fit223_ls {lazy.irt}

R Documentation

Conversion of 3PLM Items to 2PLM Items

Description

Conversion of 3PLM Items to 2PLM Items

Usage

fit223_ls(
  param3,
  wtype = 0,
  wmean = 0,
  wsd = 1,
  DinP = 1,
  npoints = 21,
  thmin = -3,
  thmax = 3,
  maxiter = 100,
  eps = 1e-06,
  print = 1,
  plot = 0,
  debug = 0
)

Arguments

param3	Item Parameter Data Frame for 3PLM Items
wtype	= 1 to use dnorm(theta) as the weight
wmean	The mean of normal distribution to be used as the weight
wsd	The sd of normal distribution to be used as the weight
DinP	= 1 to include D=1.7 in logistic function
npoints	# of discrete points for theta
thmin	Minimum value of discrete thata value
thmax	Maximum value of discrete thata value
maxiter	Maximum # of GN iterations
eps	Convergence criterion for the relative improvement of rmse
print	= 1 to print result
plot	= 1 to plot result
debug	= 1 to print intemediate result

Details

This function minimizes
rss=sum( w*( vec(icrf_3(theta)) - vec(icrf_2(theta)) )^2 )
with respec to the 2PLM item parameters,
where icrf_3(theta) is the icrf of input 3PLM items and icrf_2(theta) is the icrf of fitted 2PLM items,
and w is the weight vector normal or 1.

Unlike fitP2G_ls and fitG2P_ls, the icrf of the 0-th category is not used.
Weighted Gauss-Newton method is used for the minimization.

Value

A list of:
param3: Input 3PLM item parameter data frame (subset, type="P")
param2: 2PLM Item Parameter Data Frame (type="G")
rmse: Vector of sqrt(rss/length(theta)) for each item. grad: Gradient matrix
wtype, wmean, wsd

Examples

param2 <- fit223_ls( paramS2, plot=1, print=1 )
param21 <- fit223_ls( paramS2, plot=1, print=1, wtype=1 )

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