drafted power analysis

R/calculatePower.R

@@ -60,8 +60,12 @@ g
 
 data2$yearsOld <- data2$age / 365
 
-kmodel2 <- lm(up.fac.mean ~ mmt + milestones + yearsOld, data=data2)
+kmodel2 <- lm(up.fac.mean ~ mmt + milestones + age, data=data1)
+kmodel4 <- lm(up.fac.mean ~ mmt + age, data=data1)
+kmodel3 <- lm(up.fac.mean ~ formal.score, data=data1)
 summary(kmodel2)
+summary(kmodel3)
+summary(kmodel4)
 
 #pilotM <- glm(up.fac.mean ~ ((mmt) / (milestones/age)),   # give the anticipated regression a try
 #                  family=gaussian(link='identity'), data=data1)
@@ -73,7 +77,13 @@ pilot.b2 <- coef(summary(kmodel2))[3,1]
 pilot.b3 <- coef(summary(kmodel2))[4,1] 
 
 
+summary(pilot.b3)
+
+qqline(data1$up.fac.mean)
+
+sd(data1$up.fac.mean)
 # (3) - Set up and run the simulation
+qqline(data1$mmt)
 
 source('powerAnalysis.R') #my little "lib"
 
@@ -86,8 +96,9 @@ n <- 100 #a guess for necessary sample size (per group)
 #print("Levels are:")
 #print(levels(d$source))
 powerCheck(n, nSims)
+#powerCheck2(n, nSims) like doesn't really work
 
 #Sample values
-powerCheck(50, 100) 
+powerCheck(50, 1000) 
 powerCheck(80, 1000) 
 powerCheck(200, 5000)

R/powerAnalysis.R

@@ -16,12 +16,29 @@ l2p <- function(b) {
 
 #Matt: 
 makeDataNew <- function(n) {
-  sDF <- data.frame(
+  tDF <- data.frame(
+    Group0=rnorm(n=n, mean=-0.1296376, sd=1.479847), # up.fac.mean
+    Group1=rlnorm(n=n, mean=1.685715, sd = 0.2532059), # mmt
+    Group2=rbinom(n=n, size=1, prob=c(0.247, 0.753)), #milestones
+    Group3=rnorm(n=n, mean=4351.578, sd=1408.811) # age
   )
-  colnames(sDF) <- c('formality', 'age', 'contributors', 'collaborators', 'milestones', 'mmt', 'up.fac.mean')
+  #sDF <- melt(tDF, id.vars = 0) #AKA the index is the unique id, as far as that goes
-  return(sDF)
+  colnames(tDF) <- c('up.fac.mean', 'mmt', 'milestones', 'age')
+  return(tDF)
 }
 
+makeDataNew2 <- function(n) {
+  tDF <- data.frame(
+    Group0=rnorm(n=n, mean=-0.1296376, sd=1.479847), # up.fac.mean
+    Group1=rlnorm(n=n, mean=6.220282, sd = 2.544058) # formal.score
+  )
+  tDF[is.na(tDF) | tDF=="Inf"] = NA
+  #sDF <- melt(tDF, id.vars = 0) #AKA the index is the unique id, as far as that goes
+  colnames(tDF) <- c('up.fac.mean', 'formal.score')
+  return(tDF)
+}
+
+
 powerCheck <- function(n, nSims) { #run a power calculation on the dataset given
   #set up some empty arrays b/c R
   signif0 <- rep(NA, nSims)
@@ -32,7 +49,8 @@ powerCheck <- function(n, nSims) { #run a power calculation on the dataset given
   for (s in 1:nSims) {           # repeatedly we will....
     simData <- makeDataNew(n)       # make some data
     #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)
     p0 <- coef(summary(m1.sim))[1,4]
     p1 <- coef(summary(m1.sim))[1,4]
     p2 <- coef(summary(m1.sim))[1,4]
@@ -47,3 +65,25 @@ powerCheck <- function(n, nSims) { #run a power calculation on the dataset given
   return(power)
 }
 
+powerCheck2 <- function(n, nSims) { #run a power calculation on the dataset given
+  #set up some empty arrays b/c R
+  signif0 <- rep(NA, nSims)
+  signif1 <- rep(NA, nSims)
+  signif2 <- rep(NA, nSims)
+  signif3 <- rep(NA, nSims)
+  signifM <- rep(NA, nSims)
+  for (s in 1:nSims) {           # repeatedly we will....
+    simData <- makeDataNew2(n)       # make some data
+    #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 ~ formal.score, data=simData)
+    p0 <- coef(summary(m1.sim))[1,2]
+    p1 <- coef(summary(m1.sim))[1,2]
+    signif0[s] <- p0 <=.05
+    signif1[s] <- p1 <=.05
+    signifM[s] <- p0 <=.05 & p1 <=.05 
+  }  
+  power <- c(mean(signif0), mean(signif1), mean(signifM))
+  return(power)
+}
+