Open leungkp opened 2 years ago
Thanks for your report. I believe this is fixed in version 9.2.4 which is just now on CRAN. Could you please reinstall and confirm 9.2.4 is installed and try again?
We don't have any plans for implementing more models at this time.
Thanks so much for the reply. Unfortunately, the Error in xvar %*% beta : non-conformable arguments
error persists in version 9.2.4:
> sessionInfo()
R version 4.2.1 (2022-06-23)
Platform: x86_64-apple-darwin17.0 (64-bit)
Running under: macOS Monterey 12.3.1
Matrix products: default
LAPACK: /Library/Frameworks/R.framework/Versions/4.2/Resources/lib/libRlapack.dylib
locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] list_9.2.4 sandwich_3.0-2
loaded via a namespace (and not attached):
[1] Rcpp_1.0.8.3 mvtnorm_1.1-3 quadprog_1.5-8 lattice_0.20-45 corpcor_1.6.10 zoo_1.8-10 MASS_7.3-57 grid_4.2.1
[9] magic_1.6-0 arm_1.12-2 nlme_3.1-157 stats4_4.2.1 coda_0.19-4 minqa_1.2.4 nloptr_2.0.3 Matrix_1.4-1
[17] boot_1.3-28 splines_4.2.1 lme4_1.1-29 tools_4.2.1 abind_1.4-5 compiler_4.2.1 gamlss.dist_6.0-3 VGAM_1.1-6
Hi,
When running a multiple sensitive item design with
ictreg(..., multi.condition = "level")
, it is not currently supported bypredict.ictreg()
to make prediction:which gives an error message:
Error in xvar %*% beta : non-conformable arguments
It seems like the reason is that y_i(0) and the extra associated parameter are not taken into account in
predict.ictreg()
.In general, I manually simulated the expected probabilities of respondents choosing the sensitive items taking
y_i(0)
and the extra parameter into account (following the replication filePA_Replication.R
-- Replication R command file for: Graeme Blair and Kosuke Imai, "Statistical Analysis of List Experiments," Political Analysis, In Press. Created 27 October 2011). It seems like the inclusion of y_i(0) and the associated parameter drastically changes the expected probabilities vis-a-vis the difference-in-means estimate. Will there be any implementation of robust NLS/ML models (i.e.ictreg(...robust = TRUE)
) for multiple sensitive item design to ensure their correspondence?Thank you!