Nothing
R/fitTDAsreml.R
In statgenSTA: Single Trial Analysis (STA) of Field Trials
Defines functions
bestSpatMod
fitTDAsreml
#' Fit Single Trial Model using asreml
#'
#' Fit Single Trial Model using asreml
#'
#' @inheritParams fitTD
#'
#' @seealso \code{\link{fitTD}}
#'
#' @noRd
#' @keywords internal
fitTDAsreml <- function(TD,
trial = NULL,
traits,
what = c("fixed", "random"),
covariates = NULL,
useCheckId = FALSE,
design = "rowcol",
spatial = FALSE,
control = NULL,
checks = TRUE,
...) {
## Base check.
if (missing(TD) || !inherits(TD, "TD")) {
stop("TD should be a valid object of class TD.\n")
}
## Only perform other checks if explicitly stated.
## Provides the opportunity to skip check if already done in fitTD.
if (checks) {
## Checks.
checkOut <- modelChecks(TD = TD, trial = trial, design = design,
traits = traits, what = what,
covariates = covariates, spatial = spatial,
engine = "asreml", useCheckId = useCheckId,
control = control)
## Convert output to variables.
list2env(x = checkOut, envir = environment())
}
TDTr <- droplevels(TD[[trial]])
## Rename traits with invalid syntax.
## Mainly ()/ seem problematic, but being more strict doesn't hurt.
traitsNw <- make.names(traits)
traitsRenamed <- traits[!traits %in% traitsNw]
if (length(traitsRenamed) > 0) {
message("The following traits have been renamed since asreml doesn't ",
"allow syntactically invalid traits:\n",
paste(traitsRenamed, collapse = ", "))
colnames(TDTr)[colnames(TDTr) %in% traits] <-
make.names(colnames(TDTr)[colnames(TDTr) %in% traits])
traits <- traitsNw
}
## Should repId be used as fixed effect in the model.
useRepIdFix <- design %in% c("res.ibd", "res.rowcol", "rcbd")
## Indicate extra random effects.
if (design %in% c("ibd", "res.ibd")) {
randEff <- "subBlock"
} else if (design %in% c("rowcol", "res.rowcol")) {
randEff <- c("rowId", "colId")
} else if (design == "rcbd") {
randEff <- character()
}
# Check if spatial models can be fitted.
if (spatial) {
## Set default value for criterion
criterion <- "AIC"
if ("criterion" %in% names(control)) {
critCt <- control$criterion
if (critCt %in% c("AIC", "BIC")) {
criterion <- critCt
} else {
warning("Invalid value for control parameter criterion. ",
"Using default value instead.\n")
}
}
if (useRepIdFix) {
repTab <- table(TDTr$repId, TDTr$rowId, TDTr$colId)
} else {
repTab <- table(TDTr$rowId, TDTr$colId)
}
if (max(repTab) > 1) {
warning("There should only be one plot at each combination of ",
if (useRepIdFix) "replicate, ", "row and column.\n",
"Spatial models will not be tried")
spatial <- FALSE
}
}
## Construct formula for fixed part.
fixedForm <- paste("~",
if (useRepIdFix) "repId" else "1",
if (useCheckId) "+ checkId",
if (!is.null(covariates)) paste(c("", covariates),
collapse = "+"))
## Construct formula for random part. Include repId depending on design.
if (length(randEff) != 0) {
randomForm <- paste0(if (useRepIdFix) "repId:",
paste(randEff,
collapse = paste("+",
if (useRepIdFix) "repId:")))
} else {
randomForm <- character()
}
if (!spatial) {
## Increase max number of iterations for asreml.
maxIter <- 200
## In asreml3 na.method.X and na.method.Y are used.
## In asreml4 this is replaced by na.action.
asrArgs0 <- list(aom = TRUE, maxiter = maxIter, trace = FALSE) #, ...)
if (asreml4()) {
asrArgs0 <- c(asrArgs0, list(na.action = asreml::na.method(x = "include")))
} else {
asrArgs0 <- c(asrArgs0, list(na.method.X = "include"))
}
## Create empty base lists.
mr <- mf <- spatial <- sumTab <-
setNames(vector(mode = "list", length = length(traits)), traits)
for (trait in traits) {
if ("random" %in% what) {
## Fit model with genotype random.
mrTrait <- tryCatchExt({
if (all(is.na(TDTr[[trait]]))) {
stop("Only NA values for trait ", trait, " in trial ", trial, ".\n")
}
asrArgsR <- c(asrArgs0,
list(fixed = formula(paste0("`", trait, "`", fixedForm)),
random = formula(paste("~", randomForm,
if (length(randomForm) != 0) "+",
"genotype")),
data = TDTr))
do.call(asreml::asreml, args = asrArgsR)
})
if (!is.null(mrTrait$warning)) {
mrTrait <- chkLastIter(mrTrait)
mrTrait <- wrnToErr(mrTrait)
}
if (length(mrTrait$warning) != 0) {
warning("Warning in asreml for genotype random, trait ", trait,
" in trial ", trial, ":\n",
paste(mrTrait$warning, collapse = "\n"), "\n",
call. = FALSE)
}
if (is.null(mrTrait$error)) {
mrTrait <- mrTrait$value
} else {
warning("Error in asreml for genotype random, trait ", trait,
" in trial ", trial, ":\n",
paste(mrTrait$error, collapse = "\n"), "\n",
call. = FALSE)
mrTrait <- NULL
}
if (!is.null(mrTrait)) {
if ("fixed" %in% what) {
## Constrain variance of the variance components to be fixed as
## the values in mr.
GParamTmp <- mrTrait$G.param
for (randEf in randEff) {
## When there are no replicates the structure is
## [[randEf]][[randEf]] otherwise it is [[repId:randEf]][[repId]]
GParamTmp[[paste0(ifelse(useRepIdFix, "repId:", ""),
randEf)]][[ifelse(useRepIdFix,
"repId", randEf)]]$con <- "F"
}
}
## evaluate call terms in mr and mfTrait so predict can be run.
mrTrait$call[[1]] <- quote(asreml::asreml)
mrTrait$call$fixed <- eval(mrTrait$call$fixed)
mrTrait$call$random <- eval(mrTrait$call$random)
mrTrait$call$rcov <- eval(mrTrait$call$rcov)
mrTrait$call$data <- substitute(TDTr)
}
mr[[trait]] <- mrTrait
} # End random.
if ("fixed" %in% what) {
## Fit model with genotype fixed.
if (!"random" %in% what) {
GParamTmp <- NULL
}
mfTrait <- tryCatchExt({
if (all(is.na(TDTr[[trait]]))) {
stop("Only NA values for trait ", trait, " in trial ", trial, ".\n")
}
asrArgsF <- c(asrArgs0,
list(fixed = formula(paste0("`", trait, "`", fixedForm,
"+ genotype")),
G.param = GParamTmp, data = TDTr))
if (length(randomForm) != 0) {
asrArgsF <- c(asrArgsF,
list(random = formula(paste("~", randomForm))))
}
do.call(asreml::asreml, args = asrArgsF)
})
if (!is.null(mfTrait$warning)) {
mfTrait <- chkLastIter(mfTrait)
mfTrait <- wrnToErr(mfTrait)
}
if (length(mfTrait$warning) != 0) {
warning("Warning in asreml for genotype fixed, trait ", trait,
" in trial ", trial, ":\n",
paste(mfTrait$warning, collapse = "\n"), "\n",
call. = FALSE)
}
if (is.null(mfTrait$error)) {
mfTrait <- mfTrait$value
} else {
warning("Error in asreml for genotype fixed, trait ", trait,
" in trial ", trial, ":\n",
paste(mfTrait$error, collapse = "\n"), "\n",
call. = FALSE)
mfTrait <- NULL
}
if (!is.null(mfTrait)) {
mfTrait$call[[1]] <- quote(asreml::asreml)
mfTrait$call$fixed <- eval(mfTrait$call$fixed)
mfTrait$call$random <- eval(mfTrait$call$random)
mfTrait$call$rcov <- eval(mfTrait$call$rcov)
mfTrait$call$data <- substitute(TDTr)
## Construct assocForm for use in associate in predict.
if (useCheckId) {
assocForm <- formula("~ checkId:genotype")
} else {
assocForm <- formula("~ NULL")
}
}
mf[[trait]] <- mfTrait
} # End fixed.
spatial[trait] <- FALSE
} # End for traits.
## Create TD for output.
## Based on TDTr so dropped levels are dropped in output.
## Needs attribute from TD[trial].
TDOut <- createTD(data = TDTr)
## If trial was not a column in TDTr the name of the trial is lost and
## replaced by "TDTr". To prevent this readd the name.
names(TDOut) <- trial
attr(x = TDOut[[trial]], which = "renamedCols") <-
attr(x = TDTr, which = "renamedCols")
## Construct STA object.
return(list(mRand = if ("random" %in% what) mr else NULL,
mFix = if ("fixed" %in% what) mf else NULL, TD = TDOut,
traits = traits, design = design, spatial = spatial,
engine = "asreml", predicted = "genotype", sumTab = sumTab))
} else {# spatial
regular <- min(repTab) == 1 && max(repTab) == 1
return(bestSpatMod(TD = TD[trial], traits = traits, what = what,
regular = TRUE, criterion = criterion,
#regular = regular, criterion = criterion,
useCheckId = useCheckId, design = design,
covariates = covariates, fixedForm = fixedForm,
randomForm = randomForm, ...))
}
}
#' Helper function for calculating best spatial model using asreml.
#'
#' @noRd
#' @keywords internal
bestSpatMod <- function(TD,
traits,
what = c("fixed", "random"),
regular = TRUE,
criterion = "AIC",
useCheckId = FALSE,
design = "rowcol",
covariates = NULL,
fixedForm,
randomForm,
...) {
dotArgs <- list(...)
## Increase max number of iterations for asreml.
maxIter <- 200
TDTr <- droplevels(TD[[1]])
trial <- TDTr[["trial"]][1]
## Add empty observations.
TDTab <- as.data.frame(table(TDTr[["colId"]], TDTr[["rowId"]]))
TDTab <- TDTab[TDTab[["Freq"]] == 0, , drop = FALSE]
if (nrow(TDTab) > 0) {
extObs <- setNames(as.data.frame(matrix(nrow = nrow(TDTab),
ncol = ncol(TDTr))),
colnames(TDTr))
extObs[["trial"]] <- trial
extObs[c("colId", "rowId")] <- TDTab[c("Var1", "Var2")]
extObs[c("colCoord", "rowCoord")] <-
c(as.numeric(levels(TDTab[["Var1"]]))[TDTab[["Var1"]]],
as.numeric(levels(TDTab[["Var2"]]))[TDTab[["Var2"]]])
TDTr <- rbind(TDTr, extObs)
TDTr[["genotype"]] <- addNA(TDTr[["genotype"]])
}
## TD needs to be sorted by row and column to prevent asreml from crashing.
TDTr <- TDTr[order(TDTr[["rowId"]], TDTr[["colId"]]), ]
useRepIdFix <- design == "res.rowcol"
## Define random terms of models to try.
randTerm <- c("NULL", rep(x = c("NULL", "units"), each = 3))
if (regular) {
## Define spatial terms of models to try.
spatCh <- c("none", rep(x = c("AR1(x)id", "id(x)AR1", "AR1(x)AR1"),
times = 2))
spatTerm <- c(NA, paste("~",
rep(x = c("ar1(rowId):colId", "rowId:ar1(colId)",
"ar1(rowId):ar1(colId)"), times = 2)))
} else {
## Define spatial terms used when layout is irregular.
spatCh <- c("none", rep(x = c("exp(x)id", "id(x)exp",
"isotropic exponential"), times = 2))
spatTerm <- c(NA, paste("~", rep(x = c("exp(rowCoord):colCoord",
"rowCoord:exp(colCoord)",
"iexp(rowCoord,colCoord)"),
times = 2)))
}
## Create empty base lists.
mr <- mf <- spatial <- sumTab <- setNames(vector(mode = "list",
length = length(traits)),
traits)
btCols <- c("spatial", "random", "AIC", "BIC", "H2", "row", "col", "error",
"correlated error", "converge")
for (trait in traits) {
## Reset criterion to Inf.
criterionBest <- Inf
## Create data.frame for storing summary for current trait.
modSum <- as.data.frame(matrix(nrow = length(spatCh), ncol = length(btCols),
dimnames = list(NULL, btCols)))
## Create formula for the fixed part.
fixedFormR <- formula(paste0("`", trait, "`", fixedForm))
## Fit model with genotype random for all different random/spatial terms.
for (i in seq_along(randTerm)) {
if (length(randomForm) > 0) {
randFormR <- formula(paste("~ genotype +", randomForm, "+", randTerm[i]))
} else {
randFormR <- formula(paste("~ genotype +", randTerm[i]))
}
asrArgsR <- c(list(fixed = fixedFormR, random = randFormR, aom = TRUE,
data = TDTr, maxiter = maxIter, trace = FALSE),
dotArgs)
## In asreml3 na.method.X and na.method.Y are used.
## In asreml4 this is replaced by na.action.
asrArgsR <- c(asrArgsR, if (asreml4()) {
list(na.action = asreml::na.method(x = "include"))
} else {
list(na.method.X = "include")
})
if (!is.na(spatTerm[i])) {
## In asreml4 rcov is replaced by residual.
asrArgsR[[ifelse(asreml4(), "residual", "rcov")]] <- formula(spatTerm[i])
}
capture.output(mrTrait <- tryCatchExt({
if (all(is.na(TDTr[[trait]]))) {
stop("Only NA values for trait ", trait, " in trial ", trial, ".\n")
}
do.call(what = asreml::asreml, args = asrArgsR)
}), file = tempfile())
if (!is.null(mrTrait$warning)) {
mrTrait <- chkLastIter(mrTrait)
mrTrait <- wrnToErr(mrTrait)
}
if (length(mrTrait$warning) != 0) {
warning("Warning in asreml for model ", spatCh[i],
" genotype random, trait ", trait, " in trial ",
trial, ":\n", paste(mrTrait$warning, collapse = "\n"), "\n",
call. = FALSE)
}
if (is.null(mrTrait$error)) {
mrTrait <- mrTrait$value
} else {
warning("Error in asreml for model ", spatCh[i],
" genotype random, trait ", trait, " in trial ",
trial, ":\n", paste(mrTrait$error, collapse = "\n"), "\n",
call. = FALSE)
mrTrait <- NULL
}
## Fill model summary table.
modSum[i, "spatial"] <- spatCh[i]
modSum[i, "random"] <- if (randTerm[i] == "NULL") NA else randTerm[i]
if (!is.null(mrTrait) && mrTrait$converge) {
### Fit model with genotype fixed.
fixedFormfTrait <- update(fixedFormR, ~ . + genotype)
## Constrain variance of the variance components to be fixed as the values
## in the best model.
GParamTmp <- mrTrait$G.param
for (randEf in c("rowId", "colId")) {
## When there are no replicates the structure is [[randEf]][[randEf]]
## otherwise it is [[repId:randEf]][[repId]]
GParamTmp[[paste0(ifelse(useRepIdFix, "repId:", ""),
randEf)]][[ifelse(useRepIdFix, "repId", randEf)]]$con <- "F"
}
if (length(randomForm) > 0) {
randFormF <- formula(paste("~", randomForm, "+", randTerm[i]))
} else {
randFormF <- formula(paste("~", randTerm[i]))
}
asrArgsF <- c(list(fixed = fixedFormfTrait, random = randFormF,
G.param = GParamTmp, aom = TRUE, data = TDTr,
maxiter = maxIter, trace = FALSE), dotArgs)
## In asreml3 na.method.X and na.method.Y are used.
## In asreml4 this is replaced by na.action.
asrArgsF <- c(asrArgsF, if (asreml4()) {
list(na.action = asreml::na.method(x = "include"))
} else {
list(na.method.X = "include")
})
if (!is.na(spatTerm[i])) {
## In asreml4 parameter rcov is replaced by residual.
asrArgsF[[ifelse(asreml4(), "residual", "rcov")]] <-
formula(spatTerm[i])
}
## Fit the model with genotype fixed.
capture.output(mfTrait <- tryCatchExt({
if (all(is.na(TDTr[[trait]]))) {
stop("Only NA values for trait ", trait, " in trial ", trial, ".\n")
}
do.call(what = asreml::asreml, args = asrArgsF)}), file = tempfile())
if (!is.null(mfTrait$warning)) {
mfTrait <- chkLastIter(mfTrait)
mfTrait <- wrnToErr(mfTrait)
}
if (length(mfTrait$warning) != 0) {
warning("Warning in asreml for model ", spatCh[i],
" genotype fixed, trait ", trait, " in trial ",
TDTr$trial[1], ":\n",
paste(mfTrait$warning, collapse = "\n"), "\n", call. = FALSE)
}
if (is.null(mfTrait$error)) {
mfTrait <- mfTrait$value
} else {
warning("Error in asreml for model ", spatCh[i],
" genotype fixed, trait ", trait, " in trial ",
TDTr$trial[1], ":\n", paste(mfTrait$error, collapse = "\n"),
"\n", call. = FALSE)
mfTrait <- NULL
}
if (!is.null(mfTrait)) {
mfTrait$call[[1]] <- quote(asreml::asreml)
mfTrait$call$fixed <- eval(mfTrait$call$fixed)
mfTrait$call$random <- eval(mfTrait$call$random)
mfTrait$call$rcov <- eval(mfTrait$call$rcov)
}
if (!is.null(mfTrait) && mfTrait$converge) {
## Compute number of parameters as number of unbound rows in varcomp.
nPar <- sum(summary(mrTrait)$varcomp[["bound"]] != "B")
modSum[i, "AIC"] <- -2 * mrTrait$loglik + 2 * nPar
modSum[i, "BIC"] <- -2 * mrTrait$loglik +
log(length(fitted(mrTrait))) * nPar
## Row and column output differs for regular/non-regular.
## Always max. one of the possibilities is in summary so rowVal and
## colVal are always a single value.
summ <- summary(mrTrait)$varcomp["component"]
rowVal <- summ[rownames(summ) %in%
c("R!rowId.cor", "R!rowCoord.pow", "R!pow",
## New naming for asreml4.
"rowId:colId!rowId!cor",
"rowCoord:colCoord!rowCoord!pow",
"iexp(rowCoord,colCoord)!pow"), ]
modSum[i, "row"] <- ifelse(length(rowVal) == 0, NA, rowVal)
colVal <- summ[rownames(summ) %in%
c("R!colId.cor", "R!colCoord.pow", "R!pow",
## New naming for asreml4
"rowId:colId!colId!cor",
"rowCoord:colCoord!colCoord!pow",
"iexp(rowCoord,colCoord)!pow"), ]
modSum[i, "col"] <- ifelse(length(colVal) == 0, NA, colVal)
modSum[i, "error"] <-
ifelse(randTerm[i] == "units",
summ[rownames(summ) %in% c("units!units.var",
## New naming for asreml4.
"units"), ],
summ[rownames(summ) %in% c("R!variance",
## New naming for asreml4.
"units!R", "rowId:colId!R",
"rowCoord:colCoord!R",
"iexp(rowCoord,colCoord)!R"), ])
if (randTerm[i] == "units") {
modSum[i, "correlated error"] <-
summ[rownames(summ) %in% c("R!variance",
## New naming for asreml4.
"rowId:colId!R", "rowCoord:colCoord!R",
"iexp(rowCoord,colCoord)!R"), ]
}
## Compute heritability and add to summary table.
varGen <- if (asreml4()) {
unname(mrTrait$vparameters["genotype"] * mrTrait$sigma2)
} else {
unname(mrTrait$gammas[pattern = "genotype!genotype.var"] *
mrTrait$sigma2)
}
mrPred <- predictAsreml(model = mrTrait, classify = "genotype",
vcov = FALSE, TD = TDTr, only = "genotype",
sed = TRUE)
sedSq <- if (asreml4()) {
mrPred$sed ^ 2
} else {
mrPred$predictions$sed ^ 2
}
modSum[i, "H2"] <- 1 - mean(sedSq[lower.tri(sedSq)]) / (2 * varGen)
## If current model is better than best so far based on chosen criterion
## define best model as current model.
criterionCur <- modSum[i, criterion]
if (criterionCur < criterionBest) {
bestModfTr <- mfTrait
bestModrTr <- mrTrait
## Evaluate call terms in bestModTr so predict can be run.
## Needs to be called in every iteration to prevent final result
## from always having the values of the last iteration.
bestModrTr$call[[1]] <- quote(asreml::asreml)
bestModrTr$call$fixed <- eval(bestModrTr$call$fixed)
bestModrTr$call$random <- eval(bestModrTr$call$random)
bestModrTr$call$rcov <- eval(bestModrTr$call$rcov)
criterionBest <- criterionCur
bestMod <- i
}
}
}
## For convergence both mrTrait and mfTrait have to converge.
modSum[i, "converge"] <- isTRUE(!is.null(mrTrait) && mrTrait$converge &&
!is.null(mfTrait) & mfTrait$converge)
}
mr[[trait]] <- bestModrTr
mf[[trait]] <- bestModfTr
extraTerm <- if (randTerm[bestMod] != "NULL") paste("-", randTerm[bestMod])
spatial[[trait]] <- paste(spatCh[bestMod], extraTerm)
attr(x = modSum, which = "chosen") <- bestMod
sumTab[[trait]] <- modSum
} # End for traits.
TD[[1]] <- TDTr
return(list(mRand = if ("random" %in% what) mr else NULL,
mFix = if ("fixed" %in% what) mf else NULL, TD = TD,
traits = traits, design = design, spatial = spatial,
engine = "asreml", predicted = "genotype", sumTab = sumTab))
}
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Defines functions bestSpatMod fitTDAsreml
#' Fit Single Trial Model using asreml
#'
#' Fit Single Trial Model using asreml
#'
#' @inheritParams fitTD
#'
#' @seealso \code{\link{fitTD}}
#'
#' @noRd
#' @keywords internal
fitTDAsreml <- function(TD,
trial = NULL,
traits,
what = c("fixed", "random"),
covariates = NULL,
useCheckId = FALSE,
design = "rowcol",
spatial = FALSE,
control = NULL,
checks = TRUE,
...) {
## Base check.
if (missing(TD) || !inherits(TD, "TD")) {
stop("TD should be a valid object of class TD.\n")
}
## Only perform other checks if explicitly stated.
## Provides the opportunity to skip check if already done in fitTD.
if (checks) {
## Checks.
checkOut <- modelChecks(TD = TD, trial = trial, design = design,
traits = traits, what = what,
covariates = covariates, spatial = spatial,
engine = "asreml", useCheckId = useCheckId,
control = control)
## Convert output to variables.
list2env(x = checkOut, envir = environment())
}
TDTr <- droplevels(TD[[trial]])
## Rename traits with invalid syntax.
## Mainly ()/ seem problematic, but being more strict doesn't hurt.
traitsNw <- make.names(traits)
traitsRenamed <- traits[!traits %in% traitsNw]
if (length(traitsRenamed) > 0) {
message("The following traits have been renamed since asreml doesn't ",
"allow syntactically invalid traits:\n",
paste(traitsRenamed, collapse = ", "))
colnames(TDTr)[colnames(TDTr) %in% traits] <-
make.names(colnames(TDTr)[colnames(TDTr) %in% traits])
traits <- traitsNw
}
## Should repId be used as fixed effect in the model.
useRepIdFix <- design %in% c("res.ibd", "res.rowcol", "rcbd")
## Indicate extra random effects.
if (design %in% c("ibd", "res.ibd")) {
randEff <- "subBlock"
} else if (design %in% c("rowcol", "res.rowcol")) {
randEff <- c("rowId", "colId")
} else if (design == "rcbd") {
randEff <- character()
}
# Check if spatial models can be fitted.
if (spatial) {
## Set default value for criterion
criterion <- "AIC"
if ("criterion" %in% names(control)) {
critCt <- control$criterion
if (critCt %in% c("AIC", "BIC")) {
criterion <- critCt
} else {
warning("Invalid value for control parameter criterion. ",
"Using default value instead.\n")
}
}
if (useRepIdFix) {
repTab <- table(TDTr$repId, TDTr$rowId, TDTr$colId)
} else {
repTab <- table(TDTr$rowId, TDTr$colId)
}
if (max(repTab) > 1) {
warning("There should only be one plot at each combination of ",
if (useRepIdFix) "replicate, ", "row and column.\n",
"Spatial models will not be tried")
spatial <- FALSE
}
}
## Construct formula for fixed part.
fixedForm <- paste("~",
if (useRepIdFix) "repId" else "1",
if (useCheckId) "+ checkId",
if (!is.null(covariates)) paste(c("", covariates),
collapse = "+"))
## Construct formula for random part. Include repId depending on design.
if (length(randEff) != 0) {
randomForm <- paste0(if (useRepIdFix) "repId:",
paste(randEff,
collapse = paste("+",
if (useRepIdFix) "repId:")))
} else {
randomForm <- character()
}
if (!spatial) {
## Increase max number of iterations for asreml.
maxIter <- 200
## In asreml3 na.method.X and na.method.Y are used.
## In asreml4 this is replaced by na.action.
asrArgs0 <- list(aom = TRUE, maxiter = maxIter, trace = FALSE) #, ...)
if (asreml4()) {
asrArgs0 <- c(asrArgs0, list(na.action = asreml::na.method(x = "include")))
} else {
asrArgs0 <- c(asrArgs0, list(na.method.X = "include"))
}
## Create empty base lists.
mr <- mf <- spatial <- sumTab <-
setNames(vector(mode = "list", length = length(traits)), traits)
for (trait in traits) {
if ("random" %in% what) {
## Fit model with genotype random.
mrTrait <- tryCatchExt({
if (all(is.na(TDTr[[trait]]))) {
stop("Only NA values for trait ", trait, " in trial ", trial, ".\n")
}
asrArgsR <- c(asrArgs0,
list(fixed = formula(paste0("`", trait, "`", fixedForm)),
random = formula(paste("~", randomForm,
if (length(randomForm) != 0) "+",
"genotype")),
data = TDTr))
do.call(asreml::asreml, args = asrArgsR)
})
if (!is.null(mrTrait$warning)) {
mrTrait <- chkLastIter(mrTrait)
mrTrait <- wrnToErr(mrTrait)
}
if (length(mrTrait$warning) != 0) {
warning("Warning in asreml for genotype random, trait ", trait,
" in trial ", trial, ":\n",
paste(mrTrait$warning, collapse = "\n"), "\n",
call. = FALSE)
}
if (is.null(mrTrait$error)) {
mrTrait <- mrTrait$value
} else {
warning("Error in asreml for genotype random, trait ", trait,
" in trial ", trial, ":\n",
paste(mrTrait$error, collapse = "\n"), "\n",
call. = FALSE)
mrTrait <- NULL
}
if (!is.null(mrTrait)) {
if ("fixed" %in% what) {
## Constrain variance of the variance components to be fixed as
## the values in mr.
GParamTmp <- mrTrait$G.param
for (randEf in randEff) {
## When there are no replicates the structure is
## [[randEf]][[randEf]] otherwise it is [[repId:randEf]][[repId]]
GParamTmp[[paste0(ifelse(useRepIdFix, "repId:", ""),
randEf)]][[ifelse(useRepIdFix,
"repId", randEf)]]$con <- "F"
}
}
## evaluate call terms in mr and mfTrait so predict can be run.
mrTrait$call[[1]] <- quote(asreml::asreml)
mrTrait$call$fixed <- eval(mrTrait$call$fixed)
mrTrait$call$random <- eval(mrTrait$call$random)
mrTrait$call$rcov <- eval(mrTrait$call$rcov)
mrTrait$call$data <- substitute(TDTr)
}
mr[[trait]] <- mrTrait
} # End random.
if ("fixed" %in% what) {
## Fit model with genotype fixed.
if (!"random" %in% what) {
GParamTmp <- NULL
}
mfTrait <- tryCatchExt({
if (all(is.na(TDTr[[trait]]))) {
stop("Only NA values for trait ", trait, " in trial ", trial, ".\n")
}
asrArgsF <- c(asrArgs0,
list(fixed = formula(paste0("`", trait, "`", fixedForm,
"+ genotype")),
G.param = GParamTmp, data = TDTr))
if (length(randomForm) != 0) {
asrArgsF <- c(asrArgsF,
list(random = formula(paste("~", randomForm))))
}
do.call(asreml::asreml, args = asrArgsF)
})
if (!is.null(mfTrait$warning)) {
mfTrait <- chkLastIter(mfTrait)
mfTrait <- wrnToErr(mfTrait)
}
if (length(mfTrait$warning) != 0) {
warning("Warning in asreml for genotype fixed, trait ", trait,
" in trial ", trial, ":\n",
paste(mfTrait$warning, collapse = "\n"), "\n",
call. = FALSE)
}
if (is.null(mfTrait$error)) {
mfTrait <- mfTrait$value
} else {
warning("Error in asreml for genotype fixed, trait ", trait,
" in trial ", trial, ":\n",
paste(mfTrait$error, collapse = "\n"), "\n",
call. = FALSE)
mfTrait <- NULL
}
if (!is.null(mfTrait)) {
mfTrait$call[[1]] <- quote(asreml::asreml)
mfTrait$call$fixed <- eval(mfTrait$call$fixed)
mfTrait$call$random <- eval(mfTrait$call$random)
mfTrait$call$rcov <- eval(mfTrait$call$rcov)
mfTrait$call$data <- substitute(TDTr)
## Construct assocForm for use in associate in predict.
if (useCheckId) {
assocForm <- formula("~ checkId:genotype")
} else {
assocForm <- formula("~ NULL")
}
}
mf[[trait]] <- mfTrait
} # End fixed.
spatial[trait] <- FALSE
} # End for traits.
## Create TD for output.
## Based on TDTr so dropped levels are dropped in output.
## Needs attribute from TD[trial].
TDOut <- createTD(data = TDTr)
## If trial was not a column in TDTr the name of the trial is lost and
## replaced by "TDTr". To prevent this readd the name.
names(TDOut) <- trial
attr(x = TDOut[[trial]], which = "renamedCols") <-
attr(x = TDTr, which = "renamedCols")
## Construct STA object.
return(list(mRand = if ("random" %in% what) mr else NULL,
mFix = if ("fixed" %in% what) mf else NULL, TD = TDOut,
traits = traits, design = design, spatial = spatial,
engine = "asreml", predicted = "genotype", sumTab = sumTab))
} else {# spatial
regular <- min(repTab) == 1 && max(repTab) == 1
return(bestSpatMod(TD = TD[trial], traits = traits, what = what,
regular = TRUE, criterion = criterion,
#regular = regular, criterion = criterion,
useCheckId = useCheckId, design = design,
covariates = covariates, fixedForm = fixedForm,
randomForm = randomForm, ...))
}
}
#' Helper function for calculating best spatial model using asreml.
#'
#' @noRd
#' @keywords internal
bestSpatMod <- function(TD,
traits,
what = c("fixed", "random"),
regular = TRUE,
criterion = "AIC",
useCheckId = FALSE,
design = "rowcol",
covariates = NULL,
fixedForm,
randomForm,
...) {
dotArgs <- list(...)
## Increase max number of iterations for asreml.
maxIter <- 200
TDTr <- droplevels(TD[[1]])
trial <- TDTr[["trial"]][1]
## Add empty observations.
TDTab <- as.data.frame(table(TDTr[["colId"]], TDTr[["rowId"]]))
TDTab <- TDTab[TDTab[["Freq"]] == 0, , drop = FALSE]
if (nrow(TDTab) > 0) {
extObs <- setNames(as.data.frame(matrix(nrow = nrow(TDTab),
ncol = ncol(TDTr))),
colnames(TDTr))
extObs[["trial"]] <- trial
extObs[c("colId", "rowId")] <- TDTab[c("Var1", "Var2")]
extObs[c("colCoord", "rowCoord")] <-
c(as.numeric(levels(TDTab[["Var1"]]))[TDTab[["Var1"]]],
as.numeric(levels(TDTab[["Var2"]]))[TDTab[["Var2"]]])
TDTr <- rbind(TDTr, extObs)
TDTr[["genotype"]] <- addNA(TDTr[["genotype"]])
}
## TD needs to be sorted by row and column to prevent asreml from crashing.
TDTr <- TDTr[order(TDTr[["rowId"]], TDTr[["colId"]]), ]
useRepIdFix <- design == "res.rowcol"
## Define random terms of models to try.
randTerm <- c("NULL", rep(x = c("NULL", "units"), each = 3))
if (regular) {
## Define spatial terms of models to try.
spatCh <- c("none", rep(x = c("AR1(x)id", "id(x)AR1", "AR1(x)AR1"),
times = 2))
spatTerm <- c(NA, paste("~",
rep(x = c("ar1(rowId):colId", "rowId:ar1(colId)",
"ar1(rowId):ar1(colId)"), times = 2)))
} else {
## Define spatial terms used when layout is irregular.
spatCh <- c("none", rep(x = c("exp(x)id", "id(x)exp",
"isotropic exponential"), times = 2))
spatTerm <- c(NA, paste("~", rep(x = c("exp(rowCoord):colCoord",
"rowCoord:exp(colCoord)",
"iexp(rowCoord,colCoord)"),
times = 2)))
}
## Create empty base lists.
mr <- mf <- spatial <- sumTab <- setNames(vector(mode = "list",
length = length(traits)),
traits)
btCols <- c("spatial", "random", "AIC", "BIC", "H2", "row", "col", "error",
"correlated error", "converge")
for (trait in traits) {
## Reset criterion to Inf.
criterionBest <- Inf
## Create data.frame for storing summary for current trait.
modSum <- as.data.frame(matrix(nrow = length(spatCh), ncol = length(btCols),
dimnames = list(NULL, btCols)))
## Create formula for the fixed part.
fixedFormR <- formula(paste0("`", trait, "`", fixedForm))
## Fit model with genotype random for all different random/spatial terms.
for (i in seq_along(randTerm)) {
if (length(randomForm) > 0) {
randFormR <- formula(paste("~ genotype +", randomForm, "+", randTerm[i]))
} else {
randFormR <- formula(paste("~ genotype +", randTerm[i]))
}
asrArgsR <- c(list(fixed = fixedFormR, random = randFormR, aom = TRUE,
data = TDTr, maxiter = maxIter, trace = FALSE),
dotArgs)
## In asreml3 na.method.X and na.method.Y are used.
## In asreml4 this is replaced by na.action.
asrArgsR <- c(asrArgsR, if (asreml4()) {
list(na.action = asreml::na.method(x = "include"))
} else {
list(na.method.X = "include")
})
if (!is.na(spatTerm[i])) {
## In asreml4 rcov is replaced by residual.
asrArgsR[[ifelse(asreml4(), "residual", "rcov")]] <- formula(spatTerm[i])
}
capture.output(mrTrait <- tryCatchExt({
if (all(is.na(TDTr[[trait]]))) {
stop("Only NA values for trait ", trait, " in trial ", trial, ".\n")
}
do.call(what = asreml::asreml, args = asrArgsR)
}), file = tempfile())
if (!is.null(mrTrait$warning)) {
mrTrait <- chkLastIter(mrTrait)
mrTrait <- wrnToErr(mrTrait)
}
if (length(mrTrait$warning) != 0) {
warning("Warning in asreml for model ", spatCh[i],
" genotype random, trait ", trait, " in trial ",
trial, ":\n", paste(mrTrait$warning, collapse = "\n"), "\n",
call. = FALSE)
}
if (is.null(mrTrait$error)) {
mrTrait <- mrTrait$value
} else {
warning("Error in asreml for model ", spatCh[i],
" genotype random, trait ", trait, " in trial ",
trial, ":\n", paste(mrTrait$error, collapse = "\n"), "\n",
call. = FALSE)
mrTrait <- NULL
}
## Fill model summary table.
modSum[i, "spatial"] <- spatCh[i]
modSum[i, "random"] <- if (randTerm[i] == "NULL") NA else randTerm[i]
if (!is.null(mrTrait) && mrTrait$converge) {
### Fit model with genotype fixed.
fixedFormfTrait <- update(fixedFormR, ~ . + genotype)
## Constrain variance of the variance components to be fixed as the values
## in the best model.
GParamTmp <- mrTrait$G.param
for (randEf in c("rowId", "colId")) {
## When there are no replicates the structure is [[randEf]][[randEf]]
## otherwise it is [[repId:randEf]][[repId]]
GParamTmp[[paste0(ifelse(useRepIdFix, "repId:", ""),
randEf)]][[ifelse(useRepIdFix, "repId", randEf)]]$con <- "F"
}
if (length(randomForm) > 0) {
randFormF <- formula(paste("~", randomForm, "+", randTerm[i]))
} else {
randFormF <- formula(paste("~", randTerm[i]))
}
asrArgsF <- c(list(fixed = fixedFormfTrait, random = randFormF,
G.param = GParamTmp, aom = TRUE, data = TDTr,
maxiter = maxIter, trace = FALSE), dotArgs)
## In asreml3 na.method.X and na.method.Y are used.
## In asreml4 this is replaced by na.action.
asrArgsF <- c(asrArgsF, if (asreml4()) {
list(na.action = asreml::na.method(x = "include"))
} else {
list(na.method.X = "include")
})
if (!is.na(spatTerm[i])) {
## In asreml4 parameter rcov is replaced by residual.
asrArgsF[[ifelse(asreml4(), "residual", "rcov")]] <-
formula(spatTerm[i])
}
## Fit the model with genotype fixed.
capture.output(mfTrait <- tryCatchExt({
if (all(is.na(TDTr[[trait]]))) {
stop("Only NA values for trait ", trait, " in trial ", trial, ".\n")
}
do.call(what = asreml::asreml, args = asrArgsF)}), file = tempfile())
if (!is.null(mfTrait$warning)) {
mfTrait <- chkLastIter(mfTrait)
mfTrait <- wrnToErr(mfTrait)
}
if (length(mfTrait$warning) != 0) {
warning("Warning in asreml for model ", spatCh[i],
" genotype fixed, trait ", trait, " in trial ",
TDTr$trial[1], ":\n",
paste(mfTrait$warning, collapse = "\n"), "\n", call. = FALSE)
}
if (is.null(mfTrait$error)) {
mfTrait <- mfTrait$value
} else {
warning("Error in asreml for model ", spatCh[i],
" genotype fixed, trait ", trait, " in trial ",
TDTr$trial[1], ":\n", paste(mfTrait$error, collapse = "\n"),
"\n", call. = FALSE)
mfTrait <- NULL
}
if (!is.null(mfTrait)) {
mfTrait$call[[1]] <- quote(asreml::asreml)
mfTrait$call$fixed <- eval(mfTrait$call$fixed)
mfTrait$call$random <- eval(mfTrait$call$random)
mfTrait$call$rcov <- eval(mfTrait$call$rcov)
}
if (!is.null(mfTrait) && mfTrait$converge) {
## Compute number of parameters as number of unbound rows in varcomp.
nPar <- sum(summary(mrTrait)$varcomp[["bound"]] != "B")
modSum[i, "AIC"] <- -2 * mrTrait$loglik + 2 * nPar
modSum[i, "BIC"] <- -2 * mrTrait$loglik +
log(length(fitted(mrTrait))) * nPar
## Row and column output differs for regular/non-regular.
## Always max. one of the possibilities is in summary so rowVal and
## colVal are always a single value.
summ <- summary(mrTrait)$varcomp["component"]
rowVal <- summ[rownames(summ) %in%
c("R!rowId.cor", "R!rowCoord.pow", "R!pow",
## New naming for asreml4.
"rowId:colId!rowId!cor",
"rowCoord:colCoord!rowCoord!pow",
"iexp(rowCoord,colCoord)!pow"), ]
modSum[i, "row"] <- ifelse(length(rowVal) == 0, NA, rowVal)
colVal <- summ[rownames(summ) %in%
c("R!colId.cor", "R!colCoord.pow", "R!pow",
## New naming for asreml4
"rowId:colId!colId!cor",
"rowCoord:colCoord!colCoord!pow",
"iexp(rowCoord,colCoord)!pow"), ]
modSum[i, "col"] <- ifelse(length(colVal) == 0, NA, colVal)
modSum[i, "error"] <-
ifelse(randTerm[i] == "units",
summ[rownames(summ) %in% c("units!units.var",
## New naming for asreml4.
"units"), ],
summ[rownames(summ) %in% c("R!variance",
## New naming for asreml4.
"units!R", "rowId:colId!R",
"rowCoord:colCoord!R",
"iexp(rowCoord,colCoord)!R"), ])
if (randTerm[i] == "units") {
modSum[i, "correlated error"] <-
summ[rownames(summ) %in% c("R!variance",
## New naming for asreml4.
"rowId:colId!R", "rowCoord:colCoord!R",
"iexp(rowCoord,colCoord)!R"), ]
}
## Compute heritability and add to summary table.
varGen <- if (asreml4()) {
unname(mrTrait$vparameters["genotype"] * mrTrait$sigma2)
} else {
unname(mrTrait$gammas[pattern = "genotype!genotype.var"] *
mrTrait$sigma2)
}
mrPred <- predictAsreml(model = mrTrait, classify = "genotype",
vcov = FALSE, TD = TDTr, only = "genotype",
sed = TRUE)
sedSq <- if (asreml4()) {
mrPred$sed ^ 2
} else {
mrPred$predictions$sed ^ 2
}
modSum[i, "H2"] <- 1 - mean(sedSq[lower.tri(sedSq)]) / (2 * varGen)
## If current model is better than best so far based on chosen criterion
## define best model as current model.
criterionCur <- modSum[i, criterion]
if (criterionCur < criterionBest) {
bestModfTr <- mfTrait
bestModrTr <- mrTrait
## Evaluate call terms in bestModTr so predict can be run.
## Needs to be called in every iteration to prevent final result
## from always having the values of the last iteration.
bestModrTr$call[[1]] <- quote(asreml::asreml)
bestModrTr$call$fixed <- eval(bestModrTr$call$fixed)
bestModrTr$call$random <- eval(bestModrTr$call$random)
bestModrTr$call$rcov <- eval(bestModrTr$call$rcov)
criterionBest <- criterionCur
bestMod <- i
}
}
}
## For convergence both mrTrait and mfTrait have to converge.
modSum[i, "converge"] <- isTRUE(!is.null(mrTrait) && mrTrait$converge &&
!is.null(mfTrait) & mfTrait$converge)
}
mr[[trait]] <- bestModrTr
mf[[trait]] <- bestModfTr
extraTerm <- if (randTerm[bestMod] != "NULL") paste("-", randTerm[bestMod])
spatial[[trait]] <- paste(spatCh[bestMod], extraTerm)
attr(x = modSum, which = "chosen") <- bestMod
sumTab[[trait]] <- modSum
} # End for traits.
TD[[1]] <- TDTr
return(list(mRand = if ("random" %in% what) mr else NULL,
mFix = if ("fixed" %in% what) mf else NULL, TD = TD,
traits = traits, design = design, spatial = spatial,
engine = "asreml", predicted = "genotype", sumTab = sumTab))
}
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