R/metacorr.b.R
In kylehamilton/JamoviMeta: Meta-Analysis for JAMOVI
# This file is a generated template, your changes will not be overwritten
MetaCorrClass <- R6::R6Class(
"MetaCorrClass",
inherit = MetaCorrBase,
private = list(
# .init = function() {
#
# ready <- TRUE
# if (is.null(self$options$rcor) || is.null(self$options$samplesize) || is.null(self$options$slab) == TRUE)
# ready <- FALSE
#
# if (ready == TRUE) {
#
# private$.run()
#
# }
#
# },
.run = function() {
ri <- self$options$rcor
ni <- self$options$samplesize
mods <- self$options$moderatorcor
fsntype <- self$options$fsntype
method2 <- self$options$methodmetacor
cormeasure <- self$options$cormeasure
slab <- self$options$slab
includemods <- self$options$includemods
addcred <- self$options$addcred
addfit <- self$options$addfit
showweights <- self$options$showweights
level <- self$options$level
#yaxis <- self$options$yaxis
#data <- self$data
table <- self$results$textRICH
ready <- TRUE
if (is.null(self$options$rcor) || is.null(self$options$samplesize) || is.null(self$options$slab) == TRUE){
ready <- FALSE
# I really need to think of a better error message this is a place holder until I figure something out
jmvcore::reject("Correlations, Sample Sizes, and Study Label fields must be populated to run analysis", code='')
}
if (ready == TRUE) {
if (self$options$includemods == TRUE) {
data <- data.frame(ri = self$data[[self$options$rcor]], ni = self$data[[self$options$samplesize]], mods = self$data[[self$options$moderatorcor]], slab = self$data[[self$options$slab]])
data[[ri]] <- jmvcore::toNumeric(data[[ri]])
data[[ni]] <- jmvcore::toNumeric(data[[ni]])
data[[mods]] <- jmvcore::toNumeric(data[[mods]])
} else {
data <- data.frame(ri = self$data[[self$options$rcor]], ni = self$data[[self$options$samplesize]], slab = self$data[[self$options$slab]])
data[[ri]] <- jmvcore::toNumeric(data[[ri]])
data[[ni]] <- jmvcore::toNumeric(data[[ni]])
}
if (self$options$includemods == TRUE) {
res <- metafor::rma(ri=ri, ni=ni, method=method2, measure=cormeasure, mods= cbind(mods), data=data, slab=slab, level=level)
} else {
res <- metafor::rma(ri=ri, ni=ni, method=method2, measure=cormeasure, data=data, slab=slab, level=level)
}
#}
# if (self$options$includemods == TRUE) {
# data <- data.frame(ri = self$data[[self$options$rcor]], ni = self$data[[self$options$samplesize]], mods = self$data[[self$options$moderatorcor]], slab = self$data[[self$options$slab]])
# data[[ri]] <- jmvcore::toNumeric(data[[ri]])
# data[[ni]] <- jmvcore::toNumeric(data[[ni]])
# data[[mods]] <- jmvcore::toNumeric(data[[mods]])
# } else {
# data <- data.frame(ri = self$data[[self$options$rcor]], ni = self$data[[self$options$samplesize]], slab = self$data[[self$options$slab]])
# data[[ri]] <- jmvcore::toNumeric(data[[ri]])
# data[[ni]] <- jmvcore::toNumeric(data[[ni]])
# }
#
# if (self$options$includemods == TRUE) {
# res <- metafor::rma(ri=ri, ni=ni, method=method2, measure=cormeasure, mods=mods, data=data, slab=slab, level=level)
# } else {
# res <- metafor::rma(ri=ri, ni=ni, method=method2, measure=cormeasure, data=data, slab=slab, level=level)
# }
#Pub Bias
failsafePB <- metafor::fsn(yi=res$yi, vi=res$vi, type=fsntype)
ranktestPB <- metafor::ranktest(res)
regtestPB <- metafor::regtest(res)
fsnRICH <- self$results$pubBias$fsnRICH
fsnRICH$setRow(rowNo=1, values=list(
failSafeNumber=failsafePB$fsnum[1],
p=failsafePB$pval[1]
))
fsnTitle <- paste("Fail-Safe N Analysis (File Drawer Analysis)")
fsnNote <- paste("Fail-safe N Calculation Using the ",fsntype," Approach", sep="")
fsnRICH$setTitle(title=fsnTitle)
fsnRICH$setNote("fsnNoteTable",fsnNote)
rankRICH <- self$results$pubBias$rankRICH
rankRICH$setRow(rowNo=1, values=list(
rankTau=ranktestPB$tau[1],
p=ranktestPB$pval[1]
))
regRICH <- self$results$pubBias$regRICH
regRICH$setRow(rowNo=1, values=list(
Z=regtestPB$zval[1],
p=regtestPB$pval[1]
))
#Model Fit
modelFitRICH <- self$results$modelFitRICH
modelFitRICH$setRow(rowNo=1, values=list(
label="Maximum-Likelihood",
loglikelihood=res$fit.stats[1,1],
deviance=res$fit.stats[2,1],
AIC=res$fit.stats[3,1],
BIC=res$fit.stats[4,1],
AICc=res$fit.stats[5,1]
))
modelFitRICH$setRow(rowNo=2, values=list(
label="Restricted Maximum-Likelihood",
loglikelihood=res$fit.stats[1,2],
deviance=res$fit.stats[2,2],
AIC=res$fit.stats[3,2],
BIC=res$fit.stats[4,2],
AICc=res$fit.stats[5,2]
))
#fit statistics and information criteria
#Show if checked, hide if unchecked
if (self$options$showModelFit == TRUE) {
modelFitRICH$setVisible(visible=TRUE)
} else {
modelFitRICH$setVisible(visible=FALSE)
}
#Pub Bias Connections
#self$results$pubBias$fsn$setContent(failsafePB)
#self$results$pubBias$rank$setContent(ranktestPB)
#self$results$pubBias$reg$setContent(regtestPB)
#Data Prep: Results Table
CILB <- round(res$ci.lb[1], 3)
CIUB <- round(res$ci.ub[1], 3)
ciLBUB <- paste(CILB,"-",CIUB)
# Results Table
#table <- self$results$textRICH
# esDataNULL <- is.null(self$options$rcor)
# if (esDataNULL == TRUE){
# table$setError("error")
# }
table$setRow(rowNo=1, values=list(
Intercept="Intercept",
Estimate=as.numeric(res$b[1]),
se=res$se[1],
CILow=res$ci.lb[1],
CIHigh=res$ci.ub[1],
p=res$pval[1],
Z=res$zval[1],
k=res$k
))
if (self$options$methodmetacor == "DL"){
tau2EstimatorName = "DerSimonian-Laird"
} else if (self$options$methodmetacor == "HE"){
tau2EstimatorName = "Hedges"
} else if (self$options$methodmetacor == "HS"){
tau2EstimatorName = "Hunter-Schmidt"
} else if (self$options$methodmetacor == "SJ"){
tau2EstimatorName = "Sidik-Jonkman"
} else if (self$options$methodmetacor == "ML"){
tau2EstimatorName = "Maximum-Likelihood"
} else if (self$options$methodmetacor == "REML"){
tau2EstimatorName = "Restricted Maximum-Likelihood"
} else if (self$options$methodmetacor == "EB"){
tau2EstimatorName = "Empirical Bayes"
} else if (self$options$methodmetacor == "PM"){
tau2EstimatorName = "Paule-Mandel"
}
if (self$options$includemods == TRUE){
titleMix <- paste("Mixed-Effects Model (k = ",res$k,")",sep="")
titleMixNote <- paste("Tau\u00B2 Estimator: ",tau2EstimatorName, sep="")
table$setTitle(title=titleMix)
table$setNote("mixnote",titleMixNote)
} else if (self$options$methodmetacor == "FE"){
titleFix <- paste("Fixed-Effects Model (k = ",res$k,")",sep="")
table$setTitle(title=titleFix)
} else {
titleRan <- paste("Random-Effects Model (k = ",res$k,")",sep="")
titleRanNote <- paste("Tau\u00B2 Estimator: ",tau2EstimatorName, sep="")
table$setTitle(title=titleRan)
table$setNote("rannote",titleRanNote)
}
if (self$options$includemods == TRUE) {
modCILB <- round(res$ci.lb[2], 3)
modCIUB <- round(res$ci.ub[2], 3)
table$setRow(rowNo=2, values=list(
Intercept="Moderator",
Estimate=as.numeric(res$b[2]),
se=res$se[2],
CILow=res$ci.lb[2],
CIHigh=res$ci.ub[2],
p=res$pval[2],
Z=res$zval[2],
k=res$k
))
} else {
table$setRow(rowNo=2, values=list(
Intercept=" ",
Estimate=NULL,
se=NULL,
CILow=NULL,
CIHigh=NULL,
p=NULL,
Z=NULL,
k=NULL
))
}
#Data Prep: Heterogeneity Stats
tauSquared <- round(res$tau2, 4)
tauSquaredSE <- round(res$se.tau2, 4)
tauSqCombind <- paste(tauSquared,"(SE=",tauSquaredSE,")")
tauOnly <- round(sqrt(res$tau2), 4)
ISquStat <- paste(round(res$I2, 2),"%",sep="")
HSquStat <- round(res$H2, 4)
if (self$options$includemods == TRUE) {
RSquStat <- paste(round(res$R2, 2),"%",sep="")
} else {
RSquStat <- NULL
}
#Data Prep: Heterogeneity Test
QTestStatDF <- round(res$k - 1, 4)
#Heterogeneity Stats annd Test Table
tableTauSqaured <- self$results$tableTauSqaured
tableTauSqaured$setRow(rowNo=1, values=list(
tauSqComb=tauSqCombind,
tauSQRT=tauOnly,
ISqu=ISquStat,
HSqu=HSquStat,
RSqu=RSquStat,
QallDF=QTestStatDF,
Qall=res$QE,
QallPval=res$QEp
))
# `self$data` contains the data
# `self$options` contains the options
# `self$results` contains the results object (to populate)
image <- self$results$plot
imageFUN <- self$results$funplot
image$setState(res)
imageFUN$setState(res)
# }}))
}},
#Forest Plot Function
.plot=function(image, ...) { # <-- the plot function
plotData <- image$state
#StudyID <- self$options$studylabels
#yi <- self$options$yi
#vi <- self$options$vi
#res <- metafor::rma(yi=yi, vi=vi, data=self$data)
addcred <- self$options$addcred
addfit <- self$options$addfit
level <- self$options$level
showweights <- self$options$showweights
xlab <- self$options$xAxisTitle
order <- self$options$forestOrder
ready <- TRUE
if (is.null(self$options$rcor) || is.null(self$options$samplesize) || is.null(self$options$slab) == TRUE){
#if (is.null(self$options$rcor) == TRUE){
ready <- FALSE
}
if (ready == TRUE) {
#plot <- metafor::forest(plotData$yi, plotData$vi, addcred=addcred, addfit=addfit)
plot <- metafor::forest(plotData, addcred=addcred, addfit=addfit, level=level, showweights=showweights, xlab=xlab, order=order)
print(plot)
TRUE}
},
#Funnel Plot Function
.funplot=function(imageFUN, ...) { # <-- the plot function
plotDataFUN <- imageFUN$state
yaxis <- self$options$yaxis
yaxisInv <- self$options$yaxisInv
enhancePlot <- self$options$enhanceFunnel
ready <- TRUE
if (is.null(self$options$rcor) || is.null(self$options$samplesize) || is.null(self$options$slab) == TRUE){
#if (is.null(self$options$rcor) == TRUE){
ready <- FALSE
}
if (ready == TRUE) {
if (self$options$yaxisInv == TRUE) {
if (self$options$enhanceFunnel == TRUE) {
yaxisTrans <- paste(yaxis,"nv",sep="")
plotFUN <- metafor::funnel(plotDataFUN,yaxis=yaxisTrans,level=c(90, 95, 99), shade=c("white", "gray", "darkgray"))
} else {
yaxisTrans <- paste(yaxis,"nv",sep="")
plotFUN <- metafor::funnel(plotDataFUN,yaxis=yaxisTrans)
}
} else {
if (self$options$enhanceFunnel == TRUE) {
plotFUN <- metafor::funnel(plotDataFUN,yaxis=yaxis,level=c(90, 95, 99), shade=c("white", "gray", "darkgray"))
} else {
plotFUN <- metafor::funnel(plotDataFUN,yaxis=yaxis)
}
}
print(plotFUN)
TRUE}
})
)
kylehamilton/JamoviMeta documentation built on May 14, 2019, 2:55 p.m.
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# This file is a generated template, your changes will not be overwritten
MetaCorrClass <- R6::R6Class(
"MetaCorrClass",
inherit = MetaCorrBase,
private = list(
# .init = function() {
#
# ready <- TRUE
# if (is.null(self$options$rcor) || is.null(self$options$samplesize) || is.null(self$options$slab) == TRUE)
# ready <- FALSE
#
# if (ready == TRUE) {
#
# private$.run()
#
# }
#
# },
.run = function() {
ri <- self$options$rcor
ni <- self$options$samplesize
mods <- self$options$moderatorcor
fsntype <- self$options$fsntype
method2 <- self$options$methodmetacor
cormeasure <- self$options$cormeasure
slab <- self$options$slab
includemods <- self$options$includemods
addcred <- self$options$addcred
addfit <- self$options$addfit
showweights <- self$options$showweights
level <- self$options$level
#yaxis <- self$options$yaxis
#data <- self$data
table <- self$results$textRICH
ready <- TRUE
if (is.null(self$options$rcor) || is.null(self$options$samplesize) || is.null(self$options$slab) == TRUE){
ready <- FALSE
# I really need to think of a better error message this is a place holder until I figure something out
jmvcore::reject("Correlations, Sample Sizes, and Study Label fields must be populated to run analysis", code='')
}
if (ready == TRUE) {
if (self$options$includemods == TRUE) {
data <- data.frame(ri = self$data[[self$options$rcor]], ni = self$data[[self$options$samplesize]], mods = self$data[[self$options$moderatorcor]], slab = self$data[[self$options$slab]])
data[[ri]] <- jmvcore::toNumeric(data[[ri]])
data[[ni]] <- jmvcore::toNumeric(data[[ni]])
data[[mods]] <- jmvcore::toNumeric(data[[mods]])
} else {
data <- data.frame(ri = self$data[[self$options$rcor]], ni = self$data[[self$options$samplesize]], slab = self$data[[self$options$slab]])
data[[ri]] <- jmvcore::toNumeric(data[[ri]])
data[[ni]] <- jmvcore::toNumeric(data[[ni]])
}
if (self$options$includemods == TRUE) {
res <- metafor::rma(ri=ri, ni=ni, method=method2, measure=cormeasure, mods= cbind(mods), data=data, slab=slab, level=level)
} else {
res <- metafor::rma(ri=ri, ni=ni, method=method2, measure=cormeasure, data=data, slab=slab, level=level)
}
#}
# if (self$options$includemods == TRUE) {
# data <- data.frame(ri = self$data[[self$options$rcor]], ni = self$data[[self$options$samplesize]], mods = self$data[[self$options$moderatorcor]], slab = self$data[[self$options$slab]])
# data[[ri]] <- jmvcore::toNumeric(data[[ri]])
# data[[ni]] <- jmvcore::toNumeric(data[[ni]])
# data[[mods]] <- jmvcore::toNumeric(data[[mods]])
# } else {
# data <- data.frame(ri = self$data[[self$options$rcor]], ni = self$data[[self$options$samplesize]], slab = self$data[[self$options$slab]])
# data[[ri]] <- jmvcore::toNumeric(data[[ri]])
# data[[ni]] <- jmvcore::toNumeric(data[[ni]])
# }
#
# if (self$options$includemods == TRUE) {
# res <- metafor::rma(ri=ri, ni=ni, method=method2, measure=cormeasure, mods=mods, data=data, slab=slab, level=level)
# } else {
# res <- metafor::rma(ri=ri, ni=ni, method=method2, measure=cormeasure, data=data, slab=slab, level=level)
# }
#Pub Bias
failsafePB <- metafor::fsn(yi=res$yi, vi=res$vi, type=fsntype)
ranktestPB <- metafor::ranktest(res)
regtestPB <- metafor::regtest(res)
fsnRICH <- self$results$pubBias$fsnRICH
fsnRICH$setRow(rowNo=1, values=list(
failSafeNumber=failsafePB$fsnum[1],
p=failsafePB$pval[1]
))
fsnTitle <- paste("Fail-Safe N Analysis (File Drawer Analysis)")
fsnNote <- paste("Fail-safe N Calculation Using the ",fsntype," Approach", sep="")
fsnRICH$setTitle(title=fsnTitle)
fsnRICH$setNote("fsnNoteTable",fsnNote)
rankRICH <- self$results$pubBias$rankRICH
rankRICH$setRow(rowNo=1, values=list(
rankTau=ranktestPB$tau[1],
p=ranktestPB$pval[1]
))
regRICH <- self$results$pubBias$regRICH
regRICH$setRow(rowNo=1, values=list(
Z=regtestPB$zval[1],
p=regtestPB$pval[1]
))
#Model Fit
modelFitRICH <- self$results$modelFitRICH
modelFitRICH$setRow(rowNo=1, values=list(
label="Maximum-Likelihood",
loglikelihood=res$fit.stats[1,1],
deviance=res$fit.stats[2,1],
AIC=res$fit.stats[3,1],
BIC=res$fit.stats[4,1],
AICc=res$fit.stats[5,1]
))
modelFitRICH$setRow(rowNo=2, values=list(
label="Restricted Maximum-Likelihood",
loglikelihood=res$fit.stats[1,2],
deviance=res$fit.stats[2,2],
AIC=res$fit.stats[3,2],
BIC=res$fit.stats[4,2],
AICc=res$fit.stats[5,2]
))
#fit statistics and information criteria
#Show if checked, hide if unchecked
if (self$options$showModelFit == TRUE) {
modelFitRICH$setVisible(visible=TRUE)
} else {
modelFitRICH$setVisible(visible=FALSE)
}
#Pub Bias Connections
#self$results$pubBias$fsn$setContent(failsafePB)
#self$results$pubBias$rank$setContent(ranktestPB)
#self$results$pubBias$reg$setContent(regtestPB)
#Data Prep: Results Table
CILB <- round(res$ci.lb[1], 3)
CIUB <- round(res$ci.ub[1], 3)
ciLBUB <- paste(CILB,"-",CIUB)
# Results Table
#table <- self$results$textRICH
# esDataNULL <- is.null(self$options$rcor)
# if (esDataNULL == TRUE){
# table$setError("error")
# }
table$setRow(rowNo=1, values=list(
Intercept="Intercept",
Estimate=as.numeric(res$b[1]),
se=res$se[1],
CILow=res$ci.lb[1],
CIHigh=res$ci.ub[1],
p=res$pval[1],
Z=res$zval[1],
k=res$k
))
if (self$options$methodmetacor == "DL"){
tau2EstimatorName = "DerSimonian-Laird"
} else if (self$options$methodmetacor == "HE"){
tau2EstimatorName = "Hedges"
} else if (self$options$methodmetacor == "HS"){
tau2EstimatorName = "Hunter-Schmidt"
} else if (self$options$methodmetacor == "SJ"){
tau2EstimatorName = "Sidik-Jonkman"
} else if (self$options$methodmetacor == "ML"){
tau2EstimatorName = "Maximum-Likelihood"
} else if (self$options$methodmetacor == "REML"){
tau2EstimatorName = "Restricted Maximum-Likelihood"
} else if (self$options$methodmetacor == "EB"){
tau2EstimatorName = "Empirical Bayes"
} else if (self$options$methodmetacor == "PM"){
tau2EstimatorName = "Paule-Mandel"
}
if (self$options$includemods == TRUE){
titleMix <- paste("Mixed-Effects Model (k = ",res$k,")",sep="")
titleMixNote <- paste("Tau\u00B2 Estimator: ",tau2EstimatorName, sep="")
table$setTitle(title=titleMix)
table$setNote("mixnote",titleMixNote)
} else if (self$options$methodmetacor == "FE"){
titleFix <- paste("Fixed-Effects Model (k = ",res$k,")",sep="")
table$setTitle(title=titleFix)
} else {
titleRan <- paste("Random-Effects Model (k = ",res$k,")",sep="")
titleRanNote <- paste("Tau\u00B2 Estimator: ",tau2EstimatorName, sep="")
table$setTitle(title=titleRan)
table$setNote("rannote",titleRanNote)
}
if (self$options$includemods == TRUE) {
modCILB <- round(res$ci.lb[2], 3)
modCIUB <- round(res$ci.ub[2], 3)
table$setRow(rowNo=2, values=list(
Intercept="Moderator",
Estimate=as.numeric(res$b[2]),
se=res$se[2],
CILow=res$ci.lb[2],
CIHigh=res$ci.ub[2],
p=res$pval[2],
Z=res$zval[2],
k=res$k
))
} else {
table$setRow(rowNo=2, values=list(
Intercept=" ",
Estimate=NULL,
se=NULL,
CILow=NULL,
CIHigh=NULL,
p=NULL,
Z=NULL,
k=NULL
))
}
#Data Prep: Heterogeneity Stats
tauSquared <- round(res$tau2, 4)
tauSquaredSE <- round(res$se.tau2, 4)
tauSqCombind <- paste(tauSquared,"(SE=",tauSquaredSE,")")
tauOnly <- round(sqrt(res$tau2), 4)
ISquStat <- paste(round(res$I2, 2),"%",sep="")
HSquStat <- round(res$H2, 4)
if (self$options$includemods == TRUE) {
RSquStat <- paste(round(res$R2, 2),"%",sep="")
} else {
RSquStat <- NULL
}
#Data Prep: Heterogeneity Test
QTestStatDF <- round(res$k - 1, 4)
#Heterogeneity Stats annd Test Table
tableTauSqaured <- self$results$tableTauSqaured
tableTauSqaured$setRow(rowNo=1, values=list(
tauSqComb=tauSqCombind,
tauSQRT=tauOnly,
ISqu=ISquStat,
HSqu=HSquStat,
RSqu=RSquStat,
QallDF=QTestStatDF,
Qall=res$QE,
QallPval=res$QEp
))
# `self$data` contains the data
# `self$options` contains the options
# `self$results` contains the results object (to populate)
image <- self$results$plot
imageFUN <- self$results$funplot
image$setState(res)
imageFUN$setState(res)
# }}))
}},
#Forest Plot Function
.plot=function(image, ...) { # <-- the plot function
plotData <- image$state
#StudyID <- self$options$studylabels
#yi <- self$options$yi
#vi <- self$options$vi
#res <- metafor::rma(yi=yi, vi=vi, data=self$data)
addcred <- self$options$addcred
addfit <- self$options$addfit
level <- self$options$level
showweights <- self$options$showweights
xlab <- self$options$xAxisTitle
order <- self$options$forestOrder
ready <- TRUE
if (is.null(self$options$rcor) || is.null(self$options$samplesize) || is.null(self$options$slab) == TRUE){
#if (is.null(self$options$rcor) == TRUE){
ready <- FALSE
}
if (ready == TRUE) {
#plot <- metafor::forest(plotData$yi, plotData$vi, addcred=addcred, addfit=addfit)
plot <- metafor::forest(plotData, addcred=addcred, addfit=addfit, level=level, showweights=showweights, xlab=xlab, order=order)
print(plot)
TRUE}
},
#Funnel Plot Function
.funplot=function(imageFUN, ...) { # <-- the plot function
plotDataFUN <- imageFUN$state
yaxis <- self$options$yaxis
yaxisInv <- self$options$yaxisInv
enhancePlot <- self$options$enhanceFunnel
ready <- TRUE
if (is.null(self$options$rcor) || is.null(self$options$samplesize) || is.null(self$options$slab) == TRUE){
#if (is.null(self$options$rcor) == TRUE){
ready <- FALSE
}
if (ready == TRUE) {
if (self$options$yaxisInv == TRUE) {
if (self$options$enhanceFunnel == TRUE) {
yaxisTrans <- paste(yaxis,"nv",sep="")
plotFUN <- metafor::funnel(plotDataFUN,yaxis=yaxisTrans,level=c(90, 95, 99), shade=c("white", "gray", "darkgray"))
} else {
yaxisTrans <- paste(yaxis,"nv",sep="")
plotFUN <- metafor::funnel(plotDataFUN,yaxis=yaxisTrans)
}
} else {
if (self$options$enhanceFunnel == TRUE) {
plotFUN <- metafor::funnel(plotDataFUN,yaxis=yaxis,level=c(90, 95, 99), shade=c("white", "gray", "darkgray"))
} else {
plotFUN <- metafor::funnel(plotDataFUN,yaxis=yaxis)
}
}
print(plotFUN)
TRUE}
})
)
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