/
reliability.b.R
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reliability.b.R
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reliabilityClass <- R6::R6Class(
"reliabilityClass",
inherit = reliabilityBase,
private = list(
#### Init + run functions ----
.init = function() {
private$.initItemsTable()
},
.run = function() {
ready <- TRUE
if (is.null(self$options$vars) || length(self$options$vars) < 2)
ready <- FALSE
if (ready) {
data <- private$.cleanData()
private$.errorCheck(data)
results <- private$.compute(data)
private$.populateScaleTable(results)
private$.populateItemsTable(results)
private$.prepareCorPlot(data)
}
},
#### Compute results ----
.compute = function(data) {
suppressMessages({
suppressWarnings({
alpha <- psych::alpha(data, delete=FALSE, warnings=FALSE)
omega <- psych::omega(data, 1, flip = FALSE)
items <- self$options$vars
omegaDrop <- numeric(length(items))
if (length(items) > 2) {
for (i in seq_along(items))
omegaDrop[i] <- psych::omega(data[, colnames(data) != items[i]], 1,
flip = FALSE)$omega.tot
}
loadings <- psych::principal(data, scores=FALSE)$loadings
negCorItems <- items[loadings < 0]
}) # suppressWarnings
}) # suppressMessages
return(list('alpha'=alpha, 'omega'=omega, 'omegaDrop'=omegaDrop, 'negCorItems'=negCorItems))
},
#### Init tables ----
.initItemsTable = function() {
table <- self$results$items
items <- self$options$revItems
for (i in seq_along(items))
table$addFootnote(rowKey=items[i], 'name', 'reverse scaled item')
},
#### Populate tables ----
.populateScaleTable = function(results) {
table <- self$results$scale
alpha <- results$alpha
omega <- results$omega
negCorItems <- results$negCorItems
row <- list()
row[['alpha']] <- alpha$total$raw_alpha
row[['mean']] <- alpha$total$mean
row[['sd']] <- alpha$total$sd
row[['omega']] <- omega$omega.tot
table$setRow(rowNo=1, values=row)
if (length(negCorItems) > 0) {
if (length(negCorItems) == 1) {
note <- jmvcore::format('item {} correlates negatively with the total scale and probably should be reversed',
private$.listItems(negCorItems))
} else {
note <- jmvcore::format('items {} correlate negatively with the total scale and probably should be reversed',
private$.listItems(negCorItems))
}
table$setNote(key='negCor', note=note, init=FALSE)
}
},
.populateItemsTable = function(results) {
table <- self$results$items
items <- self$options$vars
alpha <- results$alpha
omegaDrop <- results$omegaDrop
for (i in seq_along(items)) {
row <- list()
row[["alpha"]] <- alpha$alpha.drop[items[i],"raw_alpha"]
row[["mean"]] <- alpha$item.stats[items[i],"mean"]
row[["sd"]] <- alpha$item.stats[items[i],"sd"]
row[["itemRestCor"]] <- alpha$item.stats[items[i],"r.drop"]
row[["omega"]] <- omegaDrop[i]
table$setRow(rowKey=items[i], values=row)
}
},
#### Plot functions ----
.prepareCorPlot = function(data, reorder = FALSE) {
cormat <- round(cor(data), 2)
if (reorder) {
dd <- stats::as.dist((1-cormat)/2)
hc <- stats::hclust(dd)
cormat <- cormat[hc$order, hc$order]
}
upper_tri <- cormat[lower.tri(cormat)] <- NA
melted_cormat <- reshape2::melt(cormat, na.rm = TRUE)
image <- self$results$get('corPlot')
image$setState(melted_cormat)
},
.corPlot = function(image, ...) {
if (is.null(image$state))
return(FALSE)
the <- theme_minimal() + theme(
axis.title.x = element_blank(),
axis.title.y = element_blank(),
panel.grid.major = element_blank(),
panel.border = element_blank(),
panel.background = element_blank(),
axis.ticks = element_blank(),
legend.justification = c(1, 0),
legend.position = c(0.45, 0.7),
legend.direction = "horizontal",
axis.text.x = element_text(angle = 45, vjust = 1,
size = 12, hjust = 1),
axis.text.y = element_text(size = 12)
)
p <- ggplot(data = image$state, aes(Var2, Var1, fill = value)) +
geom_tile(color = "white") +
scale_fill_gradient2(low = "#FF1919", high = "#00B233", mid = "white",
midpoint = 0, limit = c(-1,1), space = "Lab",
name="Pearson\nCorrelation") +
coord_fixed() +
geom_text(aes(Var2, Var1, label = value), color = "black", size = 4) +
guides(fill = guide_colorbar(barwidth = 7, barheight = 1,
title.position = "top", title.hjust = 0.5)) +
the
print(p)
TRUE
},
#### Helper functions ----
.cleanData = function() {
items <- self$options$vars
data <- list()
for (item in items)
data[[item]] <- jmvcore::toNumeric(self$data[[item]])
attr(data, 'row.names') <- seq_len(length(data[[1]]))
attr(data, 'class') <- 'data.frame'
data <- jmvcore::naOmit(data)
for (item in self$options$revItems) {
dataItem <- data[[item]]
minItem <- min(dataItem)
maxItem <- max(dataItem)
adjust <- minItem + maxItem
data[[item]] <- adjust - dataItem
}
return(data)
},
.errorCheck = function(data) {
items <- self$options$vars
infItems <- sapply(data, function(x) any(is.infinite(x)))
allNAItems <- sapply(data, function(x) all(is.na(x)))
noVarItems <- sapply(data, function(x) var(x, na.rm = TRUE) == 0)
error <- list()
if (any(infItems))
jmvcore::reject("Item '{}' contains infinite values", code='', items[infItems])
if (any(allNAItems))
jmvcore::reject("Item '{}' contains only missing values", code='', items[allNAItems])
if (any(noVarItems))
jmvcore::reject("Item '{}' has no variance", code='', items[noVarItems])
},
.listItems = function(items, quote = '\'') {
if (length(items) == 1) {
l <- paste0(quote, items, quote)
} else if (length(items) == 2) {
itemsQuote <- character(length(items))
for (i in seq_along(items))
itemsQuote[i] <- paste0(quote, items[i], quote)
l <- paste0(itemsQuote, collapse = ' and ')
} else {
itemsQuote <- character(length(items))
for (i in seq_along(items))
itemsQuote[i] <- paste0(quote, items[i], quote)
l <- paste0(itemsQuote[1:(length(itemsQuote)-1)], collapse = ', ')
l <- paste(l, itemsQuote[length(itemsQuote)], sep = ', and ')
}
return(l)
})
)