From 8f26cae949b9557f7350ff6605ad67e3255e6919 Mon Sep 17 00:00:00 2001
From: mjgaughan <mgaughan@proton.me>
Date: Wed, 15 Nov 2023 11:24:51 -0600
Subject: [PATCH] more fiddling w power analysis

---
 R/calculatePower.R | 9 +++++----
 R/powerAnalysis.R  | 3 ++-
 2 files changed, 7 insertions(+), 5 deletions(-)

diff --git a/R/calculatePower.R b/R/calculatePower.R
index 669addf..e48afc6 100644
--- a/R/calculatePower.R
+++ b/R/calculatePower.R
@@ -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)
 hist(as.numeric(data1$new.age))
 table(data1$formal.score)
+hist(data1$formal.score)
 kmodel1 <- lm(up.fac.mean ~ mmt, data=data1)
 summary(kmodel1)
 kmodel1 <- lm(up.fac.mean ~ old_mmt, data=data1)
 summary(kmodel1)
-kmodel1 <- lm(up.fac.mean ~ formal.score, data=data1)
-summary(kmodel1)
 kmodel1 <- lm(up.fac.mean ~ new.age, data=data1)
 summary(kmodel1)
+kmodel1 <- lm(up.fac.mean ~ milestones, data=data1)
+summary(kmodel1)
 hist(data1$formal.score)
 cor.test(data1$formal.score, data1$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)) +
   geom_point() +
-  geom_smooth()
+  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)) +
   geom_point() +
-  geom_smooth()
+  geom_smooth() 
 g
 
 data2$yearsOld <- floor(data2$age / 365)
diff --git a/R/powerAnalysis.R b/R/powerAnalysis.R
index aab5f18..6bbb903 100644
--- a/R/powerAnalysis.R
+++ b/R/powerAnalysis.R
@@ -20,6 +20,7 @@ makeDataNew <- function(n) {
     ## 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=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
     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
     ## 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 * 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
     #m1.sim <- lm(up.fac.mean ~ ((mmt)/ (milestones/age)), data=simData)
     ## could leave age out for now?