Hello all,
I have just started using fitting the PCM (Partial Credit Model) using eRm and
have 2 problems which I cannot solve, I have checked everywhere on the net, but
no joy:
Firstly,
I have fit a PCM model to 10 variables with differing response categories (3 for
the first 6 items, 4 for the following 2 and 2 for the last two items).
mat1 <- matrix(c(rasch_bart$bart_bowel, rasch_bart$bart_blad,
rasch_bart$bart_toil, rasch_bart$bart_feed, rasch_bart$bart_dress,
rasch_bart$bart_stairs, rasch_bart$bart_trans, rasch_bart$bart_mob,
rasch_bart$bart_groom, rasch_bart$bart_bath), nrow=100, ncol=10)
bart_pcm <- PCM(mat1)
summary(bart_pcm)
# Item location map
plotPImap(bart_pcm, item.subset=c(1,2))
but I can't get the person-item plot to work. I receive an error from R
saying:
Error in xb[, dim(xb)[2]] : incorrect number of dimensions.
Secondly,
How would you interpret the following parameter estimates i.e. the eta and beta
estimates?:
> summary(bart_pcm)
Results of PCM estimation:
Call: PCM(X = mat1)
Conditional log-likelihood: -206.1496
Number of iterations: 40
Number of parameters: 19
Basic Parameters (eta) with 0.95 CI:
Estimate Std. Error lower CI upper CI
eta 1 0.786 0.565 -0.320 1.893
eta 2 -0.440 0.480 -1.382 0.501
eta 3 -0.709 0.565 -1.817 0.399
eta 4 -0.547 0.428 -1.387 0.292
eta 5 -2.161 0.596 -3.330 -0.992
eta 6 5.036 0.674 3.715 6.358
eta 7 4.250 0.692 2.894 5.606
eta 8 -0.813 0.410 -1.615 -0.010
eta 9 -3.735 0.657 -5.023 -2.447
eta 10 -0.727 0.397 -1.506 0.051
eta 11 -4.671 0.709 -6.061 -3.281
eta 12 -2.417 0.463 -3.324 -1.510
eta 13 3.750 0.585 2.604 4.897
eta 14 2.499 0.613 1.297 3.700
eta 15 -0.589 0.707 -1.975 0.797
eta 16 1.637 0.515 0.627 2.647
eta 17 2.164 0.561 1.065 3.264
eta 18 -0.062 0.656 -1.347 1.223
eta 19 -2.989 0.494 -3.959 -2.020
Item Parameters (beta) with 0.95 CI:
Estimate Std. Error lower CI upper CI
beta I1.c1 -0.262 0.560 -1.360 0.835
beta I1.c2 0.786 0.565 -0.320 1.893
beta I2.c1 -0.440 0.480 -1.382 0.501
beta I2.c2 -0.709 0.565 -1.817 0.399
beta I3.c1 -0.547 0.428 -1.387 0.292
beta I3.c2 -2.161 0.596 -3.330 -0.992
beta I4.c1 5.036 0.674 3.715 6.358
beta I4.c2 4.250 0.692 2.894 5.606
beta I5.c1 -0.813 0.410 -1.615 -0.010
beta I5.c2 -3.735 0.657 -5.023 -2.447
beta I6.c1 -0.727 0.397 -1.506 0.051
beta I6.c2 -4.671 0.709 -6.061 -3.281
beta I7.c1 -2.417 0.463 -3.324 -1.510
beta I8.c1 3.750 0.585 2.604 4.897
beta I8.c2 2.499 0.613 1.297 3.700
beta I8.c3 -0.589 0.707 -1.975 0.797
beta I9.c1 1.637 0.515 0.627 2.647
beta I9.c2 2.164 0.561 1.065 3.264
beta I9.c3 -0.062 0.656 -1.347 1.223
beta I10.c1 -2.989 0.494 -3.959 -2.020
Thank you in advance for any help which is much appreciated.
Michael
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