Nothing
CalculateAndGraphForestPlot <-
function(table,
binary=TRUE, mean.sd=FALSE,
higher.is.better=TRUE,
rustlook=NA, vpos=NA, pos=NA, neg=NA, vneg=NA,
level=95,
binary.measure="RR", continuous.measure="SMD",
summary.measure="SMD", method="DL",
seed=NA, noise=0.01, sims=1,
title=NA, digits=3, ...){
# Given a table with binary event data,
# this function imputes data, calculates summary effect sizes,
# then graphs a forest plot.
#
# Args:
# level: confidence level = 1-alpha
# vpos: "very positive" outlook
#
# Returns: a forest plot for a table of pub & unpub studies with binary outcomes.
#
# Notes:
# Unlike the function GraphBinaryForestPlot(), this function allows
# the user to tweak the default effect sizes to be assigned.
# Dependencies:
# Callers: forestsens()
if(binary == TRUE){
table1 <- PrepareTableWithBinaryData(table,
higher.is.better=higher.is.better,
rustlook=rustlook,
vpos=vpos, pos=pos, neg=neg, vneg=vneg,
level=level, binary.measure=binary.measure,
summary.measure=summary.measure, method=method,
seed=seed, sims=sims)
GraphBinaryForestPlot(table=table1,
level=level, higher.is.better=higher.is.better,
digits=digits, title=title, ...)
} else {
table1 <- PrepareTableWithContinuousData(table,
mean.sd=mean.sd,
higher.is.better=higher.is.better,
vpos=vpos, pos=pos, neg=neg, vneg=vneg,
rustlook=rustlook,
level=level, continuous.measure=continuous.measure,
summary.measure=summary.measure, method=method,
seed=seed, noise=noise)
GraphContinuousForestPlot(table=table1,
level=level, higher.is.better=higher.is.better,
digits=digits, title=title, ...)
}
}
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