Last updated on 2020-02-19 10:48:48 CET.
| Flavor | Version | Tinstall | Tcheck | Ttotal | Status | Flags |
|---|---|---|---|---|---|---|
| r-devel-linux-x86_64-debian-clang | 1.0 | 3.85 | 23.34 | 27.19 | ERROR | |
| r-devel-linux-x86_64-debian-gcc | 1.0 | 2.52 | 18.43 | 20.95 | ERROR | |
| r-devel-linux-x86_64-fedora-clang | 1.0 | 32.89 | ERROR | |||
| r-devel-linux-x86_64-fedora-gcc | 1.0 | 31.72 | ERROR | |||
| r-devel-windows-ix86+x86_64 | 1.0 | 7.00 | 37.00 | 44.00 | OK | |
| r-devel-windows-ix86+x86_64-gcc8 | 1.0 | 10.00 | 57.00 | 67.00 | OK | |
| r-patched-linux-x86_64 | 1.0 | 2.95 | 24.72 | 27.67 | OK | |
| r-patched-solaris-x86 | 1.0 | 48.90 | OK | |||
| r-release-linux-x86_64 | 1.0 | 2.98 | 25.14 | 28.12 | OK | |
| r-release-windows-ix86+x86_64 | 1.0 | 5.00 | 34.00 | 39.00 | OK | |
| r-release-osx-x86_64 | 1.0 | OK | ||||
| r-oldrel-windows-ix86+x86_64 | 1.0 | 4.00 | 33.00 | 37.00 | OK | |
| r-oldrel-osx-x86_64 | 1.0 | OK |
Version: 1.0
Check: examples
Result: ERROR
Running examples in 'brm-Ex.R' failed
The error most likely occurred in:
> base::assign(".ptime", proc.time(), pos = "CheckExEnv")
> ### Name: brm
> ### Title: Fitting Binary Regression Models
> ### Aliases: brm
>
> ### ** Examples
>
> set.seed(0)
> n = 100
> alpha.true = c(0,-1)
> beta.true = c(-0.5,1)
> gamma.true = c(0.1,-0.5)
> params.true = list(alpha.true=alpha.true, beta.true=beta.true,
+ gamma.true=gamma.true)
> v.1 = rep(1,n) # intercept term
> v.2 = runif(n,-2,2)
> v = cbind(v.1,v.2)
> pscore.true = exp(v %*% gamma.true) / (1+exp(v %*% gamma.true))
> p0p1.true = t(mapply(getProbScalarRR,v %*% alpha.true,v %*% beta.true))
> x = mapply(rbinom,rep(1,n),rep(1,n),pscore.true)
> pA.true = p0p1.true[,1]
> pA.true[x==1] = p0p1.true[x==1,2]
> y = mapply(rbinom,rep(1,n),rep(1,n),pA.true)
>
> fit.mle = brm(y,x,v,v,'RR','MLE',v,TRUE)
----------- FAILURE REPORT --------------
--- failure: the condition has length > 1 ---
--- srcref ---
:
--- package (from environment) ---
brm
--- call from context ---
brm(y, x, v, v, "RR", "MLE", v, TRUE)
--- call from argument ---
if (class(va) == "formula") va = stats::model.matrix(va)
--- R stacktrace ---
where 1: brm(y, x, v, v, "RR", "MLE", v, TRUE)
--- value of length: 2 type: logical ---
[1] FALSE FALSE
--- function from context ---
function (y, x, va, vb = NULL, param, est.method = "MLE", vc = NULL,
optimal = TRUE, weights = NULL, subset = NULL, max.step = 1000,
thres = 1e-06, alpha.start = NULL, beta.start = NULL, message = FALSE)
{
if (is.null(vb))
vb = va
if (is.null(vc))
vc = va
if (class(va) == "formula")
va = stats::model.matrix(va)
if (class(vb) == "formula")
vb = stats::model.matrix(vb)
if (class(vc) == "formula")
vc = stats::model.matrix(vc)
if (is.null(weights))
weights = rep(1, length(y))
if (is.null(subset))
subset = 1:length(y)
ValidCheck(param, y, x, va, vb, vc, weights, subset, est.method,
optimal, max.step, thres, alpha.start, beta.start)
data = cbind(y, x, va, vb, vc, weights)[subset, ]
subset = subset[rowSums(is.na(data)) == 0]
y = y[subset]
x = x[subset]
va = va[subset, ]
vb = vb[subset, ]
vc = vc[subset, ]
weights = weights[subset]
pa = dim(va)[2]
pb = dim(vb)[2]
if (est.method == "MLE") {
sol = MLEst(param, y, x, va, vb, weights, max.step, thres,
alpha.start, beta.start, pa, pb)
}
if (est.method == "DR") {
if (param == "OR") {
cat("No doubly robust estimation methods for OR (with propensity score models) are available. Please refer to Tchetgen Tchetgen et al. (2010) for an alternative doubly robust estimation method. \n")
return()
}
if (is.null(alpha.start) | is.null(beta.start)) {
sol = MLEst(param, y, x, va, vb, weights, max.step,
thres, alpha.start, beta.start, pa, pb)
alpha.ml = sol$point.est[1:pa]
beta.ml = sol$point.est[(pa + 1):(pa + pb)]
beta.cov = sol$cov[(pa + 1):(pa + pb), (pa + 1):(pa +
pb)]
alpha.start = alpha.ml
}
else {
alpha.ml = alpha.start
beta.ml = beta.start
beta.cov = matrix(NA, pb, pb)
}
gamma.fit = stats::glm(x ~ vc - 1, weight = weights,
family = "binomial")
gamma = gamma.fit$coefficients
gamma.cov = summary(gamma.fit)$cov.unscaled
sol = DREst(param, y, x, va, vb, vc, alpha.ml, beta.ml,
gamma, optimal, weights, max.step, thres, alpha.start,
beta.cov, gamma.cov, message)
}
return(sol)
}
<bytecode: 0x2618fc8>
<environment: namespace:brm>
--- function search by body ---
Function brm in namespace brm has this body.
----------- END OF FAILURE REPORT --------------
Error in if (class(va) == "formula") va = stats::model.matrix(va) :
the condition has length > 1
Calls: brm
Execution halted
Flavor: r-devel-linux-x86_64-debian-clang
Version: 1.0
Check: examples
Result: ERROR
Running examples in ‘brm-Ex.R’ failed
The error most likely occurred in:
> base::assign(".ptime", proc.time(), pos = "CheckExEnv")
> ### Name: brm
> ### Title: Fitting Binary Regression Models
> ### Aliases: brm
>
> ### ** Examples
>
> set.seed(0)
> n = 100
> alpha.true = c(0,-1)
> beta.true = c(-0.5,1)
> gamma.true = c(0.1,-0.5)
> params.true = list(alpha.true=alpha.true, beta.true=beta.true,
+ gamma.true=gamma.true)
> v.1 = rep(1,n) # intercept term
> v.2 = runif(n,-2,2)
> v = cbind(v.1,v.2)
> pscore.true = exp(v %*% gamma.true) / (1+exp(v %*% gamma.true))
> p0p1.true = t(mapply(getProbScalarRR,v %*% alpha.true,v %*% beta.true))
> x = mapply(rbinom,rep(1,n),rep(1,n),pscore.true)
> pA.true = p0p1.true[,1]
> pA.true[x==1] = p0p1.true[x==1,2]
> y = mapply(rbinom,rep(1,n),rep(1,n),pA.true)
>
> fit.mle = brm(y,x,v,v,'RR','MLE',v,TRUE)
----------- FAILURE REPORT --------------
--- failure: the condition has length > 1 ---
--- srcref ---
:
--- package (from environment) ---
brm
--- call from context ---
brm(y, x, v, v, "RR", "MLE", v, TRUE)
--- call from argument ---
if (class(va) == "formula") va = stats::model.matrix(va)
--- R stacktrace ---
where 1: brm(y, x, v, v, "RR", "MLE", v, TRUE)
--- value of length: 2 type: logical ---
[1] FALSE FALSE
--- function from context ---
function (y, x, va, vb = NULL, param, est.method = "MLE", vc = NULL,
optimal = TRUE, weights = NULL, subset = NULL, max.step = 1000,
thres = 1e-06, alpha.start = NULL, beta.start = NULL, message = FALSE)
{
if (is.null(vb))
vb = va
if (is.null(vc))
vc = va
if (class(va) == "formula")
va = stats::model.matrix(va)
if (class(vb) == "formula")
vb = stats::model.matrix(vb)
if (class(vc) == "formula")
vc = stats::model.matrix(vc)
if (is.null(weights))
weights = rep(1, length(y))
if (is.null(subset))
subset = 1:length(y)
ValidCheck(param, y, x, va, vb, vc, weights, subset, est.method,
optimal, max.step, thres, alpha.start, beta.start)
data = cbind(y, x, va, vb, vc, weights)[subset, ]
subset = subset[rowSums(is.na(data)) == 0]
y = y[subset]
x = x[subset]
va = va[subset, ]
vb = vb[subset, ]
vc = vc[subset, ]
weights = weights[subset]
pa = dim(va)[2]
pb = dim(vb)[2]
if (est.method == "MLE") {
sol = MLEst(param, y, x, va, vb, weights, max.step, thres,
alpha.start, beta.start, pa, pb)
}
if (est.method == "DR") {
if (param == "OR") {
cat("No doubly robust estimation methods for OR (with propensity score models) are available. Please refer to Tchetgen Tchetgen et al. (2010) for an alternative doubly robust estimation method. \n")
return()
}
if (is.null(alpha.start) | is.null(beta.start)) {
sol = MLEst(param, y, x, va, vb, weights, max.step,
thres, alpha.start, beta.start, pa, pb)
alpha.ml = sol$point.est[1:pa]
beta.ml = sol$point.est[(pa + 1):(pa + pb)]
beta.cov = sol$cov[(pa + 1):(pa + pb), (pa + 1):(pa +
pb)]
alpha.start = alpha.ml
}
else {
alpha.ml = alpha.start
beta.ml = beta.start
beta.cov = matrix(NA, pb, pb)
}
gamma.fit = stats::glm(x ~ vc - 1, weight = weights,
family = "binomial")
gamma = gamma.fit$coefficients
gamma.cov = summary(gamma.fit)$cov.unscaled
sol = DREst(param, y, x, va, vb, vc, alpha.ml, beta.ml,
gamma, optimal, weights, max.step, thres, alpha.start,
beta.cov, gamma.cov, message)
}
return(sol)
}
<bytecode: 0x55b8b656e548>
<environment: namespace:brm>
--- function search by body ---
Function brm in namespace brm has this body.
----------- END OF FAILURE REPORT --------------
Error in if (class(va) == "formula") va = stats::model.matrix(va) :
the condition has length > 1
Calls: brm
Execution halted
Flavor: r-devel-linux-x86_64-debian-gcc
Version: 1.0
Check: examples
Result: ERROR
Running examples in ‘brm-Ex.R’ failed
The error most likely occurred in:
> ### Name: brm
> ### Title: Fitting Binary Regression Models
> ### Aliases: brm
>
> ### ** Examples
>
> set.seed(0)
> n = 100
> alpha.true = c(0,-1)
> beta.true = c(-0.5,1)
> gamma.true = c(0.1,-0.5)
> params.true = list(alpha.true=alpha.true, beta.true=beta.true,
+ gamma.true=gamma.true)
> v.1 = rep(1,n) # intercept term
> v.2 = runif(n,-2,2)
> v = cbind(v.1,v.2)
> pscore.true = exp(v %*% gamma.true) / (1+exp(v %*% gamma.true))
> p0p1.true = t(mapply(getProbScalarRR,v %*% alpha.true,v %*% beta.true))
> x = mapply(rbinom,rep(1,n),rep(1,n),pscore.true)
> pA.true = p0p1.true[,1]
> pA.true[x==1] = p0p1.true[x==1,2]
> y = mapply(rbinom,rep(1,n),rep(1,n),pA.true)
>
> fit.mle = brm(y,x,v,v,'RR','MLE',v,TRUE)
----------- FAILURE REPORT --------------
--- failure: the condition has length > 1 ---
--- srcref ---
:
--- package (from environment) ---
brm
--- call from context ---
brm(y, x, v, v, "RR", "MLE", v, TRUE)
--- call from argument ---
if (class(va) == "formula") va = stats::model.matrix(va)
--- R stacktrace ---
where 1: brm(y, x, v, v, "RR", "MLE", v, TRUE)
--- value of length: 2 type: logical ---
[1] FALSE FALSE
--- function from context ---
function (y, x, va, vb = NULL, param, est.method = "MLE", vc = NULL,
optimal = TRUE, weights = NULL, subset = NULL, max.step = 1000,
thres = 1e-06, alpha.start = NULL, beta.start = NULL, message = FALSE)
{
if (is.null(vb))
vb = va
if (is.null(vc))
vc = va
if (class(va) == "formula")
va = stats::model.matrix(va)
if (class(vb) == "formula")
vb = stats::model.matrix(vb)
if (class(vc) == "formula")
vc = stats::model.matrix(vc)
if (is.null(weights))
weights = rep(1, length(y))
if (is.null(subset))
subset = 1:length(y)
ValidCheck(param, y, x, va, vb, vc, weights, subset, est.method,
optimal, max.step, thres, alpha.start, beta.start)
data = cbind(y, x, va, vb, vc, weights)[subset, ]
subset = subset[rowSums(is.na(data)) == 0]
y = y[subset]
x = x[subset]
va = va[subset, ]
vb = vb[subset, ]
vc = vc[subset, ]
weights = weights[subset]
pa = dim(va)[2]
pb = dim(vb)[2]
if (est.method == "MLE") {
sol = MLEst(param, y, x, va, vb, weights, max.step, thres,
alpha.start, beta.start, pa, pb)
}
if (est.method == "DR") {
if (param == "OR") {
cat("No doubly robust estimation methods for OR (with propensity score models) are available. Please refer to Tchetgen Tchetgen et al. (2010) for an alternative doubly robust estimation method. \n")
return()
}
if (is.null(alpha.start) | is.null(beta.start)) {
sol = MLEst(param, y, x, va, vb, weights, max.step,
thres, alpha.start, beta.start, pa, pb)
alpha.ml = sol$point.est[1:pa]
beta.ml = sol$point.est[(pa + 1):(pa + pb)]
beta.cov = sol$cov[(pa + 1):(pa + pb), (pa + 1):(pa +
pb)]
alpha.start = alpha.ml
}
else {
alpha.ml = alpha.start
beta.ml = beta.start
beta.cov = matrix(NA, pb, pb)
}
gamma.fit = stats::glm(x ~ vc - 1, weight = weights,
family = "binomial")
gamma = gamma.fit$coefficients
gamma.cov = summary(gamma.fit)$cov.unscaled
sol = DREst(param, y, x, va, vb, vc, alpha.ml, beta.ml,
gamma, optimal, weights, max.step, thres, alpha.start,
beta.cov, gamma.cov, message)
}
return(sol)
}
<bytecode: 0x2e8e440>
<environment: namespace:brm>
--- function search by body ---
Function brm in namespace brm has this body.
----------- END OF FAILURE REPORT --------------
Error in if (class(va) == "formula") va = stats::model.matrix(va) :
the condition has length > 1
Calls: brm
Execution halted
Flavor: r-devel-linux-x86_64-fedora-clang
Version: 1.0
Check: examples
Result: ERROR
Running examples in ‘brm-Ex.R’ failed
The error most likely occurred in:
> ### Name: brm
> ### Title: Fitting Binary Regression Models
> ### Aliases: brm
>
> ### ** Examples
>
> set.seed(0)
> n = 100
> alpha.true = c(0,-1)
> beta.true = c(-0.5,1)
> gamma.true = c(0.1,-0.5)
> params.true = list(alpha.true=alpha.true, beta.true=beta.true,
+ gamma.true=gamma.true)
> v.1 = rep(1,n) # intercept term
> v.2 = runif(n,-2,2)
> v = cbind(v.1,v.2)
> pscore.true = exp(v %*% gamma.true) / (1+exp(v %*% gamma.true))
> p0p1.true = t(mapply(getProbScalarRR,v %*% alpha.true,v %*% beta.true))
> x = mapply(rbinom,rep(1,n),rep(1,n),pscore.true)
> pA.true = p0p1.true[,1]
> pA.true[x==1] = p0p1.true[x==1,2]
> y = mapply(rbinom,rep(1,n),rep(1,n),pA.true)
>
> fit.mle = brm(y,x,v,v,'RR','MLE',v,TRUE)
----------- FAILURE REPORT --------------
--- failure: the condition has length > 1 ---
--- srcref ---
:
--- package (from environment) ---
brm
--- call from context ---
brm(y, x, v, v, "RR", "MLE", v, TRUE)
--- call from argument ---
if (class(va) == "formula") va = stats::model.matrix(va)
--- R stacktrace ---
where 1: brm(y, x, v, v, "RR", "MLE", v, TRUE)
--- value of length: 2 type: logical ---
[1] FALSE FALSE
--- function from context ---
function (y, x, va, vb = NULL, param, est.method = "MLE", vc = NULL,
optimal = TRUE, weights = NULL, subset = NULL, max.step = 1000,
thres = 1e-06, alpha.start = NULL, beta.start = NULL, message = FALSE)
{
if (is.null(vb))
vb = va
if (is.null(vc))
vc = va
if (class(va) == "formula")
va = stats::model.matrix(va)
if (class(vb) == "formula")
vb = stats::model.matrix(vb)
if (class(vc) == "formula")
vc = stats::model.matrix(vc)
if (is.null(weights))
weights = rep(1, length(y))
if (is.null(subset))
subset = 1:length(y)
ValidCheck(param, y, x, va, vb, vc, weights, subset, est.method,
optimal, max.step, thres, alpha.start, beta.start)
data = cbind(y, x, va, vb, vc, weights)[subset, ]
subset = subset[rowSums(is.na(data)) == 0]
y = y[subset]
x = x[subset]
va = va[subset, ]
vb = vb[subset, ]
vc = vc[subset, ]
weights = weights[subset]
pa = dim(va)[2]
pb = dim(vb)[2]
if (est.method == "MLE") {
sol = MLEst(param, y, x, va, vb, weights, max.step, thres,
alpha.start, beta.start, pa, pb)
}
if (est.method == "DR") {
if (param == "OR") {
cat("No doubly robust estimation methods for OR (with propensity score models) are available. Please refer to Tchetgen Tchetgen et al. (2010) for an alternative doubly robust estimation method. \n")
return()
}
if (is.null(alpha.start) | is.null(beta.start)) {
sol = MLEst(param, y, x, va, vb, weights, max.step,
thres, alpha.start, beta.start, pa, pb)
alpha.ml = sol$point.est[1:pa]
beta.ml = sol$point.est[(pa + 1):(pa + pb)]
beta.cov = sol$cov[(pa + 1):(pa + pb), (pa + 1):(pa +
pb)]
alpha.start = alpha.ml
}
else {
alpha.ml = alpha.start
beta.ml = beta.start
beta.cov = matrix(NA, pb, pb)
}
gamma.fit = stats::glm(x ~ vc - 1, weight = weights,
family = "binomial")
gamma = gamma.fit$coefficients
gamma.cov = summary(gamma.fit)$cov.unscaled
sol = DREst(param, y, x, va, vb, vc, alpha.ml, beta.ml,
gamma, optimal, weights, max.step, thres, alpha.start,
beta.cov, gamma.cov, message)
}
return(sol)
}
<bytecode: 0x12b26b0>
<environment: namespace:brm>
--- function search by body ---
Function brm in namespace brm has this body.
----------- END OF FAILURE REPORT --------------
Error in if (class(va) == "formula") va = stats::model.matrix(va) :
the condition has length > 1
Calls: brm
Execution halted
Flavor: r-devel-linux-x86_64-fedora-gcc