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stubs in a place to insert the effect size

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Kaylea Champion 2023-11-14 16:12:51 -08:00
parent 03fd8dd891
commit d62f7a8b63
1 changed files with 15 additions and 8 deletions
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21
R/powerAnalysis.R
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@ -17,7 +17,8 @@ l2p <- function(b) {
#Matt: #Matt:
makeDataNew <- function(n) { makeDataNew <- function(n) {
tDF <- data.frame( tDF <- data.frame(
up.fac.mean=rnorm(n=n, mean=-0.1296376, sd=1.479847), # up.fac.mean ## don't sim the outcome
#up.fac.mean=rnorm(n=n, mean=-0.1296376, sd=1.479847), # up.fac.mean
mmt=rlnorm(n=n, mean=1.685715, sd = 0.2532059), # mmt mmt=rlnorm(n=n, mean=1.685715, sd = 0.2532059), # mmt
## this generates a 50-50 split of milestones --v ## this generates a 50-50 split of milestones --v
#milestones=rbinom(n=n, size=1, prob=c(0.247, 0.753)), #milestones #milestones=rbinom(n=n, size=1, prob=c(0.247, 0.753)), #milestones
@ -32,7 +33,8 @@ makeDataNew <- function(n) {
makeDataNew2 <- function(n) { makeDataNew2 <- function(n) {
tDF <- data.frame( tDF <- data.frame(
up.fac.mean=rnorm(n=n, mean=-0.1296376, sd=1.479847), # up.fac.mean ## don't sim the outcome
#up.fac.mean=rnorm(n=n, mean=-0.1296376, sd=1.479847), # up.fac.mean
formal.score=rlnorm(n=n, mean=6.220282, sd = 2.544058) # formal.score formal.score=rlnorm(n=n, mean=6.220282, sd = 2.544058) # formal.score
) )
tDF[is.na(tDF) | tDF=="Inf"] = NA tDF[is.na(tDF) | tDF=="Inf"] = NA
@ -51,18 +53,22 @@ powerCheck <- function(n, nSims) { #run a power calculation on the dataset given
signifM <- rep(NA, nSims) signifM <- rep(NA, nSims)
for (s in 1:nSims) { # repeatedly we will.... for (s in 1:nSims) { # repeatedly we will....
simData <- makeDataNew(n) # make some data simData <- makeDataNew(n) # make some data
## outcome goes here --v
# e.g. simData$up.fac.mean <- (usefuleffsizeA * mmt) + (usefuleffsizeB * milestones) + rnorm(n=1, mean=0, sd=1) ##plus some noise
#have updated for kkex through here, now need to look at the underproduction work #have updated for kkex through here, now need to look at the underproduction work
#m1.sim <- lm(up.fac.mean ~ ((mmt)/ (milestones/age)), data=simData) #m1.sim <- lm(up.fac.mean ~ ((mmt)/ (milestones/age)), data=simData)
m1.sim <- lm(up.fac.mean ~ mmt + milestones + age, data=simData) ## could leave age out for now?
#m1.sim <- lm(up.fac.mean ~ mmt + milestones + age, data=simData)
m1.sim <- lm(up.fac.mean ~ mmt + milestones, data=simData)
p0 <- coef(summary(m1.sim))[1,4] #intercept p0 <- coef(summary(m1.sim))[1,4] #intercept
p1 <- coef(summary(m1.sim))[2,4] #mmt p1 <- coef(summary(m1.sim))[2,4] #mmt
p2 <- coef(summary(m1.sim))[3,4] #milestones p2 <- coef(summary(m1.sim))[3,4] #milestones
p3 <- coef(summary(m1.sim))[4,4] #age #p3 <- coef(summary(m1.sim))[4,4] #age
signif0[s] <- p0 <=.05 signif0[s] <- p0 <=.05
signif1[s] <- p1 <=.05 signif1[s] <- p1 <=.05
signif2[s] <- p2 <=.05 signif2[s] <- p2 <=.05
signif3[s] <- p3 <=.05 #signif3[s] <- p3 <=.05
signifM[s] <- p0 <=.05 & p1 <=.05 & p2 <=.05 & p3 <=.05 signifM[s] <- p0 <=.05 & p1 <=.05 & p2 <=.05 #& p3 <=.05
} }
power <- c(mean(signif0), mean(signif1), mean(signif2), mean(signif3), mean(signifM)) power <- c(mean(signif0), mean(signif1), mean(signif2), mean(signif3), mean(signifM))
return(power) return(power)
@ -77,6 +83,8 @@ powerCheck2 <- function(n, nSims) { #run a power calculation on the dataset give
simData <- makeDataNew2(n) # make some data simData <- makeDataNew2(n) # make some data
#have updated for kkex through here, now need to look at the underproduction work #have updated for kkex through here, now need to look at the underproduction work
#m1.sim <- lm(up.fac.mean ~ ((mmt)/ (milestones/age)), data=simData) #m1.sim <- lm(up.fac.mean ~ ((mmt)/ (milestones/age)), data=simData)
## outcome goes here --v
## e.g. simData$up.fac.mean <- (usefuleffsizeC * formal.score) + rnorm(1, mean=0, sd=1) ##plus some noise
m1.sim <- lm(up.fac.mean ~ formal.score, data=simData) m1.sim <- lm(up.fac.mean ~ formal.score, data=simData)
p0 <- coef(summary(m1.sim))[1,4] p0 <- coef(summary(m1.sim))[1,4]
p1 <- coef(summary(m1.sim))[2,4] p1 <- coef(summary(m1.sim))[2,4]
@ -87,4 +95,3 @@ powerCheck2 <- function(n, nSims) { #run a power calculation on the dataset give
power <- c(mean(signif0), mean(signif1), mean(signifM)) power <- c(mean(signif0), mean(signif1), mean(signifM))
return(power) return(power)
} }