* using log directory 'd:/Rcompile/CRANpkg/local/2.9/FAiR.Rcheck' * using R version 2.9.2 Patched (2009-09-02 r49531) * using session charset: ISO8859-1 * checking for file 'FAiR/DESCRIPTION' ... OK * checking extension type ... Package * this is package 'FAiR' version '0.4-5' * package encoding: UTF-8 * checking package name space information ... OK * checking package dependencies ... OK * checking if this is a source package ... OK * checking whether package 'FAiR' can be installed ... OK * checking package directory ... OK * checking for portable file names ... OK * checking DESCRIPTION meta-information ... OK * checking top-level files ... OK * checking index information ... OK * checking package subdirectories ... OK * checking R files for non-ASCII characters ... OK * checking R files for syntax errors ... OK * checking whether the package can be loaded ... OK * checking whether the package can be loaded with stated dependencies ... OK * checking whether the name space can be loaded with stated dependencies ... OK * checking for unstated dependencies in R code ... OK * checking S3 generic/method consistency ... OK * checking replacement functions ... OK * checking foreign function calls ... OK * checking R code for possible problems ... NOTE Error in library.dynam(lib, package, package.lib) : shared library 'BioC_graph' not found FAiR_DAG: no visible global function definition for 'nodes' FAiR_DAG: no visible global function definition for 'addEdge' FAiR_DAG: no visible global function definition for 'subGraph' * checking Rd files ... OK * checking Rd files against version 2 parser ... OK * checking Rd cross-references ... OK * checking for missing documentation entries ... OK * checking for code/documentation mismatches ... OK * checking Rd \usage sections ... OK * checking line endings in C/C++/Fortran sources/headers ... OK * checking line endings in Makefiles ... OK * checking for portable use of $BLAS_LIBS ... OK * checking examples ... ERROR Running examples in 'FAiR-Ex.R' failed. The error most likely occurred in: > ### * 03Factanal > > flush(stderr()); flush(stdout()) > > ### Name: Factanal > ### Title: Estimate Factor Analysis Models > ### Aliases: Factanal > ### Keywords: multivariate models > > ### ** Examples > > ## Example from Venables and Ripley (2002, p. 323) > ## Previously from Bartholomew and Knott (1999, p. 68--72) > ## Originally from Smith and Stanley (1983) > ## Replicated from example(ability.cov) > > man <- make_manifest(covmat = ability.cov) Warning in FAiR_make_manifest_list(covmat, shrink) : it is strongly preferable to pass the raw data to make_manifest() > > ## Not run: > ##D ## Here is the easy way to set up a SEFA model, which uses pop-up menus > ##D res <- make_restrictions(manifest = man, factors = 2, model = "SEFA") > ## End(Not run) > > ## This is the hard way to set up a restrictions object without pop-up menus > beta <- matrix(NA_real_, nrow = nrow(cormat(man)), ncol = 2) > rownames(beta) <- rownames(cormat(man)) > free <- is.na(beta) > beta <- new("parameter.coef.SEFA", x = beta, free = free, num_free = sum(free)) > > Phi <- diag(2) > free <- lower.tri(Phi) > Phi <- new("parameter.cormat", x = Phi, free = free, num_free = sum(free)) > res <- make_restrictions(manifest = man, beta = beta, Phi = Phi) > > # This is how to make starting values where Phi is the correlation matrix > # among factors, beta is the matrix of coefficients, and the scales are > # the logarithm of the sample standard deviations. It is also the MLE. > starts <- c( 4.46294498156615e-01, # Phi_{21} + 4.67036349420035e-01, # beta_{11} + 6.42220238211291e-01, # beta_{21} + 8.88564379236454e-01, # beta_{31} + 4.77779639176941e-01, # beta_{41} + -7.13405536379741e-02, # beta_{51} + -9.47782525342137e-08, # beta_{61} + 4.04993872375487e-01, # beta_{12} + -1.04604290549591e-08, # beta_{22} + -9.44950629176182e-03, # beta_{32} + 2.63078925240678e-04, # beta_{42} + 9.38038168787216e-01, # beta_{52} + 8.43618801925473e-01, # beta_{62} + log(man@sds)) # log manifest standard deviations > > sefa <- Factanal(manifest = man, restrictions = res, + # NOTE: Do NOT specify any of the following tiny values in a + # real research situation; it is done here solely for speed + starting.values = starts, pop.size = 2, max.generations = 6, + wait.generations = 1) Thu Jan 28 14:56:50 2010 Domains: -1.000000e+00 <= X1 <= 1.000000e+00 -1.500000e+00 <= X2 <= 1.500000e+00 -1.500000e+00 <= X3 <= 1.500000e+00 -1.500000e+00 <= X4 <= 1.500000e+00 -1.500000e+00 <= X5 <= 1.500000e+00 -1.500000e+00 <= X6 <= 1.500000e+00 -1.500000e+00 <= X7 <= 1.500000e+00 -1.500000e+00 <= X8 <= 1.500000e+00 -1.500000e+00 <= X9 <= 1.500000e+00 -1.500000e+00 <= X10 <= 1.500000e+00 -1.500000e+00 <= X11 <= 1.500000e+00 -1.500000e+00 <= X12 <= 1.500000e+00 -1.500000e+00 <= X13 <= 1.500000e+00 -1.800000e+01 <= X14 <= 2.295353e+00 -1.800000e+01 <= X15 <= 1.644201e+00 -1.800000e+01 <= X16 <= 3.197901e+00 -1.800000e+01 <= X17 <= 1.964381e+00 -1.800000e+01 <= X18 <= 2.674543e+00 -1.800000e+01 <= X19 <= 3.146865e+00 Data Type: Floating Point Operators (code number, name, population) (1) Cloning........................... 1 (2) Uniform Mutation.................. 0 (3) Boundary Mutation................. 0 (4) Non-Uniform Mutation.............. 0 (5) Polytope Crossover................ 0 (6) Simple Crossover.................. 0 (7) Whole Non-Uniform Mutation........ 0 (8) Heuristic Crossover............... 0 (9) Local-Minimum Crossover........... 0 HARD Maximum Number of Generations: 6 Maximum Nonchanging Generations: 1 Population size : 2 Convergence Tolerance: 1.000000e-03 Using the BFGS Derivative Based Optimizer on the Best Individual Each Generation. Checking Gradients before Stopping. Not Using Out of Bounds Individuals But Allowing Trespassing. Minimization Problem. Generation# Solution Values (lexical) 0 -1.000000e+00 -1.000000e+00 -1.000000e+00 -1.000000e+00 6.356664e-02 'wait.generations' limit reached. No significant improvement in 1 generations. Solution Lexical Fitness Value: -1.000000e+00 -1.000000e+00 -1.000000e+00 -1.000000e+00 6.356664e-02 Parameters at the Solution (parameter, gradient): X[ 1] : 4.462945e-01 G[ 1] : -1.506061e-05 X[ 2] : 4.670363e-01 G[ 2] : -2.910233e-05 X[ 3] : 6.422202e-01 G[ 3] : -2.225256e-05 X[ 4] : 8.885644e-01 G[ 4] : -5.605264e-05 X[ 5] : 4.777796e-01 G[ 5] : -6.675071e-06 X[ 6] : -7.134055e-02 G[ 6] : -0.000000e+00 X[ 7] : -9.477825e-08 G[ 7] : -0.000000e+00 X[ 8] : 4.049939e-01 G[ 8] : -1.463011e-05 X[ 9] : -1.046043e-08 G[ 9] : -0.000000e+00 X[10] : -9.449506e-03 G[10] : -2.643417e-05 X[11] : 2.630789e-04 G[11] : 0.000000e+00 X[12] : 9.380382e-01 G[12] : 4.832929e-06 X[13] : 8.436188e-01 G[13] : -7.343759e-06 X[14] : 1.602206e+00 G[14] : 1.174018e-05 X[15] : 9.510538e-01 G[15] : 9.504571e-06 X[16] : 2.504754e+00 G[16] : 2.032664e-05 X[17] : 1.271234e+00 G[17] : 3.728702e-06 X[18] : 1.981396e+00 G[18] : 1.588892e-06 X[19] : 2.453718e+00 G[19] : 1.738531e-06 Solution Found Generation 1 Number of Generations Run 2 Thu Jan 28 14:56:50 2010 Total run time : 0 hours 0 minutes and 0 seconds Nelder-Mead resulted in no improvement; convergence presumably achieved > nsim <- 101 # number of simulations, also too small for real work > show(sefa) Call: Factanal(manifest = man, restrictions = res, starting.values = starts, pop.size = 2, max.generations = 6, wait.generations = 1) Number of observations: 112 Discrepancy: 7.055898 Semi-exploratory factor analysis with 2 factors All free factor intercorrelations are on the [-1,1] interval All coefficients on the [ -1.5 , 1.5 ] interval Zeros per factor A B zeros 2 2 Mapping rule: default Discrepancy function: MLE 6 degrees of freedom > summary(sefa, nsim = nsim) [1] "100 simulations remaining" [1] "0 simulations remaining" Call: Factanal(manifest = man, restrictions = res, starting.values = starts, pop.size = 2, max.generations = 6, wait.generations = 1) Point estimates (blanks, if any, are exact zeros): F1 F2 Uniqueness general 0.467 0.405 0.449 picture 0.642 0.588 blocks 0.889 -0.009 0.218 maze 0.478 0.772 reading 0.938 0.120 vocab 0.844 0.288 F1 1.000 0.446 F2 0.446 1.000 Upper confidence bounds (blanks, if any, are restricted) F1 F2 Uniqueness general 0.594 0.582 0.581 picture 0.789 0.731 blocks 1.080 0.275 0.473 maze 0.644 0.902 reading 0.991 0.270 vocab 0.924 0.427 F1 1.000 0.679 F2 0.679 1.000 Lower confidence bounds (blanks, if any, are restricted) F1 F2 Uniqueness general 0.301 0.249 0.325 picture 0.519 0.378 blocks 0.662 -0.241 0.034 maze 0.313 0.586 reading 0.855 0.018 vocab 0.757 0.147 F1 1.000 0.239 F2 0.239 1.000 > model_comparison(sefa, nsim = nsim) $restrictions Semi-exploratory factor analysis with 2 factors All free factor intercorrelations are on the [-1,1] interval All coefficients on the [ -1.5 , 1.5 ] interval Zeros per factor A B zeros 2 2 Mapping rule: default Discrepancy function: MLE 6 degrees of freedom $exact_fit $exact_fit$T_ML Test of Exact Fit data: T ( Swain correction ) = 6.867, df = 6, p-value = 0.3333 alternative hypothesis: true discrepancy is greater than 0 $infocriteria $infocriteria$BIC [1] 2858.139 $infocriteria$BIC_saturated [1] 2879.394 $infocriteria$BIC_null [1] 3126.466 $infocriteria$SIC [1] 1435.428 $infocriteria$SIC_saturated [1] 1508.444 $infocriteria$SIC_null [1] 1569.592 $close_fit $close_fit$RMSEA Test of Close Fit data: T ( Swain correction ) = 6.867, df = 6, p-value = 0.5018 alternative hypothesis: true discrepancy is greater than 0.05 90 percent confidence interval: 0.0003751339 0.1323741179 sample estimates: RMSEA 0.03607973 $close_fit$gamma Gamma Fit Index (Steiger) data: 90 percent confidence interval: 0.9661408 0.9999997 sample estimates: Gamma_1 0.9974033 $fit_indices $fit_indices$GFI [1] 0.9801767 $fit_indices$AGFI [1] 0.9306186 $fit_indices$McDonald Centrality Index (McDonald) data: sample estimates: Index 0.9961371 $fit_indices$SRMR [1] 0.03658248 $fit_indices$TLI T ( Swain correction ) 0.9914335 $fit_indices$CFI [1] 0.9965734 $fit_indices$NFI T ( Swain correction ) 0.9743779 $fit_indices$NNFI T ( Swain correction ) 0.9914335 > > stuff <- list() # output list for various methods > stuff$model.matrix <- model.matrix(sefa) # sample correlation matrix > stuff$fitted <- fitted(sefa, reduced = TRUE) # reduced covariance matrix > stuff$residuals <- residuals(sefa) # difference between model.matrix and fitted > stuff$rstandard <- rstandard(sefa) # normalized residual matrix > stuff$weights <- weights(sefa) # (scaled) approximate weights for residuals > stuff$influence <- influence(sefa) # weights * residuals > stuff$cormat <- cormat(sefa, matrix = "RF") # reference factor correlations > stuff$uniquenesses <- uniquenesses(sefa, standardized = FALSE) # uniquenesses > stuff$FC <- loadings(sefa, matrix = "FC") # factor contribution matrix > stuff$draws <- FA2draws(sefa, nsim = nsim) # draws from sampling distribution [1] "100 simulations remaining" [1] "0 simulations remaining" > > if(require(nFactors)) screeplot(sefa) # Enhanced scree plot Loading required package: nFactors Loading required package: MASS Loading required package: psych Loading required package: boot Attaching package: 'boot' The following object(s) are masked from package:lattice : melanoma The following object(s) are masked from package:robustbase : salinity Attaching package: 'nFactors' The following object(s) are masked from package:lattice : parallel > profile(sefa) # profile plots of non-free parameters Factors may arbitrarily be plotted in a different order than they appear in summary() > pairs(sefa) # Thurstone-style plot > if(require(Rgraphviz)) plot(sefa) # DAG Loading required package: Rgraphviz Loading required package: graph Error in library.dynam(lib, package, package.lib) : shared library 'BioC_graph' not found Error: package 'graph' could not be loaded Execution halted