diff --git a/R/calculatePower.R b/R/calculatePower.R
index e48afc6..c69389a 100644
--- a/R/calculatePower.R
+++ b/R/calculatePower.R
@@ -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) 
diff --git a/R/powerAnalysis.R b/R/powerAnalysis.R
index e4a0ccd..2c6f0ce 100644
--- a/R/powerAnalysis.R
+++ b/R/powerAnalysis.R
@@ -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)