R: Parameter Estimation of the Generalized IRT Model

guIRT {lazy.girt}

R Documentation

Parameter Estimation of the Generalized IRT Model

Description

Parameter Estimation of the Generalized IRT Model

Usage

guIRT(Uc, U = NULL, groupvar = NULL, idvar = NULL, ncat = NULL,
  type = NULL, itemname = NULL, DinP = 1, param = NULL, msn = NULL,
  fixeditems = NULL, baseform = 1, thetaphi = NULL, thphd = NULL,
  estmu = 0, estsigma = 0, npointth = 21, thmin = -4, thmax = 4,
  thdist = "NORMAL", thmean = 0, thstd = 1, npointph = 21, phmin = 0,
  phmax = 9, phdist = "GBETA", paramab = c(1, 3), maxiter = 200,
  eps = 1e-07, epsd = 1e-06, maxiter2 = 20, eps2 = 1e-04, nstrict = 9,
  maxiter22 = 5, minp1 = 0.1, maxabsparam = 20, SQUAREM = 3,
  nSQUAREM = 1, minalpha = -999, maxalpha = -1, always = 1,
  reset1 = 0, reset2 = 1, print = 2, plot = 0, smallP = 0,
  debug = 0)

Arguments

`Uc`	n x nitems+1 or +2 compressed item resopnse data frame in BILOG-MG's expanded format. (idvar and groupvar)
`U`	n x sum(ncat)+1 or +2 uncompressed irem response data frame
`groupvar`	name of the grouping varible contained as a column of Uc or NULL.
`idvar`	name of the id varible contained as a column of Uc or NULL
`ncat`	nitems x 1 # of categories for each item or NULL.
`type`	nitems x 1 vector of item types consisting of: "Bn" \| "G" \| "PN" \| "P" If NULL, P will be used.
`itemname`	nitems x 1 vector of item names or NULL.
`DinP`	= 0 to exclude 1.7 from logistic function.
`param`	nitems x max(ncat) initial value data frame for item param Only the subset of the items can be given. param$name and the colname(Uc) will be used to match the items.
`msn`	nG x 3 initial value matrix of (mean, std, n) for each group
`fixeditems`	list of items whose item parameters are to be fixed to the values given in the param data frame. Item numbers or item names
`baseform`	base form number whose theta distribution is fixed at the values given in msn[baseform,] If there are fixed items, the values of the item paramters given in the param data frame must be on the baseform scale.
`thetaphi`	Discrete theta-phi grid values. When NULL, thetaphi will be generated according to npointth etc.
`thphd`	pdf of theta-phi normalized to sum to unity. When NULL,thphd will be generated according to npointth etc.
`estmu`	= 1 to estimate multigroup theta means
`estsigma`	= 1 to estimate mul tigroup theta std
`npointth`	# of discrete points for theta.
`thmin`	Minimum value of discrete thata value.
`thmax`	Maximum value of discrete thata value.
`thdist`	theta distribution "NORMAL" or "UNIFORM"
`thmean`	The prior mean of normal theta distribution.
`npointph`	# of discrete points for phi.
`phmin`	Minimum value of discrete phi value.
`phmax`	Maximum value of discrete phi value.
`phdist`	phi distribution "GBETA"
`paramab`	The prior param for the phi distribution.
`maxiter`	max # of iterations
`eps`	eps for the relative improvement of lmlh
`epsd`	eps for the max. abs. diff. of msn Seems this is important.
`maxiter2`	max # of iterations for ordinal_reg and smn when llll <= nstrict.
`eps2`	eps for the relative improvement of llh in ordinal_reg and smn.
`nstrict`	maxiter2 will be reduced to maxiter22 after nstrict iterations.
`maxiter22`	max # of iterations for ordinal_reg and smn when llll > nstrict.
`minp1`	Minimum value of p1 parameter for type != "N" items.
`maxabsparam`	Maximum absolute value of parameters.
`SQUAREM`	= 3 : See the help of iSQUAREM in lazy.accel package.
`nSQUAREM`	when to star iSQUAREM
`minalpha`	= -999 : See the help of iSQUAREM in lazy.accel package.
`maxalpha`	= -1 : See the help of iSQUAREM in lazy.accel package.
`always`	= 0 : See the help of iSQUAREM in lazy.accel package.
`reset1`	= 1 : See the help of iSQUAREM in lazy.accel package.
`reset2`	= 2 : See the help of iSQUAREM in lazy.accel package.
`print`	= 1 to print the result
`plot`	= 1 to plot the estimated theta distributions
`smallP`	= Minimum value of P
`debug`	= 1 to print intermediate result
`theta`	npoints x 1 discrete theta points
`thd`	NOT used.
`thsd`	The prior standard deviation of normal theta distribution.

Examples


set.seed(1701)

# 30 binary items
param=paramBr
# one obs per 500 random theta-phi
resg <- gendata_gIRT( 1, param, thdist="NORMAL", phdist="GBETA", compress=1
                     , npoint=500, phmin=0, phmax=2, paramab=c(1,4) )
Uc1=as.data.frame( resg$U )
thetaphi_ORG1=resg$thetaphi

# item parameter estimation
res1=guIRT( Uc=Uc1, ncat=param$ncat, type=param$type
            , npointth=21, npointph=5, phmin=0, phmax=2
            , paramab=c(1,4), print=2 )

Print( cor( thetaphi_ORG1,res1$EAP ),
       cor( param[,c("p1","p2")], res1$param[,c("p1","p2")] ) )
plot( res1$EAP[,1], res1$EAP[,2])
dummy=freqdist1( res1$EAP[,1], npoints=101, plot=1 )
dummy=freqdist1( res1$EAP[,2], npoints=101, plot=1 )




set.seed(1701)

# 4 binary + 8 graded items
param=paramBG
# one obs per 500 random theta-phi
resg <- gendata_gIRT( 1, param, thdist="NORMAL", phdist="GBETA", compress=1
                      , npoint=500, phmin=0, phmax=2, paramab=c(1,4) )
Uc2=as.data.frame( resg$U )
thetaphi_ORG2=resg$thetaphi

# paramter estimation
res2=guIRT( Uc=Uc2, ncat=param$ncat, type=param$type
            , npointth=21, npointph=5, phmin=0, phmax=2
            , paramab=c(1,4), print=2 )

Print( cor( thetaphi_ORG2,res2$EAP ),
       param[,4:7]-res2$param[,4:7] )
plot( res2$EAP[,1], res2$EAP[,2])
dummy=freqdist1( res2$EAP[,1], npoints=101, plot=1 )
dummy=freqdist1( res2$EAP[,2], npoints=101, plot=1 )

[Package lazy.girt version 0.1.3 Index]