Last updated on 2019-11-26 00:51:58 CET.
Flavor | Version | Tinstall | Tcheck | Ttotal | Status | Flags |
---|---|---|---|---|---|---|
r-devel-linux-x86_64-debian-clang | 2.2.0 | 17.52 | 105.66 | 123.18 | ERROR | |
r-devel-linux-x86_64-debian-gcc | 2.2.0 | 15.87 | 85.67 | 101.54 | ERROR | |
r-devel-linux-x86_64-fedora-clang | 2.2.0 | 145.96 | OK | |||
r-devel-linux-x86_64-fedora-gcc | 2.2.0 | 139.89 | OK | |||
r-devel-windows-ix86+x86_64 | 2.2.0 | 34.00 | 136.00 | 170.00 | OK | |
r-devel-windows-ix86+x86_64-gcc8 | 2.2.0 | 28.00 | 104.00 | 132.00 | OK | |
r-patched-linux-x86_64 | 2.2.0 | 16.92 | 103.11 | 120.03 | OK | |
r-patched-solaris-x86 | 2.2.0 | 190.00 | OK | |||
r-release-linux-x86_64 | 2.2.0 | 16.72 | 105.47 | 122.19 | OK | |
r-release-windows-ix86+x86_64 | 2.2.0 | 33.00 | 138.00 | 171.00 | OK | |
r-release-osx-x86_64 | 2.2.0 | OK | ||||
r-oldrel-windows-ix86+x86_64 | 2.2.0 | 17.00 | 126.00 | 143.00 | OK | |
r-oldrel-osx-x86_64 | 2.2.0 | OK |
Version: 2.2.0
Check: examples
Result: ERROR
Running examples in 'mrds-Ex.R' failed
The error most likely occurred in:
> base::assign(".ptime", proc.time(), pos = "CheckExEnv")
> ### Name: ddf
> ### Title: Distance Detection Function Fitting
> ### Aliases: ddf
> ### Keywords: Models ~Statistical
>
> ### ** Examples
>
> # load data
> data(book.tee.data)
> region <- book.tee.data$book.tee.region
> egdata <- book.tee.data$book.tee.dataframe
> samples <- book.tee.data$book.tee.samples
> obs <- book.tee.data$book.tee.obs
>
> # fit a half-normal detection function
> result <- ddf(dsmodel=~mcds(key="hn", formula=~1), data=egdata, method="ds",
+ meta.data=list(width=4))
Warning in if (class(nhatend) != "try-error") { :
the condition has length > 1 and only the first element will be used
----------- FAILURE REPORT --------------
--- failure: the condition has length > 1 ---
--- srcref ---
:
--- package (from environment) ---
mrds
--- call from context ---
ddf.ds(model = dsmodel, data, meta.data = meta.data, control = control,
call = match.call())
--- call from argument ---
if (class(result$hessian) == "try-error") {
if (misc.options$mono) {
warning("First partial hessian calculation failed with monotonicity enforced, no hessian\n")
}
else {
warning("First partial hessian calculation failed; using second-partial hessian\n")
result$hessian <- lt$hessian
}
} else if (length(lt$par) > 1) {
if (class(try(solve(result$hessian), silent = TRUE)) == "try-error") {
warning("First partial hessian is singular; using second-partial hessian\n")
result$hessian <- lt$hessian
}
}
--- R stacktrace ---
where 1: ddf.ds(model = dsmodel, data, meta.data = meta.data, control = control,
call = match.call())
where 2: ddf(dsmodel = ~mcds(key = "hn", formula = ~1), data = egdata,
method = "ds", meta.data = list(width = 4))
--- value of length: 2 type: logical ---
[1] FALSE FALSE
--- function from context ---
function (model, data, meta.data = list(), control = list(),
call, method = "ds")
{
if (!is.null(meta.data$mono.strict)) {
if (meta.data$mono.strict) {
meta.data$mono <- TRUE
}
}
if (!is.null(meta.data$mono)) {
if (meta.data$mono & is.null(meta.data$mono.strict)) {
meta.data$mono <- TRUE
}
}
meta.data <- assign.default.values(meta.data, left = 0, width = NA,
binned = FALSE, int.range = NA, mono = FALSE, mono.strict = FALSE,
point = FALSE)
control <- assign.default.values(control, showit = 0, estimate = TRUE,
refit = TRUE, nrefits = 25, initial = NA, lowerbounds = NA,
upperbounds = NA, limit = TRUE, parscale = TRUE, maxiter = 12,
standardize = TRUE, mono.points = 20, mono.tol = 1e-08,
mono.delta = 1e-07, debug = FALSE, nofit = FALSE, optimx.method = "nlminb",
optimx.maxit = 300, silent = FALSE)
save.options <- options()
options(contrasts = c("contr.treatment", "contr.poly"))
if (!is.null(data$distance)) {
data <- data[!is.na(data$distance), ]
}
else {
data <- data[!is.na(data$distbegin) & !is.na(data$distend),
]
}
if (is.null(data$object)) {
stop("\nobject field is missing in the data\n")
}
datalist <- process.data(data, meta.data, check = FALSE)
xmat <- datalist$xmat
meta.data <- datalist$meta.data
if (!is.null(xmat$observer)) {
if (control$limit) {
if (length(levels(factor(xmat$observer))) > 1) {
xmat <- xmat[xmat$observer == levels(factor(xmat$observer))[1],
]
xmat$detected <- rep(1, dim(xmat)[1])
}
}
}
if (!is.null(xmat$detected)) {
if (control$limit)
xmat <- xmat[xmat$detected == 1, ]
}
else {
xmat$detected <- rep(1, dim(xmat)[1])
}
if (length(unique(xmat$object)) != length(xmat$object)) {
stop("\nSome values of object field are duplicates. They must be unique.\n")
}
if (meta.data$binned) {
breaks <- c(max(0, min(as.numeric(levels(as.factor(xmat$distbegin))))),
as.numeric(levels(as.factor(xmat$distend))))
}
else {
breaks <- NULL
}
ddfobj <- create.ddfobj(model, xmat, meta.data, control$initial)
initialvalues <- c(ddfobj$shape$parameters, ddfobj$scale$parameters,
ddfobj$adjustment$parameters)
if (!is.null(initialvalues)) {
bounds <- setbounds(control$lowerbounds, control$upperbounds,
initialvalues, ddfobj)
}
else {
bounds <- NULL
}
misc.options <- list(point = meta.data$point, int.range = meta.data$int.range,
showit = control$showit, integral.numeric = control$integral.numeric,
breaks = breaks, maxiter = control$maxiter, refit = control$refit,
nrefits = control$nrefits, parscale = control$parscale,
mono = meta.data$mono, mono.strict = meta.data$mono.strict,
binned = meta.data$binned, width = meta.data$width, standardize = control$standardize,
mono.points = control$mono.points, mono.tol = control$mono.tol,
mono.delta = control$mono.delta, debug = control$debug,
nofit = control$nofit, left = meta.data$left, silent = control$silent)
if (misc.options$showit >= 1 && !is.null(initialvalues)) {
cat("DEBUG: initial values =", round(initialvalues, 7),
"\n")
}
optim.options <- list(maxit = control$optimx.maxit, optimx.method = control$optimx.method,
parscale = control$parscale)
if (is.null(initialvalues))
misc.options$nofit <- TRUE
lt <- detfct.fit(ddfobj, optim.options, bounds, misc.options)
stored_data <- data[row.names(data) %in% row.names(xmat),
]
stored_data$detected <- 1
result <- list(call = call, data = stored_data, model = substitute(model),
meta.data = meta.data, control = control, method = method,
ds = lt, par = lt$par, lnl = -lt$value)
if (lt$converge != 0 & misc.options$debug) {
warning("No convergence, not calculating Hessian, predicted values, abundance\nReturned object is for debugging ONLY!")
options(save.options)
return(result)
}
if (is.null(lt$par)) {
lt$hessian <- NULL
}
else {
result$hessian <- try(flt.var(result$ds$aux$ddfobj, misc.options))
if (class(result$hessian) == "try-error") {
if (misc.options$mono) {
warning("First partial hessian calculation failed with monotonicity enforced, no hessian\n")
}
else {
warning("First partial hessian calculation failed; using second-partial hessian\n")
result$hessian <- lt$hessian
}
}
else if (length(lt$par) > 1) {
if (class(try(solve(result$hessian), silent = TRUE)) ==
"try-error") {
warning("First partial hessian is singular; using second-partial hessian\n")
result$hessian <- lt$hessian
}
}
}
modpaste <- paste(model)
modelvalues <- try(eval(parse(text = modpaste[2:length(modpaste)])))
class(result$ds) <- c(modelvalues$fct, "ds")
result$dsmodel <- modpaste
n <- length(xmat$distance)
npar <- length(lt$par)
result$criterion <- 2 * lt$value + 2 * npar
class(result) <- c("ds", "ddf")
if (!is.null(ddfobj$adjustment) & (ddfobj$type %in% c("hn",
"hr", "unif"))) {
result$monotonicity.check <- check.mono(result, n.pts = control$mono.points)
}
if (is.null(lt$message)) {
result$ds$message <- ""
}
if (lt$message == "FALSE CONVERGENCE") {
warning("Model fitting did not converge. Try different initial values or different model")
}
else {
result$fitted <- predict(result, esw = FALSE)$fitted
if (control$estimate) {
result$Nhat <- NCovered(result, group = TRUE)
}
}
options(save.options)
return(result)
}
<bytecode: 0xaf314b8>
<environment: namespace:mrds>
--- function search by body ---
Function ddf.ds in namespace mrds has this body.
----------- END OF FAILURE REPORT --------------
Fatal error: the condition has length > 1
Flavor: r-devel-linux-x86_64-debian-clang
Version: 2.2.0
Check: examples
Result: ERROR
Running examples in ‘mrds-Ex.R’ failed
The error most likely occurred in:
> base::assign(".ptime", proc.time(), pos = "CheckExEnv")
> ### Name: ddf
> ### Title: Distance Detection Function Fitting
> ### Aliases: ddf
> ### Keywords: Models ~Statistical
>
> ### ** Examples
>
> # load data
> data(book.tee.data)
> region <- book.tee.data$book.tee.region
> egdata <- book.tee.data$book.tee.dataframe
> samples <- book.tee.data$book.tee.samples
> obs <- book.tee.data$book.tee.obs
>
> # fit a half-normal detection function
> result <- ddf(dsmodel=~mcds(key="hn", formula=~1), data=egdata, method="ds",
+ meta.data=list(width=4))
Warning in if (class(nhatend) != "try-error") { :
the condition has length > 1 and only the first element will be used
----------- FAILURE REPORT --------------
--- failure: the condition has length > 1 ---
--- srcref ---
:
--- package (from environment) ---
mrds
--- call from context ---
ddf.ds(model = dsmodel, data, meta.data = meta.data, control = control,
call = match.call())
--- call from argument ---
if (class(result$hessian) == "try-error") {
if (misc.options$mono) {
warning("First partial hessian calculation failed with monotonicity enforced, no hessian\n")
}
else {
warning("First partial hessian calculation failed; using second-partial hessian\n")
result$hessian <- lt$hessian
}
} else if (length(lt$par) > 1) {
if (class(try(solve(result$hessian), silent = TRUE)) == "try-error") {
warning("First partial hessian is singular; using second-partial hessian\n")
result$hessian <- lt$hessian
}
}
--- R stacktrace ---
where 1: ddf.ds(model = dsmodel, data, meta.data = meta.data, control = control,
call = match.call())
where 2: ddf(dsmodel = ~mcds(key = "hn", formula = ~1), data = egdata,
method = "ds", meta.data = list(width = 4))
--- value of length: 2 type: logical ---
[1] FALSE FALSE
--- function from context ---
function (model, data, meta.data = list(), control = list(),
call, method = "ds")
{
if (!is.null(meta.data$mono.strict)) {
if (meta.data$mono.strict) {
meta.data$mono <- TRUE
}
}
if (!is.null(meta.data$mono)) {
if (meta.data$mono & is.null(meta.data$mono.strict)) {
meta.data$mono <- TRUE
}
}
meta.data <- assign.default.values(meta.data, left = 0, width = NA,
binned = FALSE, int.range = NA, mono = FALSE, mono.strict = FALSE,
point = FALSE)
control <- assign.default.values(control, showit = 0, estimate = TRUE,
refit = TRUE, nrefits = 25, initial = NA, lowerbounds = NA,
upperbounds = NA, limit = TRUE, parscale = TRUE, maxiter = 12,
standardize = TRUE, mono.points = 20, mono.tol = 1e-08,
mono.delta = 1e-07, debug = FALSE, nofit = FALSE, optimx.method = "nlminb",
optimx.maxit = 300, silent = FALSE)
save.options <- options()
options(contrasts = c("contr.treatment", "contr.poly"))
if (!is.null(data$distance)) {
data <- data[!is.na(data$distance), ]
}
else {
data <- data[!is.na(data$distbegin) & !is.na(data$distend),
]
}
if (is.null(data$object)) {
stop("\nobject field is missing in the data\n")
}
datalist <- process.data(data, meta.data, check = FALSE)
xmat <- datalist$xmat
meta.data <- datalist$meta.data
if (!is.null(xmat$observer)) {
if (control$limit) {
if (length(levels(factor(xmat$observer))) > 1) {
xmat <- xmat[xmat$observer == levels(factor(xmat$observer))[1],
]
xmat$detected <- rep(1, dim(xmat)[1])
}
}
}
if (!is.null(xmat$detected)) {
if (control$limit)
xmat <- xmat[xmat$detected == 1, ]
}
else {
xmat$detected <- rep(1, dim(xmat)[1])
}
if (length(unique(xmat$object)) != length(xmat$object)) {
stop("\nSome values of object field are duplicates. They must be unique.\n")
}
if (meta.data$binned) {
breaks <- c(max(0, min(as.numeric(levels(as.factor(xmat$distbegin))))),
as.numeric(levels(as.factor(xmat$distend))))
}
else {
breaks <- NULL
}
ddfobj <- create.ddfobj(model, xmat, meta.data, control$initial)
initialvalues <- c(ddfobj$shape$parameters, ddfobj$scale$parameters,
ddfobj$adjustment$parameters)
if (!is.null(initialvalues)) {
bounds <- setbounds(control$lowerbounds, control$upperbounds,
initialvalues, ddfobj)
}
else {
bounds <- NULL
}
misc.options <- list(point = meta.data$point, int.range = meta.data$int.range,
showit = control$showit, integral.numeric = control$integral.numeric,
breaks = breaks, maxiter = control$maxiter, refit = control$refit,
nrefits = control$nrefits, parscale = control$parscale,
mono = meta.data$mono, mono.strict = meta.data$mono.strict,
binned = meta.data$binned, width = meta.data$width, standardize = control$standardize,
mono.points = control$mono.points, mono.tol = control$mono.tol,
mono.delta = control$mono.delta, debug = control$debug,
nofit = control$nofit, left = meta.data$left, silent = control$silent)
if (misc.options$showit >= 1 && !is.null(initialvalues)) {
cat("DEBUG: initial values =", round(initialvalues, 7),
"\n")
}
optim.options <- list(maxit = control$optimx.maxit, optimx.method = control$optimx.method,
parscale = control$parscale)
if (is.null(initialvalues))
misc.options$nofit <- TRUE
lt <- detfct.fit(ddfobj, optim.options, bounds, misc.options)
stored_data <- data[row.names(data) %in% row.names(xmat),
]
stored_data$detected <- 1
result <- list(call = call, data = stored_data, model = substitute(model),
meta.data = meta.data, control = control, method = method,
ds = lt, par = lt$par, lnl = -lt$value)
if (lt$converge != 0 & misc.options$debug) {
warning("No convergence, not calculating Hessian, predicted values, abundance\nReturned object is for debugging ONLY!")
options(save.options)
return(result)
}
if (is.null(lt$par)) {
lt$hessian <- NULL
}
else {
result$hessian <- try(flt.var(result$ds$aux$ddfobj, misc.options))
if (class(result$hessian) == "try-error") {
if (misc.options$mono) {
warning("First partial hessian calculation failed with monotonicity enforced, no hessian\n")
}
else {
warning("First partial hessian calculation failed; using second-partial hessian\n")
result$hessian <- lt$hessian
}
}
else if (length(lt$par) > 1) {
if (class(try(solve(result$hessian), silent = TRUE)) ==
"try-error") {
warning("First partial hessian is singular; using second-partial hessian\n")
result$hessian <- lt$hessian
}
}
}
modpaste <- paste(model)
modelvalues <- try(eval(parse(text = modpaste[2:length(modpaste)])))
class(result$ds) <- c(modelvalues$fct, "ds")
result$dsmodel <- modpaste
n <- length(xmat$distance)
npar <- length(lt$par)
result$criterion <- 2 * lt$value + 2 * npar
class(result) <- c("ds", "ddf")
if (!is.null(ddfobj$adjustment) & (ddfobj$type %in% c("hn",
"hr", "unif"))) {
result$monotonicity.check <- check.mono(result, n.pts = control$mono.points)
}
if (is.null(lt$message)) {
result$ds$message <- ""
}
if (lt$message == "FALSE CONVERGENCE") {
warning("Model fitting did not converge. Try different initial values or different model")
}
else {
result$fitted <- predict(result, esw = FALSE)$fitted
if (control$estimate) {
result$Nhat <- NCovered(result, group = TRUE)
}
}
options(save.options)
return(result)
}
<bytecode: 0x558bc37790a0>
<environment: namespace:mrds>
--- function search by body ---
Function ddf.ds in namespace mrds has this body.
----------- END OF FAILURE REPORT --------------
Fatal error: the condition has length > 1
Flavor: r-devel-linux-x86_64-debian-gcc