50 lines
1.6 KiB
R
50 lines
1.6 KiB
R
# This is semi-generic code for doing a power analysis of a logistic regression with 4
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# levels in a factor
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# when there's some pilot values already available and defined
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#modelled heavily the simulation example explained in:
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#http://meeting.spsp.org/2016/sites/default/files/Lane%2C%20Hennes%2C%20West%20SPSP%20Power%20Workshop%202016.pdf
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library('batman')
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library('reshape')
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l2p <- function(b) {
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odds <- exp(b)
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prob <- odds/(1+odds)
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return(prob)
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}
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#Matt:
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makeDataNew <- function(n) {
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sDF <- data.frame(
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)
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colnames(sDF) <- c('formality', 'age', 'contributors', 'collaborators', 'milestones', 'mmt', 'up.fac.mean')
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return(sDF)
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}
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powerCheck <- function(n, nSims) { #run a power calculation on the dataset given
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#set up some empty arrays b/c R
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signif0 <- rep(NA, nSims)
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signif1 <- rep(NA, nSims)
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signif2 <- rep(NA, nSims)
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signif3 <- rep(NA, nSims)
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signifM <- rep(NA, nSims)
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for (s in 1:nSims) { # repeatedly we will....
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simData <- makeDataNew(n) # make some data
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#have updated for kkex through here, now need to look at the underproduction work
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m1.sim <- lm(up.fac.mean ~ ((mmt)/ (milestones/age)), data=simData)
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p0 <- coef(summary(m1.sim))[1,4]
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p1 <- coef(summary(m1.sim))[1,4]
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p2 <- coef(summary(m1.sim))[1,4]
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p3 <- coef(summary(m1.sim))[1,4]
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signif0[s] <- p0 <=.05
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signif1[s] <- p1 <=.05
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signif2[s] <- p2 <=.05
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signif3[s] <- p3 <=.05
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signifM[s] <- p0 <=.05 & p1 <=.05 & p2 <=.05 & p3 <=.05
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}
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power <- c(mean(signif0), mean(signif1), mean(signif2), mean(signif3), mean(signifM))
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return(power)
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}
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