cala {lazy.irt}  R Documentation 
Calibration of Independently Estimated Sets of Item Parameters by Minimizing the LS Criterion Defined in terms of Parameter Values
cala( indata, baseform = 0, nsubj = 0, maxiter = 100, eps = 1e05, init = 1, print = 1, debug = 0 )
indata 
data frame containing at least following: 
baseform 
form number or 0 
nsubj 
# of subjects in each form.

maxiter 
max of iteration for PCA 
eps 
eos for PCA 
init 
not used 
print 
> 0 to print results 
debug 
= 1 to print intermediate result 
This program finds u,v and b such taht
bhata[j,g] = u[g] + b[j] v[g] + error
j=1,2,..., # of items and g=1,2,..., # of forms,
using PCA with missing data, where bhat is the collection of estimated
bparameters.
a[g] will be estimated as the average of ahat[j,g]/r[g] ,
c[g] will be estimated as the average of chat[j,g] ,
where r[g]=1/v[g]
A list of:
param Data frame containing the equated item parameters
uv Transformations from common scale to each scale
qr Transformations from each scale to common scale
nfini Vector of # of forms including each item
niinf Vector of # of items included in each form
fini List of the Forms including each item.
iinf List of the Items included in each form.
iftable Matrix of item x form indicating the inclusion pattern.
toP Vector of stating locations in ParamHat
fromP Vector of ending locations in ParamHat
iterPCA # of iterations needed for convergence.
nbPCA 3 of bparameters processed by PCA.
nisolo # of items that are included in only one form.
ParamHat Item Paramter matrix analyzed by PCA
rmse LS criterion minimized.
Sayaka Arai & Shinichi Mayekawa (2011)
A comparison of equating methods and linking designs for developing
an item pool under item response theory.
Behaviormetrika, Vol. 38 No. 1, pp. 116
Shinich Mayekawa (1991) Chapter 4. Parameter Estimation. In Shiba Ed. Item Response Theory. pp87129. (in Japanese)
# Binary Items res=cala( indata=paramCal1, baseform=1, print=2 ) # Mixture of Item Types res=cala( indata=paramCal2, baseform=1, print=2 )