mgaughan/24_deb_pkg_gov
1
0
Fork 0
Code Issues Pull Requests Packages Projects Releases Wiki Activity

working power analysis

Browse Source
This commit is contained in:
mjgaughan 2023-11-15 21:31:08 -06:00
parent e72374fec0
commit 5216ca767c
2 changed files with 15 additions and 12 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

14
R/calculatePower.R
View File

@ -31,9 +31,11 @@ data1$milestones <- as.numeric(data1$milestones > 0) + 1
# (2) - Run the model on the pilot data
data1$formal.score <- data1$mmt / (data1$milestones/data1$age)
table(data1$milestones)
hist(data1$old_mmt) #inequality of participation
hist(data1$old_mmt, prob=TRUE) #inequality of participation
hist(data1$formal.score)
hist(data1$age/365)
data1$new_mmt <- data1$mmt - 1
hist(data1$new_mmt, prob=TRUE)
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)
@ -42,7 +44,7 @@ table(data1$formal.score)
hist(data1$formal.score)
kmodel1 <- lm(up.fac.mean ~ mmt, data=data1)
summary(kmodel1)
kmodel1 <- lm(up.fac.mean ~ old_mmt, data=data1)
kmodel1 <- lm(up.fac.mean ~ new_mmt, data=data1)
summary(kmodel1)
kmodel1 <- lm(up.fac.mean ~ new.age, data=data1)
summary(kmodel1)
@ -54,14 +56,14 @@ cor.test(data1$mmt, data1$up.fac.mean)
cor.test(data1$milestones, data1$up.fac.mean)
cor.test(data1$age, data1$up.fac.mean)
g <- ggplot(data1, aes(x=mmt, y=up.fac.mean)) +
g <- ggplot(data1, aes(x=new_mmt, y=up.fac.mean)) +
geom_point() +
geom_smooth()
g
data2 <- subset(data1, (data1$age / 365) < 14 )
hist(floor(data2$age))
g <- ggplot(data2, aes(x=formal.score, y=up.fac.mean)) +
g <- ggplot(data2, aes(x=mmt, y=up.fac.mean)) +
geom_point() +
geom_smooth()
g
@ -109,9 +111,9 @@ powerCheck(n, nSims)
#powerCheck2(n, nSims) like doesn't really work
#Sample values
powerCheck(50, 1000)
powerCheck(100, 1000)
powerCheck(200, 1000)
powerCheck(500, 1000)
powerCheck(300, 1000)
powerCheck2(50, 1000)
powerCheck2(200, 1000)

13
R/powerAnalysis.R
View File

@ -19,7 +19,8 @@ makeDataNew <- function(n) {
tDF <- data.frame(
## 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
new_mmt=rbeta(n=n, 5, 1),
#mmt=rlogis(n=n, location = 1.685715),
## this generates a 50-50 split of milestones --v
#milestones=rbinom(n=n, size=1, prob=c(0.247, 0.753)), #milestones
@ -57,21 +58,21 @@ powerCheck <- function(n, nSims) { #run a power calculation on the dataset given
## outcome goes here --v
# e.g. simData$up.fac.mean <- (usefuleffsizeA * mmt) + (usefuleffsizeB * milestones) + rnorm(n=1, mean=0, sd=1) ##plus some noise
#simData$up.fac.mean <- (-2.075 * simData$mmt) + (0.4284 * simData$milestones) + rnorm(n=1, mean=0, sd=1)
simData$up.fac.mean <- (0.4284 * simData$milestones) + rnorm(n=1, mean=0, sd=1)
simData$up.fac.mean <- (-2.0745 * simData$new_mmt) + (0.4284 * simData$milestones) + rnorm(n=n, mean=0, sd=1)
#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)
## 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)
m1.sim <- lm(up.fac.mean ~ new_mmt + milestones, data=simData)
p0 <- coef(summary(m1.sim))[1,4] #intercept
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
signif0[s] <- p0 <=.05
signif1[s] <- p1 <=.05
#signif2[s] <- p2 <=.05
signif2[s] <- p2 <=.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))
return(power)