Nothing
R/regr.R
In ufs: A Collection of Utilities
Defines functions
print.regr
regr
### Not exported - normally in `rosetta`
regr <- function(formula, data=NULL, conf.level=.95, digits=2,
pvalueDigits = 3, coefficients=c("raw", "scaled"),
plot=FALSE, pointAlpha = .5,
collinearity = FALSE, influential = FALSE,
ci.method = c("widest", "r.con", "olkinfinn"),
ci.method.note = FALSE, env=parent.frame()) {
dat <- data;
### Generate object to store input, intermediate outcomes, and results
res <- list(input = as.list(environment()),
intermediate = list(),
output = list());
### Extract variables from formula
res$intermediate$variableNames <- all.vars(formula);
### Convert formula to a character string
res$intermediate$formula.as.character <-
paste0(as.character(formula)[c(2, 1, 3)], collapse=" ");
if (is.null(dat)) {
### Extract variables from formula
res$intermediate$variables <-
as.character(as.list(attr(stats::terms(formula), 'variables'))[-1]);
### Store variablesnames only for naming in raw dataframe
res$intermediate$variables_namesOnly <- unlist(
lapply(strsplit(res$intermediate$variables, "\\$"), utils::tail, 1));
### Store variables in a dataframe
res$intermediate$dat.raw <- list();
for (varToGet in 1:length(res$intermediate$variables)) {
res$intermediate$dat.raw[[res$intermediate$variables_namesOnly[varToGet]]] <-
eval(parse(text=res$intermediate$variables[varToGet]), envir=env);
}
### Convert the list to a dataframe
res$intermediate$dat.raw <- data.frame(res$intermediate$dat.raw);
### Convert variable names to bare variable names
for (currentVariableIndex in 1:length(res$intermediate$variables)) {
res$intermediate$formula.as.character <-
gsub(res$intermediate$variables[currentVariableIndex],
res$intermediate$variables_namesOnly[currentVariableIndex],
res$intermediate$formula.as.character, fixed=TRUE);
}
} else {
### Store variables in a dataframe
res$intermediate$dat.raw <- dat[, res$intermediate$variableNames];
res$intermediate$variables_namesOnly <- res$intermediate$variableNames;
}
res$intermediate$formula <- formula(res$intermediate$formula.as.character,
env = environment());
### Standardize numeric variables
res$intermediate$dat.scaled <- res$intermediate$dat.raw;
res$intermediate$dat.scaled[, sapply(res$intermediate$dat.scaled, is.numeric)] <-
as.data.frame(scale(res$intermediate$dat.scaled[, sapply(res$intermediate$dat.scaled, is.numeric)]));
### Run and store lm objects
res$intermediate$lm.raw <-
stats::lm(formula=res$intermediate$formula, data=res$intermediate$dat.raw);
res$intermediate$lm.scaled <-
stats::lm(formula=res$intermediate$formula, data=res$intermediate$dat.scaled);
### R^2 confidence interval based on formula at
### http://www.danielsoper.com/statcalc3/calc.aspx?id=28
res$intermediate$rsq <- rsq <- summary(res$intermediate$lm.raw)$r.squared;
res$intermediate$k <- k <- length(res$intermediate$lm.raw$terms) - 1;
res$intermediate$n <- n <- res$intermediate$lm.raw$df.residual + k + 1;
res$intermediate$rsq.se <- sqrt((4*rsq*(1-rsq)^2*(n-k-1)^2)/
((n^2-1)*(3+n)));
res$intermediate$rsq.t.crit <- stats::qt(p=1-(1-conf.level)/2, df=n-k-1);
res$output$rsq.ci.olkinfinn <- c(res$intermediate$rsq -
res$intermediate$rsq.t.crit *
res$intermediate$rsq.se,
res$intermediate$rsq +
res$intermediate$rsq.t.crit *
res$intermediate$rsq.se);
res$output$rsq.ci.olkinfinn <- ifelse(res$output$rsq.ci.olkinfinn < 0,
0, res$output$rsq.ci.olkinfinn);
res$output$rsq.ci.r.con <- psych::r.con(sqrt(rsq), n);
res$output$rsq.ci.r.con <- ifelse(res$output$rsq.ci.r.con < 0,
0, res$output$rsq.ci.r.con) ^ 2;
if ("widest" %IN% ci.method) {
res$output$rsq.ci <- ifelse(range(res$output$rsq.ci.r.con) >
range(res$output$rsq.ci.olkinfinn),
res$output$rsq.ci.r.con,
res$output$rsq.ci.olkinfinn);
} else if ("r.con" %IN% ci.method) {
res$output$rsq.ci <- res$output$rsq.ci.r.con;
} else {
res$output$rsq.ci <- res$output$rsq.ci.olkinfinn;
}
### Run confint on lm object
res$intermediate$confint.raw <-
stats::confint(res$intermediate$lm.raw, level=conf.level);
res$intermediate$confint.scaled <-
stats::confint(res$intermediate$lm.scaled, level=conf.level);
### Run lm.influence on lm object
res$intermediate$lm.influence.raw <-
stats::lm.influence(res$intermediate$lm.raw);
res$intermediate$lm.influence.scaled <-
stats::lm.influence(res$intermediate$lm.scaled);
### Get variance inflation factors and compute tolerances
if (collinearity && (length(res$intermediate$variables) > 2)) {
if (requireNamespace('car')) {
res$intermediate$vif.raw <- car::vif(res$intermediate$lm.raw);
res$intermediate$vif.scaled <- car::vif(res$intermediate$lm.scaled);
if (is.vector(res$intermediate$vif.raw)) {
res$intermediate$tolerance.raw <- 1/res$intermediate$vif.raw;
res$intermediate$tolerance.scaled <- 1/res$intermediate$vif.scaled;
}
} else {
warning(paste0("You need to have package 'car' installed to ",
"request collineary diagnostics!"));
res$intermediate$vif.raw <- NA;
res$intermediate$vif.scaled <- NA;
}
}
### get summary for lm objects
res$intermediate$summary.raw <- summary(res$intermediate$lm.raw);
res$intermediate$summary.scaled <- summary(res$intermediate$lm.scaled);
### Generate output
res$output$coef.raw <- cbind(data.frame(res$intermediate$confint.raw[, 1],
res$intermediate$confint.raw[, 2]),
res$intermediate$summary.raw$coefficients);
res$output$coef.scaled <- cbind(data.frame(res$intermediate$confint.scaled[, 1],
res$intermediate$confint.scaled[, 2]),
res$intermediate$summary.scaled$coefficients);
names(res$output$coef.raw) <- names(res$output$coef.scaled) <-
c(paste0(conf.level*100,"% CI, lo"),
paste0(conf.level*100,"% CI, hi"),
'estimate', 'se', 't', 'p');
if (plot) {
if (length(res$intermediate$variables_namesOnly) == 2) {
res$output$plot <-
ggplot2::ggplot(res$intermediate$dat.raw,
ggplot2::aes_string(y=res$intermediate$variables_namesOnly[1],
x=res$intermediate$variables_namesOnly[2])) +
ggplot2::geom_point(alpha = pointAlpha) +
ggplot2::geom_smooth(method='lm') +
ggplot2::theme_bw();
} else if (length(res$intermediate$variables_namesOnly) == 3) {
if (is.numeric(res$intermediate$dat.raw[, res$intermediate$variables_namesOnly[2]]) &&
is.numeric(res$intermediate$dat.raw[, res$intermediate$variables_namesOnly[3]])) {
### Both numeric, so we treat the second one as moderator and compute the line
predictorName <- res$intermediate$variables_namesOnly[2];
moderatorName <- res$intermediate$variables_namesOnly[3];
predictorMean <- mean(res$intermediate$dat.raw[, predictorName],
na.rm=TRUE);
predictorSD <- stats::sd(res$intermediate$dat.raw[, predictorName],
na.rm=TRUE);
# loPredictorValue <- predictorMean - predictorSD;
# hiPredictorValue <- predictorMean + predictorSD;
loPredictorValue <- min(res$intermediate$dat.raw[, predictorName],
na.rm=TRUE);
hiPredictorValue <- max(res$intermediate$dat.raw[, predictorName],
na.rm=TRUE);
moderatorMean <- mean(res$intermediate$dat.raw[, moderatorName],
na.rm=TRUE);
moderatorSD <- stats::sd(res$intermediate$dat.raw[, moderatorName],
na.rm=TRUE);
loModeratorValue <- moderatorMean - moderatorSD;
hiModeratorValue <- moderatorMean + moderatorSD;
intercept <- res$output$coef.raw['(Intercept)', 'estimate'];
predictorSlope <- res$output$coef.raw[predictorName, 'estimate'];
moderatorSlope <- res$output$coef.raw[moderatorName, 'estimate'];
interactionSlope <- res$output$coef.raw[paste0(predictorName,
":",
moderatorName),
'estimate'];
depVarValue_loPred_loMod <- intercept +
loPredictorValue * predictorSlope +
loModeratorValue * moderatorSlope +
loPredictorValue * loModeratorValue * interactionSlope;
depVarValue_loPred_hiMod <- intercept +
loPredictorValue * predictorSlope +
hiModeratorValue * moderatorSlope +
loPredictorValue * hiModeratorValue * interactionSlope;
depVarValue_hiPred_loMod <- intercept +
hiPredictorValue * predictorSlope +
loModeratorValue * moderatorSlope +
hiPredictorValue * loModeratorValue * interactionSlope;
depVarValue_hiPred_hiMod <- intercept +
hiPredictorValue * predictorSlope +
hiModeratorValue * moderatorSlope +
hiPredictorValue * hiModeratorValue * interactionSlope;
moderatorDat <- data.frame(x = c(loPredictorValue, loPredictorValue),
xend = c(hiPredictorValue, hiPredictorValue),
y = c(depVarValue_loPred_loMod, depVarValue_loPred_hiMod),
yend = c(depVarValue_hiPred_loMod, depVarValue_hiPred_hiMod),
modVal = c("Low", "High"));
names(moderatorDat)[5] <- res$intermediate$variables_namesOnly[3];
res$output$plot <-
ggplot2::ggplot(res$intermediate$dat.raw,
ggplot2::aes_string(y=res$intermediate$variables_namesOnly[1],
x=res$intermediate$variables_namesOnly[2])) +
ggplot2::geom_point(alpha = pointAlpha) +
ggplot2::geom_smooth(method='lm') +
ggplot2::theme_bw() +
ggplot2::geom_segment(data = moderatorDat,
ggplot2::aes_string(x = 'x', xend = 'xend',
y = 'y', yend = 'yend',
group = res$intermediate$variables_namesOnly[3],
color = res$intermediate$variables_namesOnly[3]),
size=1) +
ggplot2::scale_color_viridis_d();
} else if ((is.numeric(res$intermediate$dat.raw[, res$intermediate$variables_namesOnly[2]]) &&
(is.factor(res$intermediate$dat.raw[, res$intermediate$variables_namesOnly[3]]))) ||
(is.numeric(res$intermediate$dat.raw[, res$intermediate$variables_namesOnly[3]]) &&
(is.factor(res$intermediate$dat.raw[, res$intermediate$variables_namesOnly[2]])))) {
### One of the predictors is a factor, so we jsut plot lines for each value
if (is.numeric(res$intermediate$dat.raw[, res$intermediate$variables_namesOnly[2]])) {
predictor <- res$intermediate$variables_namesOnly[2];
moderator <- res$intermediate$variables_namesOnly[3];
} else {
predictor <- res$intermediate$variables_namesOnly[3];
moderator <- res$intermediate$variables_namesOnly[2];
}
res$output$plot <-
ggplot2::ggplot(res$intermediate$dat.raw,
ggplot2::aes_string(y=res$intermediate$variables_namesOnly[1],
x=predictor)) +
ggplot2::geom_point(alpha = pointAlpha) +
ggplot2::geom_smooth(method='lm') +
ggplot2::theme_bw() +
ggplot2::geom_smooth(ggplot2::aes_string(y=res$intermediate$variables_namesOnly[1],
x=predictor,
group=moderator,
color=moderator),
method="lm",
se = FALSE) +
ggplot2::scale_color_viridis_d();
}
} else {
warning("You requested a plot, but for now plots are ",
"only available for regression analyses with one predictor.");
}
}
### Use Z = (b1 - b2) / sqrt(SE_b1^2 + SE_b2^2) to test whether
### the coefficients differ; add arguments such as
### compareCoefficients=FALSE, p.adjust="fdr" to enable user to
### request & correct this.
class(res) <- 'regr';
return(res);
}
print.regr <- function(x, digits=x$input$digits,
pvalueDigits=x$input$pvalueDigits, ...) {
cat(paste0("Regression analysis for formula: ",
x$intermediate$formula.as.character, "\n\n",
"Significance test of the entire model (all predictors together):\n",
" Multiple R-squared: [",
round(x$output$rsq.ci[1], digits), ", ",
round(x$output$rsq.ci[2], digits),
"] (point estimate = ",
round(x$intermediate$summary.raw$r.squared, digits),
", adjusted = ",
round(x$intermediate$summary.raw$adj.r.squared, digits), ")",
ifelse(x$input$ci.method.note, "*\n", "\n"),
" Test for significance: F[",
x$intermediate$summary.raw$fstatistic[2], ", ",
x$intermediate$summary.raw$fstatistic[3], "] = ",
round(x$intermediate$summary.raw$fstatistic[1], digits),
", ", ufs::formatPvalue(stats::pf(x$intermediate$summary.raw$fstatistic[1],
x$intermediate$summary.raw$fstatistic[2],
x$intermediate$summary.raw$fstatistic[3],
lower.tail=FALSE), digits=pvalueDigits), "\n"));
if ("raw" %in% x$input$coefficients) {
cat("\nRaw regression coefficients (unstandardized beta values, called 'B' in SPSS):\n\n");
tmpDat <- round(x$output$coef.raw[, 1:5], digits);
tmpDat[[1]] <- paste0("[", tmpDat[[1]], "; ", tmpDat[[2]], "]");
tmpDat[[2]] <- NULL;
names(tmpDat)[1] <- paste0(x$input$conf.level*100, "% conf. int.");
tmpDat$p <- ufs::formatPvalue(x$output$coef.raw$p,
digits=pvalueDigits,
includeP=FALSE);
print(tmpDat, ...);
}
if ("scaled" %in% x$input$coefficients) {
cat("\nScaled regression coefficients (standardized beta values, called 'Beta' in SPSS):\n\n");
tmpDat <- round(x$output$coef.scaled[, 1:5], digits);
tmpDat[[1]] <- paste0("[", tmpDat[[1]], "; ", tmpDat[[2]], "]");
tmpDat[[2]] <- NULL;
names(tmpDat)[1] <- paste0(x$input$conf.level*100, "% conf. int.");
tmpDat$p <- ufs::formatPvalue(x$output$coef.scaled$p,
digits=pvalueDigits,
includeP=FALSE);
print(tmpDat, ...);
}
if (x$input$collinearity && (!is.null(x$intermediate$vif.raw))) {
cat0("\nCollinearity diagnostics:\n\n");
if (is.vector(x$intermediate$vif.raw)) {
cat0(" For the raw regression coefficients:\n\n");
collinearityDat <- data.frame(VIF = x$intermediate$vif.raw,
Tolerance = x$intermediate$tolerance.raw);
row.names(collinearityDat) <- paste0(ufs::repStr(4), names(x$intermediate$vif.raw));
print(collinearityDat);
cat0("\n For the standardized regression coefficients:\n\n");
collinearityDat <- data.frame(VIF = x$intermediate$vif.scaled,
Tolerance = x$intermediate$tolerance.scaled);
row.names(collinearityDat) <- paste0(ufs::repStr(4), names(x$intermediate$vif.raw));
print(collinearityDat);
}
}
ciMsg <- "\n* Note that the confidence interval for R^2 is based on ";
if (x$input$ci.method[1] == 'r.con') {
ciMsg <- paste0(ciMsg,
"the confidence interval for the Pearson Correlation of ",
"the multiple correlation using r.con from the 'psych' ",
"package because that was specified using the 'ci.method' ",
"argument.");
} else if (x$input$ci.method[1] == 'olkinfinn') {
ciMsg <- paste0(ciMsg,
"the formula reported by Olkin and Finn (1995) in their Correlation ",
"Redux paper, because this was specified using the 'ci.method' ",
"argument. This may not work well for very low values. Set the ",
"argument to 'widest' to also compute the confidence interval ",
"of the multiple correlation using the r.con function from ",
"the 'psych' package and selecting the widest interval.");
} else if (identical(x$output$rsq.ci, x$output$rsq.ci.r.con)) {
ciMsg <- paste0(ciMsg,
"the confidence interval for the Pearson Correlation of ",
"the multiple correlation using r.con from the 'psych' ",
"package because that was the widest interval, which ",
"should be used because the 'ci.method' was set to 'widest'.");
} else if (identical(x$output$rsq.ci, x$output$rsq.ci.olkinfinn)) {
ciMsg <- paste0(ciMsg,
"the formula reported by Olkin and Finn (1995) in their Correlation ",
"Redux paper, because this was the widest interval, which ",
"should be used because the 'ci.method' was set to 'widest'.");
} else {
ciMsg <- paste0(ciMsg,
" -- I don't know actually, something appears to have gone wrong. ",
"The 'ci.method' argument was set to ", ufs::vecTxtQ(x$input$ci.method),
".");
}
if (x$input$ci.method.note) {
cat("\n");
cat(strwrap(ciMsg), sep="\n");
}
if (!is.null(x$output$plot)) {
print(x$output$plot);
}
invisible();
}
pander.regr <- function (x, digits = x$input$digits, pvalueDigits = x$input$pvalueDigits, ...) {
pander::pandoc.p(paste0("\n\n#### Regression analysis for formula: ", x$intermediate$formula.as.character));
pander::pandoc.p("\n\n##### Significance test of the entire model (all predictors together):\n\n");
pander::pandoc.p(paste0("Multiple R-squared: [", round(x$output$rsq.ci[1],
digits), ", ", round(x$output$rsq.ci[2], digits),
"] (point estimate = ", round(x$intermediate$summary.raw$r.squared,
digits), ", adjusted = ", round(x$intermediate$summary.raw$adj.r.squared,
digits), ")"))
pander::pandoc.p(paste0("Test for significance: F[", x$intermediate$summary.raw$fstatistic[2],
", ", x$intermediate$summary.raw$fstatistic[3], "] = ",
round(x$intermediate$summary.raw$fstatistic[1], digits),
", ", ufs::formatPvalue(stats::pf(x$intermediate$summary.raw$fstatistic[1],
x$intermediate$summary.raw$fstatistic[2], x$intermediate$summary.raw$fstatistic[3],
lower.tail = FALSE), digits = pvalueDigits), "\n"));
if ("raw" %in% x$input$coefficients) {
pander::pandoc.p("\n\n##### Raw regression coefficients (unstandardized beta values, called 'B' in SPSS):\n\n");
tmpDat <- round(x$output$coef.raw[, 1:5], digits);
tmpDat[[1]] <- paste0("[", tmpDat[[1]], "; ", tmpDat[[2]], "]");
tmpDat[[2]] <- NULL;
names(tmpDat)[1] <- paste0(x$input$conf.level * 100, "% conf. int.");
tmpDat$p <- ufs::formatPvalue(x$output$coef.raw$p, digits = pvalueDigits, includeP = FALSE);
pander::pander(tmpDat, missing="");
}
if ("scaled" %in% x$input$coefficients) {
pander::pandoc.p("\n\n##### Scaled regression coefficients (standardized beta values, called 'Beta' in SPSS):\n\n");
tmpDat <- round(x$output$coef.scaled[, 1:5], digits);
tmpDat[[1]] <- paste0("[", tmpDat[[1]], "; ", tmpDat[[2]], "]");
tmpDat[[2]] <- NULL;
names(tmpDat)[1] <- paste0(x$input$conf.level * 100, "% conf. int.");
tmpDat$p <- ufs::formatPvalue(x$output$coef.scaled$p, digits = pvalueDigits, includeP = FALSE);
pander::pander(tmpDat, missing="");
}
}
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Defines functions print.regr regr
### Not exported - normally in `rosetta`
regr <- function(formula, data=NULL, conf.level=.95, digits=2,
pvalueDigits = 3, coefficients=c("raw", "scaled"),
plot=FALSE, pointAlpha = .5,
collinearity = FALSE, influential = FALSE,
ci.method = c("widest", "r.con", "olkinfinn"),
ci.method.note = FALSE, env=parent.frame()) {
dat <- data;
### Generate object to store input, intermediate outcomes, and results
res <- list(input = as.list(environment()),
intermediate = list(),
output = list());
### Extract variables from formula
res$intermediate$variableNames <- all.vars(formula);
### Convert formula to a character string
res$intermediate$formula.as.character <-
paste0(as.character(formula)[c(2, 1, 3)], collapse=" ");
if (is.null(dat)) {
### Extract variables from formula
res$intermediate$variables <-
as.character(as.list(attr(stats::terms(formula), 'variables'))[-1]);
### Store variablesnames only for naming in raw dataframe
res$intermediate$variables_namesOnly <- unlist(
lapply(strsplit(res$intermediate$variables, "\\$"), utils::tail, 1));
### Store variables in a dataframe
res$intermediate$dat.raw <- list();
for (varToGet in 1:length(res$intermediate$variables)) {
res$intermediate$dat.raw[[res$intermediate$variables_namesOnly[varToGet]]] <-
eval(parse(text=res$intermediate$variables[varToGet]), envir=env);
}
### Convert the list to a dataframe
res$intermediate$dat.raw <- data.frame(res$intermediate$dat.raw);
### Convert variable names to bare variable names
for (currentVariableIndex in 1:length(res$intermediate$variables)) {
res$intermediate$formula.as.character <-
gsub(res$intermediate$variables[currentVariableIndex],
res$intermediate$variables_namesOnly[currentVariableIndex],
res$intermediate$formula.as.character, fixed=TRUE);
}
} else {
### Store variables in a dataframe
res$intermediate$dat.raw <- dat[, res$intermediate$variableNames];
res$intermediate$variables_namesOnly <- res$intermediate$variableNames;
}
res$intermediate$formula <- formula(res$intermediate$formula.as.character,
env = environment());
### Standardize numeric variables
res$intermediate$dat.scaled <- res$intermediate$dat.raw;
res$intermediate$dat.scaled[, sapply(res$intermediate$dat.scaled, is.numeric)] <-
as.data.frame(scale(res$intermediate$dat.scaled[, sapply(res$intermediate$dat.scaled, is.numeric)]));
### Run and store lm objects
res$intermediate$lm.raw <-
stats::lm(formula=res$intermediate$formula, data=res$intermediate$dat.raw);
res$intermediate$lm.scaled <-
stats::lm(formula=res$intermediate$formula, data=res$intermediate$dat.scaled);
### R^2 confidence interval based on formula at
### http://www.danielsoper.com/statcalc3/calc.aspx?id=28
res$intermediate$rsq <- rsq <- summary(res$intermediate$lm.raw)$r.squared;
res$intermediate$k <- k <- length(res$intermediate$lm.raw$terms) - 1;
res$intermediate$n <- n <- res$intermediate$lm.raw$df.residual + k + 1;
res$intermediate$rsq.se <- sqrt((4*rsq*(1-rsq)^2*(n-k-1)^2)/
((n^2-1)*(3+n)));
res$intermediate$rsq.t.crit <- stats::qt(p=1-(1-conf.level)/2, df=n-k-1);
res$output$rsq.ci.olkinfinn <- c(res$intermediate$rsq -
res$intermediate$rsq.t.crit *
res$intermediate$rsq.se,
res$intermediate$rsq +
res$intermediate$rsq.t.crit *
res$intermediate$rsq.se);
res$output$rsq.ci.olkinfinn <- ifelse(res$output$rsq.ci.olkinfinn < 0,
0, res$output$rsq.ci.olkinfinn);
res$output$rsq.ci.r.con <- psych::r.con(sqrt(rsq), n);
res$output$rsq.ci.r.con <- ifelse(res$output$rsq.ci.r.con < 0,
0, res$output$rsq.ci.r.con) ^ 2;
if ("widest" %IN% ci.method) {
res$output$rsq.ci <- ifelse(range(res$output$rsq.ci.r.con) >
range(res$output$rsq.ci.olkinfinn),
res$output$rsq.ci.r.con,
res$output$rsq.ci.olkinfinn);
} else if ("r.con" %IN% ci.method) {
res$output$rsq.ci <- res$output$rsq.ci.r.con;
} else {
res$output$rsq.ci <- res$output$rsq.ci.olkinfinn;
}
### Run confint on lm object
res$intermediate$confint.raw <-
stats::confint(res$intermediate$lm.raw, level=conf.level);
res$intermediate$confint.scaled <-
stats::confint(res$intermediate$lm.scaled, level=conf.level);
### Run lm.influence on lm object
res$intermediate$lm.influence.raw <-
stats::lm.influence(res$intermediate$lm.raw);
res$intermediate$lm.influence.scaled <-
stats::lm.influence(res$intermediate$lm.scaled);
### Get variance inflation factors and compute tolerances
if (collinearity && (length(res$intermediate$variables) > 2)) {
if (requireNamespace('car')) {
res$intermediate$vif.raw <- car::vif(res$intermediate$lm.raw);
res$intermediate$vif.scaled <- car::vif(res$intermediate$lm.scaled);
if (is.vector(res$intermediate$vif.raw)) {
res$intermediate$tolerance.raw <- 1/res$intermediate$vif.raw;
res$intermediate$tolerance.scaled <- 1/res$intermediate$vif.scaled;
}
} else {
warning(paste0("You need to have package 'car' installed to ",
"request collineary diagnostics!"));
res$intermediate$vif.raw <- NA;
res$intermediate$vif.scaled <- NA;
}
}
### get summary for lm objects
res$intermediate$summary.raw <- summary(res$intermediate$lm.raw);
res$intermediate$summary.scaled <- summary(res$intermediate$lm.scaled);
### Generate output
res$output$coef.raw <- cbind(data.frame(res$intermediate$confint.raw[, 1],
res$intermediate$confint.raw[, 2]),
res$intermediate$summary.raw$coefficients);
res$output$coef.scaled <- cbind(data.frame(res$intermediate$confint.scaled[, 1],
res$intermediate$confint.scaled[, 2]),
res$intermediate$summary.scaled$coefficients);
names(res$output$coef.raw) <- names(res$output$coef.scaled) <-
c(paste0(conf.level*100,"% CI, lo"),
paste0(conf.level*100,"% CI, hi"),
'estimate', 'se', 't', 'p');
if (plot) {
if (length(res$intermediate$variables_namesOnly) == 2) {
res$output$plot <-
ggplot2::ggplot(res$intermediate$dat.raw,
ggplot2::aes_string(y=res$intermediate$variables_namesOnly[1],
x=res$intermediate$variables_namesOnly[2])) +
ggplot2::geom_point(alpha = pointAlpha) +
ggplot2::geom_smooth(method='lm') +
ggplot2::theme_bw();
} else if (length(res$intermediate$variables_namesOnly) == 3) {
if (is.numeric(res$intermediate$dat.raw[, res$intermediate$variables_namesOnly[2]]) &&
is.numeric(res$intermediate$dat.raw[, res$intermediate$variables_namesOnly[3]])) {
### Both numeric, so we treat the second one as moderator and compute the line
predictorName <- res$intermediate$variables_namesOnly[2];
moderatorName <- res$intermediate$variables_namesOnly[3];
predictorMean <- mean(res$intermediate$dat.raw[, predictorName],
na.rm=TRUE);
predictorSD <- stats::sd(res$intermediate$dat.raw[, predictorName],
na.rm=TRUE);
# loPredictorValue <- predictorMean - predictorSD;
# hiPredictorValue <- predictorMean + predictorSD;
loPredictorValue <- min(res$intermediate$dat.raw[, predictorName],
na.rm=TRUE);
hiPredictorValue <- max(res$intermediate$dat.raw[, predictorName],
na.rm=TRUE);
moderatorMean <- mean(res$intermediate$dat.raw[, moderatorName],
na.rm=TRUE);
moderatorSD <- stats::sd(res$intermediate$dat.raw[, moderatorName],
na.rm=TRUE);
loModeratorValue <- moderatorMean - moderatorSD;
hiModeratorValue <- moderatorMean + moderatorSD;
intercept <- res$output$coef.raw['(Intercept)', 'estimate'];
predictorSlope <- res$output$coef.raw[predictorName, 'estimate'];
moderatorSlope <- res$output$coef.raw[moderatorName, 'estimate'];
interactionSlope <- res$output$coef.raw[paste0(predictorName,
":",
moderatorName),
'estimate'];
depVarValue_loPred_loMod <- intercept +
loPredictorValue * predictorSlope +
loModeratorValue * moderatorSlope +
loPredictorValue * loModeratorValue * interactionSlope;
depVarValue_loPred_hiMod <- intercept +
loPredictorValue * predictorSlope +
hiModeratorValue * moderatorSlope +
loPredictorValue * hiModeratorValue * interactionSlope;
depVarValue_hiPred_loMod <- intercept +
hiPredictorValue * predictorSlope +
loModeratorValue * moderatorSlope +
hiPredictorValue * loModeratorValue * interactionSlope;
depVarValue_hiPred_hiMod <- intercept +
hiPredictorValue * predictorSlope +
hiModeratorValue * moderatorSlope +
hiPredictorValue * hiModeratorValue * interactionSlope;
moderatorDat <- data.frame(x = c(loPredictorValue, loPredictorValue),
xend = c(hiPredictorValue, hiPredictorValue),
y = c(depVarValue_loPred_loMod, depVarValue_loPred_hiMod),
yend = c(depVarValue_hiPred_loMod, depVarValue_hiPred_hiMod),
modVal = c("Low", "High"));
names(moderatorDat)[5] <- res$intermediate$variables_namesOnly[3];
res$output$plot <-
ggplot2::ggplot(res$intermediate$dat.raw,
ggplot2::aes_string(y=res$intermediate$variables_namesOnly[1],
x=res$intermediate$variables_namesOnly[2])) +
ggplot2::geom_point(alpha = pointAlpha) +
ggplot2::geom_smooth(method='lm') +
ggplot2::theme_bw() +
ggplot2::geom_segment(data = moderatorDat,
ggplot2::aes_string(x = 'x', xend = 'xend',
y = 'y', yend = 'yend',
group = res$intermediate$variables_namesOnly[3],
color = res$intermediate$variables_namesOnly[3]),
size=1) +
ggplot2::scale_color_viridis_d();
} else if ((is.numeric(res$intermediate$dat.raw[, res$intermediate$variables_namesOnly[2]]) &&
(is.factor(res$intermediate$dat.raw[, res$intermediate$variables_namesOnly[3]]))) ||
(is.numeric(res$intermediate$dat.raw[, res$intermediate$variables_namesOnly[3]]) &&
(is.factor(res$intermediate$dat.raw[, res$intermediate$variables_namesOnly[2]])))) {
### One of the predictors is a factor, so we jsut plot lines for each value
if (is.numeric(res$intermediate$dat.raw[, res$intermediate$variables_namesOnly[2]])) {
predictor <- res$intermediate$variables_namesOnly[2];
moderator <- res$intermediate$variables_namesOnly[3];
} else {
predictor <- res$intermediate$variables_namesOnly[3];
moderator <- res$intermediate$variables_namesOnly[2];
}
res$output$plot <-
ggplot2::ggplot(res$intermediate$dat.raw,
ggplot2::aes_string(y=res$intermediate$variables_namesOnly[1],
x=predictor)) +
ggplot2::geom_point(alpha = pointAlpha) +
ggplot2::geom_smooth(method='lm') +
ggplot2::theme_bw() +
ggplot2::geom_smooth(ggplot2::aes_string(y=res$intermediate$variables_namesOnly[1],
x=predictor,
group=moderator,
color=moderator),
method="lm",
se = FALSE) +
ggplot2::scale_color_viridis_d();
}
} else {
warning("You requested a plot, but for now plots are ",
"only available for regression analyses with one predictor.");
}
}
### Use Z = (b1 - b2) / sqrt(SE_b1^2 + SE_b2^2) to test whether
### the coefficients differ; add arguments such as
### compareCoefficients=FALSE, p.adjust="fdr" to enable user to
### request & correct this.
class(res) <- 'regr';
return(res);
}
print.regr <- function(x, digits=x$input$digits,
pvalueDigits=x$input$pvalueDigits, ...) {
cat(paste0("Regression analysis for formula: ",
x$intermediate$formula.as.character, "\n\n",
"Significance test of the entire model (all predictors together):\n",
" Multiple R-squared: [",
round(x$output$rsq.ci[1], digits), ", ",
round(x$output$rsq.ci[2], digits),
"] (point estimate = ",
round(x$intermediate$summary.raw$r.squared, digits),
", adjusted = ",
round(x$intermediate$summary.raw$adj.r.squared, digits), ")",
ifelse(x$input$ci.method.note, "*\n", "\n"),
" Test for significance: F[",
x$intermediate$summary.raw$fstatistic[2], ", ",
x$intermediate$summary.raw$fstatistic[3], "] = ",
round(x$intermediate$summary.raw$fstatistic[1], digits),
", ", ufs::formatPvalue(stats::pf(x$intermediate$summary.raw$fstatistic[1],
x$intermediate$summary.raw$fstatistic[2],
x$intermediate$summary.raw$fstatistic[3],
lower.tail=FALSE), digits=pvalueDigits), "\n"));
if ("raw" %in% x$input$coefficients) {
cat("\nRaw regression coefficients (unstandardized beta values, called 'B' in SPSS):\n\n");
tmpDat <- round(x$output$coef.raw[, 1:5], digits);
tmpDat[[1]] <- paste0("[", tmpDat[[1]], "; ", tmpDat[[2]], "]");
tmpDat[[2]] <- NULL;
names(tmpDat)[1] <- paste0(x$input$conf.level*100, "% conf. int.");
tmpDat$p <- ufs::formatPvalue(x$output$coef.raw$p,
digits=pvalueDigits,
includeP=FALSE);
print(tmpDat, ...);
}
if ("scaled" %in% x$input$coefficients) {
cat("\nScaled regression coefficients (standardized beta values, called 'Beta' in SPSS):\n\n");
tmpDat <- round(x$output$coef.scaled[, 1:5], digits);
tmpDat[[1]] <- paste0("[", tmpDat[[1]], "; ", tmpDat[[2]], "]");
tmpDat[[2]] <- NULL;
names(tmpDat)[1] <- paste0(x$input$conf.level*100, "% conf. int.");
tmpDat$p <- ufs::formatPvalue(x$output$coef.scaled$p,
digits=pvalueDigits,
includeP=FALSE);
print(tmpDat, ...);
}
if (x$input$collinearity && (!is.null(x$intermediate$vif.raw))) {
cat0("\nCollinearity diagnostics:\n\n");
if (is.vector(x$intermediate$vif.raw)) {
cat0(" For the raw regression coefficients:\n\n");
collinearityDat <- data.frame(VIF = x$intermediate$vif.raw,
Tolerance = x$intermediate$tolerance.raw);
row.names(collinearityDat) <- paste0(ufs::repStr(4), names(x$intermediate$vif.raw));
print(collinearityDat);
cat0("\n For the standardized regression coefficients:\n\n");
collinearityDat <- data.frame(VIF = x$intermediate$vif.scaled,
Tolerance = x$intermediate$tolerance.scaled);
row.names(collinearityDat) <- paste0(ufs::repStr(4), names(x$intermediate$vif.raw));
print(collinearityDat);
}
}
ciMsg <- "\n* Note that the confidence interval for R^2 is based on ";
if (x$input$ci.method[1] == 'r.con') {
ciMsg <- paste0(ciMsg,
"the confidence interval for the Pearson Correlation of ",
"the multiple correlation using r.con from the 'psych' ",
"package because that was specified using the 'ci.method' ",
"argument.");
} else if (x$input$ci.method[1] == 'olkinfinn') {
ciMsg <- paste0(ciMsg,
"the formula reported by Olkin and Finn (1995) in their Correlation ",
"Redux paper, because this was specified using the 'ci.method' ",
"argument. This may not work well for very low values. Set the ",
"argument to 'widest' to also compute the confidence interval ",
"of the multiple correlation using the r.con function from ",
"the 'psych' package and selecting the widest interval.");
} else if (identical(x$output$rsq.ci, x$output$rsq.ci.r.con)) {
ciMsg <- paste0(ciMsg,
"the confidence interval for the Pearson Correlation of ",
"the multiple correlation using r.con from the 'psych' ",
"package because that was the widest interval, which ",
"should be used because the 'ci.method' was set to 'widest'.");
} else if (identical(x$output$rsq.ci, x$output$rsq.ci.olkinfinn)) {
ciMsg <- paste0(ciMsg,
"the formula reported by Olkin and Finn (1995) in their Correlation ",
"Redux paper, because this was the widest interval, which ",
"should be used because the 'ci.method' was set to 'widest'.");
} else {
ciMsg <- paste0(ciMsg,
" -- I don't know actually, something appears to have gone wrong. ",
"The 'ci.method' argument was set to ", ufs::vecTxtQ(x$input$ci.method),
".");
}
if (x$input$ci.method.note) {
cat("\n");
cat(strwrap(ciMsg), sep="\n");
}
if (!is.null(x$output$plot)) {
print(x$output$plot);
}
invisible();
}
pander.regr <- function (x, digits = x$input$digits, pvalueDigits = x$input$pvalueDigits, ...) {
pander::pandoc.p(paste0("\n\n#### Regression analysis for formula: ", x$intermediate$formula.as.character));
pander::pandoc.p("\n\n##### Significance test of the entire model (all predictors together):\n\n");
pander::pandoc.p(paste0("Multiple R-squared: [", round(x$output$rsq.ci[1],
digits), ", ", round(x$output$rsq.ci[2], digits),
"] (point estimate = ", round(x$intermediate$summary.raw$r.squared,
digits), ", adjusted = ", round(x$intermediate$summary.raw$adj.r.squared,
digits), ")"))
pander::pandoc.p(paste0("Test for significance: F[", x$intermediate$summary.raw$fstatistic[2],
", ", x$intermediate$summary.raw$fstatistic[3], "] = ",
round(x$intermediate$summary.raw$fstatistic[1], digits),
", ", ufs::formatPvalue(stats::pf(x$intermediate$summary.raw$fstatistic[1],
x$intermediate$summary.raw$fstatistic[2], x$intermediate$summary.raw$fstatistic[3],
lower.tail = FALSE), digits = pvalueDigits), "\n"));
if ("raw" %in% x$input$coefficients) {
pander::pandoc.p("\n\n##### Raw regression coefficients (unstandardized beta values, called 'B' in SPSS):\n\n");
tmpDat <- round(x$output$coef.raw[, 1:5], digits);
tmpDat[[1]] <- paste0("[", tmpDat[[1]], "; ", tmpDat[[2]], "]");
tmpDat[[2]] <- NULL;
names(tmpDat)[1] <- paste0(x$input$conf.level * 100, "% conf. int.");
tmpDat$p <- ufs::formatPvalue(x$output$coef.raw$p, digits = pvalueDigits, includeP = FALSE);
pander::pander(tmpDat, missing="");
}
if ("scaled" %in% x$input$coefficients) {
pander::pandoc.p("\n\n##### Scaled regression coefficients (standardized beta values, called 'Beta' in SPSS):\n\n");
tmpDat <- round(x$output$coef.scaled[, 1:5], digits);
tmpDat[[1]] <- paste0("[", tmpDat[[1]], "; ", tmpDat[[2]], "]");
tmpDat[[2]] <- NULL;
names(tmpDat)[1] <- paste0(x$input$conf.level * 100, "% conf. int.");
tmpDat$p <- ufs::formatPvalue(x$output$coef.scaled$p, digits = pvalueDigits, includeP = FALSE);
pander::pander(tmpDat, missing="");
}
}
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