CRAN Package Check Results for Package mBvs

Last updated on 2021-05-27 12:54:17 CEST.

Flavor Version Tinstall Tcheck Ttotal Status Flags
r-devel-linux-x86_64-debian-clang 1.4 10.93 33.39 44.32 ERROR
r-devel-linux-x86_64-debian-gcc 1.4 10.22 25.49 35.71 ERROR
r-devel-linux-x86_64-fedora-clang 1.4 67.41 ERROR
r-devel-linux-x86_64-fedora-gcc 1.4 59.53 ERROR
r-devel-windows-x86_64 1.4 22.00 50.00 72.00 OK
r-devel-windows-x86_64-gcc10-UCRT 1.4 OK
r-patched-linux-x86_64 1.4 11.87 33.98 45.85 OK
r-patched-solaris-x86 1.4 92.50 OK
r-release-linux-x86_64 1.4 11.73 34.11 45.84 OK
r-release-windows-ix86+x86_64 1.4 32.00 49.00 81.00 OK
r-oldrel-macos-x86_64 1.4 OK
r-oldrel-windows-ix86+x86_64 1.4 28.00 48.00 76.00 OK

Check Details

Version: 1.4
Check: examples
Result: ERROR
    Running examples in 'mBvs-Ex.R' failed
    The error most likely occurred in:
    
    > base::assign(".ptime", proc.time(), pos = "CheckExEnv")
    > ### Name: mvnBvs
    > ### Title: The function to perform variable selection for multivariate
    > ### normal responses
    > ### Aliases: mvnBvs
    > ### Keywords: multivariate Bayesian variable selection multivariate
    > ### continuous data
    >
    > ### ** Examples
    >
    >
    > # loading a data set
    > data(simData_cont)
    > Y <- simData_cont$Y
    > data <- simData_cont$X
    > form1 <- as.formula( ~ cov.1+cov.2)
    > form2 <- as.formula( ~ 1)
    > lin.pred <- list(form1, form2)
    >
    > p <- dim(data)[2]
    > p_adj <- 0
    > q <- dim(Y)[2]
    >
    > #####################
    > ## Hyperparameters ##
    >
    > ## Common hyperparameters
    > ##
    > eta = 0.1
    > v = rep(10, q)
    > omega = rep(log(0.5/(1-0.5)), p-p_adj)
    > common.beta0 <- c(rep(0, q), 10^6)
    >
    > ## Unstructured model
    > ##
    > rho0 <- q + 4
    > Psi0 <- diag(3, q)
    > US.Sigma <- c(rho0, Psi0)
    >
    > ## Factor-analytic model
    > ##
    > FA.lam <- c(rep(0, q), 10^6)
    > FA.sigSq <- c(2, 1)
    >
    > ##
    > hyperParams <- list(eta=eta, v=v, omega=omega, beta0=common.beta0,
    + US=list(US.Sigma=US.Sigma),
    + FA=list(lambda=FA.lam, sigmaSq=FA.sigSq))
    >
    > ###################
    > ## MCMC SETTINGS ##
    >
    > ## Setting for the overall run
    > ##
    > numReps <- 50
    > thin <- 1
    > burninPerc <- 0.5
    >
    > ## Tuning parameters for specific updates
    > ##
    > ## - those common to all models
    > mhProp_beta_var <- matrix(0.5, p+p_adj, q)
    > ##
    > ## - those specific to the unstructured model
    > mhrho_prop <- 1000
    > mhPsi_prop <- diag(1, q)
    > ##
    > ## - those specific to the factor-analytic model
    > mhProp_lambda_var <- 0.5
    >
    > ##
    > mcmc.US <- list(run=list(numReps=numReps, thin=thin, burninPerc=burninPerc),
    + tuning=list(mhProp_beta_var=mhProp_beta_var,
    + mhrho_prop=mhrho_prop, mhPsi_prop=mhPsi_prop))
    >
    > ##
    > mcmc.FA <- list(run=list(numReps=numReps, thin=thin, burninPerc=burninPerc),
    + tuning=list(mhProp_beta_var=mhProp_beta_var,
    + mhProp_lambda_var=mhProp_lambda_var))
    >
    > #####################
    > ## Starting Values ##
    >
    > ## - those common to all models
    > beta0 <- rep(0, q)
    > B <- matrix(sample(x=c(0.3, 0), size=q, replace = TRUE), p+p_adj, q)
    > gamma <- B
    > gamma[gamma != 0] <- 1
    > ##
    > ## - those specific to the unstructured model
    > Sigma <- diag(1, q)
    > ##
    > ## - those specific to the factor-analytic model
    > lambda <- rep(0.5, q)
    > sigmaSq <- 1
    >
    > ####################################
    > ## Fitting the unstructured model ##
    > ####################################
    >
    > startValues <- vector("list", 2)
    >
    > startValues[[1]] <- as.vector(c(beta0, B, gamma, Sigma))
    >
    > beta0 <- rep(0.2, q)
    > Sigma <- diag(0.5, q)
    >
    > startValues[[2]] <- as.vector(c(beta0, B, gamma, Sigma))
    >
    > fit.us <- mvnBvs(Y, lin.pred, data, model="unstructured", hyperParams,
    + startValues, mcmcParams=mcmc.US)
    chain: 1
    chain: 2
    >
    > fit.us
    Error in matrix(x$chain1$B.p[, , 1], 1, q) :
     data length differs from size of matrix: [20 != 1 x 10]
    Calls: <Anonymous> ... print.mvnBvs -> colnames -> is.data.frame -> matrix
    Execution halted
Flavors: r-devel-linux-x86_64-debian-clang, r-devel-linux-x86_64-debian-gcc

Version: 1.4
Check: examples
Result: ERROR
    Running examples in ‘mBvs-Ex.R’ failed
    The error most likely occurred in:
    
    > ### Name: mvnBvs
    > ### Title: The function to perform variable selection for multivariate
    > ### normal responses
    > ### Aliases: mvnBvs
    > ### Keywords: multivariate Bayesian variable selection multivariate
    > ### continuous data
    >
    > ### ** Examples
    >
    >
    > # loading a data set
    > data(simData_cont)
    > Y <- simData_cont$Y
    > data <- simData_cont$X
    > form1 <- as.formula( ~ cov.1+cov.2)
    > form2 <- as.formula( ~ 1)
    > lin.pred <- list(form1, form2)
    >
    > p <- dim(data)[2]
    > p_adj <- 0
    > q <- dim(Y)[2]
    >
    > #####################
    > ## Hyperparameters ##
    >
    > ## Common hyperparameters
    > ##
    > eta = 0.1
    > v = rep(10, q)
    > omega = rep(log(0.5/(1-0.5)), p-p_adj)
    > common.beta0 <- c(rep(0, q), 10^6)
    >
    > ## Unstructured model
    > ##
    > rho0 <- q + 4
    > Psi0 <- diag(3, q)
    > US.Sigma <- c(rho0, Psi0)
    >
    > ## Factor-analytic model
    > ##
    > FA.lam <- c(rep(0, q), 10^6)
    > FA.sigSq <- c(2, 1)
    >
    > ##
    > hyperParams <- list(eta=eta, v=v, omega=omega, beta0=common.beta0,
    + US=list(US.Sigma=US.Sigma),
    + FA=list(lambda=FA.lam, sigmaSq=FA.sigSq))
    >
    > ###################
    > ## MCMC SETTINGS ##
    >
    > ## Setting for the overall run
    > ##
    > numReps <- 50
    > thin <- 1
    > burninPerc <- 0.5
    >
    > ## Tuning parameters for specific updates
    > ##
    > ## - those common to all models
    > mhProp_beta_var <- matrix(0.5, p+p_adj, q)
    > ##
    > ## - those specific to the unstructured model
    > mhrho_prop <- 1000
    > mhPsi_prop <- diag(1, q)
    > ##
    > ## - those specific to the factor-analytic model
    > mhProp_lambda_var <- 0.5
    >
    > ##
    > mcmc.US <- list(run=list(numReps=numReps, thin=thin, burninPerc=burninPerc),
    + tuning=list(mhProp_beta_var=mhProp_beta_var,
    + mhrho_prop=mhrho_prop, mhPsi_prop=mhPsi_prop))
    >
    > ##
    > mcmc.FA <- list(run=list(numReps=numReps, thin=thin, burninPerc=burninPerc),
    + tuning=list(mhProp_beta_var=mhProp_beta_var,
    + mhProp_lambda_var=mhProp_lambda_var))
    >
    > #####################
    > ## Starting Values ##
    >
    > ## - those common to all models
    > beta0 <- rep(0, q)
    > B <- matrix(sample(x=c(0.3, 0), size=q, replace = TRUE), p+p_adj, q)
    > gamma <- B
    > gamma[gamma != 0] <- 1
    > ##
    > ## - those specific to the unstructured model
    > Sigma <- diag(1, q)
    > ##
    > ## - those specific to the factor-analytic model
    > lambda <- rep(0.5, q)
    > sigmaSq <- 1
    >
    > ####################################
    > ## Fitting the unstructured model ##
    > ####################################
    >
    > startValues <- vector("list", 2)
    >
    > startValues[[1]] <- as.vector(c(beta0, B, gamma, Sigma))
    >
    > beta0 <- rep(0.2, q)
    > Sigma <- diag(0.5, q)
    >
    > startValues[[2]] <- as.vector(c(beta0, B, gamma, Sigma))
    >
    > fit.us <- mvnBvs(Y, lin.pred, data, model="unstructured", hyperParams,
    + startValues, mcmcParams=mcmc.US)
    chain: 1
    chain: 2
    >
    > fit.us
    Error in matrix(x$chain1$B.p[, , 1], 1, q) :
     data length differs from size of matrix: [20 != 1 x 10]
    Calls: <Anonymous> ... print.mvnBvs -> colnames -> is.data.frame -> matrix
    Execution halted
Flavors: r-devel-linux-x86_64-fedora-clang, r-devel-linux-x86_64-fedora-gcc