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more fiddling w power analysis

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mjgaughan 2023-11-15 11:24:51 -06:00
parent 27856dfd2d
commit 8f26cae949
2 changed files with 7 additions and 5 deletions
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9
R/calculatePower.R
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@ -39,14 +39,15 @@ data1$new.age <- as.numeric(cut(data1$age/365, breaks=c(0,9,12,15,17), labels=c(
data1$formal.score <- data1$mmt / (data1$milestones/data1$new.age) data1$formal.score <- data1$mmt / (data1$milestones/data1$new.age)
hist(as.numeric(data1$new.age)) hist(as.numeric(data1$new.age))
table(data1$formal.score) table(data1$formal.score)
hist(data1$formal.score)
kmodel1 <- lm(up.fac.mean ~ mmt, data=data1) kmodel1 <- lm(up.fac.mean ~ mmt, data=data1)
summary(kmodel1) summary(kmodel1)
kmodel1 <- lm(up.fac.mean ~ old_mmt, data=data1) kmodel1 <- lm(up.fac.mean ~ old_mmt, data=data1)
summary(kmodel1) summary(kmodel1)
kmodel1 <- lm(up.fac.mean ~ formal.score, data=data1)
summary(kmodel1)
kmodel1 <- lm(up.fac.mean ~ new.age, data=data1) kmodel1 <- lm(up.fac.mean ~ new.age, data=data1)
summary(kmodel1) summary(kmodel1)
kmodel1 <- lm(up.fac.mean ~ milestones, data=data1)
summary(kmodel1)
hist(data1$formal.score) hist(data1$formal.score)
cor.test(data1$formal.score, data1$up.fac.mean) cor.test(data1$formal.score, data1$up.fac.mean)
cor.test(data1$mmt, data1$up.fac.mean) cor.test(data1$mmt, data1$up.fac.mean)
@ -55,14 +56,14 @@ cor.test(data1$age, data1$up.fac.mean)
g <- ggplot(data1, aes(x=mmt, y=up.fac.mean)) + g <- ggplot(data1, aes(x=mmt, y=up.fac.mean)) +
geom_point() + geom_point() +
geom_smooth() geom_smooth()
g g
data2 <- subset(data1, (data1$age / 365) < 14 ) data2 <- subset(data1, (data1$age / 365) < 14 )
hist(floor(data2$age)) hist(floor(data2$age))
g <- ggplot(data2, aes(x=formal.score, y=up.fac.mean)) + g <- ggplot(data2, aes(x=formal.score, y=up.fac.mean)) +
geom_point() + geom_point() +
geom_smooth() geom_smooth()
g g
data2$yearsOld <- floor(data2$age / 365) data2$yearsOld <- floor(data2$age / 365)

3
R/powerAnalysis.R
View File

@ -20,6 +20,7 @@ makeDataNew <- function(n) {
## don't sim the outcome ## don't sim the outcome
#up.fac.mean=rnorm(n=n, mean=-0.1296376, sd=1.479847), # up.fac.mean #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
#mmt=rlogis(n=n, location = 1.685715),
## 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
milestones=rbinom(n=n, size=1, prob=.247), #milestones milestones=rbinom(n=n, size=1, prob=.247), #milestones
@ -55,7 +56,7 @@ powerCheck <- function(n, nSims) { #run a power calculation on the dataset given
simData <- makeDataNew(n) # make some data simData <- makeDataNew(n) # make some data
## outcome goes here --v ## outcome goes here --v
# e.g. simData$up.fac.mean <- (usefuleffsizeA * mmt) + (usefuleffsizeB * milestones) + rnorm(n=1, mean=0, sd=1) ##plus some noise # e.g. simData$up.fac.mean <- (usefuleffsizeA * mmt) + (usefuleffsizeB * milestones) + rnorm(n=1, mean=0, sd=1) ##plus some noise
simData$up.fac.mean <- (2 * simData$mmt) + (1.5 * simData$milestones) + rnorm(n=1, mean=0, sd=1) simData$up.fac.mean <- (-2.075 * simData$mmt) + (0.4284 * simData$milestones) + rnorm(n=1, mean=0, sd=1)
#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)
## could leave age out for now? ## could leave age out for now?