103 lines
4.1 KiB
R
103 lines
4.1 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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tDF <- data.frame(
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## don't sim the outcome
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#up.fac.mean=rnorm(n=n, mean=-0.1296376, sd=1.479847), # up.fac.mean
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#mmt=rlnorm(n=n, mean=1.685715, sd = 0.2532059), # mmt
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new_mmt=rbeta(n=n, 5, 1),
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#mmt=rlogis(n=n, location = 1.685715),
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## this generates a 50-50 split of milestones --v
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#milestones=rbinom(n=n, size=1, prob=c(0.247, 0.753)), #milestones
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milestones=rbinom(n=n, size=1, prob=.247), #milestones
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age=rnorm(n=n, mean=4351.578, sd=1408.811) # age
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)
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#sDF <- melt(tDF, id.vars = 0) #AKA the index is the unique id, as far as that goes
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## can name these in the data.frame constructor method directly
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#colnames(tDF) <- c('up.fac.mean', 'mmt', 'milestones', 'age')
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return(tDF)
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}
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makeDataNew2 <- function(n) {
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tDF <- data.frame(
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## don't sim the outcome
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#up.fac.mean=rnorm(n=n, mean=-0.1296376, sd=1.479847), # up.fac.mean
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#formal.score=rlnorm(n=n, mean=6.220282, sd = 2.544058) # formal.score
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formal.score=rbeta(n=n, 1, 3) * 10000
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)
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tDF[is.na(tDF) | tDF=="Inf"] = NA
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#sDF <- melt(tDF, id.vars = 0) #AKA the index is the unique id, as far as that goes
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##colnames(tDF) <- c('up.fac.mean', 'formal.score')
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return(tDF)
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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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## outcome goes here --v
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# e.g. simData$up.fac.mean <- (usefuleffsizeA * mmt) + (usefuleffsizeB * milestones) + rnorm(n=1, mean=0, sd=1) ##plus some noise
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#simData$up.fac.mean <- (-2.075 * simData$mmt) + (0.4284 * simData$milestones) + rnorm(n=1, mean=0, sd=1)
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simData$up.fac.mean <- (-1.38 * simData$new_mmt) + (0.40 * simData$milestones) + rnorm(n=n, mean=0, sd=1)
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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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## could leave age out for now?
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#m1.sim <- lm(up.fac.mean ~ mmt + milestones + age, data=simData)
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m1.sim <- lm(up.fac.mean ~ new_mmt + milestones, data=simData)
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p0 <- coef(summary(m1.sim))[1,4] #intercept
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p1 <- coef(summary(m1.sim))[2,4] #mmt
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p2 <- coef(summary(m1.sim))[3,4] #milestones
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#p3 <- coef(summary(m1.sim))[4,4] #age
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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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powerCheck2 <- 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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signifM <- rep(NA, nSims)
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for (s in 1:nSims) { # repeatedly we will....
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simData <- makeDataNew2(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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## outcome goes here --v
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simData$up.fac.mean <- (0.00017 * simData$formal.score) + rnorm(n, mean=0, sd=1) ##plus some noise
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m1.sim <- lm(up.fac.mean ~ formal.score, data=simData)
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p0 <- coef(summary(m1.sim))[1,4]
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p1 <- coef(summary(m1.sim))[2,4]
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signif0[s] <- p0 <=.05
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signif1[s] <- p1 <=.05
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signifM[s] <- p0 <=.05 & p1 <=.05
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}
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power <- c(mean(signif0), mean(signif1), mean(signifM))
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return(power)
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}
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