CRAN Package Check Results for Package designGLMM

Last updated on 2020-02-19 10:48:50 CET.

Flavor Version Tinstall Tcheck Ttotal Status Flags
r-devel-linux-x86_64-debian-clang 0.1.0 2.03 21.87 23.90 ERROR
r-devel-linux-x86_64-debian-gcc 0.1.0 1.77 17.47 19.24 ERROR
r-devel-linux-x86_64-fedora-clang 0.1.0 29.86 ERROR
r-devel-linux-x86_64-fedora-gcc 0.1.0 29.22 ERROR
r-devel-windows-ix86+x86_64 0.1.0 6.00 37.00 43.00 OK
r-devel-windows-ix86+x86_64-gcc8 0.1.0 7.00 51.00 58.00 OK
r-patched-linux-x86_64 0.1.0 1.59 22.41 24.00 OK
r-patched-solaris-x86 0.1.0 43.30 OK
r-release-linux-x86_64 0.1.0 1.69 22.65 24.34 OK
r-release-windows-ix86+x86_64 0.1.0 5.00 50.00 55.00 OK
r-release-osx-x86_64 0.1.0 OK
r-oldrel-windows-ix86+x86_64 0.1.0 2.00 33.00 35.00 OK
r-oldrel-osx-x86_64 0.1.0 OK

Check Details

Version: 0.1.0
Check: examples
Result: ERROR
    Running examples in 'designGLMM-Ex.R' failed
    The error most likely occurred in:
    
    > base::assign(".ptime", proc.time(), pos = "CheckExEnv")
    > ### Name: findOptimalBlockDesign
    > ### Title: Find an efficient block design for a Poisson GLMM
    > ### Aliases: findOptimalBlockDesign
    >
    > ### ** Examples
    >
    >
    > ## Constructing a D-optimal block design with 4 blocks of size 3 with seven treatments
    > ## with means c(5,5.5,6,5.5,7,10,4) with between block standard deviation 0.3
    > ## and no overdispersion (sigma=0). In each round of simulated annealing, we use 1000
    > ## iterations
    >
    > findOptimalBlockDesign(numblock = 4, blksize = 3, means = c(5,5.5,6,5.5,7,10,4),
    + sigma = 0, sigmaB = 0.3,iter=1000)
    
    
    The optimal design is:
     [,1] [,2] [,3]
    [1,] 1 2 3
    [2,] 1 6 7
    [3,] 2 5 7
    [4,] 4 5 6
    The determinant of the information matrix is: 0.0789
    Progressvec is: 0.0788975256241403
     Progressvec is: 0.0788975256241403
    $design
     [,1] [,2] [,3]
    [1,] 1 2 3
    [2,] 1 6 7
    [3,] 2 5 7
    [4,] 4 5 6
    
    $value
    [1] 0.07889753
    
    $iter
    [1] 0.07889753 0.07889753
    
    >
    > ## Constructing an A-optimal design with the same means
    >
    > findOptimalBlockDesign(numblock = 4, blksize = 3, means = c(5,5.5,6,5.5,7,10,4),
    + sigma = 0, sigmaB = 0.3, criterion = "A",iter=1000)
     ----------- FAILURE REPORT --------------
     --- failure: the condition has length > 1 ---
     --- srcref ---
    :
     --- package (from environment) ---
    designGLMM
     --- call from context ---
    fn(par, ...)
     --- call from argument ---
    if (class(inversematrix) == "try-error") ret <- 1e+160 else ret <- sum(diag(inversematrix)) -
     inversematrix[1, 1]
     --- R stacktrace ---
    where 1: fn(par, ...)
    where 2: (function (par)
    fn(par, ...))(c(4, 1, 1, 6, 3, 2, 2, 5, 7, 3, 6, 7))
    where 3: stats::optim(workingdes, objfnA_BD, updateDesign_BD, length(means),
     blksize, sigmaB, sigma, means, probs, method = "SANN", control = list(maxit = iter,
     temp = temp, trace = trace, REPORT = 1, tmax = tmax))
    where 4: findOptimalBlockDesign(numblock = 4, blksize = 3, means = c(5,
     5.5, 6, 5.5, 7, 10, 4), sigma = 0, sigmaB = 0.3, criterion = "A",
     iter = 1000)
    
     --- value of length: 2 type: logical ---
    [1] FALSE FALSE
     --- function from context ---
    function (des, ntmt, blksz, sigb, sige, means, probs = c(1))
    {
     nblk <- length(des)/blksz
     xmeans <- means[des]
     diagall <- sigb^2/(sige^2 + 1/xmeans)
     ellvec <- matrix(diagall, nrow = blksz)
     blk <- apply(ellvec, 2, function(x) diag(x)/sigb^2 - x %*%
     t(x) * 1/(sigb^2 * (1 + sqrt(t(x)) %*% sqrt(x)))[1, 1])
     oP <- matrix(0, length(des), length(des))
     for (q in 1:nblk) {
     oP[((q - 1) * blksz + 1):((q - 1) * blksz + blksz), ((q -
     1) * blksz + 1):((q - 1) * blksz + blksz)] <- matrix(blk[,
     q], nrow = blksz)
     }
     Xmaster <- cbind(rep(1, ntmt), diag(1, ntmt))
     X <- t(sapply(des, function(x) Xmaster[x, ]))
     cm <- matrix(0, nrow = ntmt, ncol = ntmt)
     cm[upper.tri(cm, diag = FALSE)] <- -1
     cm <- cm + diag((ntmt - 1):0)
     cm <- cbind(rep(0, ntmt), cm)
     cm <- matrix(cm[1:(ntmt - 1), ], nrow = (ntmt - 1))
     cm <- rbind(c(1, rep(0, ntmt)), cm)
     cm <- cm/rowSums(cm^2)
     inversematrix <- try(solve(cm %*% t(X) %*% oP %*% X %*% t(cm)),
     silent = TRUE)
     if (class(inversematrix) == "try-error")
     ret <- 1e+160
     else ret <- sum(diag(inversematrix)) - inversematrix[1, 1]
     ret
    }
    <bytecode: 0x3709630>
    <environment: namespace:designGLMM>
     --- function search by body ---
    Function objfnA_BD in namespace designGLMM has this body.
     ----------- END OF FAILURE REPORT --------------
    Error in if (class(inversematrix) == "try-error") ret <- 1e+160 else ret <- sum(diag(inversematrix)) - :
     the condition has length > 1
    Calls: findOptimalBlockDesign -> <Anonymous> -> <Anonymous> -> fn
    Execution halted
Flavor: r-devel-linux-x86_64-debian-clang

Version: 0.1.0
Check: examples
Result: ERROR
    Running examples in ‘designGLMM-Ex.R’ failed
    The error most likely occurred in:
    
    > base::assign(".ptime", proc.time(), pos = "CheckExEnv")
    > ### Name: findOptimalBlockDesign
    > ### Title: Find an efficient block design for a Poisson GLMM
    > ### Aliases: findOptimalBlockDesign
    >
    > ### ** Examples
    >
    >
    > ## Constructing a D-optimal block design with 4 blocks of size 3 with seven treatments
    > ## with means c(5,5.5,6,5.5,7,10,4) with between block standard deviation 0.3
    > ## and no overdispersion (sigma=0). In each round of simulated annealing, we use 1000
    > ## iterations
    >
    > findOptimalBlockDesign(numblock = 4, blksize = 3, means = c(5,5.5,6,5.5,7,10,4),
    + sigma = 0, sigmaB = 0.3,iter=1000)
    
    
    The optimal design is:
     [,1] [,2] [,3]
    [1,] 1 2 3
    [2,] 1 6 7
    [3,] 2 5 7
    [4,] 4 5 6
    The determinant of the information matrix is: 0.0789
    Progressvec is: 0.0788975256241403
     Progressvec is: 0.0788975256241403
    $design
     [,1] [,2] [,3]
    [1,] 1 2 3
    [2,] 1 6 7
    [3,] 2 5 7
    [4,] 4 5 6
    
    $value
    [1] 0.07889753
    
    $iter
    [1] 0.07889753 0.07889753
    
    >
    > ## Constructing an A-optimal design with the same means
    >
    > findOptimalBlockDesign(numblock = 4, blksize = 3, means = c(5,5.5,6,5.5,7,10,4),
    + sigma = 0, sigmaB = 0.3, criterion = "A",iter=1000)
     ----------- FAILURE REPORT --------------
     --- failure: the condition has length > 1 ---
     --- srcref ---
    :
     --- package (from environment) ---
    designGLMM
     --- call from context ---
    fn(par, ...)
     --- call from argument ---
    if (class(inversematrix) == "try-error") ret <- 1e+160 else ret <- sum(diag(inversematrix)) -
     inversematrix[1, 1]
     --- R stacktrace ---
    where 1: fn(par, ...)
    where 2: (function (par)
    fn(par, ...))(c(4, 1, 1, 6, 3, 2, 2, 5, 7, 3, 6, 7))
    where 3: stats::optim(workingdes, objfnA_BD, updateDesign_BD, length(means),
     blksize, sigmaB, sigma, means, probs, method = "SANN", control = list(maxit = iter,
     temp = temp, trace = trace, REPORT = 1, tmax = tmax))
    where 4: findOptimalBlockDesign(numblock = 4, blksize = 3, means = c(5,
     5.5, 6, 5.5, 7, 10, 4), sigma = 0, sigmaB = 0.3, criterion = "A",
     iter = 1000)
    
     --- value of length: 2 type: logical ---
    [1] FALSE FALSE
     --- function from context ---
    function (des, ntmt, blksz, sigb, sige, means, probs = c(1))
    {
     nblk <- length(des)/blksz
     xmeans <- means[des]
     diagall <- sigb^2/(sige^2 + 1/xmeans)
     ellvec <- matrix(diagall, nrow = blksz)
     blk <- apply(ellvec, 2, function(x) diag(x)/sigb^2 - x %*%
     t(x) * 1/(sigb^2 * (1 + sqrt(t(x)) %*% sqrt(x)))[1, 1])
     oP <- matrix(0, length(des), length(des))
     for (q in 1:nblk) {
     oP[((q - 1) * blksz + 1):((q - 1) * blksz + blksz), ((q -
     1) * blksz + 1):((q - 1) * blksz + blksz)] <- matrix(blk[,
     q], nrow = blksz)
     }
     Xmaster <- cbind(rep(1, ntmt), diag(1, ntmt))
     X <- t(sapply(des, function(x) Xmaster[x, ]))
     cm <- matrix(0, nrow = ntmt, ncol = ntmt)
     cm[upper.tri(cm, diag = FALSE)] <- -1
     cm <- cm + diag((ntmt - 1):0)
     cm <- cbind(rep(0, ntmt), cm)
     cm <- matrix(cm[1:(ntmt - 1), ], nrow = (ntmt - 1))
     cm <- rbind(c(1, rep(0, ntmt)), cm)
     cm <- cm/rowSums(cm^2)
     inversematrix <- try(solve(cm %*% t(X) %*% oP %*% X %*% t(cm)),
     silent = TRUE)
     if (class(inversematrix) == "try-error")
     ret <- 1e+160
     else ret <- sum(diag(inversematrix)) - inversematrix[1, 1]
     ret
    }
    <bytecode: 0x55a24e5f67e0>
    <environment: namespace:designGLMM>
     --- function search by body ---
    Function objfnA_BD in namespace designGLMM has this body.
     ----------- END OF FAILURE REPORT --------------
    Error in if (class(inversematrix) == "try-error") ret <- 1e+160 else ret <- sum(diag(inversematrix)) - :
     the condition has length > 1
    Calls: findOptimalBlockDesign -> <Anonymous> -> <Anonymous> -> fn
    Execution halted
Flavor: r-devel-linux-x86_64-debian-gcc

Version: 0.1.0
Check: examples
Result: ERROR
    Running examples in ‘designGLMM-Ex.R’ failed
    The error most likely occurred in:
    
    > ### Name: findOptimalBlockDesign
    > ### Title: Find an efficient block design for a Poisson GLMM
    > ### Aliases: findOptimalBlockDesign
    >
    > ### ** Examples
    >
    >
    > ## Constructing a D-optimal block design with 4 blocks of size 3 with seven treatments
    > ## with means c(5,5.5,6,5.5,7,10,4) with between block standard deviation 0.3
    > ## and no overdispersion (sigma=0). In each round of simulated annealing, we use 1000
    > ## iterations
    >
    > findOptimalBlockDesign(numblock = 4, blksize = 3, means = c(5,5.5,6,5.5,7,10,4),
    + sigma = 0, sigmaB = 0.3,iter=1000)
    
    
    The optimal design is:
     [,1] [,2] [,3]
    [1,] 1 2 3
    [2,] 1 6 7
    [3,] 2 5 7
    [4,] 4 5 6
    The determinant of the information matrix is: 0.0789
    Progressvec is: 0.0788975256241403
     Progressvec is: 0.0788975256241403
    $design
     [,1] [,2] [,3]
    [1,] 1 2 3
    [2,] 1 6 7
    [3,] 2 5 7
    [4,] 4 5 6
    
    $value
    [1] 0.07889753
    
    $iter
    [1] 0.07889753 0.07889753
    
    >
    > ## Constructing an A-optimal design with the same means
    >
    > findOptimalBlockDesign(numblock = 4, blksize = 3, means = c(5,5.5,6,5.5,7,10,4),
    + sigma = 0, sigmaB = 0.3, criterion = "A",iter=1000)
     ----------- FAILURE REPORT --------------
     --- failure: the condition has length > 1 ---
     --- srcref ---
    :
     --- package (from environment) ---
    designGLMM
     --- call from context ---
    fn(par, ...)
     --- call from argument ---
    if (class(inversematrix) == "try-error") ret <- 1e+160 else ret <- sum(diag(inversematrix)) -
     inversematrix[1, 1]
     --- R stacktrace ---
    where 1: fn(par, ...)
    where 2: (function (par)
    fn(par, ...))(c(4, 1, 1, 6, 3, 2, 2, 5, 7, 3, 6, 7))
    where 3: stats::optim(workingdes, objfnA_BD, updateDesign_BD, length(means),
     blksize, sigmaB, sigma, means, probs, method = "SANN", control = list(maxit = iter,
     temp = temp, trace = trace, REPORT = 1, tmax = tmax))
    where 4: findOptimalBlockDesign(numblock = 4, blksize = 3, means = c(5,
     5.5, 6, 5.5, 7, 10, 4), sigma = 0, sigmaB = 0.3, criterion = "A",
     iter = 1000)
    
     --- value of length: 2 type: logical ---
    [1] FALSE FALSE
     --- function from context ---
    function (des, ntmt, blksz, sigb, sige, means, probs = c(1))
    {
     nblk <- length(des)/blksz
     xmeans <- means[des]
     diagall <- sigb^2/(sige^2 + 1/xmeans)
     ellvec <- matrix(diagall, nrow = blksz)
     blk <- apply(ellvec, 2, function(x) diag(x)/sigb^2 - x %*%
     t(x) * 1/(sigb^2 * (1 + sqrt(t(x)) %*% sqrt(x)))[1, 1])
     oP <- matrix(0, length(des), length(des))
     for (q in 1:nblk) {
     oP[((q - 1) * blksz + 1):((q - 1) * blksz + blksz), ((q -
     1) * blksz + 1):((q - 1) * blksz + blksz)] <- matrix(blk[,
     q], nrow = blksz)
     }
     Xmaster <- cbind(rep(1, ntmt), diag(1, ntmt))
     X <- t(sapply(des, function(x) Xmaster[x, ]))
     cm <- matrix(0, nrow = ntmt, ncol = ntmt)
     cm[upper.tri(cm, diag = FALSE)] <- -1
     cm <- cm + diag((ntmt - 1):0)
     cm <- cbind(rep(0, ntmt), cm)
     cm <- matrix(cm[1:(ntmt - 1), ], nrow = (ntmt - 1))
     cm <- rbind(c(1, rep(0, ntmt)), cm)
     cm <- cm/rowSums(cm^2)
     inversematrix <- try(solve(cm %*% t(X) %*% oP %*% X %*% t(cm)),
     silent = TRUE)
     if (class(inversematrix) == "try-error")
     ret <- 1e+160
     else ret <- sum(diag(inversematrix)) - inversematrix[1, 1]
     ret
    }
    <bytecode: 0x1b81628>
    <environment: namespace:designGLMM>
     --- function search by body ---
    Function objfnA_BD in namespace designGLMM has this body.
     ----------- END OF FAILURE REPORT --------------
    Error in if (class(inversematrix) == "try-error") ret <- 1e+160 else ret <- sum(diag(inversematrix)) - :
     the condition has length > 1
    Calls: findOptimalBlockDesign -> <Anonymous> -> <Anonymous> -> fn
    Execution halted
Flavor: r-devel-linux-x86_64-fedora-clang

Version: 0.1.0
Check: examples
Result: ERROR
    Running examples in ‘designGLMM-Ex.R’ failed
    The error most likely occurred in:
    
    > ### Name: findOptimalBlockDesign
    > ### Title: Find an efficient block design for a Poisson GLMM
    > ### Aliases: findOptimalBlockDesign
    >
    > ### ** Examples
    >
    >
    > ## Constructing a D-optimal block design with 4 blocks of size 3 with seven treatments
    > ## with means c(5,5.5,6,5.5,7,10,4) with between block standard deviation 0.3
    > ## and no overdispersion (sigma=0). In each round of simulated annealing, we use 1000
    > ## iterations
    >
    > findOptimalBlockDesign(numblock = 4, blksize = 3, means = c(5,5.5,6,5.5,7,10,4),
    + sigma = 0, sigmaB = 0.3,iter=1000)
    
    
    The optimal design is:
     [,1] [,2] [,3]
    [1,] 1 2 3
    [2,] 1 6 7
    [3,] 2 5 7
    [4,] 4 5 6
    The determinant of the information matrix is: 0.0789
    Progressvec is: 0.0788975256241403
     Progressvec is: 0.0788975256241403
    $design
     [,1] [,2] [,3]
    [1,] 1 2 3
    [2,] 1 6 7
    [3,] 2 5 7
    [4,] 4 5 6
    
    $value
    [1] 0.07889753
    
    $iter
    [1] 0.07889753 0.07889753
    
    >
    > ## Constructing an A-optimal design with the same means
    >
    > findOptimalBlockDesign(numblock = 4, blksize = 3, means = c(5,5.5,6,5.5,7,10,4),
    + sigma = 0, sigmaB = 0.3, criterion = "A",iter=1000)
     ----------- FAILURE REPORT --------------
     --- failure: the condition has length > 1 ---
     --- srcref ---
    :
     --- package (from environment) ---
    designGLMM
     --- call from context ---
    fn(par, ...)
     --- call from argument ---
    if (class(inversematrix) == "try-error") ret <- 1e+160 else ret <- sum(diag(inversematrix)) -
     inversematrix[1, 1]
     --- R stacktrace ---
    where 1: fn(par, ...)
    where 2: (function (par)
    fn(par, ...))(c(4, 1, 1, 6, 3, 2, 2, 5, 7, 3, 6, 7))
    where 3: stats::optim(workingdes, objfnA_BD, updateDesign_BD, length(means),
     blksize, sigmaB, sigma, means, probs, method = "SANN", control = list(maxit = iter,
     temp = temp, trace = trace, REPORT = 1, tmax = tmax))
    where 4: findOptimalBlockDesign(numblock = 4, blksize = 3, means = c(5,
     5.5, 6, 5.5, 7, 10, 4), sigma = 0, sigmaB = 0.3, criterion = "A",
     iter = 1000)
    
     --- value of length: 2 type: logical ---
    [1] FALSE FALSE
     --- function from context ---
    function (des, ntmt, blksz, sigb, sige, means, probs = c(1))
    {
     nblk <- length(des)/blksz
     xmeans <- means[des]
     diagall <- sigb^2/(sige^2 + 1/xmeans)
     ellvec <- matrix(diagall, nrow = blksz)
     blk <- apply(ellvec, 2, function(x) diag(x)/sigb^2 - x %*%
     t(x) * 1/(sigb^2 * (1 + sqrt(t(x)) %*% sqrt(x)))[1, 1])
     oP <- matrix(0, length(des), length(des))
     for (q in 1:nblk) {
     oP[((q - 1) * blksz + 1):((q - 1) * blksz + blksz), ((q -
     1) * blksz + 1):((q - 1) * blksz + blksz)] <- matrix(blk[,
     q], nrow = blksz)
     }
     Xmaster <- cbind(rep(1, ntmt), diag(1, ntmt))
     X <- t(sapply(des, function(x) Xmaster[x, ]))
     cm <- matrix(0, nrow = ntmt, ncol = ntmt)
     cm[upper.tri(cm, diag = FALSE)] <- -1
     cm <- cm + diag((ntmt - 1):0)
     cm <- cbind(rep(0, ntmt), cm)
     cm <- matrix(cm[1:(ntmt - 1), ], nrow = (ntmt - 1))
     cm <- rbind(c(1, rep(0, ntmt)), cm)
     cm <- cm/rowSums(cm^2)
     inversematrix <- try(solve(cm %*% t(X) %*% oP %*% X %*% t(cm)),
     silent = TRUE)
     if (class(inversematrix) == "try-error")
     ret <- 1e+160
     else ret <- sum(diag(inversematrix)) - inversematrix[1, 1]
     ret
    }
    <bytecode: 0x2de2678>
    <environment: namespace:designGLMM>
     --- function search by body ---
    Function objfnA_BD in namespace designGLMM has this body.
     ----------- END OF FAILURE REPORT --------------
    Error in if (class(inversematrix) == "try-error") ret <- 1e+160 else ret <- sum(diag(inversematrix)) - :
     the condition has length > 1
    Calls: findOptimalBlockDesign -> <Anonymous> -> <Anonymous> -> fn
    Execution halted
Flavor: r-devel-linux-x86_64-fedora-gcc