0 = beta0, w0 = w0)
if (is.null(lambda1))
lambda1 = lambda$lambda1
if (is.null(lambda2))
lambda2 = lambda$lambda2
}
if (search == "grid") {
res1 <- pawls_grid(x = XX, y = yy, penalty1 = penalty1,
penalty2 = penalty2, lambda1 = lambda1, lambda2 = lambda2,
beta0 = beta0, w0 = w0, delta = delta, maxIter = maxIter,
intercept = intercept)
res2 <- BIC_grid(res1$wloss, res1$beta, res1$w)
fit <- list(beta = res2$beta, w = res2$w, lambda1 = lambda1,
lambda2 = lambda2, opt.lambda1 = lambda1[res2$index1],
opt.lambda2 = lambda2[res2$index2], iter = res1$iter,
ws = res1$w, betas = res1$betas, raw.bic = res2$raw.bic,
bic = res2$bic)
class(fit) <- "pawls.grid"
}
else {
res = pawls_cross(x = XX, y = yy, penalty1 = penalty1,
penalty2 = penalty2, lambda1 = lambda1, lambda2 = lambda2,
beta0 = beta0, w0 = w0, delta = delta, maxIter = maxIter,
intercept = intercept, criterion = criterion, startBeta = startBeta,
startW = startW)
fit <- list(beta = res$beta, w = res$w, lambda1 = lambda1,
lambda2 = lambda2, opt.lambda1 = res$opt.lambda1,
opt.lambda2 = res$opt.lambda2, iter = res$iter)
class(fit) <- "pawls.cross"
}
if (standardize) {
scale = ifelse(scale == 0, 0, 1/scale)
fit$beta = fit$beta * scale
}
fit
}
<bytecode: 0x2a139a0>
<environment: namespace:pawls>
--- function search by body ---
Function pawls in namespace pawls has this body.
----------- END OF FAILURE REPORT --------------
Error in if (class(x) != "matrix") { : the condition has length > 1
Calls: pawls
Execution halted
Flavor: r-devel-linux-x86_64-debian-clang
Version: 1.0.0
Check: examples
Result: ERROR
Running examples in ‘pawls-Ex.R’ failed
The error most likely occurred in:
> base::assign(".ptime", proc.time(), pos = "CheckExEnv")
> ### Name: pawls
> ### Title: Penalized adaptive weighted least squares regression
> ### Aliases: pawls
>
> ### ** Examples
>
> ## generate data
> library("mvtnorm")
> set.seed(123) # for reproducibility
> n = 100 # number of observations
> p = 8 # number of variables
> beta = c(1, 2, 3, 0, 0, 0, 0, 0) # coefficients
> sigma <- 0.5 # controls signal-to-noise ratio
> epsilon <- 0.1 # contamination level
> Sigma <- 0.5^t(sapply(1:p, function(i, j) abs(i-j), 1:p))
> x <- rmvnorm(n, sigma=Sigma) # predictor matrix
> e <- rnorm(n) # error terms
> i <- 1:ceiling(epsilon*n) # observations to be contaminated
> e[i] <- e[i] + 5 # vertical outliers
> y <- c(x %*% beta + sigma * e) # response
> x[i,] <- x[i,] + 5 # bad leverage points
>
> ## fit pawls model over a find grid of tuning parameters
> pawls(x,y)
----------- FAILURE REPORT --------------
--- failure: the condition has length > 1 ---
--- srcref ---
:
--- package (from environment) ---
pawls
--- call from context ---
pawls(x, y)
--- call from argument ---
if (class(x) != "matrix") {
tmp <- try(x <- as.matrix(x), silent = TRUE)
if (class(tmp)[1] == "try-error")
stop("x must be a matrix or able to be coerced to a matrix")
}
--- R stacktrace ---
where 1: pawls(x, y)
--- value of length: 2 type: logical ---
[1] FALSE TRUE
--- function from context ---
function (x, y, nlambda1 = 100, nlambda2 = 50, lambda1 = NULL,
lambda2 = NULL, lambda1.min = 0.05, lambda2.min = 0.001,
beta0 = NULL, w0 = NULL, initial = c("uniform", "PAWLS"),
delta = 1e-06, maxIter = 1000, intercept = TRUE, standardize = TRUE,
search = c("grid", "cross"))
{
n = length(y)
p = dim(x)[2]
if (class(x) != "matrix") {
tmp <- try(x <- as.matrix(x), silent = TRUE)
if (class(tmp)[1] == "try-error")
stop("x must be a matrix or able to be coerced to a matrix")
}
if (class(y) != "numeric") {
tmp <- try(y <- as.numeric(y), silent = TRUE)
if (class(tmp)[1] == "try-error")
stop("y must numeric or able to be coerced to numeric")
}
if (any(is.na(y)) | any(is.na(x)))
stop("Missing data (NA's) detected.Take actions to eliminate missing data before passing \n X and y to pawls.")
initial <- match.arg(initial)
search <- match.arg(search)
penalty2 <- "L1"
penalty1 <- "L1"
criterion <- "BIC"
startBeta <- NULL
startW <- NULL
if (!is.null(lambda1))
nlambda1 <- length(lambda1)
if (!is.null(lambda2))
nlambda2 <- length(lambda2)
if (initial == "PAWLS") {
init = pawls(x, y, lambda1.min = 0.05, lambda2.min = 0.001,
intercept = intercept, search = "grid")
beta0 = SetBeta0(init$beta)
w0 = ifelse(init$w == 1, 0.99, init$w)
}
else if (initial == "uniform") {
if (is.null(beta0)) {
beta0 = rep(1, p)
if (intercept)
beta0 = c(1, beta0)
}
if (is.null(w0))
w0 = rep(0.99, n)
beta0 = SetBeta0(beta0)
w0 = ifelse(w0 == 1, 0.99, w0)
}
if (intercept) {
x = AddIntercept(x)
}
std = 0
scale = 0
if (standardize) {
std <- .Call("Standardize", x, y)
XX <- std[[1]]
yy <- std[[2]]
scale <- std[[3]]
}
else {
XX = x
yy = y
}
if (is.null(lambda1) || is.null(lambda2)) {
lambda = setup_parameter(x = XX, y = yy, nlambda1 = nlambda1,
nlambda2 = nlambda2, lambda1.min = lambda1.min, lambda2.min = lambda2.min,
beta0 = beta0, w0 = w0)
if (is.null(lambda1))
lambda1 = lambda$lambda1
if (is.null(lambda2))
lambda2 = lambda$lambda2
}
if (search == "grid") {
res1 <- pawls_grid(x = XX, y = yy, penalty1 = penalty1,
penalty2 = penalty2, lambda1 = lambda1, lambda2 = lambda2,
beta0 = beta0, w0 = w0, delta = delta, maxIter = maxIter,
intercept = intercept)
res2 <- BIC_grid(res1$wloss, res1$beta, res1$w)
fit <- list(beta = res2$beta, w = res2$w, lambda1 = lambda1,
lambda2 = lambda2, opt.lambda1 = lambda1[res2$index1],
opt.lambda2 = lambda2[res2$index2], iter = res1$iter,
ws = res1$w, betas = res1$betas, raw.bic = res2$raw.bic,
bic = res2$bic)
class(fit) <- "pawls.grid"
}
else {
res = pawls_cross(x = XX, y = yy, penalty1 = penalty1,
penalty2 = penalty2, lambda1 = lambda1, lambda2 = lambda2,
beta0 = beta0, w0 = w0, delta = delta, maxIter = maxIter,
intercept = intercept, criterion = criterion, startBeta = startBeta,
startW = startW)
fit <- list(beta = res$beta, w = res$w, lambda1 = lambda1,
lambda2 = lambda2, opt.lambda1 = res$opt.lambda1,
opt.lambda2 = res$opt.lambda2, iter = res$iter)
class(fit) <- "pawls.cross"
}
if (standardize) {
scale = ifelse(scale == 0, 0, 1/scale)
fit$beta = fit$beta * scale
}
fit
}
<bytecode: 0x55632041bf00>
<environment: namespace:pawls>
--- function search by body ---
Function pawls in namespace pawls has this body.
----------- END OF FAILURE REPORT --------------
Error in if (class(x) != "matrix") { : the condition has length > 1
Calls: pawls
Execution halted
Flavor: r-devel-linux-x86_64-debian-gcc
Version: 1.0.0
Check: examples
Result: ERROR
Running examples in ‘pawls-Ex.R’ failed
The error most likely occurred in:
> ### Name: pawls
> ### Title: Penalized adaptive weighted least squares regression
> ### Aliases: pawls
>
> ### ** Examples
>
> ## generate data
> library("mvtnorm")
> set.seed(123) # for reproducibility
> n = 100 # number of observations
> p = 8 # number of variables
> beta = c(1, 2, 3, 0, 0, 0, 0, 0) # coefficients
> sigma <- 0.5 # controls signal-to-noise ratio
> epsilon <- 0.1 # contamination level
> Sigma <- 0.5^t(sapply(1:p, function(i, j) abs(i-j), 1:p))
> x <- rmvnorm(n, sigma=Sigma) # predictor matrix
> e <- rnorm(n) # error terms
> i <- 1:ceiling(epsilon*n) # observations to be contaminated
> e[i] <- e[i] + 5 # vertical outliers
> y <- c(x %*% beta + sigma * e) # response
> x[i,] <- x[i,] + 5 # bad leverage points
>
> ## fit pawls model over a find grid of tuning parameters
> pawls(x,y)
----------- FAILURE REPORT --------------
--- failure: the condition has length > 1 ---
--- srcref ---
:
--- package (from environment) ---
pawls
--- call from context ---
pawls(x, y)
--- call from argument ---
if (class(x) != "matrix") {
tmp <- try(x <- as.matrix(x), silent = TRUE)
if (class(tmp)[1] == "try-error")
stop("x must be a matrix or able to be coerced to a matrix")
}
--- R stacktrace ---
where 1: pawls(x, y)
--- value of length: 2 type: logical ---
[1] FALSE TRUE
--- function from context ---
function (x, y, nlambda1 = 100, nlambda2 = 50, lambda1 = NULL,
lambda2 = NULL, lambda1.min = 0.05, lambda2.min = 0.001,
beta0 = NULL, w0 = NULL, initial = c("uniform", "PAWLS"),
delta = 1e-06, maxIter = 1000, intercept = TRUE, standardize = TRUE,
search = c("grid", "cross"))
{
n = length(y)
p = dim(x)[2]
if (class(x) != "matrix") {
tmp <- try(x <- as.matrix(x), silent = TRUE)
if (class(tmp)[1] == "try-error")
stop("x must be a matrix or able to be coerced to a matrix")
}
if (class(y) != "numeric") {
tmp <- try(y <- as.numeric(y), silent = TRUE)
if (class(tmp)[1] == "try-error")
stop("y must numeric or able to be coerced to numeric")
}
if (any(is.na(y)) | any(is.na(x)))
stop("Missing data (NA's) detected.Take actions to eliminate missing data before passing \n X and y to pawls.")
initial <- match.arg(initial)
search <- match.arg(search)
penalty2 <- "L1"
penalty1 <- "L1"
criterion <- "BIC"
startBeta <- NULL
startW <- NULL
if (!is.null(lambda1))
nlambda1 <- length(lambda1)
if (!is.null(lambda2))
nlambda2 <- length(lambda2)
if (initial == "PAWLS") {
init = pawls(x, y, lambda1.min = 0.05, lambda2.min = 0.001,
intercept = intercept, search = "grid")
beta0 = SetBeta0(init$beta)
w0 = ifelse(init$w == 1, 0.99, init$w)
}
else if (initial == "uniform") {
if (is.null(beta0)) {
beta0 = rep(1, p)
if (intercept)
beta0 = c(1, beta0)
}
if (is.null(w0))
w0 = rep(0.99, n)
beta0 = SetBeta0(beta0)
w0 = ifelse(w0 == 1, 0.99, w0)
}
if (intercept) {
x = AddIntercept(x)
}
std = 0
scale = 0
if (standardize) {
std <- .Call("Standardize", x, y)
XX <- std[[1]]
yy <- std[[2]]
scale <- std[[3]]
}
else {
XX = x
yy = y
}
if (is.null(lambda1) || is.null(lambda2)) {
lambda = setup_parameter(x = XX, y = yy, nlambda1 = nlambda1,
nlambda2 = nlambda2, lambda1.min = lambda1.min, lambda2.min = lambda2.min,
beta0 = beta0, w0 = w0)
if (is.null(lambda1))
lambda1 = lambda$lambda1
if (is.null(lambda2))
lambda2 = lambda$lambda2
}
if (search == "grid") {
res1 <- pawls_grid(x = XX, y = yy, penalty1 = penalty1,
penalty2 = penalty2, lambda1 = lambda1, lambda2 = lambda2,
beta0 = beta0, w0 = w0, delta = delta, maxIter = maxIter,
intercept = intercept)
res2 <- BIC_grid(res1$wloss, res1$beta, res1$w)
fit <- list(beta = res2$beta, w = res2$w, lambda1 = lambda1,
lambda2 = lambda2, opt.lambda1 = lambda1[res2$index1],
opt.lambda2 = lambda2[res2$index2], iter = res1$iter,
ws = res1$w, betas = res1$betas, raw.bic = res2$raw.bic,
bic = res2$bic)
class(fit) <- "pawls.grid"
}
else {
res = pawls_cross(x = XX, y = yy, penalty1 = penalty1,
penalty2 = penalty2, lambda1 = lambda1, lambda2 = lambda2,
beta0 = beta0, w0 = w0, delta = delta, maxIter = maxIter,
intercept = intercept, criterion = criterion, startBeta = startBeta,
startW = startW)
fit <- list(beta = res$beta, w = res$w, lambda1 = lambda1,
lambda2 = lambda2, opt.lambda1 = res$opt.lambda1,
opt.lambda2 = res$opt.lambda2, iter = res$iter)
class(fit) <- "pawls.cross"
}
if (standardize) {
scale = ifelse(scale == 0, 0, 1/scale)
fit$beta = fit$beta * scale
}
fit
}
<bytecode: 0x28d4850>
<environment: namespace:pawls>
--- function search by body ---
Function pawls in namespace pawls has this body.
----------- END OF FAILURE REPORT --------------
Error in if (class(x) != "matrix") { : the condition has length > 1
Calls: pawls
Execution halted
Flavor: r-devel-linux-x86_64-fedora-clang
Version: 1.0.0
Check: examples
Result: ERROR
Running examples in ‘pawls-Ex.R’ failed
The error most likely occurred in:
> ### Name: pawls
> ### Title: Penalized adaptive weighted least squares regression
> ### Aliases: pawls
>
> ### ** Examples
>
> ## generate data
> library("mvtnorm")
> set.seed(123) # for reproducibility
> n = 100 # number of observations
> p = 8 # number of variables
> beta = c(1, 2, 3, 0, 0, 0, 0, 0) # coefficients
> sigma <- 0.5 # controls signal-to-noise ratio
> epsilon <- 0.1 # contamination level
> Sigma <- 0.5^t(sapply(1:p, function(i, j) abs(i-j), 1:p))
> x <- rmvnorm(n, sigma=Sigma) # predictor matrix
> e <- rnorm(n) # error terms
> i <- 1:ceiling(epsilon*n) # observations to be contaminated
> e[i] <- e[i] + 5 # vertical outliers
> y <- c(x %*% beta + sigma * e) # response
> x[i,] <- x[i,] + 5 # bad leverage points
>
> ## fit pawls model over a find grid of tuning parameters
> pawls(x,y)
----------- FAILURE REPORT --------------
--- failure: the condition has length > 1 ---
--- srcref ---
:
--- package (from environment) ---
pawls
--- call from context ---
pawls(x, y)
--- call from argument ---
if (class(x) != "matrix") {
tmp <- try(x <- as.matrix(x), silent = TRUE)
if (class(tmp)[1] == "try-error")
stop("x must be a matrix or able to be coerced to a matrix")
}
--- R stacktrace ---
where 1: pawls(x, y)
--- value of length: 2 type: logical ---
[1] FALSE TRUE
--- function from context ---
function (x, y, nlambda1 = 100, nlambda2 = 50, lambda1 = NULL,
lambda2 = NULL, lambda1.min = 0.05, lambda2.min = 0.001,
beta0 = NULL, w0 = NULL, initial = c("uniform", "PAWLS"),
delta = 1e-06, maxIter = 1000, intercept = TRUE, standardize = TRUE,
search = c("grid", "cross"))
{
n = length(y)
p = dim(x)[2]
if (class(x) != "matrix") {
tmp <- try(x <- as.matrix(x), silent = TRUE)
if (class(tmp)[1] == "try-error")
stop("x must be a matrix or able to be coerced to a matrix")
}
if (class(y) != "numeric") {
tmp <- try(y <- as.numeric(y), silent = TRUE)
if (class(tmp)[1] == "try-error")
stop("y must numeric or able to be coerced to numeric")
}
if (any(is.na(y)) | any(is.na(x)))
stop("Missing data (NA's) detected.Take actions to eliminate missing data before passing \n X and y to pawls.")
initial <- match.arg(initial)
search <- match.arg(search)
penalty2 <- "L1"
penalty1 <- "L1"
criterion <- "BIC"
startBeta <- NULL
startW <- NULL
if (!is.null(lambda1))
nlambda1 <- length(lambda1)
if (!is.null(lambda2))
nlambda2 <- length(lambda2)
if (initial == "PAWLS") {
init = pawls(x, y, lambda1.min = 0.05, lambda2.min = 0.001,
intercept = intercept, search = "grid")
beta0 = SetBeta0(init$beta)
w0 = ifelse(init$w == 1, 0.99, init$w)
}
else if (initial == "uniform") {
if (is.null(beta0)) {
beta0 = rep(1, p)
if (intercept)
beta0 = c(1, beta0)
}
if (is.null(w0))
w0 = rep(0.99, n)
beta0 = SetBeta0(beta0)
w0 = ifelse(w0 == 1, 0.99, w0)
}
if (intercept) {
x = AddIntercept(x)
}
std = 0
scale = 0
if (standardize) {
std <- .Call("Standardize", x, y)
XX <- std[[1]]
yy <- std[[2]]
scale <- std[[3]]
}
else {
XX = x
yy = y
}
if (is.null(lambda1) || is.null(lambda2)) {
lambda = setup_parameter(x = XX, y = yy, nlambda1 = nlambda1,
nlambda2 = nlambda2, lambda1.min = lambda1.min, lambda2.min = lambda2.min,
beta0 = beta0, w0 = w0)
if (is.null(lambda1))
lambda1 = lambda$lambda1
if (is.null(lambda2))
lambda2 = lambda$lambda2
}
if (search == "grid") {
res1 <- pawls_grid(x = XX, y = yy, penalty1 = penalty1,
penalty2 = penalty2, lambda1 = lambda1, lambda2 = lambda2,
beta0 = beta0, w0 = w0, delta = delta, maxIter = maxIter,
intercept = intercept)
res2 <- BIC_grid(res1$wloss, res1$beta, res1$w)
fit <- list(beta = res2$beta, w = res2$w, lambda1 = lambda1,
lambda2 = lambda2, opt.lambda1 = lambda1[res2$index1],
opt.lambda2 = lambda2[res2$index2], iter = res1$iter,
ws = res1$w, betas = res1$betas, raw.bic = res2$raw.bic,
bic = res2$bic)
class(fit) <- "pawls.grid"
}
else {
res = pawls_cross(x = XX, y = yy, penalty1 = penalty1,
penalty2 = penalty2, lambda1 = lambda1, lambda2 = lambda2,
beta0 = beta0, w0 = w0, delta = delta, maxIter = maxIter,
intercept = intercept, criterion = criterion, startBeta = startBeta,
startW = startW)
fit <- list(beta = res$beta, w = res$w, lambda1 = lambda1,
lambda2 = lambda2, opt.lambda1 = res$opt.lambda1,
opt.lambda2 = res$opt.lambda2, iter = res$iter)
class(fit) <- "pawls.cross"
}
if (standardize) {
scale = ifelse(scale == 0, 0, 1/scale)
fit$beta = fit$beta * scale
}
fit
}
<bytecode: 0x154ece0>
<environment: namespace:pawls>
--- function search by body ---
Function pawls in namespace pawls has this body.
----------- END OF FAILURE REPORT --------------
Error in if (class(x) != "matrix") { : the condition has length > 1
Calls: pawls
Execution halted
Flavor: r-devel-linux-x86_64-fedora-gcc