R/CRSM.R

Defines functions CRSM

Documented in CRSM

#' Estimation of Continuous Rating Scale Model (Mueller, 1987)
#'
#' Estimation of the Rating Scale Model for continuous data by Mueller (1987).
#'
#' \deqn{P_{vi}(a \leq X \leq b) = \frac{\int_a^b exp[x \mu + x(2c-x) \theta]
#' dx}{\int_{c-\frac{d}{2}}^{c+\frac{d}{2}} exp[t \mu + t(2c-t) \theta] dt}}
#'
#' Parameters are estimated by a pairwise conditional likelihood estimation (a pseudo-likelihood approach, described in Mueller, 1999).
#'
#' The parameters of the Continuous Rating Scale Model are estimated by a pairwise cml approach using Newton-Raphson iterations for optimizing.
#'
#' @aliases CRSM summary.CRSM print.CRSM
#' @param data Data matrix or data frame; rows represent observations
#' (persons), columns represent the items.
#' @param low The minimum value of the response scale (on which the data are
#' based).
#' @param high The maximum value of the response scale (on which the data are
#' based).
#' @param start Starting values for parameter estimation. If missing, a vector
#' of 0 is used as starting values.
#' @param conv Convergence criterium for parameter estimation.
#' @return \item{data}{data matrix according to the input} \item{data_p}{data
#' matrix with data transformed to a response interval between 0 and 1}
#' \item{itempar}{estimated item parameters} \item{itempar_se_low}{estimated
#' lower boundary for standard errors of estimated item parameters}
#' \item{itempar_se_up}{estimated upper boundary for standard errors of
#' estimated item parameters} \item{itempar_se}{estimated mean standard errors
#' of estimated item parameters} \item{disppar}{estimated dispersion
#' parameter} \item{disppar_se_low}{estimated lower boundary for standard
#' errors of estimated dispersion parameter} \item{disppar_se_up}{estimated
#' upper boundary for standard errors of estimated dispersion parameter}
#' \item{itempar_se}{estimated mean standard errors of estimated item
#' parameter} \item{disp_est}{estimated dispersion parameters for all item pairs}\item{iterations}{Number of Newton-Raphson iterations for each
#' item pair} \item{low}{minimal data value entered in call}\item{high}{maximal data value entered in call}\item{call}{call of the CRSM function}
#' @author Christine Hohensinn
#' @references Mueller, H. (1987). A Rasch model for continuous ratings.
#' Psychometrika, 52, 165-181.
#'
#' Mueller, H. (1999). Probabilistische Testmodelle fuer diskrete und
#' kontinuierliche Ratingskalen. [Probabilistic models for discrete and
#' continuous rating scales]. Bern: Huber.
#' @keywords continuous rating scale model
#' @examples
#'
#' #estimate CRSM item parameters
#' data(analog)
#' res_crsm <- CRSM(extraversion, low=-10, high=10)
#'
#' summary(res_crsm)
#'
#' @export CRSM
#' @rdname crsm
CRSM <-
function(data, low, high, start, conv=0.0001){

call <- match.call()

if(is.data.frame(data)) {data <- as.matrix(data)}
if(missing(low)){stop("Error: enter lowest possible value of items")}
if(missing(high)){stop("Error: enter highest possible value of items")}

# bring data in interval (0,1)
data_p <- apply(data, 2, function(d) {(d-low)/(high-low)})

#check if few oberservations for item / person
if (any(apply(data_p, 2, function(dr) sum(dr != 0 & dr != 1)) %in% c(0,1)))
{cat("warning: there is at least 1 item with only 1 observation containing no extreme rawscore.
     This will probably cause estimation problems")}
if (any(apply(data_p, 1, function(dc) sum(dc != 0 & dc != 1)) %in% c(0,1)))
{cat("warning: there is at least 1 person with only 1 observation containing no extreme rawscore.
     This will probably cause estimation problems")}

if(missing(start)){
  para1     <- rep(0, 2)
} else {
  para1     <- start
}

combis <- combn(ncol(data_p),2)



iterations <- vector(mode="numeric",length=ncol(combis))

S0n <- function(t, paraI)      {exp(-(t/2)*paraI[1] - (t^2/2)*paraI[2])}
S1n <- function(t, paraI) {t*   exp(-(t/2)*paraI[1] - (t^2/2)*paraI[2])}
S2n <- function(t, paraI) {t^2* exp(-(t/2)*paraI[1] - (t^2/2)*paraI[2])}
S3n <- function(t, paraI) {t^3* exp(-(t/2)*paraI[1] - (t^2/2)*paraI[2])}
S4n <- function(t, paraI) {t^4* exp(-(t/2)*paraI[1] - (t^2/2)*paraI[2])}



parlist <- lapply(1:ncol(combis), function(m) {
  zwi <- data_p[,c(combis[1,m],combis[2,m])]
  zwi2 <- zwi[rowSums(zwi)!=0 & rowSums(zwi)!=2,, drop=FALSE] #delete extreme scores

  uij <- zwi2[,1]-zwi2[,2]
  sij <- sum(uij)

  bound       <- round(1-abs(rowSums(zwi2)-1),6) #interval width
  bound.v     <- sort(unique(bound))
  bound.n     <- unname(table(bound))

  iter        <- 0
  para        <- NA

  while( is.na(para) || max(abs(para1-para)) > conv){
    para <- para1

    S0n.i <- sapply(bound.v, function(b) integrate(S0n,paraI=para,lower=-b, upper=b, stop.on.error=F)$value)
    S1n.i <- sapply(bound.v, function(b) integrate(S1n,paraI=para,lower=-b, upper=b, stop.on.error=F)$value)
    S2n.i <- sapply(bound.v, function(b) integrate(S2n,paraI=para,lower=-b, upper=b, stop.on.error=F)$value)
    S3n.i <- sapply(bound.v, function(b) integrate(S3n,paraI=para,lower=-b, upper=b, stop.on.error=F)$value)
    S4n.i <- sapply(bound.v, function(b) integrate(S4n,paraI=para,lower=-b, upper=b, stop.on.error=F)$value)


    abl1    <- vector(length=2)
    abl1[1] <- -sij/2 + 0.5*sum((S1n.i/S0n.i)*bound.n)
    abl1[2] <- -sum(uij^2)/2 + 0.5*sum((S2n.i/S0n.i)*bound.n)

    abl2        <- matrix(NA,ncol=2,nrow=2)
    abl2[1,1]   <- -0.25 * sum(((S2n.i/S0n.i)-(S1n.i/S0n.i)^2)*bound.n)
    abl2[1,2]   <- -0.25 * sum(((S3n.i/S0n.i)-((S1n.i*S2n.i)/S0n.i^2))*bound.n)
    abl2[2,1]   <- abl2[1,2]
    abl2[2,2]   <- -0.25 * sum(((S4n.i/S0n.i)-(S2n.i/S0n.i)^2)*bound.n)

    para1    <- as.vector(para - abl1%*%solve(abl2))
    iter <- iter+1
  }
  return(list(para1=para1, iterations=iter, hessian=abl2))
}
)


beta       <- sapply(parlist, function(l) l$para1[1])
lambda.all <- sapply(parlist, function(la) la$para1[2])
iterations <- sapply(parlist, function(it) it$iterations)

#calculate item parameters
if(any(lambda.all <= 0)){
    cat("warning: at least one dispersion parameter is 0 or negativ!")
}

tc <- t(combis)

lambda_est <- data.frame(item_pair=paste(tc[,1], tc[,2], sep='_'), lambda=lambda.all)

lambda     <- mean(lambda.all)

betas <- sapply(1:ncol(data_p), function(be) {
  sum(beta[combis[1,]==be],beta[combis[2,]==be]*(-1))/ncol(data_p)
}
)
betas <- betas - mean(betas)
names(betas) <- paste(rep("beta ", length(betas)), colnames(data))

#standard errors for items
varDiff.item <- sapply(parlist, function(s) diag(solve(s$hessian)*(-1))[1])
sdDiff.item <- sapply(parlist, function(s) sqrt(diag(solve(s$hessian))*(-1))[1])
varDiff.distr <- sapply(parlist, function(s) diag(solve(s$hessian)*(-1))[2])
sdDiff.distr <- sapply(parlist, function(s) sqrt(diag(solve(s$hessian)*(-1))[2]))

se.item.up <- sapply(1:ncol(data_p), function(di) {
  sqrt((sum(sdDiff.item[combis[1,]==di],sdDiff.item[combis[2,]==di]))^2/(ncol(data_p))^2)
}
)
names(se.item.up) <- paste(rep("SE(beta) up ", length(betas)), colnames(data))

se.item.low <- sapply(1:ncol(data_p), function(di) {
  sqrt(sum(varDiff.item[combis[1,]==di],varDiff.item[combis[2,]==di])/(ncol(data_p))^2)
}
)
names(se.item.low) <- paste(rep("SE(beta) low ", length(betas)), colnames(data))

se.item.mean <- (se.item.up+se.item.low)/2
names(se.item.mean) <- paste(rep("SE(beta) ", length(betas)), colnames(data))

se.distr.low <- sqrt(sum(varDiff.distr)/(ncol(combis))^2)
names(se.distr.low) <- "SE(lambda) low"

indmat <- apply(combis, 2, function(co){
  colSums(matrix(combis %in% co,nrow=2))
})
indmat[indmat==2] <- 1
indmat.n <- t(indmat*sdDiff.distr)

se.distr.up <- sqrt(sum(indmat.n*sdDiff.distr)/ncol(combis)^2)
names(se.distr.up) <- "SE(lambda) up"

se.distr.mean <- mean(c(se.distr.low, se.distr.up))
names(se.distr.mean) <- "SE(lambda)"




res_all   <- list(data=data, data_p=data_p, itempar=betas,itempar_se_low=se.item.low, itempar_se_up=se.item.up, itempar_se=se.item.mean, disppar=lambda, disppar_se_low=se.distr.low, disppar_se_up=se.distr.up,  disppar_se=se.distr.mean, disp_est=lambda_est, iterations=iterations, low=low, high=high,call=call)

class(res_all) <- "CRSM"

res_all

}

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pcIRT documentation built on April 30, 2018, 5:03 p.m.