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consistent influence tables + covariance + export residuals #293

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consistent influence tables + covariance + export residuals #293

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6ecf63c
consistent influence table + covariance
JohnnyDoorn Mar 27, 2024
f1ccec6
move durbin watson to model summary table
JohnnyDoorn Mar 27, 2024
e635bfb
add options for verified test
JohnnyDoorn Mar 27, 2024
c3b83c9
Update helpfile
JohnnyDoorn Mar 27, 2024
c81ec1e
add mahalanobis
JohnnyDoorn Mar 27, 2024
58f9858
bug cook
JohnnyDoorn Mar 28, 2024
891b321
Merge branch 'mahalanobis' into influentialCases
JohnnyDoorn Mar 28, 2024
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43 changes: 26 additions & 17 deletions R/correlation.R
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Original file line number Diff line number Diff line change
Expand Up @@ -115,7 +115,7 @@ CorrelationInternal <- function(jaspResults, dataset, options){
mainTable$dependOn(c("variables", "partialOutVariables",
"pearson", "spearman", "kendallsTauB", "pairwiseDisplay", "significanceReport",
"significanceFlagged", "sampleSize",
"ci", "ciLevel",
"ci", "ciLevel", "covariance",
"vovkSellke", "alternative", "naAction",
"ciBootstrap", "ciBootstrapSamples", "effectSize"))

Expand Down Expand Up @@ -201,7 +201,14 @@ CorrelationInternal <- function(jaspResults, dataset, options){

if(options$significanceReport)
mainTable$addColumnInfo(name = paste0(test, "_p.value"), title = gettext("p"), type = "pvalue", overtitle = overtitle)


if(options$vovkSellke){
mainTable$addColumnInfo(name = paste0(test, "_vsmpr"), title = gettext("VS-MPR"), type = "number", overtitle = overtitle)

mainTable$addFootnote(message = .corrGetTexts()$footnotes$VSMPR, symbol = "\u2020", colNames = paste0(test, "_vsmpr"))
mainTable$addCitation(.corrGetTexts()$references$Sellke_etal_2001)
}

if(options$ci){
mainTable$addColumnInfo(name = paste0(test, "_lower.ci"),
title = gettextf("Lower %s%% CI", 100*options$ciLevel), type = "number",
Expand All @@ -211,19 +218,16 @@ CorrelationInternal <- function(jaspResults, dataset, options){
overtitle = overtitle)
}

if(options$vovkSellke){
mainTable$addColumnInfo(name = paste0(test, "_vsmpr"), title = gettext("VS-MPR"), type = "number", overtitle = overtitle)

mainTable$addFootnote(message = .corrGetTexts()$footnotes$VSMPR, symbol = "\u2020", colNames = paste0(test, "_vsmpr"))
mainTable$addCitation(.corrGetTexts()$references$Sellke_etal_2001)
}

if(options$effectSize){
mainTable$addColumnInfo(name = paste0(test, "_effect.size"), title = gettext("Effect size (Fisher's z)"), type = "number", overtitle = overtitle)
mainTable$addColumnInfo(name = paste0(test, "_se.effect.size"), title = gettext("SE Effect size"), type = "number", overtitle = overtitle)
}
}
}

if(options$covariance){
mainTable$addColumnInfo(name = "covariance", title = gettext("Covariance"), type = "number")
}
}

.corrTitlerer <- function(test, nTests){
Expand Down Expand Up @@ -252,13 +256,19 @@ CorrelationInternal <- function(jaspResults, dataset, options){
overtitle <- paste(vi, variables[vi], sep = ". ")

if(options$sampleSize) {
mainTable$addColumnInfo(name = paste(.v(variables[vi]), "sample.size", sep = "_"), title = "n",
mainTable$addColumnInfo(name = paste(variables[vi], "sample.size", sep = "_"), title = "n",
type = "integer", overtitle = overtitle)
}

for(ti in seq_along(tests)){
.corrInitCorrelationTableRowAsColumn(mainTable, options, variables[vi], testsTitles[ti], tests[ti], overtitle)
}

if(options$covariance) {
mainTable$addColumnInfo(name = paste(variables[vi], "covariance", sep = "_"), gettextf("Covariance"),
type = "number", overtitle = overtitle)
}

mainTable$setRowName(vi, .v(variables[vi]))
}
}
Expand Down Expand Up @@ -290,11 +300,11 @@ CorrelationInternal <- function(jaspResults, dataset, options){
title = gettextf("Lower %s%% CI", 100*options$ciLevel),
type = "number", overtitle = overtitle)
}

if(options$effectSize){
mainTable$addColumnInfo(name = sprintf(name, "effect.size"), title = gettextf("Effect size (Fisher's z)"), type = "number", overtitle = overtitle)
mainTable$addColumnInfo(name = sprintf(name, "se.effect.size"), title = gettext("SE Effect size"), type = "number", overtitle = overtitle)
}
}
}

### Compute results ----
Expand Down Expand Up @@ -343,7 +353,9 @@ CorrelationInternal <- function(jaspResults, dataset, options){
currentResults <- list()
testErrors <- list()
currentResults[['sample.size']] <- nrow(data)

if (isFALSE(errors))
currentResults[['covariance']] <- cov(x = data[,1], y = data[,2])

# even if we do not want the specific tests results
# we still want the output as NaN - to fill the jaspTables correctly
# so we still loop over all tests - .corr.test() returns empty lists if isFALSE(compute)
Expand Down Expand Up @@ -466,7 +478,6 @@ CorrelationInternal <- function(jaspResults, dataset, options){
statsNames <- c("estimate", "p.value", "lower.ci", "upper.ci", "vsmpr", "effect.size", "se.effect.size")
alternative <- match.arg(alternative)


if(isFALSE(compute)){
result <- rep(NaN, length(statsNames))
names(result) <- statsNames
Expand Down Expand Up @@ -513,9 +524,6 @@ CorrelationInternal <- function(jaspResults, dataset, options){
result$se.effect.size <- sqrt((2 / (n * (n - 1))) * (1 - (4 * (s1^2 / pi^2)) + (2 * (n - 2) * ((1/9) - (4 * (s2^2 / pi^2))))))
}




result <- unlist(result[stats], use.names = FALSE)
names(result) <- statsNames
}
Expand Down Expand Up @@ -705,6 +713,7 @@ CorrelationInternal <- function(jaspResults, dataset, options){
testErrors <- lapply(corrResults, function(x) x[['testErrors']])

mainTable[['sample.size']] <- sapply(results, function(x) x[['sample.size']])
mainTable[['covariance']] <- sapply(results, function(x) x[['covariance']])

# would be nice to be able to fill table cell-wise, i.e., mainTable[[row, col]] <- value
colNames <- character() # this is for error footnotes
Expand Down
117 changes: 80 additions & 37 deletions R/generalizedlinearmodel.R
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Original file line number Diff line number Diff line change
Expand Up @@ -470,7 +470,14 @@ GeneralizedLinearModelInternal <- function(jaspResults, dataset = NULL, options,
.glmOutlierTable(jaspResults, dataset, options, ready, position = 8, residType = "standardized deviance")
.glmOutlierTable(jaspResults, dataset, options, ready, position = 8, residType = "studentized deviance")

.glmInfluenceTable(jaspResults, dataset, options, ready, position = 9)
.glmInfluenceTable(jaspResults[["diagnosticsContainer"]],
jaspResults[["glmModels"]][["object"]][["fullModel"]],
dataset, options, ready, position = 9)

.regressionExportResiduals(jaspResults,
jaspResults[["glmModels"]][["object"]][["fullModel"]],
dataset, options, ready)

.glmMulticolliTable(jaspResults, dataset, options, ready, position = 10)

return()
Expand Down Expand Up @@ -896,29 +903,31 @@ GeneralizedLinearModelInternal <- function(jaspResults, dataset = NULL, options,


# Table: Influential cases
.glmInfluenceTable <- function(jaspResults, dataset, options, ready, position) {
.glmInfluenceTable <- function(jaspResults, model, dataset, options, ready, position, linRegAnalysis = FALSE) {

tableOptionsOn <- c(options[["dfbetas"]],
options[["dffits"]],
options[["covarianceRatio"]],
options[["cooksDistance"]],
options[["leverage"]])
options[["leverage"]],
options[["mahalanobis"]])


if (!ready | !any(tableOptionsOn))
if (!ready | !options[["residualCasewiseDiagnostic"]])
return()


tableOptions <- c("dfbetas", "dffits", "covarianceRatio", "cooksDistance", "leverage")
tableOptions <- c("dfbetas", "dffits", "covarianceRatio", "leverage", "mahalanobis")
tableOptionsClicked <- tableOptions[tableOptionsOn]
tableOptionsClicked <- c("cooksDistance", tableOptionsClicked)

if (is.null(jaspResults[["diagnosticsContainer"]][["influenceTable"]])) {
if (is.null(jaspResults[["influenceTable"]])) {
influenceTable <- createJaspTable(gettext("Table: Influential Cases"))
influenceTable$dependOn(optionsFromObject = jaspResults[["modelSummary"]],
options = tableOptions)
influenceTable$dependOn(c("residualCasewiseDiagnostic", "residualCasewiseDiagnosticType",
"residualCasewiseDiagnosticZThreshold", "residualCasewiseDiagnosticCooksDistanceThreshold"))
influenceTable$position <- position
influenceTable$showSpecifiedColumnsOnly <- TRUE
jaspResults[["diagnosticsContainer"]][["influenceTable"]] <- influenceTable
jaspResults[["influenceTable"]] <- influenceTable
}

tableOptionToColName <- function(x) {
Expand All @@ -927,41 +936,52 @@ GeneralizedLinearModelInternal <- function(jaspResults, dataset = NULL, options,
"dffits" = "DFFITS",
"covarianceRatio" = "Covariance Ratio",
"cooksDistance" = "Cook's Distance",
"leverage" = "Leverage")
"leverage" = "Leverage",
"mahalanobis" = "Mahalanobis")
}

if (is.null(jaspResults[["glmModels"]])) {
if (is.null(model)) {
for (option in tableOptionsClicked) {
colTitle <- tableOptionToColName(option)
jaspResults[["influenceTable"]]$addColumnInfo(name = option, title = gettext(colTitle), type = "number")
influenceTable$addColumnInfo(name = option, title = gettext(colTitle), type = "number")
}
} else {
glmFullModel <- jaspResults[["glmModels"]][["object"]][["fullModel"]]

colNameList <- c()
jaspResults[["diagnosticsContainer"]][["influenceTable"]]$addColumnInfo(name = "caseN", title = "Case Number", type = "integer")
influenceTable$addColumnInfo(name = "caseN", title = "Case Number", type = "integer")
influenceTable$addColumnInfo(name = "stdResidual", title = gettext("Std. Residual"), type = "number", format = "dp:3")
influenceTable$addColumnInfo(name = "dependent", title = options$dependent, type = "number")
influenceTable$addColumnInfo(name = "predicted", title = gettext("Predicted Value"), type = "number")
influenceTable$addColumnInfo(name = "residual", title = gettext("Residual"), type = "number", format = "dp:3")

alwaysPresent <- c("caseN", "stdResidual", "dependent", "predicted", "residual")
for (option in tableOptionsClicked) {
if (option == "dfbetas") {
predictors <- names(glmFullModel$coefficients)
predictors <- names(model$coefficients)
for (predictor in predictors) {
dfbetasName <- gettextf("DFBETAS_%1s", predictor)
colNameList <- c(colNameList, dfbetasName)
if (predictor == "(Intercept)")
dfbetasTitle <- gettext("DFBETAS:Intercept")
else
dfbetasTitle <- gettextf("DFBETAS:%1s", gsub(":", "*", predictor))
jaspResults[["diagnosticsContainer"]][["influenceTable"]]$addColumnInfo(name = dfbetasName, title = dfbetasTitle, type = "number")
influenceTable$addColumnInfo(name = dfbetasName, title = dfbetasTitle, type = "number")
}
} else {
colNameList <- c(colNameList, option)
colTitle <- tableOptionToColName(option)
jaspResults[["diagnosticsContainer"]][["influenceTable"]]$addColumnInfo(name = option, title = gettext(colTitle), type = "number")
influenceTable$addColumnInfo(name = option, title = gettext(colTitle), type = "number")
}
}
.glmInfluenceTableFill(jaspResults, dataset, options, ready, model = glmFullModel, influenceMeasures = tableOptionsClicked, colNames = colNameList)
.glmInfluenceTableFill(influenceTable, dataset, options, ready,
model = model,
influenceMeasures = tableOptionsClicked,
colNames = c(colNameList, alwaysPresent))
}
}

.glmInfluenceTableFill <- function(jaspResults, dataset, options, ready, model, influenceMeasures, colNames) {
.glmInfluenceTableFill <- function(influenceTable, dataset, options, ready, model, influenceMeasures, colNames) {

influenceRes <- influence.measures(model)
nDFBETAS <- length(names(model$coefficients))

Expand All @@ -978,25 +998,48 @@ GeneralizedLinearModelInternal <- function(jaspResults, dataset = NULL, options,
colInd <- c(colInd, optionToColInd(measure, nDFBETAS))
}

influenceResData <- as.data.frame(influenceRes[["infmat"]][, colInd])
names(influenceResData) <- colNames
caseN <- seq.int(nrow(influenceResData))
influenceResData <- cbind(caseN, influenceResData)

influenceResSig <- influenceRes[["is.inf"]][, colInd]

if (length(colInd) > 1) {
influenceResDataFinal <- influenceResData[rowSums(influenceResSig) > 0, , drop = FALSE]
} else {
influenceResDataFinal <- influenceResData[influenceResSig > 0, , drop = FALSE]
}

nRowInfluential <- nrow(influenceResDataFinal)

if (nRowInfluential == 0)
jaspResults[["diagnosticsContainer"]][["influenceTable"]]$addFootnote(gettext("No influential cases found."))
influenceResData <- as.data.frame(influenceRes[["infmat"]][, colInd])
colnames(influenceResData)[1:length(colInd)] <- colNames[1:length(colInd)]

influenceResData[["caseN"]] <- seq.int(nrow(influenceResData))
influenceResData[["stdResidual"]] <- rstandard(model)
influenceResData[["dependent"]] <- model.frame(model)[[options$dependent]]
influenceResData[["predicted"]] <- model$fitted.values
influenceResData[["residual"]] <- model$residual
# browser()
modelMatrix <- as.data.frame(model.matrix(model))
modelMatrix <- modelMatrix[colnames(modelMatrix) != "(Intercept)"]
influenceResData[["mahalanobis"]] <- mahalanobis(modelMatrix, center = colMeans(modelMatrix), cov = cov(modelMatrix))

if (options$residualCasewiseDiagnosticType == "cooksDistance")
index <- which(abs(influenceResData[["cooksDistance"]]) > options$residualCasewiseDiagnosticCooksDistanceThreshold)
else if (options$residualCasewiseDiagnosticType == "outliersOutside")
index <- which(abs(influenceResData[["stdResidual"]]) > options$residualCasewiseDiagnosticZThreshold)
else # all
index <- seq.int(nrow(influenceResData))

# funky statement to ensure a df even if only 1 row
influenceResSig <- subset(influenceRes[["is.inf"]], 1:nrow(influenceResData) %in% index, select = colInd)
colnames(influenceResSig) <- colNames[1:length(colInd)]

influenceResData <- influenceResData[index, ]

if (length(index) == 0)
influenceTable$addFootnote(gettext("No influential cases found."))
else {
jaspResults[["diagnosticsContainer"]][["influenceTable"]]$setData(influenceResDataFinal)
influenceTable$setData(influenceResData)
# if any other metrix show influence, add footnotes:
if (sum(influenceResSig) > 0) {
for (thisCol in colnames(influenceResSig)) {
if (sum(influenceResSig[, thisCol]) > 0)
influenceTable$addFootnote(
gettext("Potentially influential case, according to the selected influence measure."),
colNames = thisCol,
rowNames = rownames(influenceResData)[influenceResSig[, thisCol]],
symbol = "*"
)
}
}
}
}

Expand Down
17 changes: 17 additions & 0 deletions R/glmCommonFunctions.R
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Original file line number Diff line number Diff line change
Expand Up @@ -198,6 +198,23 @@
}
}

.regressionExportResiduals <- function(container, model, dataset, options, ready) {

if (isFALSE(options[["residualsSavedToData"]]))
return()

if (is.null(container[["residualsSavedToDataColumn"]]) && options[["residualsSavedToDataColumn"]] != "") {

residuals <- model[["residuals"]] # extract residuals

container[["residualsSavedToDataColumn"]] <- createJaspColumn(columnName = options[["residualsSavedToDataColumn"]])
container[["residualsSavedToDataColumn"]]$dependOn(options = c("residualsSavedToDataColumn", "residualsSavedToData"))
container[["residualsSavedToDataColumn"]]$setScale(residuals)

}

}

.constInfoTransform <- function(family, x) {
switch(family,
"bernoulli" = 1/(sin(sqrt(x))),
Expand Down
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