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Confirmatory factor analysis using the sem package. TLI CFI and RMSEA absent from model summary.

5 messages · Hervé Guyon, Kevin Cheung, John Fox

Original
Kevin Cheung
# 
Hi R-help,

I am using the sem package to run confirmatory factor analysis (cfa) on some questionnaire data collected from 307 participants. I have been running R-2.15.3 in Windows in conjunction with R studio. The model I am using was developed from exploratory factor analysis of a separate dataset (n=439); it includes 18 items that load onto 3 factors. I have used the sem package documentation and this video (http://vimeo.com/38941937) to run the cfa and obtain a chi-square statistic for the model. However, when I use the summary() function, the model does not provide indices of fit; I need these as part of my analysis output. In particular, I am looking for the Tucker Lewis Index (TLI), Comparative Fit Index (CFI), & the Root Mean Square of Approximation (RMSEA). I have checked the documentation and cannot seem to find any reason for this; none of the arguments listed with the sem command indicate that I have to specify these as part of the output. In addition, the analysis example from the video includes these indices as part of the output, but my analysis does not provide them. I have included my code with comments below:

________________________________________

library(sem)

validation.data <-
structure(list(V1 = c(5L, 4L, 2L, 4L, 5L, 6L, 6L, 4L, 5L, 3L,
6L, 5L, 4L, 5L, 3L, 5L, 5L, 5L, 5L, 5L, 5L, 4L, 5L, 5L, 5L, 4L,
5L, 4L, 4L, 5L, 4L, 4L, 5L, 5L, 5L, 5L, 2L, 6L, 5L, 6L, 4L, 5L,
6L, 5L, 5L, 4L, 5L, 5L, 3L, 5L, 5L, 5L, 5L, 4L, 2L, 5L, 5L, 5L,
4L, 6L, 4L, 6L, 5L, 5L, 5L, 4L, 5L, 5L, 4L, 5L, 6L, 4L, 5L, 4L,
5L, 5L, 5L, 3L, 5L, 5L, 3L, 5L, 4L, 5L, 2L, 6L, 4L, 4L, 4L, 5L,
5L, 4L, 6L, 2L, 4L, 5L, 4L, 5L, 4L, 5L, 5L, 5L, 5L, 5L, 5L, 5L,
5L, 4L, 2L, 4L, 4L, 5L, 5L, 4L, 4L, 5L, 5L, 5L, 5L, 5L, 5L, 4L,
5L, 5L, 5L, 5L, 5L, 6L, 5L, 5L, 4L, 4L, 5L, 4L, 4L, 5L, 4L, 4L,
4L, 5L, 5L, 6L, 5L, 5L, 5L, 4L, 4L, 3L, 4L, 5L, 5L, 5L, 2L, 5L,
5L, 5L, 4L, 5L, 5L, 5L, 5L, 6L, 4L, 5L, 5L, 6L, 5L, 5L, 5L, 5L,
4L, 5L, 4L, 5L, 5L, 4L, 4L, 4L, 4L, 5L, 2L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 5L, 4L, 4L, 5L, 4L, 5L, 5L, 4L, 5L, 5L, 4L, 4L, 5L,
5L, 5L, 5L, 4L, 4L, 2L, 6L, 5L, 5L, 5L, 6L, 4L, 3L, 5L, 5L, 5L,
5L, 4L, 4L, 6L, 6L, 5L, 5L, 5L, 3L, 6L, 5L, 5L, 5L, 4L, 5L, 5L,
4L, 5L, 4L, 6L, 5L, 4L, 4L, 5L, 4L, 3L, 4L, 5L, 4L, 5L, 6L, 2L,
4L, 4L, 5L, 4L, 4L, 4L, 5L, 6L, 5L, 4L, 4L, 4L, 6L, 6L, 4L, 5L,
5L, 5L, 2L, 4L, 4L, 5L, 4L, 5L, 5L, 4L, 5L, 3L, 5L, 6L, 5L, 4L,
4L, 3L, 3L, 4L, 5L, 5L, 1L, 4L, 5L, 3L, 5L, 1L, 6L, 5L, 4L, 4L,
5L, 5L, 4L, 5L, 5L, 6L, 5L, 5L, 5L), V2 = c(5L, 5L, 6L, 6L, 6L,
6L, 6L, 6L, 5L, 5L, 6L, 6L, 6L, 6L, 5L, 6L, 6L, 6L, 5L, 6L, 6L,
4L, 5L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 5L, 6L, 6L, 5L,
5L, 5L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 5L, 5L, 6L, 5L, 6L, 5L,
5L, 5L, 6L, 6L, 6L, 6L, 6L, 4L, 6L, 5L, 6L, 5L, 5L, 6L, 6L, 6L,
6L, 5L, 6L, 6L, 6L, 6L, 6L, 5L, 6L, 6L, 6L, 6L, 6L, 5L, 6L, 5L,
6L, 6L, 5L, 6L, 6L, 5L, 4L, 6L, 5L, 6L, 5L, 5L, 6L, 5L, 6L, 6L,
5L, 6L, 6L, 6L, 5L, 5L, 6L, 6L, 5L, 4L, 6L, 4L, 6L, 6L, 6L, 6L,
6L, 6L, 5L, 5L, 5L, 6L, 6L, 6L, 5L, 5L, 6L, 6L, 5L, 6L, 5L, 6L,
6L, 5L, 6L, 6L, 5L, 6L, 6L, 6L, 6L, 5L, 6L, 5L, 5L, 5L, 6L, 5L,
6L, 5L, 5L, 5L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 4L, 2L, 5L, 6L, 4L,
5L, 5L, 6L, 6L, 5L, 6L, 4L, 6L, 5L, 5L, 6L, 5L, 6L, 6L, 5L, 6L,
5L, 6L, 6L, 6L, 6L, 6L, 5L, 6L, 6L, 6L, 6L, 5L, 6L, 5L, 6L, 6L,
5L, 6L, 5L, 6L, 5L, 6L, 6L, 5L, 5L, 6L, 6L, 6L, 5L, 5L, 6L, 6L,
6L, 6L, 5L, 5L, 6L, 6L, 6L, 6L, 4L, 6L, 6L, 6L, 6L, 6L, 6L, 6L,
6L, 6L, 5L, 6L, 6L, 3L, 5L, 6L, 6L, 6L, 5L, 6L, 5L, 5L, 6L, 6L,
6L, 5L, 6L, 6L, 6L, 6L, 6L, 5L, 6L, 5L, 6L, 6L, 6L, 6L, 5L, 5L,
6L, 6L, 6L, 6L, 6L, 5L, 5L, 6L, 4L, 5L, 6L, 6L, 5L, 6L, 6L, 6L,
6L, 6L, 6L, 6L, 5L, 6L, 6L, 5L, 5L, 6L, 5L, 5L, 6L, 5L, 5L, 6L,
5L, 5L, 6L, 6L, 6L, 6L, 6L, 6L, 5L, 6L, 6L, 5L, 6L, 6L), V3 = c(5L,
5L, 3L, 6L, 5L, 2L, 4L, 4L, 4L, 4L, 6L, 5L, 5L, 4L, 4L, 4L, 4L,
5L, 4L, 4L, 1L, 3L, 4L, 5L, 5L, 4L, 4L, 5L, 5L, 5L, 4L, 5L, 4L,
5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 4L, 1L, 5L, 5L, 4L, 5L, 4L, 5L,
5L, 4L, 5L, 4L, 3L, 2L, 5L, 6L, 6L, 5L, 5L, 5L, 6L, 5L, 6L, 5L,
4L, 5L, 5L, 5L, 4L, 5L, 5L, 5L, 5L, 3L, 5L, 4L, 5L, 5L, 5L, 3L,
5L, 5L, 5L, 3L, 5L, 4L, 5L, 4L, 5L, 4L, 3L, 5L, 3L, 5L, 3L, 4L,
4L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 2L,
5L, 4L, 5L, 4L, 6L, 4L, 5L, 5L, 4L, 5L, 4L, 5L, 5L, 4L, 6L, 5L,
5L, 5L, 5L, 4L, 5L, 4L, 5L, 4L, 4L, 5L, 5L, 4L, 6L, 4L, 6L, 5L,
4L, 4L, 5L, 5L, 5L, 4L, 4L, 1L, 5L, 4L, 4L, 5L, 5L, 4L, 6L, 3L,
4L, 4L, 5L, 4L, 4L, 5L, 3L, 5L, 6L, 5L, 4L, 4L, 5L, 5L, 5L, 5L,
3L, 5L, 4L, 6L, 5L, 4L, 6L, 3L, 4L, 2L, 4L, 4L, 5L, 4L, 4L, 5L,
3L, 4L, 5L, 5L, 4L, 3L, 3L, 5L, 5L, 5L, 5L, 4L, 3L, 4L, 2L, 5L,
5L, 6L, 4L, 5L, 4L, 4L, 5L, 4L, 6L, 6L, 4L, 4L, 4L, 5L, 5L, 6L,
5L, 4L, 6L, 5L, 5L, 5L, 4L, 6L, 6L, 3L, 2L, 3L, 6L, 4L, 5L, 3L,
6L, 3L, 4L, 4L, 5L, 4L, 6L, 4L, 5L, 4L, 5L, 5L, 5L, 5L, 5L, 5L,
5L, 4L, 4L, 5L, 5L, 6L, 5L, 4L, 4L, 5L, 5L, 5L, 5L, 4L, 3L, 5L,
5L, 4L, 5L, 5L, 5L, 5L, 6L, 5L, 4L, 4L, 3L, 4L, 5L, 5L, 4L, 1L,
6L, 4L, 4L, 4L, 2L, 6L, 5L, 4L, 5L, 5L, 5L, 5L, 5L, 4L, 6L, 5L,
5L, 6L), V4 = c(5L, 3L, 4L, 6L, 5L, 4L, 6L, 4L, 4L, 3L, 5L, 4L,
4L, 4L, 6L, 3L, 5L, 5L, 5L, 5L, 2L, 4L, 5L, 5L, 5L, 4L, 6L, 5L,
4L, 5L, 4L, 6L, 5L, 5L, 4L, 5L, 5L, 5L, 5L, 6L, 5L, 5L, 3L, 4L,
5L, 3L, 4L, 5L, 4L, 5L, 4L, 4L, 3L, 3L, 3L, 5L, 5L, 6L, 4L, 5L,
5L, 6L, 5L, 4L, 5L, 4L, 4L, 5L, 4L, 5L, 5L, 5L, 5L, 5L, 3L, 5L,
5L, 5L, 5L, 4L, 5L, 5L, 5L, 5L, 4L, 5L, 5L, 6L, 5L, 5L, 4L, 5L,
5L, 5L, 4L, 4L, 4L, 6L, 5L, 5L, 5L, 6L, 4L, 5L, 5L, 5L, 4L, 6L,
5L, 5L, 4L, 6L, 2L, 5L, 6L, 4L, 5L, 6L, 5L, 4L, 5L, 4L, 5L, 4L,
5L, 5L, 6L, 6L, 4L, 4L, 5L, 4L, 4L, 5L, 4L, 6L, 4L, 4L, 5L, 5L,
4L, 5L, 5L, 5L, 4L, 4L, 5L, 5L, 5L, 5L, 4L, 5L, 2L, 5L, 5L, 6L,
5L, 2L, 3L, 6L, 4L, 1L, 4L, 5L, 4L, 5L, 5L, 3L, 4L, 5L, 5L, 4L,
4L, 4L, 4L, 5L, 4L, 4L, 5L, 4L, 4L, 5L, 4L, 6L, 5L, 5L, 1L, 4L,
4L, 5L, 4L, 4L, 4L, 4L, 4L, 4L, 5L, 5L, 4L, 3L, 5L, 5L, 5L, 5L,
4L, 3L, 5L, 3L, 6L, 5L, 5L, 5L, 5L, 5L, 4L, 5L, 4L, 6L, 5L, 4L,
4L, 4L, 6L, 5L, 5L, 4L, 4L, 6L, 5L, 5L, 5L, 5L, 5L, 5L, 4L, 3L,
4L, 6L, 5L, 5L, 5L, 5L, 4L, 6L, 5L, 5L, 5L, 5L, 4L, 6L, 4L, 5L,
5L, 5L, 5L, 4L, 4L, 6L, 5L, 4L, 5L, 5L, 6L, 5L, 5L, 5L, 5L, 5L,
5L, 5L, 5L, 4L, 6L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 4L, 6L, 4L, 4L,
5L, 5L, 5L, 5L, 1L, 6L, 4L, 2L, 5L, 5L, 5L, 5L, 4L, 4L, 5L, 5L,
5L, 6L, 5L, 5L, 5L, 4L, 6L), V5 = c(6L, 6L, 5L, 6L, 6L, 6L, 6L,
5L, 5L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 5L, 5L, 5L, 6L, 6L, 5L, 5L,
6L, 6L, 6L, 5L, 6L, 6L, 6L, 5L, 6L, 6L, 5L, 5L, 6L, 6L, 5L, 6L,
6L, 6L, 5L, 6L, 6L, 4L, 5L, 6L, 5L, 5L, 6L, 5L, 6L, 5L, 4L, 6L,
6L, 6L, 6L, 6L, 6L, 5L, 6L, 6L, 5L, 6L, 5L, 6L, 6L, 5L, 5L, 6L,
5L, 5L, 6L, 5L, 6L, 5L, 6L, 5L, 6L, 5L, 6L, 5L, 6L, 5L, 5L, 5L,
6L, 6L, 5L, 6L, 5L, 6L, 5L, 5L, 4L, 5L, 6L, 5L, 5L, 6L, 6L, 5L,
5L, 6L, 6L, 5L, 6L, 5L, 5L, 5L, 6L, 5L, 5L, 6L, 6L, 6L, 6L, 6L,
5L, 5L, 5L, 6L, 6L, 5L, 5L, 5L, 6L, 6L, 5L, 6L, 5L, 5L, 5L, 5L,
6L, 6L, 5L, 6L, 6L, 6L, 6L, 5L, 6L, 5L, 5L, 5L, 6L, 6L, 4L, 5L,
6L, 5L, 6L, 6L, 6L, 6L, 3L, 6L, 6L, 6L, 4L, 5L, 6L, 6L, 6L, 5L,
6L, 6L, 5L, 5L, 5L, 6L, 5L, 5L, 6L, 5L, 5L, 6L, 5L, 6L, 4L, 6L,
6L, 6L, 6L, 5L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 5L, 6L, 6L, 5L, 5L,
6L, 5L, 5L, 6L, 6L, 5L, 5L, 6L, 5L, 5L, 6L, 6L, 6L, 5L, 5L, 5L,
5L, 3L, 6L, 5L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 5L, 6L, 6L, 5L,
5L, 6L, 6L, 5L, 4L, 4L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L,
6L, 5L, 6L, 6L, 6L, 6L, 6L, 5L, 6L, 5L, 6L, 6L, 6L, 5L, 6L, 6L,
6L, 5L, 6L, 5L, 5L, 6L, 4L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L,
6L, 6L, 6L, 6L, 6L, 6L, 5L, 6L, 6L, 1L, 6L, 5L, 5L, 6L, 4L, 6L,
6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L), V6 = c(6L, 6L,
5L, 6L, 6L, 5L, 6L, 4L, 5L, 5L, 6L, 6L, 6L, 6L, 6L, 6L, 5L, 6L,
5L, 6L, 6L, 2L, 4L, 5L, 5L, 5L, 4L, 5L, 5L, 6L, 5L, 6L, 4L, 4L,
5L, 6L, 5L, 4L, 5L, 5L, 6L, 6L, 6L, 6L, 5L, 4L, 6L, 5L, 5L, 5L,
4L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 6L, 6L, 5L, 6L, 5L, 6L, 6L, 6L,
6L, 4L, 5L, 6L, 6L, 3L, 6L, 6L, 5L, 5L, 4L, 6L, 5L, 5L, 5L, 3L,
4L, 5L, 5L, 5L, 2L, 5L, 5L, 4L, 5L, 6L, 3L, 6L, 4L, 4L, 5L, 5L,
3L, 6L, 6L, 5L, 5L, 5L, 6L, 5L, 4L, 3L, 6L, 5L, 4L, 6L, 5L, 6L,
5L, 5L, 4L, 6L, 4L, 6L, 5L, 4L, 5L, 5L, 5L, 6L, 6L, 6L, 5L, 6L,
5L, 4L, 4L, 5L, 3L, 5L, 6L, 5L, 6L, 6L, 6L, 6L, 6L, 6L, 5L, 6L,
6L, 6L, 4L, 4L, 5L, 6L, 2L, 4L, 4L, 6L, 4L, 6L, 6L, 5L, 5L, 4L,
5L, 5L, 6L, 6L, 4L, 5L, 5L, 6L, 5L, 5L, 5L, 6L, 5L, 5L, 6L, 4L,
6L, 5L, 3L, 4L, 6L, 4L, 4L, 5L, 4L, 4L, 5L, 6L, 4L, 3L, 6L, 5L,
5L, 4L, 4L, 5L, 6L, 4L, 5L, 5L, 6L, 6L, 5L, 4L, 5L, 2L, 6L, 6L,
6L, 5L, 5L, 6L, 4L, 5L, 5L, 5L, 6L, 6L, 4L, 3L, 6L, 6L, 6L, 5L,
4L, 6L, 5L, 6L, 5L, 4L, 6L, 6L, 3L, 6L, 3L, 5L, 6L, 4L, 3L, 6L,
6L, 6L, 4L, 6L, 6L, 6L, 5L, 5L, 6L, 6L, 5L, 5L, 4L, 4L, 2L, 6L,
6L, 3L, 4L, 6L, 6L, 6L, 5L, 6L, 5L, 4L, 5L, 4L, 6L, 4L, 4L, 6L,
6L, 5L, 5L, 5L, 6L, 6L, 6L, 5L, 3L, 5L, 5L, 5L, 6L, 5L, 1L, 5L,
4L, 5L, 6L, 4L, 5L, 6L, 5L, 5L, 6L, 5L, 5L, 6L, 5L, 6L, 6L, 4L,
6L), V7 = c(4, 1, 1, 5, 3, 2, 6, 3, 3, 2, 6, 3, 3, 5, 5, 1, 3,
3, 3, 5, 6, 1, 2, 3.5, 5, 2, 2, 3, 2, 4, 4, 2, 4, 4, 2, 5, 3,
4, 4, 4, 4, 2, 5, 3, 2, 2, 4, 4, 2, 5, 3, 2, 4, 2, 4, 2, 4, 5,
5, 5, 2, 6, 4, 2, 4, 2, 3, 1, 5, 4, 4, 2, 5, 5, 4, 4, 2, 5, 6,
4, 4, 1, 3, 2, 2, 4, 2, 3, 4, 3, 3, 3, 2, 4, 2, 1, 4, 4, 3, 3,
5, 4, 4, 5, 5, 2, 2, 3, 2, 4, 3, 5, 2, 1, 2, 3, 2, 6, 4, 2, 2,
3, 4, 4, 4, 3, 3, 5, 1, 5, 3, 3, 1, 2, 3, 2, 6, 2, 4, 4, 5, 2,
5, 2, 5, 3, 1, 6, 3, 3, 2, 4, 1, 1, 1, 6, 2, 2, 2, 4, 3, 1, 4,
4, 4, 4, 3, 2, 4, 3, 4, 4, 2, 2, 4, 4, 4, 2, 1, 3, 2, 6, 2, 2.5,
3, 3, 2, 2, 4, 4, 1, 2, 2, 1, 3, 3, 2, 2, 4, 2, 5, 3, 6, 4, 3,
2, 2, 1, 6, 5, 3, 2, 2, 5, 2, 3, 2, 4, 4, 2, 3, 1, 4, 3, 6, 1,
3, 6, 4, 5, 3, 3, 4, 5, 1, 4, 3, 4, 3, 3, 3, 4, 1, 6, 3, 4, 2,
5, 2, 3, 4, 5, 3, 2, 2, 3, 1, 4, 2, 4, 3, 4, 6, 5, 3, 4, 3, 2,
2, 4, 3, 2, 4, 4, 6, 4, 5, 3, 4, 4, 4, 5, 2, 2, 3, 5, 4, 5, 1,
4, 3, 4, 5, 2, 4, 2, 1, 4, 3, 2, 2, 5, 3, 4, 2, 2, 5), V8 = c(5,
5, 6, 6, 5, 6, 6, 6, 5, 5, 6, 6, 5, 6, 6, 5, 6, 5, 6, 6, 5, 5,
5, 6, 6, 5, 6, 5, 6, 6, 6, 6, 6, 5, 5, 6, 6, 4, 2, 6, 6, 4, 6,
6, 5, 6, 6, 5, 5, 5, 5, 6, 6, 5, 6, 6, 5, 6, 6, 6, 5, 6, 5, 6,
5, 5, 6, 6, 6, 6, 6, 6, 6, 6, 5, 6, 6, 6, 6, 6, 5, 6, 5, 5, 5,
5, 6, 6, 6, 6, 6, 6, 5, 5, 6, 5, 5, 6, 5, 6, 5, 5, 6, 6, 6, 5,
6, 6, 6, 5, 5, 6, 2, 4, 6, 6, 6, 6, 6, 5, 5, 5, 6, 6, 6, 5, 6,
6, 6, 5, 6, 5, 6, 4, 5, 6, 6, 5, 5, 6, 6, 6, 5, 6, 5, 5, 5, 6,
5, 6, 4, 4, 6, 5, 6, 5, 6, 6, 6, 6, 6, 4, 6, 5, 4, 6, 5, 6, 6,
5.5, 5, 5, 4, 5, 4, 6, 5, 5, 5, 5, 6, 4, 6, 4, 6, 6, 6, 4, 6,
6, 6, 6, 5, 6, 5, 6, 5, 4, 5, 6, 6, 6, 6, 6, 5, 4, 5, 6, 5, 5,
5, 4, 4, 5, 6, 5, 1, 6, 6, 6, 6, 6, 6, 6, 6, 5, 6, 6, 6, 6, 6,
6, 6, 6, 3, 6, 6, 6, 6, 5, 6, 6, 6, 6, 6, 5, 5, 6, 5, 5, 6, 5,
5, 6, 5, 5, 5, 6, 5, 6, 5, 6, 6, 6, 6, 6, 6, 5, 6, 5, 5, 6, 6,
5, 6, 6, 6, 6, 6, 6, 5, 5, 5, 6, 5, 5, 6, 6, 5, 6, 4, 6, 6, 6,
6, 5, 5, 5, 5, 5, 6, 6, 6, 6, 5, 6, 6), V9 = c(5, 4, 2, 6, 4,
6, 6, 4, 5, 2, 6, 5, 4, 5, 4, 5, 5, 5, 6, 5, 6, 4, 3, 5, 5, 4,
5, 4, 6, 5, 4, 5, 5, 5, 5, 5, 2, 6, 5, 6, 5, 5, 6, 5, 2, 4, 6,
5, 3, 5, 5, 5, 6, 4, 3, 5, 6, 5, 4, 6, 5, 6, 5, 5, 4, 4, 5, 5,
5, 6, 6, 6, 4, 4, 4, 5, 5, 4, 4, 5, 3, 6, 5, 5, 3, 5, 4, 5, 4,
4, 5, 4, 6, 5, 4, 5, 4, 4, 5, 5, 5, 5, 6, 5, 5, 5, 5, 4, 2, 5,
4, 6, 5, 4, 4, 4.5, 5, 6, 5, 6, 5, 5, 5, 4, 6, 5, 5, 6, 4, 5,
4, 5, 6, 4, 5, 5, 4, 5, 4, 5, 6, 5, 5, 5, 6, 5, 4, 5, 5, 5, 5,
6, 2, 5, 4, 5, 5, 5, 6, 5, 4, 6, 4, 5, 4, 6, 4, 5, 6, 5, 5, 6,
5, 5, 6, 5, 5, 6, 3, 5, 3, 4, 4, 4, 5, 5, 4, 4, 5, 6, 5, 4, 5,
4, 5, 4, 4, 5, 6, 4, 5, 4, 6, 5, 5, 4, 5, 2, 5, 5, 5, 6, 5, 4,
4, 5, 5, 5, 5, 4, 5, 6, 6, 5, 5, 5, 4, 5, 5, 5, 5, 4, 6, 6, 3,
5, 5, 6, 5, 4, 3, 4, 5, 3, 4, 5, 5, 5, 5, 5, 4, 5, 5, 5, 5, 4,
5, 5, 5, 4, 5, 5, 6, 6, 4, 5, 5, 5, 2, 5, 4, 5, 4, 5, 6, 5, 5,
4, 6, 6, 5, 5, 5, 5, 4, 4, 5, 5, 1, 5, 4, 5, 5, 4, 4, 6, 4, 5,
5, 5, 4, 5, 6, 6, 6, 5, 6), V10 = c(5L, 5L, 3L, 6L, 5L, 6L, 6L,
5L, 5L, 4L, 5L, 6L, 6L, 3L, 4L, 5L, 5L, 6L, 5L, 5L, 6L, 4L, 5L,
6L, 6L, 5L, 5L, 5L, 5L, 6L, 5L, 6L, 6L, 5L, 6L, 5L, 5L, 6L, 5L,
5L, 6L, 5L, 6L, 5L, 4L, 6L, 4L, 4L, 5L, 5L, 6L, 5L, 6L, 5L, 5L,
5L, 6L, 6L, 6L, 6L, 5L, 6L, 6L, 4L, 6L, 5L, 5L, 6L, 4L, 6L, 6L,
4L, 4L, 5L, 5L, 5L, 5L, 5L, 5L, 6L, 5L, 5L, 5L, 5L, 5L, 5L, 4L,
4L, 5L, 5L, 5L, 5L, 6L, 5L, 5L, 5L, 4L, 5L, 4L, 6L, 5L, 6L, 6L,
6L, 6L, 5L, 6L, 5L, 6L, 5L, 5L, 6L, 4L, 5L, 5L, 5L, 5L, 6L, 5L,
5L, 5L, 5L, 6L, 6L, 5L, 5L, 6L, 6L, 2L, 6L, 5L, 6L, 5L, 5L, 5L,
6L, 4L, 5L, 6L, 6L, 6L, 6L, 6L, 5L, 6L, 4L, 5L, 5L, 5L, 5L, 5L,
5L, 4L, 6L, 5L, 6L, 5L, 6L, 5L, 5L, 1L, 4L, 5L, 5L, 5L, 6L, 4L,
2L, 6L, 5L, 5L, 6L, 5L, 5L, 6L, 5L, 5L, 5L, 4L, 4L, 5L, 5L, 5L,
3L, 4L, 5L, 4L, 4L, 6L, 6L, 4L, 5L, 4L, 2L, 5L, 3L, 4L, 5L, 5L,
5L, 5L, 6L, 6L, 5L, 5L, 4L, 5L, 5L, 6L, 5L, 6L, 6L, 5L, 5L, 4L,
5L, 5L, 6L, 6L, 2L, 5L, 4L, 6L, 6L, 6L, 6L, 5L, 6L, 4L, 5L, 5L,
4L, 6L, 6L, 4L, 5L, 5L, 6L, 5L, 5L, 5L, 5L, 4L, 4L, 5L, 5L, 5L,
5L, 3L, 5L, 5L, 4L, 6L, 5L, 5L, 5L, 6L, 6L, 6L, 2L, 5L, 5L, 5L,
4L, 5L, 5L, 5L, 5L, 5L, 5L, 6L, 5L, 5L, 5L, 6L, 6L, 5L, 5L, 5L,
6L, 5L, 6L, 3L, 4L, 4L, 5L, 6L, 5L, 5L, 6L, 5L, 4L, 5L, 5L, 6L,
6L, 5L, 5L, 5L, 4L, 5L, 5L, 5L, 5L, 6L, 6L, 6L), V11 = c(5, 6,
5, 6, 5, 5, 6, 4, 4, 4, 6, 6, 6, 4, 6, 4, 5, 5, 4, 5, 6, 5, 2,
5, 6, 5, 3, 5, 5, 6, 5, 6, 6, 5, 6, 5, 5, 5, 5, 6, 6, 4, 6, 5,
4, 5, 6, 5, 4, 5, 6, 4, 4, 6, 5, 6, 4, 6, 5, 6, 5, 6, 6, 6, 3,
5, 6, 5, 5, 6, 5, 4, 5, 6, 2, 5, 3, 6, 5, 6, 5, 2, 5, 5, 5, 6,
5, 4, 4, 4, 5, 6, 2, 5, 4, 3, 4, 4, 4, 6, 6, 5, 6, 6, 6, 5, 4,
4.5, 5, 4, 5, 5, 4, 6, 5, 5, 5, 6, 5, 5, 4, 4, 5, 5, 4, 5, 6,
5, 5, 6, 4, 4, 5, 5, 4, 2, 6, 4, 6, 6, 6, 5, 6, 4, 4, 5, 5, 5,
4, 5, 5, 6, 2, 3, 3, 6, 5, 6, 5, 5, 1, 4, 4, 4, 6, 6, 5, 2, 6,
5, 5, 6, 5, 5, 5, 4, 6, 3, 4, 5, 3, 5, 6, 3, 4, 3, 3, 5, 5, 3,
6, 4, 3, 6, 5, 4, 4, 5, 6, 5, 5, 4, 6, 5, 4, 5, 5, 5, 6, 6, 6,
4, 5, 6, 5, 4, 5, 6, 5, 5, 5, 6, 5, 6, 6, 5, 5, 5, 5, 6, 5, 4,
6, 6, 3, 5, 3, 6, 5, 4, 5, 4, 5, 5, 4, 6, 5, 5, 4, 5, 6, 6, 5,
5, 5, 5, 6, 6, 5, 4, 5, 5, 6, 5, 5, 6, 5, 3, 5, 4, 5, 4, 5, 5,
6, 5, 5, 5, 5, 6, 5, 6, 2, 5, 5, 5, 5, 5, 1, 5, 3, 5, 5, 4, 6,
6, 5, 5, 5, 5, 5, 5, 4, 6, 6, 6, 6), V12 = c(4L, 6L, 3L, 6L,
5L, 5L, 6L, 4L, 5L, 4L, 6L, 5L, 5L, 5L, 5L, 5L, 4L, 5L, 4L, 5L,
6L, 5L, 4L, 5L, 5L, 4L, 4L, 5L, 5L, 6L, 3L, 6L, 6L, 5L, 6L, 5L,
4L, 4L, 4L, 4L, 4L, 5L, 6L, 5L, 5L, 4L, 4L, 5L, 5L, 3L, 5L, 4L,
4L, 6L, 4L, 4L, 5L, 6L, 6L, 6L, 4L, 6L, 6L, 4L, 5L, 3L, 4L, 5L,
5L, 4L, 4L, 4L, 4L, 5L, 3L, 5L, 3L, 6L, 5L, 6L, 4L, 3L, 5L, 2L,
4L, 5L, 5L, 4L, 5L, 4L, 5L, 2L, 2L, 5L, 4L, 4L, 4L, 4L, 5L, 6L,
5L, 5L, 5L, 6L, 6L, 5L, 4L, 5L, 5L, 4L, 4L, 5L, 4L, 5L, 5L, 5L,
5L, 6L, 5L, 5L, 5L, 4L, 5L, 6L, 6L, 6L, 6L, 6L, 3L, 6L, 5L, 5L,
5L, 4L, 4L, 5L, 4L, 4L, 6L, 5L, 6L, 5L, 5L, 4L, 6L, 5L, 4L, 6L,
4L, 5L, 5L, 3L, 2L, 4L, 4L, 5L, 5L, 2L, 3L, 5L, 4L, 6L, 5L, 5L,
6L, 6L, 4L, 2L, 6L, 5L, 5L, 6L, 5L, 5L, 5L, 5L, 6L, 4L, 3L, 5L,
3L, 4L, 2L, 3L, 4L, 3L, 4L, 4L, 5L, 2L, 5L, 4L, 5L, 5L, 5L, 4L,
4L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 4L, 4L, 5L, 4L, 6L, 6L, 5L, 4L,
5L, 5L, 4L, 5L, 5L, 6L, 5L, 5L, 5L, 6L, 5L, 5L, 6L, 6L, 4L, 6L,
5L, 2L, 5L, 3L, 6L, 6L, 3L, 4L, 5L, 5L, 5L, 5L, 5L, 6L, 1L, 4L,
4L, 4L, 5L, 5L, 4L, 5L, 4L, 5L, 4L, 4L, 4L, 3L, 5L, 5L, 5L, 4L,
4L, 5L, 6L, 5L, 5L, 6L, 4L, 4L, 4L, 5L, 5L, 5L, 4L, 5L, 6L, 5L,
5L, 4L, 6L, 5L, 4L, 6L, 4L, 4L, 4L, 5L, 6L, 5L, 5L, 5L, 4L, 4L,
5L, 3L, 5L, 6L, 5L, 5L, 5L, 3L, 5L, 5L, 6L, 6L, 5L, 4L, 5L),
    V13 = c(5L, 5L, 4L, 6L, 5L, 5L, 6L, 4L, 4L, 3L, 6L, 5L, 4L,
    5L, 4L, 3L, 4L, 5L, 4L, 4L, 4L, 4L, 3L, 4L, 6L, 5L, 5L, 5L,
    5L, 6L, 5L, 5L, 5L, 5L, 5L, 4L, 4L, 4L, 4L, 5L, 3L, 4L, 6L,
    5L, 4L, 4L, 4L, 3L, 4L, 4L, 4L, 4L, 3L, 2L, 5L, 5L, 5L, 6L,
    6L, 6L, 4L, 6L, 6L, 4L, 4L, 4L, 4L, 4L, 5L, 5L, 4L, 5L, 6L,
    6L, 3L, 4L, 2L, 6L, 6L, 5L, 4L, 3L, 5L, 5L, 5L, 5L, 4L, 4L,
    3L, 4L, 3L, 5L, 4L, 4L, 4L, 3L, 4L, 5L, 5L, 5L, 4L, 6L, 5L,
    4L, 5L, 5L, 4L, 5L, 5L, 4L, 4L, 5L, 4L, 4L, 4L, 5L, 4L, 5L,
    5L, 4L, 5L, 4L, 5L, 4L, 5L, 5L, 5L, 6L, 4L, 6L, 5L, 5L, 4L,
    4L, 3L, 5L, 4L, 3L, 5L, 4L, 5L, 4L, 4L, 5L, 4L, 5L, 5L, 5L,
    4L, 4L, 6L, 4L, 2L, 4L, 2L, 5L, 4L, 5L, 3L, 4L, 4L, 3L, 4L,
    5L, 3L, 6L, 4L, 2L, 5L, 6L, 5L, 5L, 5L, 5L, 4L, 6L, 5L, 4L,
    5L, 4L, 3L, 4L, 5L, 3L, 5L, 5L, 2L, 4L, 5L, 5L, 3L, 4L, 6L,
    5L, 5L, 4L, 4L, 4L, 4L, 3L, 5L, 5L, 5L, 5L, 4L, 3L, 4L, 3L,
    5L, 5L, 4L, 5L, 4L, 4L, 4L, 4L, 5L, 5L, 5L, 5L, 3L, 6L, 5L,
    5L, 5L, 4L, 4L, 4L, 6L, 2L, 5L, 5L, 6L, 5L, 3L, 5L, 2L, 5L,
    5L, 4L, 5L, 6L, 5L, 4L, 4L, 4L, 4L, 5L, 3L, 4L, 4L, 5L, 4L,
    2L, 4L, 3L, 3L, 6L, 5L, 4L, 5L, 5L, 6L, 4L, 4L, 4L, 5L, 5L,
    5L, 5L, 5L, 3L, 4L, 5L, 5L, 4L, 5L, 5L, 4L, 5L, 5L, 6L, 3L,
    3L, 4L, 5L, 5L, 5L, 1L, 5L, 3L, 4L, 4L, 1L, 6L, 4L, 5L, 5L,
    5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L), V14 = c(4L, 5L, 4L,
    6L, 5L, 5L, 6L, 5L, 4L, 4L, 6L, 4L, 4L, 5L, 5L, 5L, 5L, 5L,
    5L, 4L, 6L, 4L, 4L, 5L, 5L, 5L, 4L, 5L, 5L, 6L, 5L, 5L, 5L,
    5L, 5L, 5L, 5L, 4L, 5L, 6L, 4L, 4L, 6L, 5L, 5L, 4L, 4L, 5L,
    5L, 4L, 5L, 4L, 4L, 4L, 5L, 5L, 5L, 6L, 6L, 5L, 5L, 6L, 5L,
    4L, 5L, 4L, 4L, 5L, 4L, 5L, 6L, 5L, 5L, 5L, 4L, 5L, 3L, 6L,
    5L, 5L, 4L, 4L, 5L, 5L, 2L, 4L, 5L, 4L, 4L, 5L, 3L, 5L, 5L,
    4L, 5L, 2L, 4L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 6L, 5L, 5L, 5L,
    5L, 5L, 4L, 5L, 4L, 5L, 4L, 4L, 4L, 5L, 5L, 5L, 5L, 4L, 6L,
    4L, 5L, 5L, 5L, 6L, 4L, 5L, 5L, 5L, 4L, 5L, 2L, 5L, 4L, 4L,
    5L, 6L, 6L, 5L, 4L, 5L, 4L, 4L, 2L, 5L, 4L, 5L, 4L, 5L, 1L,
    5L, 3L, 5L, 5L, 5L, 3L, 5L, 5L, 4L, 4L, 5L, 4L, 5L, 4L, 5L,
    6L, 6L, 5L, 4L, 6L, 5L, 5L, 5L, 5L, 3L, 5L, 4L, 4L, 4L, 5L,
    3L, 4L, 5L, 4L, 4L, 5L, 5L, 5L, 4L, 6L, 3L, 5L, 4L, 5L, 5L,
    5L, 4L, 5L, 5L, 5L, 5L, 4L, 4L, 4L, 4L, 6L, 5L, 4L, 6L, 5L,
    4L, 4L, 5L, 5L, 6L, 5L, 5L, 4L, 5L, 5L, 5L, 6L, 5L, 4L, 6L,
    4L, 4L, 5L, 4L, 6L, 6L, 2L, 5L, 4L, 6L, 5L, 4L, 4L, 5L, 4L,
    4L, 4L, 5L, 4L, 5L, 5L, 5L, 4L, 4L, 5L, 4L, 5L, 3L, 5L, 5L,
    5L, 4L, 5L, 5L, 6L, 4L, 5L, 4L, 5L, 5L, 5L, 4L, 4L, 4L, 4L,
    5L, 6L, 5L, 5L, 5L, 4L, 5L, 4L, 2L, 4L, 4L, 4L, 5L, 5L, 5L,
    5L, 5L, 4L, 4L, 4L, 1L, 5L, 5L, 4L, 5L, 5L, 5L, 5L, 5L, 5L,
    5L, 5L, 6L, 5L), V15 = c(5, 4, 4, 6, 5, 2, 6, 4, 5, 4, 5,
    4, 4, 5, 4, 5, 4, 4, 3, 3, 2, 4, 5, 5, 5, 5, 4, 5, 5, 5,
    4, 5, 5, 5, 5, 5, 4, 3, 2, 4, 5, 5, 4, 5, 5, 4, 5, 4, 5,
    4, 5, 5, 4, 4, 2, 5, 5, 6, 6, 5, 5, 6, 5, 4, 4, 4, 5, 5,
    4, 4, 6, 5, 5, 5, 4, 5, 4, 5, 5, 5, 5, 3, 5, 5, 4, 5, 5,
    4, 5, 5, 4, 4, 6, 4, 4, 3, 4, 6, 3, 5, 5, 5, 4, 5, 6, 5,
    4, 5, 5, 4, 4, 5, 4, 5, 5, 4.5, 4, 5, 5, 5, 5, 4, 5, 5, 5,
    5, 6, 6, 3, 6, 5, 4, 3, 5, 3, 6, 4, 4, 5, 5, 4, 5, 4, 4,
    4, 4, 4, 5, 4, 6, 5, 5, 3, 4, 4, 5, 5, 5, 4, 5, 3, 4, 5,
    6, 4, 6, 5, 2, 6, 4, 5, 4, 5, 5, 4, 6, 5, 5, 3, 4, 4, 4,
    4, 3, 4, 4, 2, 4, 5, 5, 4, 4, 5, 4, 5, 4, 5, 4, 5, 5, 5,
    5, 3, 4, 4, 3, 4, 4, 6, 5, 4, 5, 5, 4, 3, 4, 3, 5, 5, 5,
    4, 6, 4, 5, 6, 5, 4, 6, 5, 2, 5, 4, 3, 6, 5, 5, 3, 6, 5,
    4, 5, 5, 5, 4, 4, 5, 3, 5, 3, 5, 6, 4, 4, 5, 4, 3, 5, 5,
    5, 4, 5, 5, 6, 5, 4, 4, 3, 5, 4, 5, 4, 2, 5, 5, 5, 5, 5,
    5, 3, 5, 4, 5, 4, 4, 4, 4, 5, 5, 1, 5, 4, 4, 5, 3, 6, 2,
    4, 5, 5, 5, 5, 6, 5, 5, 5, 5, 4), V16 = c(5L, 6L, 4L, 6L,
    5L, 5L, 6L, 4L, 3L, 3L, 6L, 4L, 6L, 5L, 6L, 4L, 5L, 4L, 4L,
    5L, 6L, 3L, 4L, 5L, 5L, 5L, 3L, 5L, 4L, 5L, 5L, 5L, 4L, 4L,
    5L, 5L, 5L, 4L, 5L, 6L, 5L, 2L, 6L, 5L, 4L, 4L, 5L, 5L, 5L,
    5L, 5L, 4L, 4L, 4L, 4L, 2L, 5L, 6L, 5L, 6L, 4L, 6L, 6L, 5L,
    4L, 3L, 3L, 4L, 5L, 6L, 4L, 2L, 5L, 6L, 4L, 5L, 3L, 6L, 6L,
    4L, 4L, 1L, 4L, 5L, 4L, 4L, 4L, 3L, 5L, 4L, 2L, 6L, 2L, 5L,
    4L, 2L, 4L, 5L, 3L, 5L, 6L, 5L, 4L, 6L, 6L, 5L, 3L, 3L, 2L,
    4L, 4L, 5L, 4L, 6L, 5L, 4L, 2L, 6L, 5L, 6L, 5L, 4L, 5L, 5L,
    5L, 6L, 5L, 5L, 5L, 6L, 4L, 4L, 4L, 4L, 2L, 4L, 6L, 5L, 6L,
    6L, 6L, 6L, 5L, 5L, 5L, 5L, 6L, 4L, 3L, 5L, 5L, 5L, 1L, 4L,
    4L, 6L, 4L, 6L, 5L, 5L, 4L, 4L, 5L, 5L, 5L, 6L, 4L, 5L, 5L,
    6L, 5L, 5L, 4L, 5L, 5L, 4L, 6L, 2L, 3L, 5L, 3L, 5L, 6L, 3L,
    4L, 4L, 4L, 4L, 5L, 5L, 4L, 4L, 6L, 5L, 3L, 3L, 4L, 5L, 6L,
    4L, 5L, 4L, 5L, 5L, 4L, 4L, 5L, 4L, 6L, 5L, 5L, 4L, 5L, 5L,
    3L, 5L, 5L, 5L, 5L, 6L, 4L, 2L, 5L, 6L, 6L, 5L, 4L, 5L, 5L,
    6L, 5L, 4L, 4L, 6L, 2L, 6L, 3L, 6L, 5L, 4L, 4L, 4L, 5L, 6L,
    3L, 5L, 4L, 5L, 5L, 4L, 5L, 5L, 5L, 5L, 3L, 4L, 1L, 5L, 5L,
    4L, 4L, 5L, 6L, 5L, 5L, 6L, 4L, 3L, 5L, 4L, 4L, 4L, 5L, 4L,
    4L, 5L, 5L, 4L, 5L, 5L, 5L, 5L, 2L, 4L, 5L, 4L, 5L, 5L, 1L,
    5L, 3L, 4L, 6L, 4L, 5L, 2L, 4L, 5L, 5L, 4L, 5L, 5L, 5L, 5L,
    6L, 4L, 6L), V17 = c(5, 5, 6, 6, 5, 6, 6, 5, 4, 6, 6, 6,
    6, 6, 6, 4, 6, 1, 5, 6, 5, 4, 5, 5, 6, 5, 4, 5, 6, 6, 6,
    5, 6, 5, 5, 6, 6, 4, 2, 6, 6, 2, 5, 6, 4, 5, 6, 5, 5, 6,
    5, 5, 5, 5, 6, 4, 5, 6, 6, 6, 4, 6, 6, 5, 6, 4, 6, 6, 5,
    5, 6, 6, 6, 6, 4, 6, 5, 5, 5, 5, 5, 6, 5, 6, 3, 4, 5, 6,
    6, 5, 6, 6, 5, 5, 5, 4, 5, 6, 5, 6, 6, 5, 6, 5, 6, 5, 3,
    6, 4, 4, 4, 5, 4, 4, 6, 6, 4, 6, 5, 5, 5, 5, 5, 6, 5, 5,
    6, 6, 5, 5, 6, 5, 5, 5, 4, 6, 6, 5, 6, 6, 6, 6, 6, 6, 5,
    5, 4, 6, 6, 6, 5, 5, 5, 5, 6, 6, 6, 3, 6, 5, 4, 4, 5, 5,
    6, 6, 5, 5, 6, 5, 5, 3, 5, 4, 4, 6, 5, 5, 5, 5, 6, 5, 6,
    5.5, 5, 6, 5, 5, 5, 6, 5, 6, 5, 6, 5, 5, 6, 4, 5, 6, 6, 6,
    6, 5, 5, 5, 6, 6, 6, 5, 5, 4, 4, 5, 4, 5, 1, 5, 5, 5, 5,
    5, 6, 5, 6, 4, 6, 4, 6, 6, 5, 6, 5, 6, 5, 5, 4, 6, 5, 5,
    6, 6, 6, 6, 5, 6, 6, 6, 5, 4, 6, 5, 5, 6, 5, 5, 5, 5, 5,
    3, 5, 6, 6, 5, 6, 6, 6, 5, 5, 4, 6, 5, 5, 5, 5, 6, 6, 6,
    5, 6, 5, 5, 6, 6, 5, 5, 6, 5, 5, 6, 4, 6, 6, 6, 6, 4, 5,
    6, 6, 5, 5, 6, 5, 6, 5, 5, 6), V18 = c(5L, 6L, 6L, 6L, 5L,
    5L, 6L, 5L, 5L, 5L, 6L, 6L, 6L, 6L, 5L, 5L, 6L, 3L, 5L, 6L,
    6L, 1L, 6L, 6L, 6L, 5L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 5L, 6L,
    6L, 5L, 4L, 2L, 5L, 6L, 2L, 5L, 6L, 4L, 6L, 6L, 4L, 6L, 5L,
    4L, 4L, 5L, 5L, 6L, 4L, 4L, 6L, 6L, 6L, 4L, 6L, 5L, 6L, 5L,
    4L, 6L, 6L, 5L, 5L, 6L, 6L, 5L, 6L, 3L, 6L, 4L, 5L, 6L, 5L,
    5L, 4L, 5L, 6L, 3L, 4L, 6L, 5L, 6L, 5L, 6L, 6L, 5L, 5L, 5L,
    4L, 4L, 6L, 5L, 5L, 5L, 6L, 6L, 5L, 6L, 5L, 3L, 6L, 4L, 5L,
    5L, 5L, 4L, 4L, 6L, 6L, 6L, 6L, 5L, 6L, 4L, 4L, 5L, 6L, 5L,
    4L, 5L, 6L, 6L, 5L, 6L, 5L, 4L, 6L, 5L, 6L, 6L, 5L, 6L, 6L,
    6L, 6L, 6L, 5L, 5L, 4L, 3L, 4L, 5L, 6L, 6L, 6L, 6L, 5L, 6L,
    4L, 6L, 5L, 6L, 5L, 4L, 4L, 5L, 6L, 6L, 5L, 5L, 5L, 6L, 5L,
    5L, 4L, 6L, 4L, 3L, 6L, 6L, 6L, 5L, 4L, 6L, 4L, 6L, 5L, 5L,
    6L, 6L, 5L, 6L, 6L, 6L, 6L, 5L, 6L, 5L, 6L, 6L, 5L, 4L, 6L,
    6L, 6L, 6L, 6L, 4L, 6L, 5L, 6L, 6L, 5L, 2L, 6L, 4L, 6L, 5L,
    5L, 1L, 4L, 5L, 4L, 4L, 5L, 6L, 5L, 6L, 3L, 6L, 4L, 6L, 6L,
    6L, 6L, 5L, 6L, 4L, 5L, 5L, 6L, 5L, 5L, 6L, 5L, 6L, 6L, 5L,
    6L, 6L, 6L, 4L, 3L, 6L, 6L, 5L, 5L, 5L, 4L, 5L, 5L, 5L, 6L,
    4L, 6L, 6L, 5L, 6L, 6L, 6L, 5L, 5L, 5L, 6L, 6L, 6L, 6L, 6L,
    6L, 6L, 6L, 5L, 6L, 5L, 2L, 6L, 6L, 5L, 5L, 6L, 5L, 1L, 6L,
    5L, 6L, 6L, 6L, 6L, 6L, 5L, 5L, 5L, 5L, 6L, 6L, 5L, 6L, 5L,
    5L, 5L)), .Names = c("V1", "V2", "V3", "V4", "V5", "V6",
"V7", "V8", "V9", "V10", "V11", "V12", "V13", "V14", "V15", "V16",
"V17", "V18"), class = "data.frame", row.names = c(NA, -307L))

## data set included using dump() command. Note that there is no missing data here as small amounts of na data have been replaced using linear interpolation.


cov.validation <- cov(validation.data)  ## covariance matrix to be used as the S argument in sem function

cfa.validation <- specifyModel()        ## copy and paste this command separately into R before copying the model
ABILITY -> V12, ability0
ABILITY -> V9, ability1
ABILITY -> V14, ability2
ABILITY -> V13, ability3
ABILITY -> V3, ability4
ABILITY -> V1, ability5
ABILITY -> V15, ability6
ABILITY -> V10, ability7
VALUES -> V17, values0
VALUES ->V18, values1
VALUES -> V8, values2
VALUES -> V2, values3
VALUES -> V5, values4
IDENTITY -> V16, identity0
IDENTITY -> V6, identity1
IDENTITY -> V11, identity2
IDENTITY -> V7, identity3
ABILITY <-> ABILITY, NA, 1
VALUES <-> VALUES, NA, 1
IDENTITY <-> IDENTITY, NA, 1
V1 <-> V1, error1
V2 <-> V2, error2
V3 <-> V3, error3
V4 <-> V4, error4
V5 <-> V5, error5
V6 <-> V6, error6
V7 <-> V7, error7
V8 <-> V8, error8
V9 <-> V9, error9
V10 <-> V10, error10
V11 <-> V11, error11
V12 <-> V12, error12
V13 <-> V13, error13
V14 <-> V14, error14
V15 <-> V15, error15
V16 <-> V16, error16
V17 <-> V17, error17
V18 <-> V18, error18
ABILITY <-> VALUES, cov1
ABILITY <-> IDENTITY, cov2
VALUES <-> IDENTITY, cov3

## model specified using standardised factor variances. Analysis has also been run after setting the first item score for each factor to 1, with no difference
## line numbers for the model have been omitted for ease of copying and pasting into R

cfa.validation.output <- sem(cfa.validation, cov.validation, nrow( validation.data))  ## nrow() function used to specify the number of observations.

summary(cfa.validation.output)

______________________________________________________________


The summary that I obtain reads as follows:

Model Chisquare =  561.2528   Df =  133 Pr(>Chisq) = 5.854301e-54
 AIC =  637.2528
 BIC =  -200.418

 Normalized Residuals
    Min.  1st Qu.   Median     Mean  3rd Qu.     Max.
-2.51200 -0.43180  0.02767  0.66300  1.47200  9.78700

 R-square for Endogenous Variables
   V12     V9    V14    V13     V3     V1    V15    V10    V17    V18     V8
0.3193 0.2699 0.4813 0.4904 0.3310 0.3021 0.3544 0.2525 0.6333 0.5825 0.4169
    V2     V5    V16     V6    V11     V7
0.2248 0.3106 0.6653 0.5932 0.4485 0.3899

 Parameter Estimates
          Estimate  Std Error  z value   Pr(>|z|)
ability0  0.5454256 0.05495730  9.924534 3.256189e-23 V12 <--- ABILITY
ability1  0.4648402 0.05171841  8.987906 2.519863e-19 V9 <--- ABILITY
ability2  0.5751229 0.04485033 12.823158 1.216427e-37 V14 <--- ABILITY
ability3  0.6667419 0.05135888 12.982018 1.547491e-38 V13 <--- ABILITY
ability4  0.5430359 0.05354916 10.140887 3.637813e-24 V3 <--- ABILITY
ability5  0.4946864 0.05151662  9.602464 7.805609e-22 V1 <--- ABILITY
ability6  0.5364778 0.05075407 10.570143 4.098707e-26 V15 <--- ABILITY
ability7  0.4247777 0.04912394  8.647061 5.284253e-18 V10 <--- ABILITY
values0   0.6726096 0.04487096 14.989865 8.552626e-51 V17 <--- VALUES
values1   0.7427623 0.05225037 14.215445 7.348274e-46 V18 <--- VALUES
values2   0.4703353 0.04077475 11.534966 8.792193e-31 V8 <--- VALUES
values3   0.2867428 0.03579227  8.011306 1.134969e-15 V2 <--- VALUES
values4   0.3602499 0.03731974  9.653065 4.770800e-22 V5 <--- VALUES
identity0 0.8873503 0.05543298 16.007622 1.130485e-57 V16 <--- IDENTITY
identity1 0.7475428 0.05048877 14.806122 1.337368e-49 V6 <--- IDENTITY
identity2 0.6753142 0.05482191 12.318327 7.217620e-35 V11 <--- IDENTITY
identity3 0.8376139 0.07429317 11.274439 1.754934e-29 V7 <--- IDENTITY
error1    0.5652955 0.04986735 11.335985 8.704746e-30 V1 <--> V1
error2    0.2835150 0.02444977 11.595816 4.327216e-31 V2 <--> V2
error3    0.5960018 0.05327544 11.187177 4.711963e-29 V3 <--> V3
error4    0.7766920 0.06279183 12.369317 3.830654e-35 V4 <--> V4
error5    0.2880738 0.02581887 11.157491 6.582297e-29 V5 <--> V5
error6    0.3832292 0.04263115  8.989418 2.485441e-19 V6 <--> V6
error7    1.0980209 0.10041134 10.935227 7.820970e-28 V7 <--> V7
error8    0.3094475 0.02970430 10.417601 2.060859e-25 V8 <--> V8
error9    0.5844651 0.05087751 11.487691 1.521236e-30 V9 <--> V9
error10   0.5342599 0.04619898 11.564324 6.248167e-31 V10 <--> V10
error11   0.5607651 0.05324925 10.530948 6.220486e-26 V11 <--> V11
error12   0.6341278 0.05637253 11.248880 2.345511e-29 V12 <--> V12
error13   0.4619288 0.04592463 10.058410 8.434950e-24 V13 <--> V13
error14   0.3564872 0.03515096 10.141605 3.611160e-24 V14 <--> V14
error15   0.5242402 0.04741430 11.056583 2.037115e-28 V15 <--> V15
error16   0.3961271 0.05073686  7.807481 5.834244e-15 V16 <--> V16
error17   0.2619686 0.03471455  7.546364 4.475775e-14 V17 <--> V17
error18   0.3954005 0.04696524  8.419004 3.796997e-17 V18 <--> V18
cov1      0.2758005 0.06547343  4.212403 2.526678e-05 VALUES <--> ABILITY
cov2      0.6920402 0.04301632 16.087854 3.104127e-58 IDENTITY <--> ABILITY
cov3      0.3573852 0.06216556  5.748926 8.981225e-09 IDENTITY <--> VALUES

 Iterations =  30
_________________________________________________________
As far as I can tell, the analysis has estimated parameters in the model, but I cannot obtain the fit indices. I have also used the stdCoef() command to obtain standardised coefficients. I have searched for similar issues on the R-help archive and on a number of forums, but haven't found anything useful. I have also examined the documentation for these packages and cannot find the problem. I am starting to think that I have missed something very simple, but I have gone over every step very closely and carefully. Any help with this issue would be greatly appreciated.

With regards,
Kevin Yet Fong Cheung

Kevin Yet Fong Cheung, Bsc., MRes., MBPsS.
Postgraduate Researcher
Centre for Psychological Research
University of Derby
Kedleston Road
Derby DE22 1GB
k.cheung at derby.ac.uk<mailto:k.cheung at derby.ac.uk>
01332592081

http://derby.academia.edu/KevinCheung


_____________________________________________________________________
The University of Derby has a published policy regarding email and reserves the right to monitor email traffic. If you believe this email was sent to you in error, please notify the sender and delete this email. Please direct any concerns to Infosec at derby.ac.uk.
John Fox
# 
Dear Kevin,

See ?summary.objectiveML, and in particular the description of the fit.indices argument. By default, the summary() method doesn't print many fit indices, but many are available optionally.

I hope this helps,
 John

------------------------------------------------
John Fox
Sen. William McMaster Prof. of Social Statistics
Department of Sociology
McMaster University
Hamilton, Ontario, Canada
http://socserv.mcmaster.ca/jfox/

On Mon, 18 Mar 2013 15:00:06 +0000
Kevin Cheung <K.Cheung at derby.ac.uk> wrote:
f!
Hervé Guyon
# 
Hi Kevin,

With sem package use : summary(X,fit.indices=c("RMSEA",...)) to get 
RMSEA or anothers fit indices.
See the section "ML.methods" in sem.pdf

Herv?

Herv?
Le 18/03/2013 16:00, Kevin Cheung a ?crit :
Kevin Cheung
# 
Dear John,

Thank you for taking the time to help me with this, I have been able to obtain fit indices using the information that you provided.

Note to users searching archived R-help posts about this issue: The instructional video I looked at (http://vimeo.com/38941937) gave fit indices using the default summary() command without any additional arguments. This may have been due to a different version of R (I noticed that the instructor was using a mac based OS).

With regards,
Kevin

Kevin Yet Fong Cheung, Bsc., MRes., MBPsS.
Postgraduate Researcher
Centre for Psychological Research
University of Derby
Kedleston Road
Derby DE22 1GB
k.cheung at derby.ac.uk<mailto:k.cheung at derby.ac.uk>
01332592081

http://derby.academia.edu/KevinCheung


-----Original Message-----
From: John Fox [mailto:jfox at mcmaster.ca]
Sent: 18 March 2013 15:55
To: Kevin Cheung
Cc: r-help at r-project.org
Subject: Re: [R] Confirmatory factor analysis using the sem package. TLI CFI and RMSEA absent from model summary.

Dear Kevin,

See ?summary.objectiveML, and in particular the description of the fit.indices argument. By default, the summary() method doesn't print many fit indices, but many are available optionally.

I hope this helps,
 John

------------------------------------------------
John Fox
Sen. William McMaster Prof. of Social Statistics Department of Sociology McMaster University Hamilton, Ontario, Canada http://socserv.mcmaster.ca/jfox/

On Mon, 18 Mar 2013 15:00:06 +0000
Kevin Cheung <K.Cheung at derby.ac.uk> wrote:
f!
_____________________________________________________________________
The University of Derby has a published policy regarding email and reserves the right to monitor email traffic. If you believe this email was sent to you in error, please notify the sender and delete this email. Please direct any concerns to Infosec at derby.ac.uk.
John Fox
# 
Dear Kevin,

In earlier versions of the sem package, the summary() method for model objects produced many (and from version to version, an increasing number of) fit indices by default. For various reasons, I decided to provide only the LR chisquare test for the model, the BIC, and the AIC by default, and to leave it up the user to decide what fit indices to show. This can be controlled with the fit.indices option as well as the fit.indices argument to the summary() method.

Best,
 John

On Mon, 18 Mar 2013 16:46:03 +0000
Kevin Cheung <K.Cheung at derby.ac.uk> wrote:
le!
------------------------------------------------
John Fox
Sen. William McMaster Prof. of Social Statistics
Department of Sociology
McMaster University
Hamilton, Ontario, Canada
http://socserv.mcmaster.ca/jfox/