ben-domingue

bigf<-function(df) {
    df<-df[!is.na(df$rt),]
    df<-df[df$rt<60*5,]
    x<-irw::long2resp(df)
    id<-x$id
    x$id<-NULL
    m<-mirt::mirt(x,1,'Rasch')
    th<-mirt::fscores(m)
    th<-data.frame(id=id,th=th[,1])
    df<-merge(df,th)

ben-domingue

library(TestGardener)
convert<-function(resp) {
    n<-ncol(resp)
    N<-nrow(resp)
    for (i in 1:ncol(resp)) {
        vals<-sample(2:4,N,replace=TRUE)
        resp[,i]<-ifelse(resp[,i]==1,resp[,i],vals)
    }
    optList <- list()
    grbg <- matrix(0,n,1)

ben-domingue

##a helper function we'll use along the way.
##kr20 is a function that computes the kr20 version of cronbach's alpha
kr20<-function(resp) {
    k<-ncol(resp)
    p<-colMeans(resp,na.rm=TRUE)
    q<-1-p
    o<-rowSums(resp)
    (k/(k-1))*(1-sum(p*q)/var(o))
}

ben-domingue

library(schoolgrowth)

## > growthscores
##    stuid school grade year subject  Y
## 1  s0102  sch01     4    1     ela 50
## 2  s0196  sch01     4    1     ela 46
## 3  s0227  sch01     4    1     ela  5
## 4  s0284  sch01     4    1     ela 18
## 5  s0353  sch01     4    1     ela 15

ben-domingue

simfun<-function(N,a1,beta.inter=.1) {
    ff<-function(i,N,a1,a2,beta.inter) {
        add.error<-function(t,alpha) {
            ve<-var(t)/alpha-var(t)
            e<-rnorm(N,mean=0,sd=sqrt(ve))
            t+e
        }
        g<-rnorm(N)
        e<-rnorm(N)
        y<-.2*g+.5*e+beta.inter*g*e+rnorm(N)

ben-domingue

#this contains the funtion that does the ML estimation
ratio.test<-function(df,
                     hess=FALSE, #if FALSE, compute empirical hessian
                     ctl.list=list(maxit=1000,reltol=sqrt(.Machine$double.eps)/1000)
                     ) {
    ##
    anal_vcov <- function(pars, df){
        neg_hess <- function(pars,df) { #computes the negative hessian
            for (nm in names(pars)) assign(nm,pars[[nm]])
            mu <- m*df$E + pi0*df$g + pi1*df$g*df$E

ben-domingue

out<-list()
for (s1 in seq(0,.2,by=.01)) {
    print(s1)
    test<-numeric()
    for (i in 1:250) {
        N=1000
        h=sqrt(.6)
        e=sqrt(1-h^2)
        m=.35
        s0=sqrt(1-m^2)

ben-domingue

interplay<-function(x,std.time.in.item=FALSE,nspl=4,plot.den=TRUE,top.plot=TRUE,...) {
    ##x needs to have columns:
    ## item [item id]
    ## id [person id]
    ## diff [item difficulty]
    ## th [person theta]
    ## pv [irt-based p-value]
    ## rt [response time in metric you want to analyze]
    ##resp [item response]
    #####################################################################

ben-domingue

#get genetic risk score
read.table("/hrsshare/alz_context/pgs/alz_nc.profile",header=TRUE)->score
score[,c(2,6)]->score
names(score)<-c("subjectID","polygenic_score")

#rand fat files
load("/hrsshare/v2_hrs_linked.Rdata")
x[,c("subjectID","hhidpn","family","raracem","rahispan")]->ids
ids[ids$raracem=="1.white/caucasian" & ids$rahispan=="0. not hispanic",]->ids
merge(ids,score)->x

ben-domingue

make_grs<-function(nm,df,fam.id="family",id="subjectID",
                   threshold.seq,
                   plink="/hrsshare/analytic_sample/hrs_geno_final",
                   resid.fm=NULL) {
  dir.nm<-paste0("/tmp/",nm)
  system(paste("mkdir",dir.nm))
  getwd()->dir.save
  setwd(dir.nm)
  if (!is.null(resid.fm)) {
    paste(nm,"~",resid.fm)->resid.fm