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binning age and trying to figure out power

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mjgaughan 2023-11-14 22:54:32 -06:00
parent 68653a6470
commit 27856dfd2d
2 changed files with 11 additions and 3 deletions
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11
R/calculatePower.R
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@ -34,12 +34,19 @@ table(data1$milestones)
hist(data1$old_mmt) #inequality of participation hist(data1$old_mmt) #inequality of participation
hist(data1$formal.score) hist(data1$formal.score)
hist(data1$age/365) hist(data1$age/365)
data1$new.age <- as.numeric(cut(data1$age/365, breaks=c(0,9,12,15,17), labels=c(1,2,3,4)))
data1$formal.score <- data1$mmt / (data1$milestones/data1$new.age)
hist(as.numeric(data1$new.age))
table(data1$formal.score)
kmodel1 <- lm(up.fac.mean ~ mmt, data=data1) kmodel1 <- lm(up.fac.mean ~ mmt, data=data1)
summary(kmodel1) summary(kmodel1)
kmodel1 <- lm(up.fac.mean ~ old_mmt, data=data1) kmodel1 <- lm(up.fac.mean ~ old_mmt, data=data1)
summary(kmodel1) summary(kmodel1)
kmodel1 <- lm(up.fac.mean ~ formal.score, data=data1) kmodel1 <- lm(up.fac.mean ~ formal.score, data=data1)
summary(kmodel1) summary(kmodel1)
kmodel1 <- lm(up.fac.mean ~ new.age, data=data1)
summary(kmodel1)
hist(data1$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$up.fac.mean)
@ -52,13 +59,13 @@ g <- ggplot(data1, aes(x=mmt, y=up.fac.mean)) +
g g
data2 <- subset(data1, (data1$age / 365) < 14 ) data2 <- subset(data1, (data1$age / 365) < 14 )
hist(data2$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 <- data2$age / 365 data2$yearsOld <- floor(data2$age / 365)
kmodel2 <- lm(up.fac.mean ~ mmt + milestones + age, data=data1) kmodel2 <- lm(up.fac.mean ~ mmt + milestones + age, data=data1)
kmodel5 <- lm(up.fac.mean ~ mmt + milestones, data=data1) kmodel5 <- lm(up.fac.mean ~ mmt + milestones, data=data1)

3
R/powerAnalysis.R
View File

@ -55,6 +55,7 @@ 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$milestones) + rnorm(n=1, mean=0, sd=1)
#have updated for kkex through here, now need to look at the underproduction work #have updated for kkex through here, now need to look at the underproduction work
#m1.sim <- lm(up.fac.mean ~ ((mmt)/ (milestones/age)), data=simData) #m1.sim <- lm(up.fac.mean ~ ((mmt)/ (milestones/age)), data=simData)
## could leave age out for now? ## could leave age out for now?
@ -84,7 +85,7 @@ powerCheck2 <- function(n, nSims) { #run a power calculation on the dataset give
#have updated for kkex through here, now need to look at the underproduction work #have updated for kkex through here, now need to look at the underproduction work
#m1.sim <- lm(up.fac.mean ~ ((mmt)/ (milestones/age)), data=simData) #m1.sim <- lm(up.fac.mean ~ ((mmt)/ (milestones/age)), data=simData)
## outcome goes here --v ## outcome goes here --v
## e.g. simData$up.fac.mean <- (usefuleffsizeC * formal.score) + rnorm(1, mean=0, sd=1) ##plus some noise simData$up.fac.mean <- (0.5 * simData$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]