CRAN Package Check Results for Package nmaINLA

Last updated on 2020-02-19 10:49:01 CET.

Check Details

Version: 0.1.2
Check: package dependencies
Result: NOTE
    Package suggested but not available for checking: 'INLA'
Flavors: r-devel-linux-x86_64-debian-clang, r-devel-linux-x86_64-debian-gcc, r-patched-linux-x86_64, r-release-linux-x86_64, r-release-osx-x86_64, r-oldrel-osx-x86_64

Version: 0.1.2
Check: re-building of vignette outputs
Result: WARN
    Error(s) in re-building vignettes:
    --- re-building ‘nmaINLA.Rnw’ using knitr
    Loading required package: INLA
    Loading required package: Matrix
    Loading required package: sp
    Loading required package: parallel
    This is INLA_19.09.03 built 2020-02-15 02:59:58 UTC.
    See www.r-inla.org/contact-us for how to get help.
    To enable PARDISO sparse library; see inla.pardiso()
    Loading required package: nmaINLA
     ----------- FAILURE REPORT --------------
     --- failure: the condition has length > 1 ---
     --- srcref ---
    :
     --- package (from environment) ---
    nmaINLA
     --- call from context ---
    nma_inla(SmokdatINLA, likelihood = "binomial", fixed.par = c(0,
     1000), tau.prior = "uniform", tau.par = c(0, 5), type = "consistency")
     --- call from argument ---
    if (class(try.tau) != "try-error") {
     tau.stdev = sqrt(INLA::inla.emarginal(function(x) x^2, tau) -
     INLA::inla.emarginal(function(x) x^1, tau)^2)
    } else {
     tau.stdev = NA
    }
     --- R stacktrace ---
    where 1 at <text>#12: nma_inla(SmokdatINLA, likelihood = "binomial", fixed.par = c(0,
     1000), tau.prior = "uniform", tau.par = c(0, 5), type = "consistency")
    where 2: eval(expr, envir, enclos)
    where 3: eval(expr, envir, enclos)
    where 4: withVisible(eval(expr, envir, enclos))
    where 5: withCallingHandlers(withVisible(eval(expr, envir, enclos)), warning = wHandler,
     error = eHandler, message = mHandler)
    where 6: handle(ev <- withCallingHandlers(withVisible(eval(expr, envir,
     enclos)), warning = wHandler, error = eHandler, message = mHandler))
    where 7: timing_fn(handle(ev <- withCallingHandlers(withVisible(eval(expr,
     envir, enclos)), warning = wHandler, error = eHandler, message = mHandler)))
    where 8: evaluate_call(expr, parsed$src[[i]], envir = envir, enclos = enclos,
     debug = debug, last = i == length(out), use_try = stop_on_error !=
     2L, keep_warning = keep_warning, keep_message = keep_message,
     output_handler = output_handler, include_timing = include_timing)
    where 9: evaluate::evaluate(...)
    where 10: evaluate(code, envir = env, new_device = FALSE, keep_warning = !isFALSE(options$warning),
     keep_message = !isFALSE(options$message), stop_on_error = if (options$error &&
     options$include) 0L else 2L, output_handler = knit_handlers(options$render,
     options))
    where 11: in_dir(input_dir(), evaluate(code, envir = env, new_device = FALSE,
     keep_warning = !isFALSE(options$warning), keep_message = !isFALSE(options$message),
     stop_on_error = if (options$error && options$include) 0L else 2L,
     output_handler = knit_handlers(options$render, options)))
    where 12: block_exec(params)
    where 13: call_block(x)
    where 14: process_group.block(group)
    where 15: process_group(group)
    where 16: withCallingHandlers(if (tangle) process_tangle(group) else process_group(group),
     error = function(e) {
     setwd(wd)
     cat(res, sep = "\n", file = output %n% "")
     message("Quitting from lines ", paste(current_lines(i),
     collapse = "-"), " (", knit_concord$get("infile"),
     ") ")
     })
    where 17: process_file(text, output)
    where 18: (if (grepl("\\.[Rr]md$", file)) knit2html_v1 else if (grepl("\\.[Rr]rst$",
     file)) knit2pandoc else knit)(file, encoding = encoding,
     quiet = quiet, envir = globalenv(), ...)
    where 19: engine$weave(file, quiet = quiet, encoding = enc)
    where 20: doTryCatch(return(expr), name, parentenv, handler)
    where 21: tryCatchOne(expr, names, parentenv, handlers[[1L]])
    where 22: tryCatchList(expr, classes, parentenv, handlers)
    where 23: tryCatch({
     engine$weave(file, quiet = quiet, encoding = enc)
     setwd(startdir)
     output <- find_vignette_product(name, by = "weave", engine = engine)
     if (!have.makefile && vignette_is_tex(output)) {
     texi2pdf(file = output, clean = FALSE, quiet = quiet)
     output <- find_vignette_product(name, by = "texi2pdf",
     engine = engine)
     }
     outputs <- c(outputs, output)
    }, error = function(e) {
     thisOK <<- FALSE
     fails <<- c(fails, file)
     message(gettextf("Error: processing vignette '%s' failed with diagnostics:\n%s",
     file, conditionMessage(e)))
    })
    where 24: tools:::buildVignettes(dir = "/data/gannet/ripley/R/packages/tests-clang/nmaINLA.Rcheck/vign_test/nmaINLA",
     ser_elibs = "/tmp/RtmpPYeqrb/file8d892fa2eafc.rds")
    
     --- value of length: 2 type: logical ---
    [1] TRUE TRUE
     --- function from context ---
    function (datINLA, likelihood = NULL, fixed.par = c(0, 1000),
     tau.prior = "uniform", tau.par = c(0, 5), kappa.prior = "uniform",
     kappa.par = c(0, 5), mreg = FALSE, type = "consistency",
     verbose = FALSE, inla.strategy = "simplified.laplace", improve.hyperpar.dz = 0.75,
     correct = FALSE, correct.factor = 10, improve.hyperpar = TRUE)
    {
     if (requireNamespace("INLA", quietly = TRUE)) {
     if (!(sum(search() == "package:INLA")) == 1) {
     stop("INLA need to be loaded! \n\n Please use the following command to load INLA,\n\n library(INLA) \n")
     }
     if (!is.data.frame(datINLA)) {
     stop("Data MUST be a data frame!!!")
     }
     if (likelihood %in% c("binomial", "normal", "poisson") ==
     FALSE) {
     stop("Function argument \"likelihood\" must be equal to \"binomial\" or \"normal\" or \"poisson\" !!!")
     }
     if (type %in% c("FE", "consistency", "jackson") == FALSE) {
     stop("Function argument \"type\" must be equal to \"FE\" or \"consistency\" or \"jackson\" !!!")
     }
     if (tau.prior %in% c("uniform", "half-normal") == FALSE) {
     stop("Function argument \"tau.prior\" must be equal to \"uniform\" or \"half-normal\" !!!")
     }
     if (kappa.prior %in% c("uniform", "half-normal") == FALSE) {
     stop("Function argument \"kappa.prior\" must be equal to \"uniform\" or \"half-normal\" !!!")
     }
     cor <- 0.5
     ngroup <- max(datINLA$na) - 1
     cor.inla.init <- log((1 + cor * (ngroup - 1))/(1 - cor))
     N <- max(datINLA$study)
     d_params <- grep("d1", names(datINLA), value = TRUE)
     N_d_params <- length(d_params)
     inla.form <- paste("Y ~ -1 + mu +", paste(d_params, collapse = "+",
     sep = " "), sep = " ")
     if (mreg == TRUE) {
     inla.form <- paste(inla.form, " + cov", sep = "")
     }
     if (type %in% c("consistency", "jackson") == TRUE) {
     hyperunif.function <- function(x) {
     ifelse(exp(x)^-0.5 < tau.par[2] & exp(x)^-0.5 >
     tau.par[1], logdens <- log(1/(tau.par[2] -
     tau.par[1])), logdens <- log(9.98012604599318e-322))
     logdenst <- logdens + log(0.5 * exp(-x/2))
     return(logdenst)
     }
     lprec <- seq(from = -40, to = 40, len = 20000)
     unif.prior.table <- paste(c("table:", cbind(lprec,
     sapply(lprec, FUN = hyperunif.function))), sep = "",
     collapse = " ")
     if (tau.prior == "uniform") {
     het.expr <- " + f(het, model=\"iid\", hyper = list(theta = list(prior = unif.prior.table)"
     }
     if (tau.prior == "half-normal") {
     tau.par[2] <- 1/tau.par[2]
     het.expr <- " + f(het, model=\"iid\", hyper = list(theta = list(prior = \"logtnormal\", param = tau.par)"
     }
     multi.arm.expr <- ", group = g, control.group = list(model = \"exchangeable\", hyper = list(rho = list(fixed = TRUE, initial = cor.inla.init)))"
     inla.form <- paste(inla.form, het.expr, ")", sep = "")
     inla.form <- paste(inla.form, multi.arm.expr, ")",
     sep = "")
     }
     if (type == "jackson") {
     if (kappa.prior == "uniform") {
     inc.expr <- " + f(inc, model=\"iid\", hyper = list(theta = list(prior = unif.prior.table)"
     }
     if (kappa.prior == "half-normal") {
     kappa.par[2] <- 1/kappa.par[2]
     inc.expr <- " + f(inc, model=\"iid\", hyper = list(theta = list(prior = \"logtnormal\", param = kappa.par)"
     }
     inla.form <- paste(inla.form, inc.expr, ")", sep = "")
     inla.form <- paste(inla.form, multi.arm.expr, ")",
     sep = "")
     }
     if (likelihood == "binomial") {
     datINLA$Y = datINLA$responders
     Ntrials = datINLA$sampleSize
     fit.inla <- INLA::inla(stats::as.formula(inla.form),
     data = datINLA, family = "binomial", verbose = verbose,
     control.fixed = list(expand.factor.strategy = "inla",
     mean = fixed.par[1], prec = 1/fixed.par[2]),
     Ntrials = Ntrials, control.compute = list(dic = TRUE,
     waic = TRUE, cpo = TRUE, mlik = TRUE, config = TRUE),
     control.inla = list(strategy = inla.strategy,
     correct = correct, correct.factor = correct.factor))
     }
     if (likelihood == "normal") {
     datINLA$Y = datINLA$mean
     prec = 1/datINLA$std.err^2
     fit.inla <- INLA::inla(stats::as.formula(inla.form),
     family = "normal", verbose = verbose, data = datINLA,
     control.fixed = list(expand.factor.strategy = "inla",
     mean = fixed.par[1], prec = 1/fixed.par[2]),
     control.family = list(hyper = list(prec = list(fixed = TRUE,
     initial = 0))), scale = prec, control.compute = list(dic = TRUE,
     waic = TRUE, cpo = TRUE, mlik = TRUE, config = TRUE),
     control.inla = list(strategy = inla.strategy,
     correct = correct, correct.factor = correct.factor))
     }
     if (likelihood == "poisson") {
     datINLA$Y = datINLA$responders
     E = datINLA$exposure
     fit.inla <- INLA::inla(stats::as.formula(inla.form),
     data = datINLA, family = "poisson", verbose = verbose,
     control.fixed = list(expand.factor.strategy = "inla",
     mean = fixed.par[1], prec = 1/fixed.par[2]),
     E = E, control.compute = list(dic = TRUE, waic = TRUE,
     cpo = TRUE, mlik = TRUE, config = TRUE), control.inla = list(strategy = inla.strategy,
     correct = correct, correct.factor = correct.factor))
     }
     if (!fit.inla$ok) {
     stop("Something wrong while running model with data! Please set verbose = TRUE to check!!!!")
     }
     if (type %in% c("consistency", "jackson") == TRUE) {
     if (improve.hyperpar == TRUE) {
     fit.inla <- INLA::inla.hyperpar(fit.inla, dz = improve.hyperpar.dz)
     }
     }
     d_params <- as.matrix(fit.inla$summary.fixed[(N + 1):(N +
     N_d_params), c(1, 2, 3, 4, 5)])
     if (mreg == TRUE) {
     cov <- as.matrix(fit.inla$summary.fixed[N + N_d_params +
     1, c(1, 2, 3, 4, 5)])
     }
     if (type %in% c("consistency", "jackson") == TRUE) {
     tau.mean <- INLA::inla.emarginal(function(x) 1/sqrt(x),
     fit.inla$marginals.hyperpar[["Precision for het"]])
     try.tau = try(tau <- INLA::inla.tmarginal(function(x) 1/sqrt(x),
     fit.inla$marginals.hyperpar[["Precision for het"]]),
     silent = TRUE)
     if (class(try.tau) != "try-error") {
     tau.stdev = sqrt(INLA::inla.emarginal(function(x) x^2,
     tau) - INLA::inla.emarginal(function(x) x^1,
     tau)^2)
     }
     else {
     tau.stdev = NA
     }
     tau.quant <- as.numeric(rev(sqrt((1/summary(fit.inla)$hyperpar[1,
     c(3, 4, 5)]))))
     if (type == "jackson") {
     kappa.mean <- INLA::inla.emarginal(function(x) 1/sqrt(x),
     fit.inla$marginals.hyperpar[["Precision for inc"]])
     try.kappa = try(kappa <- INLA::inla.tmarginal(function(x) 1/sqrt(x),
     fit.inla$marginals.hyperpar[["Precision for inc"]]),
     silent = TRUE)
     if (class(try.kappa) != "try-error") {
     kappa.stdev = sqrt(INLA::inla.emarginal(function(x) x^2,
     kappa) - INLA::inla.emarginal(function(x) x^1,
     kappa)^2)
     }
     else {
     kappa.stdev = NA
     }
     kappa.quant <- as.numeric(rev(sqrt((1/summary(fit.inla)$hyperpar[2,
     c(3, 4, 5)]))))
     tab <- matrix(NA, 2, 5)
     tab[1, ] <- c(tau.mean, tau.stdev, tau.quant[1],
     tau.quant[2], tau.quant[3])
     tab[2, ] <- c(kappa.mean, kappa.stdev, kappa.quant[1],
     kappa.quant[2], kappa.quant[3])
     }
     else {
     tab <- matrix(NA, 1, 5)
     rownames(tab) <- "tau"
     }
     tab[1, ] <- c(tau.mean, tau.stdev, tau.quant[1],
     tau.quant[2], tau.quant[3])
     colnames(tab) <- c("mean", "sd", "0.025quant", "0.5quant",
     "0.975quant")
     }
     else tab <- NA
     fit.inla$d_params <- d_params
     if (mreg == TRUE) {
     fit.inla$cov <- cov
     }
     fit.inla$hyperpar <- tab
     fit.inla$fixed.par <- fixed.par
     fit.inla$tau.prior <- tau.prior
     fit.inla$tau.par <- tau.par
     fit.inla$kappa.prior <- kappa.prior
     fit.inla$kappa.par <- kappa.par
     fit.inla$type <- type
     fit.inla$mreg <- mreg
     fit.inla$inla.strategy <- inla.strategy
     fit.inla$N <- N
     fit.inla$N_d_params <- N_d_params
     class(fit.inla) <- "nma_inla"
     return(fit.inla)
     }
     else {
     stop("INLA need to be installed and loaded!\n\nPlease use the following command to install and load INLA,\n\ninstall.packages(\"INLA\", repos=c(getOption(\"repos\"), INLA=\"https://inla.r-inla-download.org/R/stable\"), dep=TRUE) \n\nlibrary(INLA) \n")
     }
    }
    <bytecode: 0xdacdc08>
    <environment: namespace:nmaINLA>
     --- function search by body ---
    Function nma_inla in namespace nmaINLA has this body.
     ----------- END OF FAILURE REPORT --------------
    Quitting from lines 99-142 (nmaINLA.Rnw)
    Error: processing vignette 'nmaINLA.Rnw' failed with diagnostics:
    the condition has length > 1
    --- failed re-building ‘nmaINLA.Rnw’
    
    SUMMARY: processing the following file failed:
     ‘nmaINLA.Rnw’
    
    Error: Vignette re-building failed.
    Execution halted
Flavor: r-devel-linux-x86_64-fedora-clang

Version: 0.1.2
Check: re-building of vignette outputs
Result: WARN
    Error(s) in re-building vignettes:
    --- re-building ‘nmaINLA.Rnw’ using knitr
    Loading required package: INLA
    Loading required package: Matrix
    Loading required package: sp
    Loading required package: parallel
    This is INLA_19.09.03 built 2020-02-16 10:27:38 UTC.
    See www.r-inla.org/contact-us for how to get help.
    To enable PARDISO sparse library; see inla.pardiso()
    Loading required package: nmaINLA
     ----------- FAILURE REPORT --------------
     --- failure: the condition has length > 1 ---
     --- srcref ---
    :
     --- package (from environment) ---
    nmaINLA
     --- call from context ---
    nma_inla(SmokdatINLA, likelihood = "binomial", fixed.par = c(0,
     1000), tau.prior = "uniform", tau.par = c(0, 5), type = "consistency")
     --- call from argument ---
    if (class(try.tau) != "try-error") {
     tau.stdev = sqrt(INLA::inla.emarginal(function(x) x^2, tau) -
     INLA::inla.emarginal(function(x) x^1, tau)^2)
    } else {
     tau.stdev = NA
    }
     --- R stacktrace ---
    where 1 at <text>#12: nma_inla(SmokdatINLA, likelihood = "binomial", fixed.par = c(0,
     1000), tau.prior = "uniform", tau.par = c(0, 5), type = "consistency")
    where 2: eval(expr, envir, enclos)
    where 3: eval(expr, envir, enclos)
    where 4: withVisible(eval(expr, envir, enclos))
    where 5: withCallingHandlers(withVisible(eval(expr, envir, enclos)), warning = wHandler,
     error = eHandler, message = mHandler)
    where 6: handle(ev <- withCallingHandlers(withVisible(eval(expr, envir,
     enclos)), warning = wHandler, error = eHandler, message = mHandler))
    where 7: timing_fn(handle(ev <- withCallingHandlers(withVisible(eval(expr,
     envir, enclos)), warning = wHandler, error = eHandler, message = mHandler)))
    where 8: evaluate_call(expr, parsed$src[[i]], envir = envir, enclos = enclos,
     debug = debug, last = i == length(out), use_try = stop_on_error !=
     2L, keep_warning = keep_warning, keep_message = keep_message,
     output_handler = output_handler, include_timing = include_timing)
    where 9: evaluate::evaluate(...)
    where 10: evaluate(code, envir = env, new_device = FALSE, keep_warning = !isFALSE(options$warning),
     keep_message = !isFALSE(options$message), stop_on_error = if (options$error &&
     options$include) 0L else 2L, output_handler = knit_handlers(options$render,
     options))
    where 11: in_dir(input_dir(), evaluate(code, envir = env, new_device = FALSE,
     keep_warning = !isFALSE(options$warning), keep_message = !isFALSE(options$message),
     stop_on_error = if (options$error && options$include) 0L else 2L,
     output_handler = knit_handlers(options$render, options)))
    where 12: block_exec(params)
    where 13: call_block(x)
    where 14: process_group.block(group)
    where 15: process_group(group)
    where 16: withCallingHandlers(if (tangle) process_tangle(group) else process_group(group),
     error = function(e) {
     setwd(wd)
     cat(res, sep = "\n", file = output %n% "")
     message("Quitting from lines ", paste(current_lines(i),
     collapse = "-"), " (", knit_concord$get("infile"),
     ") ")
     })
    where 17: process_file(text, output)
    where 18: (if (grepl("\\.[Rr]md$", file)) knit2html_v1 else if (grepl("\\.[Rr]rst$",
     file)) knit2pandoc else knit)(file, encoding = encoding,
     quiet = quiet, envir = globalenv(), ...)
    where 19: engine$weave(file, quiet = quiet, encoding = enc)
    where 20: doTryCatch(return(expr), name, parentenv, handler)
    where 21: tryCatchOne(expr, names, parentenv, handlers[[1L]])
    where 22: tryCatchList(expr, classes, parentenv, handlers)
    where 23: tryCatch({
     engine$weave(file, quiet = quiet, encoding = enc)
     setwd(startdir)
     output <- find_vignette_product(name, by = "weave", engine = engine)
     if (!have.makefile && vignette_is_tex(output)) {
     texi2pdf(file = output, clean = FALSE, quiet = quiet)
     output <- find_vignette_product(name, by = "texi2pdf",
     engine = engine)
     }
     outputs <- c(outputs, output)
    }, error = function(e) {
     thisOK <<- FALSE
     fails <<- c(fails, file)
     message(gettextf("Error: processing vignette '%s' failed with diagnostics:\n%s",
     file, conditionMessage(e)))
    })
    where 24: tools:::buildVignettes(dir = "/data/gannet/ripley/R/packages/tests-devel/nmaINLA.Rcheck/vign_test/nmaINLA",
     ser_elibs = "/tmp/RtmpIDn2gk/file3a885afc1254.rds")
    
     --- value of length: 2 type: logical ---
    [1] TRUE TRUE
     --- function from context ---
    function (datINLA, likelihood = NULL, fixed.par = c(0, 1000),
     tau.prior = "uniform", tau.par = c(0, 5), kappa.prior = "uniform",
     kappa.par = c(0, 5), mreg = FALSE, type = "consistency",
     verbose = FALSE, inla.strategy = "simplified.laplace", improve.hyperpar.dz = 0.75,
     correct = FALSE, correct.factor = 10, improve.hyperpar = TRUE)
    {
     if (requireNamespace("INLA", quietly = TRUE)) {
     if (!(sum(search() == "package:INLA")) == 1) {
     stop("INLA need to be loaded! \n\n Please use the following command to load INLA,\n\n library(INLA) \n")
     }
     if (!is.data.frame(datINLA)) {
     stop("Data MUST be a data frame!!!")
     }
     if (likelihood %in% c("binomial", "normal", "poisson") ==
     FALSE) {
     stop("Function argument \"likelihood\" must be equal to \"binomial\" or \"normal\" or \"poisson\" !!!")
     }
     if (type %in% c("FE", "consistency", "jackson") == FALSE) {
     stop("Function argument \"type\" must be equal to \"FE\" or \"consistency\" or \"jackson\" !!!")
     }
     if (tau.prior %in% c("uniform", "half-normal") == FALSE) {
     stop("Function argument \"tau.prior\" must be equal to \"uniform\" or \"half-normal\" !!!")
     }
     if (kappa.prior %in% c("uniform", "half-normal") == FALSE) {
     stop("Function argument \"kappa.prior\" must be equal to \"uniform\" or \"half-normal\" !!!")
     }
     cor <- 0.5
     ngroup <- max(datINLA$na) - 1
     cor.inla.init <- log((1 + cor * (ngroup - 1))/(1 - cor))
     N <- max(datINLA$study)
     d_params <- grep("d1", names(datINLA), value = TRUE)
     N_d_params <- length(d_params)
     inla.form <- paste("Y ~ -1 + mu +", paste(d_params, collapse = "+",
     sep = " "), sep = " ")
     if (mreg == TRUE) {
     inla.form <- paste(inla.form, " + cov", sep = "")
     }
     if (type %in% c("consistency", "jackson") == TRUE) {
     hyperunif.function <- function(x) {
     ifelse(exp(x)^-0.5 < tau.par[2] & exp(x)^-0.5 >
     tau.par[1], logdens <- log(1/(tau.par[2] -
     tau.par[1])), logdens <- log(9.98012604599318e-322))
     logdenst <- logdens + log(0.5 * exp(-x/2))
     return(logdenst)
     }
     lprec <- seq(from = -40, to = 40, len = 20000)
     unif.prior.table <- paste(c("table:", cbind(lprec,
     sapply(lprec, FUN = hyperunif.function))), sep = "",
     collapse = " ")
     if (tau.prior == "uniform") {
     het.expr <- " + f(het, model=\"iid\", hyper = list(theta = list(prior = unif.prior.table)"
     }
     if (tau.prior == "half-normal") {
     tau.par[2] <- 1/tau.par[2]
     het.expr <- " + f(het, model=\"iid\", hyper = list(theta = list(prior = \"logtnormal\", param = tau.par)"
     }
     multi.arm.expr <- ", group = g, control.group = list(model = \"exchangeable\", hyper = list(rho = list(fixed = TRUE, initial = cor.inla.init)))"
     inla.form <- paste(inla.form, het.expr, ")", sep = "")
     inla.form <- paste(inla.form, multi.arm.expr, ")",
     sep = "")
     }
     if (type == "jackson") {
     if (kappa.prior == "uniform") {
     inc.expr <- " + f(inc, model=\"iid\", hyper = list(theta = list(prior = unif.prior.table)"
     }
     if (kappa.prior == "half-normal") {
     kappa.par[2] <- 1/kappa.par[2]
     inc.expr <- " + f(inc, model=\"iid\", hyper = list(theta = list(prior = \"logtnormal\", param = kappa.par)"
     }
     inla.form <- paste(inla.form, inc.expr, ")", sep = "")
     inla.form <- paste(inla.form, multi.arm.expr, ")",
     sep = "")
     }
     if (likelihood == "binomial") {
     datINLA$Y = datINLA$responders
     Ntrials = datINLA$sampleSize
     fit.inla <- INLA::inla(stats::as.formula(inla.form),
     data = datINLA, family = "binomial", verbose = verbose,
     control.fixed = list(expand.factor.strategy = "inla",
     mean = fixed.par[1], prec = 1/fixed.par[2]),
     Ntrials = Ntrials, control.compute = list(dic = TRUE,
     waic = TRUE, cpo = TRUE, mlik = TRUE, config = TRUE),
     control.inla = list(strategy = inla.strategy,
     correct = correct, correct.factor = correct.factor))
     }
     if (likelihood == "normal") {
     datINLA$Y = datINLA$mean
     prec = 1/datINLA$std.err^2
     fit.inla <- INLA::inla(stats::as.formula(inla.form),
     family = "normal", verbose = verbose, data = datINLA,
     control.fixed = list(expand.factor.strategy = "inla",
     mean = fixed.par[1], prec = 1/fixed.par[2]),
     control.family = list(hyper = list(prec = list(fixed = TRUE,
     initial = 0))), scale = prec, control.compute = list(dic = TRUE,
     waic = TRUE, cpo = TRUE, mlik = TRUE, config = TRUE),
     control.inla = list(strategy = inla.strategy,
     correct = correct, correct.factor = correct.factor))
     }
     if (likelihood == "poisson") {
     datINLA$Y = datINLA$responders
     E = datINLA$exposure
     fit.inla <- INLA::inla(stats::as.formula(inla.form),
     data = datINLA, family = "poisson", verbose = verbose,
     control.fixed = list(expand.factor.strategy = "inla",
     mean = fixed.par[1], prec = 1/fixed.par[2]),
     E = E, control.compute = list(dic = TRUE, waic = TRUE,
     cpo = TRUE, mlik = TRUE, config = TRUE), control.inla = list(strategy = inla.strategy,
     correct = correct, correct.factor = correct.factor))
     }
     if (!fit.inla$ok) {
     stop("Something wrong while running model with data! Please set verbose = TRUE to check!!!!")
     }
     if (type %in% c("consistency", "jackson") == TRUE) {
     if (improve.hyperpar == TRUE) {
     fit.inla <- INLA::inla.hyperpar(fit.inla, dz = improve.hyperpar.dz)
     }
     }
     d_params <- as.matrix(fit.inla$summary.fixed[(N + 1):(N +
     N_d_params), c(1, 2, 3, 4, 5)])
     if (mreg == TRUE) {
     cov <- as.matrix(fit.inla$summary.fixed[N + N_d_params +
     1, c(1, 2, 3, 4, 5)])
     }
     if (type %in% c("consistency", "jackson") == TRUE) {
     tau.mean <- INLA::inla.emarginal(function(x) 1/sqrt(x),
     fit.inla$marginals.hyperpar[["Precision for het"]])
     try.tau = try(tau <- INLA::inla.tmarginal(function(x) 1/sqrt(x),
     fit.inla$marginals.hyperpar[["Precision for het"]]),
     silent = TRUE)
     if (class(try.tau) != "try-error") {
     tau.stdev = sqrt(INLA::inla.emarginal(function(x) x^2,
     tau) - INLA::inla.emarginal(function(x) x^1,
     tau)^2)
     }
     else {
     tau.stdev = NA
     }
     tau.quant <- as.numeric(rev(sqrt((1/summary(fit.inla)$hyperpar[1,
     c(3, 4, 5)]))))
     if (type == "jackson") {
     kappa.mean <- INLA::inla.emarginal(function(x) 1/sqrt(x),
     fit.inla$marginals.hyperpar[["Precision for inc"]])
     try.kappa = try(kappa <- INLA::inla.tmarginal(function(x) 1/sqrt(x),
     fit.inla$marginals.hyperpar[["Precision for inc"]]),
     silent = TRUE)
     if (class(try.kappa) != "try-error") {
     kappa.stdev = sqrt(INLA::inla.emarginal(function(x) x^2,
     kappa) - INLA::inla.emarginal(function(x) x^1,
     kappa)^2)
     }
     else {
     kappa.stdev = NA
     }
     kappa.quant <- as.numeric(rev(sqrt((1/summary(fit.inla)$hyperpar[2,
     c(3, 4, 5)]))))
     tab <- matrix(NA, 2, 5)
     tab[1, ] <- c(tau.mean, tau.stdev, tau.quant[1],
     tau.quant[2], tau.quant[3])
     tab[2, ] <- c(kappa.mean, kappa.stdev, kappa.quant[1],
     kappa.quant[2], kappa.quant[3])
     }
     else {
     tab <- matrix(NA, 1, 5)
     rownames(tab) <- "tau"
     }
     tab[1, ] <- c(tau.mean, tau.stdev, tau.quant[1],
     tau.quant[2], tau.quant[3])
     colnames(tab) <- c("mean", "sd", "0.025quant", "0.5quant",
     "0.975quant")
     }
     else tab <- NA
     fit.inla$d_params <- d_params
     if (mreg == TRUE) {
     fit.inla$cov <- cov
     }
     fit.inla$hyperpar <- tab
     fit.inla$fixed.par <- fixed.par
     fit.inla$tau.prior <- tau.prior
     fit.inla$tau.par <- tau.par
     fit.inla$kappa.prior <- kappa.prior
     fit.inla$kappa.par <- kappa.par
     fit.inla$type <- type
     fit.inla$mreg <- mreg
     fit.inla$inla.strategy <- inla.strategy
     fit.inla$N <- N
     fit.inla$N_d_params <- N_d_params
     class(fit.inla) <- "nma_inla"
     return(fit.inla)
     }
     else {
     stop("INLA need to be installed and loaded!\n\nPlease use the following command to install and load INLA,\n\ninstall.packages(\"INLA\", repos=c(getOption(\"repos\"), INLA=\"https://inla.r-inla-download.org/R/stable\"), dep=TRUE) \n\nlibrary(INLA) \n")
     }
    }
    <bytecode: 0xd65ed08>
    <environment: namespace:nmaINLA>
     --- function search by body ---
    Function nma_inla in namespace nmaINLA has this body.
     ----------- END OF FAILURE REPORT --------------
    Quitting from lines 99-142 (nmaINLA.Rnw)
    Error: processing vignette 'nmaINLA.Rnw' failed with diagnostics:
    the condition has length > 1
    --- failed re-building ‘nmaINLA.Rnw’
    
    SUMMARY: processing the following file failed:
     ‘nmaINLA.Rnw’
    
    Error: Vignette re-building failed.
    Execution halted
Flavor: r-devel-linux-x86_64-fedora-gcc