ls()
dir()
dir()
dir()
rm(list=ls())#
load("n2bnd_dvmat.rda")#
library(reshape)#
library(lme4)#
source("plmer.R")
d.all$item <- rep(1:1552,times=30) # 1552 words x 30 subjecs (160 Sätze/Subject; all words)#
nrow(d.all) # 46560#
#
# twx: 0: word n, 1: n+1, 2: n+2, 3: n+3, 9: other#
TargetRegion <- which(d.all$twx<3)  # word n, n+1, n+2; 3*160*30 = 14400  #
d.red1 <- d.all[TargetRegion, ]#
nrow(d.red1)#
#
# Missing sentences: 1596, 1596/14400=.1108, 14400-1596=12804#
validSn <- which(d.red1$prevw >=0 )#
d.red2 <- d.red1[validSn, ]#
nrow(d.red2) # 12804#
#
# Display change too late#
validBnd5 <- which(d.red2$bnd5 == 1) # valid display change,  82%  of possible measures#
d <- d.red2[validBnd5, ]  # 9096 valid words #
nrow(d) # 9096
str(d)
d$x1 <- d$cnpl1 - 1.5  # lex status word n+1 -.5: cw, +.5: fw#
d$pv <- d$prevw - 0.5  # preview -.5: id, +.5: nw
str(d)
d.rs <- melt(d, id=c("id", "item", "x1", "pv", "twx"), measure=c("FFDr", "GZD", "ILP") )#
cast(d.rs, x1+pv ~ variable, function(x) c(round(mean(x, na.rm=TRUE)), round(sd(x, na.rm=TRUE))),#
     subset=twx==0 )
cast(d.rs, x1+pv ~ variable, function(x) c(mean(x, na.rm=TRUE), sd(x, na.rm=TRUE)),#
     subset=twx==0 )
d.rs <- melt(d, id=c("id", "item", "x1", "pv", "twx"), measure=c("FFDr", "GZD") )#
cast(d.rs, x1+pv ~ variable, function(x) c(round(mean(x, na.rm=TRUE)), round(sd(x, na.rm=TRUE))),#
     subset=twx==0 )
m0.ffd <- lmer(FFDr ~ x1*pv + (1|id) + (1|item), data=d, subset=twx==0)#
pvals.fnc(m0.ffd)
m0.ffd
source("ffd.fake.R")
source("n2power.R")
ls()
ffd.fake
prb1 <- 2880/4800  # .60 for n and n+2
prb1
prb2 <- 1600/4800  # .33 for n+1
prb2
K <- 160#
J <-  30
n2.power
J
K
prop=1.0
n.sims=1000
	signif.x1   <- rep(NA, n.sims)#
	signif.pv   <- rep(NA, n.sims)#
	signif.x1pv <- rep(NA, n.sims)#
	N <- J*K
N
s <- 1
ffd.fake
		fake <- ffd.fake(J,K)
head(fake)
str(fake)
mean(y)
mean(fake$y)
sd(fake$y)
tapply(fake$y,fake$x1,mean)
tapply(fake$y,fake$pv,mean)
tapply(fake$y,fake$x1pv,mean)
tapply(fake$y,list(fake$x1,fake$pv),mean)
tapply(d$FFDr,list(d$x1,d$pv),mean)
		lme.power <- lmer(y ~ x1*pv + (1|id) + (1|item), data=fake)
lme.power
		theta.x1.hat <- fixef(lme.power)["x1"]
theta.x1.hat
		theta.x1.se  <- sqrt(vcov(lme.power)["x1","x1"])
theta.x1.se
		signif.x1[s] <- (theta.x1.hat + 2*theta.x1.se) < 0 # returns TRUE or FALSE
signif.x1[1]
		theta.pv.hat <- fixef(lme.power)["pv"]#
		theta.pv.se  <- sqrt(vcov(lme.power)["pv","pv"])#
		signif.pv[s] <- (theta.pv.hat - 2*theta.pv.se) > 0 # returns TRUE or FALSE
		theta.x1pv.hat <- fixef(lme.power)["x1:pv"]#
		theta.x1pv.se  <- sqrt(vcov(lme.power)["x1:pv","x1:pv"])#
		signif.x1pv[s] <- (theta.x1pv.hat + 2*theta.x1pv.se) < 0  # returns T or F
	signif<-cbind(signif.x1, signif.pv, signif.x1pv)
signif
ffd.pow100 <- n2.power(J, K, prop=1,   n.sims=1000)    # x1: -7, pv: 7, x1*pv: -7#
for (i in 1:3) print(mean(ffd.pow100[,i]))            #  .977,  .979,   .476
ffd.pow100 <- n2.power(J, K, prop=1,   n.sims=1000)    # x1: -7, pv: 7, x1*pv: -7
source("n2power.R")
ffd.pow100 <- n2.power(J, K, prop=1,   n.sims=1000)    # x1: -7, pv: 7, x1*pv: -7
for (i in 1:3) print(mean(ffd.pow100[,i]))            #  .977,  .979,   .476
library(lme4)
sessionInfo(lme4)
sessionInfo()
