Last updated on 2019-12-05 00:51:48 CET.
| Flavor | Version | Tinstall | Tcheck | Ttotal | Status | Flags |
|---|---|---|---|---|---|---|
| r-devel-linux-x86_64-debian-clang | 5.1.2 | 48.98 | 178.65 | 227.63 | ERROR | |
| r-devel-linux-x86_64-debian-gcc | 5.1.2 | 41.23 | 138.06 | 179.29 | ERROR | |
| r-devel-linux-x86_64-fedora-clang | 5.1.2 | 283.44 | OK | |||
| r-devel-linux-x86_64-fedora-gcc | 5.1.2 | 282.93 | OK | |||
| r-devel-windows-ix86+x86_64 | 5.1.2 | 93.00 | 227.00 | 320.00 | OK | |
| r-devel-windows-ix86+x86_64-gcc8 | 5.1.2 | 98.00 | 228.00 | 326.00 | OK | |
| r-patched-linux-x86_64 | 5.1.2 | 43.71 | 162.01 | 205.72 | OK | |
| r-patched-solaris-x86 | 5.1.2 | 344.30 | OK | |||
| r-release-linux-x86_64 | 5.1.2 | 44.51 | 171.76 | 216.27 | OK | |
| r-release-windows-ix86+x86_64 | 5.1.2 | 88.00 | 234.00 | 322.00 | OK | |
| r-release-osx-x86_64 | 5.1.2 | OK | ||||
| r-oldrel-windows-ix86+x86_64 | 5.1.2 | 67.00 | 202.00 | 269.00 | OK | |
| r-oldrel-osx-x86_64 | 5.1.2 | OK |
Version: 5.1.2
Check: examples
Result: ERROR
Running examples in 'CARBayes-Ex.R' failed
The error most likely occurred in:
> base::assign(".ptime", proc.time(), pos = "CheckExEnv")
> ### Name: MVS.CARleroux
> ### Title: Fit a multivariate spatial generalised linear mixed model to
> ### data, where the random effects are modelled by a multivariate
> ### conditional autoregressive model.
> ### Aliases: MVS.CARleroux
>
> ### ** Examples
>
> #################################################
> #### Run the model on simulated data on a lattice
> #################################################
> #### Load other libraries required
> library(MASS)
>
> #### Set up a square lattice region
> x.easting <- 1:10
> x.northing <- 1:10
> Grid <- expand.grid(x.easting, x.northing)
> K <- nrow(Grid)
>
> #### set up distance and neighbourhood (W, based on sharing a common border) matrices
> distance <- as.matrix(dist(Grid))
> W <-array(0, c(K,K))
> W[distance==1] <-1
> K <- nrow(W)
>
> #### Generate the correlation structures
> Q.W <- 0.99 * (diag(apply(W, 2, sum)) - W) + 0.01 * diag(rep(1,K))
> Q.W.inv <- solve(Q.W)
>
> Sigma <- matrix(c(1,0.5,0, 0.5,1,0.3, 0, 0.3, 1), nrow=3)
> Sigma.inv <- solve(Sigma)
> J <- nrow(Sigma)
> N.all <- K * J
>
> precision.phi <- kronecker(Q.W, Sigma.inv)
> var.phi <- solve(precision.phi)
>
> #### Generate the covariate component
> x1 <- rnorm(K)
> x2 <- rnorm(K)
> XB <- cbind(0.1 * x1 - 0.1*x2, -0.1 * x1 + 0.1*x2, 0.1 * x1 - 0.1*x2)
>
> #### Generate the random effects
> phi <- mvrnorm(n=1, mu=rep(0,N.all), Sigma=var.phi)
>
>
> #### Generate the response data
> lp <-as.numeric(t(XB)) + phi
> prob <- exp(lp) / (1 + exp(lp))
> trials.vec <- rep(100,N.all)
> Y.vec <- rbinom(n=N.all, size=trials.vec, prob=prob)
>
> #### Turn the data and trials into matrices where each row is an area.
> Y <- matrix(Y.vec, nrow=K, ncol=J, byrow=TRUE)
> trials <- matrix(trials.vec, nrow=K, ncol=J, byrow=TRUE)
>
> #### Run the Leroux model
> formula <- Y ~ x1 + x2
> ## Not run:
> ##D model <- MVS.CARleroux(formula=formula, family="binomial",
> ##D trials=trials, W=W, burnin=20000, n.sample=100000)
> ## End(Not run)
>
> #### Toy example for checking
> model <- MVS.CARleroux(formula=formula, family="binomial",
+ trials=trials, W=W, burnin=10, n.sample=50)
Setting up the model.
----------- FAILURE REPORT --------------
--- failure: the condition has length > 1 ---
--- srcref ---
:
--- package (from environment) ---
CARBayes
--- call from context ---
common.frame(formula, data, "binomial")
--- call from argument ---
if (class(X) == "try-error") stop("the covariate matrix contains inappropriate values.",
call. = FALSE)
--- R stacktrace ---
where 1: common.frame(formula, data, "binomial")
where 2: binomial.MVlerouxCAR(formula = formula, data = data, trials = trials,
W = W, burnin = burnin, n.sample = n.sample, thin = thin,
prior.mean.beta = prior.mean.beta, prior.var.beta = prior.var.beta,
prior.Sigma.df = prior.Sigma.df, prior.Sigma.scale = prior.Sigma.scale,
rho = rho, MALA = MALA, verbose = verbose)
where 3: MVS.CARleroux(formula = formula, family = "binomial", trials = trials,
W = W, burnin = 10, n.sample = 50)
--- value of length: 2 type: logical ---
[1] FALSE FALSE
--- function from context ---
function (formula, data, family)
{
frame <- try(suppressWarnings(model.frame(formula, data = data,
na.action = na.pass)), silent = TRUE)
if (class(frame) == "try-error")
stop("the formula inputted contains an error, e.g the variables may be different lengths.",
call. = FALSE)
X <- try(suppressWarnings(model.matrix(object = attr(frame,
"terms"), data = frame)), silent = TRUE)
if (class(X) == "try-error")
stop("the covariate matrix contains inappropriate values.",
call. = FALSE)
if (sum(is.na(X)) > 0)
stop("the covariate matrix contains missing 'NA' values.",
call. = FALSE)
n <- nrow(X)
p <- ncol(X)
cor.X <- suppressWarnings(cor(X))
diag(cor.X) <- 0
if (max(cor.X, na.rm = TRUE) == 1)
stop("the covariate matrix has two exactly linearly related columns.",
call. = FALSE)
if (min(cor.X, na.rm = TRUE) == -1)
stop("the covariate matrix has two exactly linearly related columns.",
call. = FALSE)
if (p > 1) {
if (sort(apply(X, 2, sd))[2] == 0)
stop("the covariate matrix has two intercept terms.",
call. = FALSE)
}
else {
}
X.standardised <- X
X.sd <- apply(X, 2, sd)
X.mean <- apply(X, 2, mean)
X.indicator <- rep(NA, p)
for (j in 1:p) {
if (length(table(X[, j])) > 2) {
X.indicator[j] <- 1
X.standardised[, j] <- (X[, j] - mean(X[, j]))/sd(X[,
j])
}
else if (length(table(X[, j])) == 1) {
X.indicator[j] <- 2
}
else {
X.indicator[j] <- 0
}
}
Y <- model.response(frame)
J <- length(Y)/n
which.miss <- matrix(as.numeric(!is.na(Y)), nrow = n, ncol = J)
if (J == 1)
which.miss <- as.numeric(which.miss)
n.miss <- n * J - sum(which.miss)
if (family == "binomial") {
if (!is.numeric(Y))
stop("the response variable has non-numeric values.",
call. = FALSE)
int.check <- n * J - n.miss - sum(ceiling(Y) == floor(Y),
na.rm = TRUE)
if (int.check > 0)
stop("the response variable has non-integer values.",
call. = FALSE)
if (min(Y, na.rm = TRUE) < 0)
stop("the response variable has negative values.",
call. = FALSE)
}
else if (family == "gaussian") {
if (!is.numeric(Y))
stop("the response variable has non-numeric values.",
call. = FALSE)
}
else if (family == "poisson") {
if (!is.numeric(Y))
stop("the response variable has non-numeric values.",
call. = FALSE)
int.check <- n * J - n.miss - sum(ceiling(Y) == floor(Y),
na.rm = TRUE)
if (int.check > 0)
stop("the response variable has non-integer values.",
call. = FALSE)
if (min(Y, na.rm = TRUE) < 0)
stop("the response variable has negative values.",
call. = FALSE)
}
else if (family == "multinomial") {
if (!is.numeric(Y))
stop("the response variable has non-numeric values.",
call. = FALSE)
int.check <- n * J - n.miss - sum(ceiling(Y) == floor(Y),
na.rm = TRUE)
if (int.check > 0)
stop("the response variable has non-integer values.",
call. = FALSE)
if (min(Y, na.rm = TRUE) < 0)
stop("the response variable has negative values.",
call. = FALSE)
}
else {
}
offset <- try(model.offset(frame), silent = TRUE)
if (class(offset) == "try-error")
stop("the offset is not numeric.", call. = FALSE)
if (family == "multinomial") {
if (is.null(offset))
offset <- array(0, c(n, (J - 1)))
}
else {
if (is.null(offset))
offset <- array(0, c(n, J))
}
if (sum(is.na(offset)) > 0)
stop("the offset has missing 'NA' values.", call. = FALSE)
if (!is.numeric(offset))
stop("the offset variable has non-numeric values.", call. = FALSE)
results <- list(n = n, p = p, X = X, X.standardised = X.standardised,
X.sd = X.sd, X.mean = X.mean, X.indicator = X.indicator,
offset = offset, Y = Y, which.miss = which.miss, n.miss = n.miss)
return(results)
}
<bytecode: 0xd893fa8>
<environment: namespace:CARBayes>
--- function search by body ---
Function common.frame in namespace CARBayes has this body.
----------- END OF FAILURE REPORT --------------
Fatal error: the condition has length > 1
Flavor: r-devel-linux-x86_64-debian-clang
Version: 5.1.2
Check: examples
Result: ERROR
Running examples in ‘CARBayes-Ex.R’ failed
The error most likely occurred in:
> base::assign(".ptime", proc.time(), pos = "CheckExEnv")
> ### Name: MVS.CARleroux
> ### Title: Fit a multivariate spatial generalised linear mixed model to
> ### data, where the random effects are modelled by a multivariate
> ### conditional autoregressive model.
> ### Aliases: MVS.CARleroux
>
> ### ** Examples
>
> #################################################
> #### Run the model on simulated data on a lattice
> #################################################
> #### Load other libraries required
> library(MASS)
>
> #### Set up a square lattice region
> x.easting <- 1:10
> x.northing <- 1:10
> Grid <- expand.grid(x.easting, x.northing)
> K <- nrow(Grid)
>
> #### set up distance and neighbourhood (W, based on sharing a common border) matrices
> distance <- as.matrix(dist(Grid))
> W <-array(0, c(K,K))
> W[distance==1] <-1
> K <- nrow(W)
>
> #### Generate the correlation structures
> Q.W <- 0.99 * (diag(apply(W, 2, sum)) - W) + 0.01 * diag(rep(1,K))
> Q.W.inv <- solve(Q.W)
>
> Sigma <- matrix(c(1,0.5,0, 0.5,1,0.3, 0, 0.3, 1), nrow=3)
> Sigma.inv <- solve(Sigma)
> J <- nrow(Sigma)
> N.all <- K * J
>
> precision.phi <- kronecker(Q.W, Sigma.inv)
> var.phi <- solve(precision.phi)
>
> #### Generate the covariate component
> x1 <- rnorm(K)
> x2 <- rnorm(K)
> XB <- cbind(0.1 * x1 - 0.1*x2, -0.1 * x1 + 0.1*x2, 0.1 * x1 - 0.1*x2)
>
> #### Generate the random effects
> phi <- mvrnorm(n=1, mu=rep(0,N.all), Sigma=var.phi)
>
>
> #### Generate the response data
> lp <-as.numeric(t(XB)) + phi
> prob <- exp(lp) / (1 + exp(lp))
> trials.vec <- rep(100,N.all)
> Y.vec <- rbinom(n=N.all, size=trials.vec, prob=prob)
>
> #### Turn the data and trials into matrices where each row is an area.
> Y <- matrix(Y.vec, nrow=K, ncol=J, byrow=TRUE)
> trials <- matrix(trials.vec, nrow=K, ncol=J, byrow=TRUE)
>
> #### Run the Leroux model
> formula <- Y ~ x1 + x2
> ## Not run:
> ##D model <- MVS.CARleroux(formula=formula, family="binomial",
> ##D trials=trials, W=W, burnin=20000, n.sample=100000)
> ## End(Not run)
>
> #### Toy example for checking
> model <- MVS.CARleroux(formula=formula, family="binomial",
+ trials=trials, W=W, burnin=10, n.sample=50)
Setting up the model.
----------- FAILURE REPORT --------------
--- failure: the condition has length > 1 ---
--- srcref ---
:
--- package (from environment) ---
CARBayes
--- call from context ---
common.frame(formula, data, "binomial")
--- call from argument ---
if (class(X) == "try-error") stop("the covariate matrix contains inappropriate values.",
call. = FALSE)
--- R stacktrace ---
where 1: common.frame(formula, data, "binomial")
where 2: binomial.MVlerouxCAR(formula = formula, data = data, trials = trials,
W = W, burnin = burnin, n.sample = n.sample, thin = thin,
prior.mean.beta = prior.mean.beta, prior.var.beta = prior.var.beta,
prior.Sigma.df = prior.Sigma.df, prior.Sigma.scale = prior.Sigma.scale,
rho = rho, MALA = MALA, verbose = verbose)
where 3: MVS.CARleroux(formula = formula, family = "binomial", trials = trials,
W = W, burnin = 10, n.sample = 50)
--- value of length: 2 type: logical ---
[1] FALSE FALSE
--- function from context ---
function (formula, data, family)
{
frame <- try(suppressWarnings(model.frame(formula, data = data,
na.action = na.pass)), silent = TRUE)
if (class(frame) == "try-error")
stop("the formula inputted contains an error, e.g the variables may be different lengths.",
call. = FALSE)
X <- try(suppressWarnings(model.matrix(object = attr(frame,
"terms"), data = frame)), silent = TRUE)
if (class(X) == "try-error")
stop("the covariate matrix contains inappropriate values.",
call. = FALSE)
if (sum(is.na(X)) > 0)
stop("the covariate matrix contains missing 'NA' values.",
call. = FALSE)
n <- nrow(X)
p <- ncol(X)
cor.X <- suppressWarnings(cor(X))
diag(cor.X) <- 0
if (max(cor.X, na.rm = TRUE) == 1)
stop("the covariate matrix has two exactly linearly related columns.",
call. = FALSE)
if (min(cor.X, na.rm = TRUE) == -1)
stop("the covariate matrix has two exactly linearly related columns.",
call. = FALSE)
if (p > 1) {
if (sort(apply(X, 2, sd))[2] == 0)
stop("the covariate matrix has two intercept terms.",
call. = FALSE)
}
else {
}
X.standardised <- X
X.sd <- apply(X, 2, sd)
X.mean <- apply(X, 2, mean)
X.indicator <- rep(NA, p)
for (j in 1:p) {
if (length(table(X[, j])) > 2) {
X.indicator[j] <- 1
X.standardised[, j] <- (X[, j] - mean(X[, j]))/sd(X[,
j])
}
else if (length(table(X[, j])) == 1) {
X.indicator[j] <- 2
}
else {
X.indicator[j] <- 0
}
}
Y <- model.response(frame)
J <- length(Y)/n
which.miss <- matrix(as.numeric(!is.na(Y)), nrow = n, ncol = J)
if (J == 1)
which.miss <- as.numeric(which.miss)
n.miss <- n * J - sum(which.miss)
if (family == "binomial") {
if (!is.numeric(Y))
stop("the response variable has non-numeric values.",
call. = FALSE)
int.check <- n * J - n.miss - sum(ceiling(Y) == floor(Y),
na.rm = TRUE)
if (int.check > 0)
stop("the response variable has non-integer values.",
call. = FALSE)
if (min(Y, na.rm = TRUE) < 0)
stop("the response variable has negative values.",
call. = FALSE)
}
else if (family == "gaussian") {
if (!is.numeric(Y))
stop("the response variable has non-numeric values.",
call. = FALSE)
}
else if (family == "poisson") {
if (!is.numeric(Y))
stop("the response variable has non-numeric values.",
call. = FALSE)
int.check <- n * J - n.miss - sum(ceiling(Y) == floor(Y),
na.rm = TRUE)
if (int.check > 0)
stop("the response variable has non-integer values.",
call. = FALSE)
if (min(Y, na.rm = TRUE) < 0)
stop("the response variable has negative values.",
call. = FALSE)
}
else if (family == "multinomial") {
if (!is.numeric(Y))
stop("the response variable has non-numeric values.",
call. = FALSE)
int.check <- n * J - n.miss - sum(ceiling(Y) == floor(Y),
na.rm = TRUE)
if (int.check > 0)
stop("the response variable has non-integer values.",
call. = FALSE)
if (min(Y, na.rm = TRUE) < 0)
stop("the response variable has negative values.",
call. = FALSE)
}
else {
}
offset <- try(model.offset(frame), silent = TRUE)
if (class(offset) == "try-error")
stop("the offset is not numeric.", call. = FALSE)
if (family == "multinomial") {
if (is.null(offset))
offset <- array(0, c(n, (J - 1)))
}
else {
if (is.null(offset))
offset <- array(0, c(n, J))
}
if (sum(is.na(offset)) > 0)
stop("the offset has missing 'NA' values.", call. = FALSE)
if (!is.numeric(offset))
stop("the offset variable has non-numeric values.", call. = FALSE)
results <- list(n = n, p = p, X = X, X.standardised = X.standardised,
X.sd = X.sd, X.mean = X.mean, X.indicator = X.indicator,
offset = offset, Y = Y, which.miss = which.miss, n.miss = n.miss)
return(results)
}
<bytecode: 0x5642ecc52ea8>
<environment: namespace:CARBayes>
--- function search by body ---
Function common.frame in namespace CARBayes has this body.
----------- END OF FAILURE REPORT --------------
Fatal error: the condition has length > 1
Flavor: r-devel-linux-x86_64-debian-gcc