Kevin Cheung
2013-Mar-18 15:00 UTC
[R] Confirmatory factor analysis using the sem package. TLI CFI and RMSEA absent from model summary.
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
2013-Mar-18 15:55 UTC
[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:> 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!f!> rom 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. > > ______________________________________________ > R-help at r-project.org mailing list > https://stat.ethz.ch/mailman/listinfo/r-help > PLEASE do read the posting guide http://www.R-project.org/posting-guide.html > and provide commented, minimal, self-contained, reproducible code.
Hervé Guyon
2013-Mar-18 16:12 UTC
[R] Confirmatory factor analysis using the sem package. TLI CFI and RMSEA absent from model summary.
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 :> 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 f! > rom 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. > > ______________________________________________ > R-help at r-project.org mailing list > https://stat.ethz.ch/mailman/listinfo/r-help > PLEASE do read the posting guide http://www.R-project.org/posting-guide.html > and provide commented, minimal, self-contained, reproducible code. >