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[Linear regression] Only check weights cov when there are predictors #352

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merged 2 commits into from
Nov 27, 2024
Merged

[Linear regression] Only check weights cov when there are predictors #352

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e604a52
Only check weights cov when there are predictors
JohnnyDoorn Nov 26, 2024
6217d19
update format coefficients cov table
JohnnyDoorn Nov 27, 2024
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51 changes: 27 additions & 24 deletions R/regressionlinear.R
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Original file line number Diff line number Diff line change
Expand Up @@ -16,7 +16,8 @@
#

RegressionLinearInternal <- function(jaspResults, dataset = NULL, options) {
ready <- options$dependent != "" && (length(unlist(options$modelTerms)) > 0 || options$interceptTerm)
nModels <- length(options[["modelTerms"]])
ready <- options$dependent != "" && (length(options[["modelTerms"]][[nModels]][["components"]]) > 0 || options$interceptTerm)

if (ready) {
dataset <- .linregReadDataset(dataset, options)
Expand Down Expand Up @@ -51,11 +52,11 @@ RegressionLinearInternal <- function(jaspResults, dataset = NULL, options) {
# these output elements show statistics of the "final model" (lm fit with all predictors in enter method and last lm fit in stepping methods)
finalModel <- model[[length(model)]]

if (options$residualCasewiseDiagnostic && is.null(modelContainer[["influenceTable"]]))
if (options$residualCasewiseDiagnostic && is.null(modelContainer[["influenceTable"]]))
.glmInfluenceTable(modelContainer, finalModel$fit, dataset, options, ready = ready, position = 9)
.regressionExportResiduals(modelContainer, finalModel$fit, dataset, options, ready = ready)


if (options$residualStatistic && is.null(modelContainer[["residualsTable"]]))
.linregCreateResidualsTable(modelContainer, finalModel, options, position = 10)

Expand Down Expand Up @@ -128,12 +129,12 @@ RegressionLinearInternal <- function(jaspResults, dataset = NULL, options) {
return(gettext("Stepwise procedures are not supported for models containing factors with more than 2 levels; retry the analysis using dummy variables"))
}
},

checkIfFactorInteractions = function() {
if (any(vapply(options[["modelTerms"]], .linregCheckIfInteractionWithFactors, logical(1L), factorVariables = options[["factors"]]))) {
return(gettext("Stepwise procedures are not supported for interactions containing more than 1 factor"))
}
},
},

checkIfPEntryIsValid = function() {
if (options$steppingMethodCriteriaType == "pValue" && options$steppingMethodCriteriaPEntry > options$steppingMethodCriteriaPRemoval)
Expand Down Expand Up @@ -171,9 +172,11 @@ RegressionLinearInternal <- function(jaspResults, dataset = NULL, options) {
factorLevels.target = options$factors,
factorLevels.amount = '< 2',
exitAnalysisIfErrors = TRUE)
covwt <- function(...) return(stats::cov.wt(..., wt = dataset[[.v(options[["weights"]])]])$cov)
.hasErrors(dataset[, -which(colnames(dataset) %in% c(.v(options$weights)))], type = "varCovData", varCovData.corFun = covwt,
exitAnalysisIfErrors = TRUE)
if (length(c(options$covariates, options$factors)) != 0) {
covwt <- function(...) return(stats::cov.wt(..., wt = dataset[[.v(options[["weights"]])]])$cov)
.hasErrors(dataset[, -which(colnames(dataset) %in% c(.v(options$weights)))], type = "varCovData", varCovData.corFun = covwt,
exitAnalysisIfErrors = TRUE)
}
}
}

Expand Down Expand Up @@ -209,7 +212,7 @@ RegressionLinearInternal <- function(jaspResults, dataset = NULL, options) {
summaryTable$addColumnInfo(name = "AIC", title = gettext("AIC"), type = "number", format = "dp:3")
summaryTable$addColumnInfo(name = "BIC", title = gettext("BIC"), type = "number", format = "dp:3")
}

if (options$rSquaredChange || options$fChange) {
if (options$rSquaredChange)
summaryTable$addColumnInfo(name = "R2c", title = gettextf("R%s Change", "\u00B2"), type = "number", format = "dp:3")
Expand Down Expand Up @@ -572,7 +575,7 @@ RegressionLinearInternal <- function(jaspResults, dataset = NULL, options) {
collDiagTable$addColumnInfo(name = "condIndex", title = gettext("Condition Index"), type = "number")

if (!is.null(model)) {
.linregAddPredictorsAsColumns(collDiagTable, model, options[["interceptTerm"]], overtitle = gettext("Variance Proportions"), format = "dp:3")
.linregAddPredictorsAsColumns(collDiagTable, model, options[["interceptTerm"]], overtitle = gettext("Variance Proportions"), format = "number")
.linregAddInterceptNotShownFootnote(collDiagTable, model, options)
.linregFillCollinearityDiagnosticsTable(collDiagTable, model, options)
}
Expand Down Expand Up @@ -714,7 +717,7 @@ RegressionLinearInternal <- function(jaspResults, dataset = NULL, options) {
if (!is.null(finalModel) && !is.null(finalModel$fit)) {
fit <- finalModel$fit

.linregInsertPlot(residQQPlot, .glmFillPlotResQQ, model = fit,
.linregInsertPlot(residQQPlot, .glmFillPlotResQQ, model = fit,
residType = "deviance", options = options)
}
}
Expand Down Expand Up @@ -807,13 +810,13 @@ RegressionLinearInternal <- function(jaspResults, dataset = NULL, options) {

predictorsInNull <- .linregGetPredictors(options$modelTerms[[1]][["components"]])
predictorsInFull <- .linregGetPredictors(options$modelTerms[[nModels]][["components"]]) # these include the null terms


if (options$weights != "")
weights <- dataset[[options$weights]]
else
weights <- rep(1, length(dataset[[dependent]]))

if (options$method %in% c("backward", "forward", "stepwise") && length(predictorsInFull) > 0)
model <- .linregGetModelSteppingMethod(dependent, predictorsInFull, predictorsInNull, dataset, options, weights)
else
Expand Down Expand Up @@ -858,10 +861,10 @@ RegressionLinearInternal <- function(jaspResults, dataset = NULL, options) {
model <- .linregForwardRegression(dependent, predictors, predictorsInNull, dataset, options, weights)
else # stepwise
model <- .linregStepwiseRegression(dependent, predictors, predictorsInNull, dataset, options, weights)

for (i in seq_along(model))
model[[i]] <- c(model[[i]], number = i)
model[[i]] <- c(model[[i]], number = i)

return(model)
}

Expand Down Expand Up @@ -992,8 +995,8 @@ RegressionLinearInternal <- function(jaspResults, dataset = NULL, options) {
if (grepl(candidatePredictors[i], pattern = ":")) {
variables <- unlist(strsplit(candidatePredictors[i], ":")) # split up interaction
permutations <- combinat::permn(variables) # realize all orderings
myPattern <- paste(sapply(permutations,
function(perm) paste(paste0(perm, ".?"), collapse = ":")),
myPattern <- paste(sapply(permutations,
function(perm) paste(paste0(perm, ".?"), collapse = ":")),
collapse = "|") # paste together with "|"
} else {
myPattern <- candidatePredictors[i]
Expand Down Expand Up @@ -1857,7 +1860,7 @@ RegressionLinearInternal <- function(jaspResults, dataset = NULL, options) {
.linregGetIndicesOfModelsWithPredictors <- function(model, options) {
predictorsInNull <- model[[1]]$predictors
indices <- seq_along(model)

if (options$method == "enter") {
if (length(model) >= 1 && options$interceptTerm && length(predictorsInNull) == 0)
indices <- indices[-1]
Expand Down Expand Up @@ -2123,7 +2126,7 @@ RegressionLinearInternal <- function(jaspResults, dataset = NULL, options) {
mapping = ggplot2::aes(x = x, y = y, group = group),
size = 1) +
ggplot2::xlab(xlab) +
ggplot2::ylab(ylab)
ggplot2::ylab(ylab)

factorPoints <- jaspGraphs::geom_point(data = d_factor,
mapping = ggplot2::aes(x = x, y = y))
Expand All @@ -2132,7 +2135,7 @@ RegressionLinearInternal <- function(jaspResults, dataset = NULL, options) {

} else {
xBreaks <- jaspGraphs::getPrettyAxisBreaks(xVar)

basicMarginalPlot <- ggplot2::ggplot() +
ggplot2::geom_line(data = d,
mapping = ggplot2::aes(x = x, y = y),
Expand Down Expand Up @@ -2200,7 +2203,7 @@ RegressionLinearInternal <- function(jaspResults, dataset = NULL, options) {

# base y-axis breaks on y and the prediction and/or confidence interval
yBreaks <- jaspGraphs::getPrettyAxisBreaks(jaspGraphs::getPrettyAxisBreaks(unlist(d[, -1])))

finalMarginalPlot <- basicMarginalPlot +
confidenceBounds +
predictionBound1 +
Expand Down
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