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Type 'q()' to quit R. > pkgname <- "fmrs" > source(file.path(R.home("share"), "R", "examples-header.R")) > options(warn = 1) > library('fmrs') fmrs package, Version 1.0.9, Released 2016-07-01 Provides parameter estimation as well as variable selection in Finite Mixture of Accelerated Failure Time Regression and Finite Mixture of Regression Models. Furthermore, this package provides Ridge Regression and Elastic Net. BugReports: https://github.com/shokoohi/fmrs/issues > > base::assign(".oldSearch", base::search(), pos = 'CheckExEnv') > base::assign(".old_wd", base::getwd(), pos = 'CheckExEnv') > cleanEx() > nameEx("BIC-methods") > ### * BIC-methods > > flush(stderr()); flush(stdout()) > > ### Name: BIC > ### Title: BIC method > ### Aliases: BIC BIC,BIC-method BIC,fmrsfit-method > > ### ** Examples > > set.seed(1980) > nComp = 2 > nCov = 10 > nObs = 500 > dispersion = c(1, 1) > mixProp = c(0.4, 0.6) > rho = 0.5 > coeff1 = c( 2, 2, -1, -2, 1, 2, 0, 0, 0, 0, 0) > coeff2 = c(-1, -1, 1, 2, 0, 0, 0, 0, -1, 2, -2) > umax = 40 > > dat <- fmrs.gendata(nObs = nObs, nComp = nComp, nCov = nCov, + coeff = c(coeff1, coeff2), dispersion = dispersion, + mixProp = mixProp, rho = rho, umax = umax, + disFamily = "lnorm") > > res.mle <- fmrs.mle(y = dat$y, x = dat$x, delta = dat$delta, + nComp = nComp, disFamily = "lnorm", + initCoeff = rnorm(nComp*nCov+nComp), + initDispersion = rep(1, nComp), + initmixProp = rep(1/nComp, nComp)) fmrs.c:500:66: runtime error: index 501 out of bounds for type 'int [NCOV + 1][NCOMP]' #0 0x7f6968b37ba5 in FMR_Norm_Surv_EM_MLE /data/gannet/ripley/R/packages/tests-clang-SAN/fmrs/src/fmrs.c:500:66 #1 0x744de8 in do_dotCode /data/gannet/ripley/R/svn/R-devel/src/main/dotcode.c #2 0x820998 in Rf_eval /data/gannet/ripley/R/svn/R-devel/src/main/eval.c:791:9 #3 0x894b93 in do_set /data/gannet/ripley/R/svn/R-devel/src/main/eval.c:2920:8 #4 0x8203ac in Rf_eval /data/gannet/ripley/R/svn/R-devel/src/main/eval.c:763:12 #5 0x893a52 in do_begin /data/gannet/ripley/R/svn/R-devel/src/main/eval.c:2471:10 #6 0x8203ac in Rf_eval /data/gannet/ripley/R/svn/R-devel/src/main/eval.c:763:12 #7 0x8203ac in Rf_eval /data/gannet/ripley/R/svn/R-devel/src/main/eval.c:763:12 #8 0x8203ac in Rf_eval /data/gannet/ripley/R/svn/R-devel/src/main/eval.c:763:12 #9 0x893a52 in do_begin /data/gannet/ripley/R/svn/R-devel/src/main/eval.c:2471:10 #10 0x8203ac in Rf_eval /data/gannet/ripley/R/svn/R-devel/src/main/eval.c:763:12 #11 0x8882ab in R_execClosure /data/gannet/ripley/R/svn/R-devel/src/main/eval.c #12 0x883707 in Rf_applyClosure /data/gannet/ripley/R/svn/R-devel/src/main/eval.c:1779:16 #13 0x8428ce in bcEval /data/gannet/ripley/R/svn/R-devel/src/main/eval.c:7022:12 #14 0x81fd59 in Rf_eval /data/gannet/ripley/R/svn/R-devel/src/main/eval.c:688:8 #15 0x8882ab in R_execClosure /data/gannet/ripley/R/svn/R-devel/src/main/eval.c #16 0x88cdba in R_execMethod /data/gannet/ripley/R/svn/R-devel/src/main/eval.c:2029:11 #17 0x7f696dd7ebde in R_dispatchGeneric /data/gannet/ripley/R/svn/R-devel/src/library/methods/src/methods_list_dispatch.c:1050:19 #18 0x9b6b96 in do_standardGeneric /data/gannet/ripley/R/svn/R-devel/src/main/objects.c:1285:13 #19 0x83481c in bcEval /data/gannet/ripley/R/svn/R-devel/src/main/eval.c:7011:12 #20 0x81fd59 in Rf_eval /data/gannet/ripley/R/svn/R-devel/src/main/eval.c:688:8 #21 0x8882ab in R_execClosure /data/gannet/ripley/R/svn/R-devel/src/main/eval.c #22 0x883707 in Rf_applyClosure /data/gannet/ripley/R/svn/R-devel/src/main/eval.c:1779:16 #23 0x820b92 in Rf_eval /data/gannet/ripley/R/svn/R-devel/src/main/eval.c:811:12 #24 0x894b93 in do_set /data/gannet/ripley/R/svn/R-devel/src/main/eval.c:2920:8 #25 0x8203ac in Rf_eval /data/gannet/ripley/R/svn/R-devel/src/main/eval.c:763:12 #26 0x954fb9 in Rf_ReplIteration /data/gannet/ripley/R/svn/R-devel/src/main/main.c:264:2 #27 0x959400 in R_ReplConsole /data/gannet/ripley/R/svn/R-devel/src/main/main.c:314:11 #28 0x9591e5 in run_Rmainloop /data/gannet/ripley/R/svn/R-devel/src/main/main.c:1113:5 #29 0x4dbb4a in main /data/gannet/ripley/R/svn/R-devel/src/main/Rmain.c:29:5 #30 0x7f69795d9f42 in __libc_start_main (/lib64/libc.so.6+0x23f42) #31 0x43036d in _start (/data/gannet/ripley/R/R-clang-SAN/bin/exec/R+0x43036d) SUMMARY: UndefinedBehaviorSanitizer: undefined-behavior fmrs.c:500:66 in > BIC(res.mle) [1] -827.0648 > > > > cleanEx() > nameEx("coefficients-methods") > ### * coefficients-methods > > flush(stderr()); flush(stdout()) > > ### Name: coefficients > ### Title: coefficients method > ### Aliases: coefficients coefficients,coefficients-method > ### coefficients,fmrsfit-method > > ### ** Examples > > set.seed(1980) > nComp = 2 > nCov = 10 > nObs = 500 > dispersion = c(1, 1) > mixProp = c(0.4, 0.6) > rho = 0.5 > coeff1 = c( 2, 2, -1, -2, 1, 2, 0, 0, 0, 0, 0) > coeff2 = c(-1, -1, 1, 2, 0, 0, 0, 0, -1, 2, -2) > umax = 40 > > dat <- fmrs.gendata(nObs = nObs, nComp = nComp, nCov = nCov, + coeff = c(coeff1, coeff2), dispersion = dispersion, + mixProp = mixProp, rho = rho, umax = umax, + disFamily = "lnorm") > > res.mle <- fmrs.mle(y = dat$y, x = dat$x, delta = dat$delta, + nComp = nComp, disFamily = "lnorm", + initCoeff = rnorm(nComp*nCov+nComp), + initDispersion = rep(1, nComp), + initmixProp = rep(1/nComp, nComp)) > coefficients(res.mle) Comp.1 Comp.2 Intercept -1.023594579 2.16213365 X.1 -0.947852679 1.88477727 X.2 0.789705795 -0.87406530 X.3 2.150263722 -2.14019456 X.4 0.006755311 0.98534042 X.5 0.057400637 1.97234015 X.6 -0.059509907 0.25060224 X.7 -0.129978021 0.03928839 X.8 -0.878291946 -0.05872176 X.9 1.928299499 -0.04921801 X.10 -2.048083361 0.06100636 > > > > cleanEx() > nameEx("dispersion-methods") > ### * dispersion-methods > > flush(stderr()); flush(stdout()) > > ### Name: dispersion > ### Title: dispersion method > ### Aliases: dispersion dispersion,dispersion-method > ### dispersion,fmrsfit-method > > ### ** Examples > > set.seed(1980) > nComp = 2 > nCov = 10 > nObs = 500 > dispersion = c(1, 1) > mixProp = c(0.4, 0.6) > rho = 0.5 > coeff1 = c( 2, 2, -1, -2, 1, 2, 0, 0, 0, 0, 0) > coeff2 = c(-1, -1, 1, 2, 0, 0, 0, 0, -1, 2, -2) > umax = 40 > > dat <- fmrs.gendata(nObs = nObs, nComp = nComp, nCov = nCov, + coeff = c(coeff1, coeff2), dispersion = dispersion, + mixProp = mixProp, rho = rho, umax = umax, + disFamily = "lnorm") > > res.mle <- fmrs.mle(y = dat$y, x = dat$x, delta = dat$delta, + nComp = nComp, disFamily = "lnorm", + initCoeff = rnorm(nComp*nCov+nComp), + initDispersion = rep(1, nComp), + initmixProp = rep(1/nComp, nComp)) > dispersion(res.mle) Comp.1 Comp.2 [1,] 0.8846201 0.9351782 > > > > cleanEx() > nameEx("fitted-methods") > ### * fitted-methods > > flush(stderr()); flush(stdout()) > > ### Name: fitted > ### Title: fitted method > ### Aliases: fitted fitted,fitted-method fitted,fmrsfit-method > > ### ** Examples > > set.seed(1980) > nComp = 2 > nCov = 10 > nObs = 500 > dispersion = c(1, 1) > mixProp = c(0.4, 0.6) > rho = 0.5 > coeff1 = c( 2, 2, -1, -2, 1, 2, 0, 0, 0, 0, 0) > coeff2 = c(-1, -1, 1, 2, 0, 0, 0, 0, -1, 2, -2) > umax = 40 > > dat <- fmrs.gendata(nObs = nObs, nComp = nComp, nCov = nCov, + coeff = c(coeff1, coeff2), dispersion = dispersion, + mixProp = mixProp, rho = rho, umax = umax, + disFamily = "lnorm") > > res.mle <- fmrs.mle(y = dat$y, x = dat$x, delta = dat$delta, + nComp = nComp, disFamily = "lnorm", + initCoeff = rnorm(nComp*nCov+nComp), + initDispersion = rep(1, nComp), + initmixProp = rep(1/nComp, nComp)) > head(fitted(res.mle)) Comp.1 Comp.2 [1,] 1.127924e+02 1.677727 [2,] 2.188275e-01 115.838327 [3,] 2.448557e-03 1.121822 [4,] 2.595668e-01 111.464290 [5,] 2.345457e+00 14.048199 [6,] 1.116815e-01 2.534667 > > > > cleanEx() > nameEx("fmrs.gendata-methods") > ### * fmrs.gendata-methods > > flush(stderr()); flush(stdout()) > > ### Name: fmrs.gendata > ### Title: fmrs.gendata method > ### Aliases: fmrs.gendata fmrs.gendata,ANY-method fmrs.gendata-method > ### Keywords: AFT Censored Data FMRs Generation > > ### ** Examples > > set.seed(1980) > nComp = 2 > nCov = 10 > nObs = 500 > REP = 500 > dispersion = c(1, 1) > mixProp = c(0.4, 0.6) > rho = 0.5 > coeff1 = c( 2, 2, -1, -2, 1, 2, 0, 0, 0, 0, 0) > coeff2 = c(-1, -1, 1, 2, 0, 0, 0, 0, -1, 2, -2) > umax = 40 > > dat <- fmrs.gendata(nObs = nObs, nComp = nComp, nCov = nCov, + coeff = c(coeff1, coeff2), dispersion = dispersion, + mixProp =mixProp, rho = rho, umax = umax, + disFamily = "lnorm") > > > > cleanEx() > nameEx("fmrs.mle-methods") > ### * fmrs.mle-methods > > flush(stderr()); flush(stdout()) > > ### Name: fmrs.mle > ### Title: fmrs.mle method > ### Aliases: fmrs.mle fmrs.mle,ANY-method fmrs.mle-method > ### Keywords: AFT Censored EM FMRs NR Ridge > > ### ** Examples > > set.seed(1980) > nComp = 2 > nCov = 10 > nObs = 500 > dispersion = c(1, 1) > mixProp = c(0.4, 0.6) > rho = 0.5 > coeff1 = c( 2, 2, -1, -2, 1, 2, 0, 0, 0, 0, 0) > coeff2 = c(-1, -1, 1, 2, 0, 0, 0, 0, -1, 2, -2) > umax = 40 > > dat <- fmrs.gendata(nObs = nObs, nComp = nComp, nCov = nCov, + coeff = c(coeff1, coeff2), dispersion = dispersion, + mixProp = mixProp, rho = rho, umax = umax, + disFamily = "lnorm") > > res.mle <- fmrs.mle(y = dat$y, x = dat$x, delta = dat$delta, + nComp = nComp, disFamily = "lnorm", + initCoeff = rnorm(nComp*nCov+nComp), + initDispersion = rep(1, nComp), + initmixProp = rep(1/nComp, nComp)) > summary(res.mle) ------------------------------------------- Fitted Model: ------------------------------------------- Finite Mixture of Accelerated Failure Time Regression Models Log-Normal Sub-Distributions 2 Components; 10 Covariates; 500 samples. Coefficients: Comp.1 Comp.2 Intercept -1.023594646 2.16213209 X.1 -0.947852642 1.88477635 X.2 0.789705744 -0.87406443 X.3 2.150263743 -2.14019382 X.4 0.006755298 0.98533979 X.5 0.057400643 1.97233892 X.6 -0.059509943 0.25060228 X.7 -0.129978039 0.03928863 X.8 -0.878291959 -0.05872164 X.9 1.928299514 -0.04921806 X.10 -2.048083339 0.06100657 Active Set: Comp.1 Comp.2 Intercept 1 1 X.1 1 1 X.2 1 1 X.3 1 1 X.4 1 1 X.5 1 1 X.6 1 1 X.7 1 1 X.8 1 1 X.9 1 1 X.10 1 1 Dispersions: Comp.1 Comp.2 [1,] 0.8846201 0.9351782 Mixing Proportions: Comp.1 Comp.2 [1,] 0.5831783 0.4168217 LogLik: -764.9188; BIC: -827.0648 > > > > cleanEx() > nameEx("fmrs.tunsel-methods") > ### * fmrs.tunsel-methods > > flush(stderr()); flush(stdout()) > > ### Name: fmrs.tunsel > ### Title: fmrs.tunsel method > ### Aliases: fmrs.tunsel fmrs.tunsel,ANY-method fmrs.tunsel-method > ### Keywords: AFT Adaptive Censored FMRs LASSO MCP Regression Ridge SCAD > ### SICA Tuning > > ### ** Examples > > set.seed(1980) > nComp = 2 > nCov = 10 > nObs = 500 > dispersion = c(1, 1) > mixProp = c(0.4, 0.6) > rho = 0.5 > coeff1 = c( 2, 2, -1, -2, 1, 2, 0, 0, 0, 0, 0) > coeff2 = c(-1, -1, 1, 2, 0, 0, 0, 0, -1, 2, -2) > umax = 40 > > dat <- fmrs.gendata(nObs = nObs, nComp = nComp, nCov = nCov, + coeff = c(coeff1, coeff2), dispersion = dispersion, + mixProp = mixProp, rho = rho, umax = umax, + disFamily = "lnorm") > > res.mle <- fmrs.mle(y = dat$y, x = dat$x, delta = dat$delta, + nComp = nComp, disFamily = "lnorm", + initCoeff = rnorm(nComp*nCov+nComp), + initDispersion = rep(1, nComp), + initmixProp = rep(1/nComp, nComp)) > > res.lam <- fmrs.tunsel(y = dat$y, x = dat$x, delta = dat$delta, + nComp = nComp, disFamily = "lnorm", + initCoeff = c(coefficients(res.mle)), + initDispersion = dispersion(res.mle), + initmixProp = mixProp(res.mle), + penFamily = "adplasso") > show(res.lam) An object of class 'fmrstunpar' Finite Mixture of Accelerated Failure Time Regression Models Log-Normal Sub-Distributions 2 Components; adplasso Penalty; > > > > cleanEx() > nameEx("fmrs.varsel-methods") > ### * fmrs.varsel-methods > > flush(stderr()); flush(stdout()) > > ### Name: fmrs.varsel > ### Title: fmrs.varsel method > ### Aliases: fmrs.varsel fmrs.varsel,ANY-method fmrs.varsel-method > ### Keywords: AFT Adaptive Algorithm Censored EM ElasticNet FMR LASSO MCP > ### Regression Ridge SCAD SICA Selection > > ### ** Examples > > set.seed(1980) > nComp = 2 > nCov = 10 > nObs = 500 > dispersion = c(1, 1) > mixProp = c(0.4, 0.6) > rho = 0.5 > coeff1 = c( 2, 2, -1, -2, 1, 2, 0, 0, 0, 0, 0) > coeff2 = c(-1, -1, 1, 2, 0, 0, 0, 0, -1, 2, -2) > umax = 40 > > dat <- fmrs.gendata(nObs = nObs, nComp = nComp, nCov = nCov, + coeff = c(coeff1, coeff2), dispersion = dispersion, + mixProp =mixProp, rho = rho, umax = umax, + disFamily = "lnorm") > > res.mle <- fmrs.mle(y = dat$y, x = dat$x, delta = dat$delta, + nComp = nComp, disFamily = "lnorm", + initCoeff = rnorm(nComp*nCov+nComp), + initDispersion = rep(1, nComp), + initmixProp = rep(1/nComp, nComp)) > > res.lam <- fmrs.tunsel(y = dat$y, x = dat$x, delta = dat$delta, + nComp = ncomp(res.mle), disFamily = "lnorm", + initCoeff=c(coefficients(res.mle)), + initDispersion = dispersion(res.mle), + initmixProp = mixProp(res.mle), + penFamily = "adplasso") > res.var <- fmrs.varsel(y = dat$y, x = dat$x, delta = dat$delta, + nComp = ncomp(res.mle), disFamily = "lnorm", + initCoeff=c(coefficients(res.mle)), + initDispersion = dispersion(res.mle), + initmixProp = mixProp(res.mle), + penFamily = "adplasso", + lambPen = slot(res.lam, "lambPen")) fmrs.c:964:66: runtime error: index 501 out of bounds for type 'int [NCOV + 1][NCOMP]' #0 0x7f6968b4d5da in FMR_Norm_Surv_EM_VarSel /data/gannet/ripley/R/packages/tests-clang-SAN/fmrs/src/fmrs.c:964:66 #1 0x734f7b in do_dotCode /data/gannet/ripley/R/svn/R-devel/src/main/dotcode.c #2 0x820998 in Rf_eval /data/gannet/ripley/R/svn/R-devel/src/main/eval.c:791:9 #3 0x894b93 in do_set /data/gannet/ripley/R/svn/R-devel/src/main/eval.c:2920:8 #4 0x8203ac in Rf_eval /data/gannet/ripley/R/svn/R-devel/src/main/eval.c:763:12 #5 0x893a52 in do_begin /data/gannet/ripley/R/svn/R-devel/src/main/eval.c:2471:10 #6 0x8203ac in Rf_eval /data/gannet/ripley/R/svn/R-devel/src/main/eval.c:763:12 #7 0x8203ac in Rf_eval /data/gannet/ripley/R/svn/R-devel/src/main/eval.c:763:12 #8 0x8203ac in Rf_eval /data/gannet/ripley/R/svn/R-devel/src/main/eval.c:763:12 #9 0x893a52 in do_begin /data/gannet/ripley/R/svn/R-devel/src/main/eval.c:2471:10 #10 0x8203ac in Rf_eval /data/gannet/ripley/R/svn/R-devel/src/main/eval.c:763:12 #11 0x8882ab in R_execClosure /data/gannet/ripley/R/svn/R-devel/src/main/eval.c #12 0x883707 in Rf_applyClosure /data/gannet/ripley/R/svn/R-devel/src/main/eval.c:1779:16 #13 0x8428ce in bcEval /data/gannet/ripley/R/svn/R-devel/src/main/eval.c:7022:12 #14 0x81fd59 in Rf_eval /data/gannet/ripley/R/svn/R-devel/src/main/eval.c:688:8 #15 0x8882ab in R_execClosure /data/gannet/ripley/R/svn/R-devel/src/main/eval.c #16 0x88cdba in R_execMethod /data/gannet/ripley/R/svn/R-devel/src/main/eval.c:2029:11 #17 0x7f696dd7ebde in R_dispatchGeneric /data/gannet/ripley/R/svn/R-devel/src/library/methods/src/methods_list_dispatch.c:1050:19 #18 0x9b6b96 in do_standardGeneric /data/gannet/ripley/R/svn/R-devel/src/main/objects.c:1285:13 #19 0x83481c in bcEval /data/gannet/ripley/R/svn/R-devel/src/main/eval.c:7011:12 #20 0x81fd59 in Rf_eval /data/gannet/ripley/R/svn/R-devel/src/main/eval.c:688:8 #21 0x8882ab in R_execClosure /data/gannet/ripley/R/svn/R-devel/src/main/eval.c #22 0x883707 in Rf_applyClosure /data/gannet/ripley/R/svn/R-devel/src/main/eval.c:1779:16 #23 0x820b92 in Rf_eval /data/gannet/ripley/R/svn/R-devel/src/main/eval.c:811:12 #24 0x894b93 in do_set /data/gannet/ripley/R/svn/R-devel/src/main/eval.c:2920:8 #25 0x8203ac in Rf_eval /data/gannet/ripley/R/svn/R-devel/src/main/eval.c:763:12 #26 0x954fb9 in Rf_ReplIteration /data/gannet/ripley/R/svn/R-devel/src/main/main.c:264:2 #27 0x959400 in R_ReplConsole /data/gannet/ripley/R/svn/R-devel/src/main/main.c:314:11 #28 0x9591e5 in run_Rmainloop /data/gannet/ripley/R/svn/R-devel/src/main/main.c:1113:5 #29 0x4dbb4a in main /data/gannet/ripley/R/svn/R-devel/src/main/Rmain.c:29:5 #30 0x7f69795d9f42 in __libc_start_main (/lib64/libc.so.6+0x23f42) #31 0x43036d in _start (/data/gannet/ripley/R/R-clang-SAN/bin/exec/R+0x43036d) SUMMARY: UndefinedBehaviorSanitizer: undefined-behavior fmrs.c:964:66 in > > coefficients(res.var)[-1,] Comp.1 Comp.2 X.1 -8.629639e-01 1.843139e+00 X.2 6.958970e-01 -8.463501e-01 X.3 2.150083e+00 -2.075233e+00 X.4 3.212255e-15 9.358463e-01 X.5 4.197090e-15 2.021649e+00 X.6 -1.663208e-13 1.344485e-01 X.7 -6.437494e-13 1.808071e-12 X.8 -8.610983e-01 6.267395e-13 X.9 1.852416e+00 -4.750971e-13 X.10 -2.003814e+00 1.372679e-12 > round(coefficients(res.var)[-1,],5) Comp.1 Comp.2 X.1 -0.86296 1.84314 X.2 0.69590 -0.84635 X.3 2.15008 -2.07523 X.4 0.00000 0.93585 X.5 0.00000 2.02165 X.6 0.00000 0.13445 X.7 0.00000 0.00000 X.8 -0.86110 0.00000 X.9 1.85242 0.00000 X.10 -2.00381 0.00000 > > > > cleanEx() > nameEx("logLik-methods") > ### * logLik-methods > > flush(stderr()); flush(stdout()) > > ### Name: logLik > ### Title: logLik method > ### Aliases: logLik logLik,fmrsfit-method logLik,logLik-method > > ### ** Examples > > set.seed(1980) > nComp = 2 > nCov = 10 > nObs = 500 > dispersion = c(1, 1) > mixProp = c(0.4, 0.6) > rho = 0.5 > coeff1 = c( 2, 2, -1, -2, 1, 2, 0, 0, 0, 0, 0) > coeff2 = c(-1, -1, 1, 2, 0, 0, 0, 0, -1, 2, -2) > umax = 40 > > dat <- fmrs.gendata(nObs = nObs, nComp = nComp, nCov = nCov, + coeff = c(coeff1, coeff2), dispersion = dispersion, + mixProp = mixProp, rho = rho, umax = umax, + disFamily = "lnorm") > > res.mle <- fmrs.mle(y = dat$y, x = dat$x, delta = dat$delta, + nComp = nComp, disFamily = "lnorm", + initCoeff = rnorm(nComp*nCov+nComp), + initDispersion = rep(1, nComp), + initmixProp = rep(1/nComp, nComp)) > logLik(res.mle) [1] -764.9188 > > > > cleanEx() > nameEx("mixProp-methods") > ### * mixProp-methods > > flush(stderr()); flush(stdout()) > > ### Name: mixProp > ### Title: mixProp method > ### Aliases: mixProp mixProp,fmrsfit-method mixProp,mixProp-method > > ### ** Examples > > set.seed(1980) > nComp = 2 > nCov = 10 > nObs = 500 > dispersion = c(1, 1) > mixProp = c(0.4, 0.6) > rho = 0.5 > coeff1 = c( 2, 2, -1, -2, 1, 2, 0, 0, 0, 0, 0) > coeff2 = c(-1, -1, 1, 2, 0, 0, 0, 0, -1, 2, -2) > umax = 40 > > dat <- fmrs.gendata(nObs = nObs, nComp = nComp, nCov = nCov, + coeff = c(coeff1, coeff2), dispersion = dispersion, + mixProp = mixProp, rho = rho, umax = umax, + disFamily = "lnorm") > > res.mle <- fmrs.mle(y = dat$y, x = dat$x, delta = dat$delta, + nComp = nComp, disFamily = "lnorm", + initCoeff = rnorm(nComp*nCov+nComp), + initDispersion = rep(1, nComp), + initmixProp = rep(1/nComp, nComp)) > mixProp(res.mle) Comp.1 Comp.2 [1,] 0.5831783 0.4168217 > > > > cleanEx() > nameEx("ncomp-methods") > ### * ncomp-methods > > flush(stderr()); flush(stdout()) > > ### Name: ncomp > ### Title: ncomp method > ### Aliases: ncomp ncomp,fmrsfit-method ncomp,ncomp-method > > ### ** Examples > > set.seed(1980) > nComp = 2 > nCov = 10 > nObs = 500 > dispersion = c(1, 1) > mixProp = c(0.4, 0.6) > rho = 0.5 > coeff1 = c( 2, 2, -1, -2, 1, 2, 0, 0, 0, 0, 0) > coeff2 = c(-1, -1, 1, 2, 0, 0, 0, 0, -1, 2, -2) > umax = 40 > > dat <- fmrs.gendata(nObs = nObs, nComp = nComp, nCov = nCov, + coeff = c(coeff1, coeff2), dispersion = dispersion, + mixProp = mixProp, rho = rho, umax = umax, + disFamily = "lnorm") > > res.mle <- fmrs.mle(y = dat$y, x = dat$x, delta = dat$delta, + nComp = nComp, disFamily = "lnorm", + initCoeff = rnorm(nComp*nCov+nComp), + initDispersion = rep(1, nComp), + initmixProp = rep(1/nComp, nComp)) > ncomp(res.mle) [1] 2 > > > > cleanEx() > nameEx("ncov-methods") > ### * ncov-methods > > flush(stderr()); flush(stdout()) > > ### Name: ncov > ### Title: ncov method > ### Aliases: ncov ncov,fmrsfit-method ncov,ncov-method > > ### ** Examples > > set.seed(1980) > nComp = 2 > nCov = 10 > nObs = 500 > dispersion = c(1, 1) > mixProp = c(0.4, 0.6) > rho = 0.5 > coeff1 = c( 2, 2, -1, -2, 1, 2, 0, 0, 0, 0, 0) > coeff2 = c(-1, -1, 1, 2, 0, 0, 0, 0, -1, 2, -2) > umax = 40 > > dat <- fmrs.gendata(nObs = nObs, nComp = nComp, nCov = nCov, + coeff = c(coeff1, coeff2), dispersion = dispersion, + mixProp = mixProp, rho = rho, umax = umax, + disFamily = "lnorm") > > res.mle <- fmrs.mle(y = dat$y, x = dat$x, delta = dat$delta, + nComp = nComp, disFamily = "lnorm", + initCoeff = rnorm(nComp*nCov+nComp), + initDispersion = rep(1, nComp), + initmixProp = rep(1/nComp, nComp)) > ncov(res.mle) [1] 10 > > > > cleanEx() > nameEx("nobs-methods") > ### * nobs-methods > > flush(stderr()); flush(stdout()) > > ### Name: nobs > ### Title: nobs method > ### Aliases: nobs nobs,fmrsfit-method nobs,nobs-method > > ### ** Examples > > set.seed(1980) > nComp = 2 > nCov = 10 > nObs = 500 > dispersion = c(1, 1) > mixProp = c(0.4, 0.6) > rho = 0.5 > coeff1 = c( 2, 2, -1, -2, 1, 2, 0, 0, 0, 0, 0) > coeff2 = c(-1, -1, 1, 2, 0, 0, 0, 0, -1, 2, -2) > umax = 40 > > dat <- fmrs.gendata(nObs = nObs, nComp = nComp, nCov = nCov, + coeff = c(coeff1, coeff2), dispersion = dispersion, + mixProp = mixProp, rho = rho, umax = umax, + disFamily = "lnorm") > > res.mle <- fmrs.mle(y = dat$y, x = dat$x, delta = dat$delta, + nComp = nComp, disFamily = "lnorm", + initCoeff = rnorm(nComp*nCov+nComp), + initDispersion = rep(1, nComp), + initmixProp = rep(1/nComp, nComp)) > nobs(res.mle) [1] 500 > > > > cleanEx() > nameEx("residuals-methods") > ### * residuals-methods > > flush(stderr()); flush(stdout()) > > ### Name: residuals > ### Title: residuals method > ### Aliases: residuals residuals,fmrsfit-method residuals,residuals-method > > ### ** Examples > > set.seed(1980) > nComp = 2 > nCov = 10 > nObs = 500 > dispersion = c(1, 1) > mixProp = c(0.4, 0.6) > rho = 0.5 > coeff1 = c( 2, 2, -1, -2, 1, 2, 0, 0, 0, 0, 0) > coeff2 = c(-1, -1, 1, 2, 0, 0, 0, 0, -1, 2, -2) > umax = 40 > > dat <- fmrs.gendata(nObs = nObs, nComp = nComp, nCov = nCov, + coeff = c(coeff1, coeff2), dispersion = dispersion, + mixProp = mixProp, rho = rho, umax = umax, + disFamily = "lnorm") > > res.mle <- fmrs.mle(y = dat$y, x = dat$x, delta = dat$delta, + nComp = nComp, disFamily = "lnorm", + initCoeff = rnorm(nComp*nCov+nComp), + initDispersion = rep(1, nComp), + initmixProp = rep(1/nComp, nComp)) > head(residuals(res.mle)) Comp.1 Comp.2 [1,] -1.103058e+02 0.8088913 [2,] 2.892735e-02 -115.5905722 [3,] 9.527391e-03 -1.1098461 [4,] 3.584864e+01 -75.3560781 [5,] 1.430063e+00 -10.2726788 [6,] 1.033225e-02 -2.4126537 > > > > cleanEx() > nameEx("show-methods") > ### * show-methods > > flush(stderr()); flush(stdout()) > > ### Name: show > ### Title: show method > ### Aliases: show show,fmrsfit-method show,fmrstunpar-method > ### show,show-method > > ### ** Examples > > set.seed(1980) > nComp = 2 > nCov = 10 > nObs = 500 > dispersion = c(1, 1) > mixProp = c(0.4, 0.6) > rho = 0.5 > coeff1 = c( 2, 2, -1, -2, 1, 2, 0, 0, 0, 0, 0) > coeff2 = c(-1, -1, 1, 2, 0, 0, 0, 0, -1, 2, -2) > umax = 40 > > dat <- fmrs.gendata(nObs = nObs, nComp = nComp, nCov = nCov, + coeff = c(coeff1, coeff2), dispersion = dispersion, + mixProp = mixProp, rho = rho, umax = umax, + disFamily = "lnorm") > > res.mle <- fmrs.mle(y = dat$y, x = dat$x, delta = dat$delta, + nComp = nComp, disFamily = "lnorm", + initCoeff = rnorm(nComp*nCov+nComp), + initDispersion = rep(1, nComp), + initmixProp = rep(1/nComp, nComp)) > show(res.mle) An object of class 'fmrsfit' Finite Mixture of Accelerated Failure Time Regression Models Log-Normal Sub-Distributions 2 Components; 10 Covariates; 500 samples. > res.lam <- fmrs.tunsel(y = dat$y, x = dat$x, delta = dat$delta, + nComp = nComp, disFamily = "lnorm", + initCoeff = c(coefficients(res.mle)), + initDispersion = dispersion(res.mle), + initmixProp = mixProp(res.mle), + penFamily = "adplasso") > show(res.lam) An object of class 'fmrstunpar' Finite Mixture of Accelerated Failure Time Regression Models Log-Normal Sub-Distributions 2 Components; adplasso Penalty; > > > > cleanEx() > nameEx("summary-methods") > ### * summary-methods > > flush(stderr()); flush(stdout()) > > ### Name: summary > ### Title: summary method > ### Aliases: summary summary,fmrsfit-method summary,fmrstunpar-method > ### summary,summary-method > > ### ** Examples > > set.seed(1980) > nComp = 2 > nCov = 10 > nObs = 500 > dispersion = c(1, 1) > mixProp = c(0.4, 0.6) > rho = 0.5 > coeff1 = c( 2, 2, -1, -2, 1, 2, 0, 0, 0, 0, 0) > coeff2 = c(-1, -1, 1, 2, 0, 0, 0, 0, -1, 2, -2) > umax = 40 > > dat <- fmrs.gendata(nObs = nObs, nComp = nComp, nCov = nCov, + coeff = c(coeff1, coeff2), dispersion = dispersion, + mixProp = mixProp, rho = rho, umax = umax, + disFamily = "lnorm") > > res.mle <- fmrs.mle(y = dat$y, x = dat$x, delta = dat$delta, + nComp = nComp, disFamily = "lnorm", + initCoeff = rnorm(nComp*nCov+nComp), + initDispersion = rep(1, nComp), + initmixProp = rep(1/nComp, nComp)) > summary(res.mle) ------------------------------------------- Fitted Model: ------------------------------------------- Finite Mixture of Accelerated Failure Time Regression Models Log-Normal Sub-Distributions 2 Components; 10 Covariates; 500 samples. Coefficients: Comp.1 Comp.2 Intercept -1.023594646 2.16213209 X.1 -0.947852642 1.88477635 X.2 0.789705744 -0.87406443 X.3 2.150263743 -2.14019382 X.4 0.006755298 0.98533979 X.5 0.057400643 1.97233892 X.6 -0.059509943 0.25060228 X.7 -0.129978039 0.03928863 X.8 -0.878291959 -0.05872164 X.9 1.928299514 -0.04921806 X.10 -2.048083339 0.06100657 Active Set: Comp.1 Comp.2 Intercept 1 1 X.1 1 1 X.2 1 1 X.3 1 1 X.4 1 1 X.5 1 1 X.6 1 1 X.7 1 1 X.8 1 1 X.9 1 1 X.10 1 1 Dispersions: Comp.1 Comp.2 [1,] 0.8846201 0.9351782 Mixing Proportions: Comp.1 Comp.2 [1,] 0.5831783 0.4168217 LogLik: -764.9188; BIC: -827.0648 > res.lam <- fmrs.tunsel(y = dat$y, x = dat$x, delta = dat$delta, + nComp = nComp, disFamily = "lnorm", + initCoeff = c(coefficients(res.mle)), + initDispersion = dispersion(res.mle), + initmixProp = mixProp(res.mle), + penFamily = "adplasso") > summary(res.lam) ------------------------------------------- Selected Tuning Parameters: ------------------------------------------- Finite Mixture of Accelerated Failure Time Regression Models Log-Normal Sub-Distributions 2 Components; adplasso Penalty; Component-wise lambda: Comp.1 Comp.2 [1,] 0.03 0.01 Ridge lambda: [1] 0 MCP's Extra Tuning Parameter: [1] 4 SICA's Extra Tuning Parameter: [1] 5 Active Set: Comp.1 Comp.2 Intercept 1 1 X.1 1 1 X.2 1 1 X.3 1 1 X.4 1 1 X.5 1 1 X.6 1 1 X.7 1 1 X.8 1 1 X.9 1 1 X.10 1 1 > > > > cleanEx() > nameEx("weights-methods") > ### * weights-methods > > flush(stderr()); flush(stdout()) > > ### Name: weights > ### Title: weights method > ### Aliases: weights weights,fmrsfit-method weights,weights-method > > ### ** Examples > > set.seed(1980) > nComp = 2 > nCov = 10 > nObs = 500 > dispersion = c(1, 1) > mixProp = c(0.4, 0.6) > rho = 0.5 > coeff1 = c( 2, 2, -1, -2, 1, 2, 0, 0, 0, 0, 0) > coeff2 = c(-1, -1, 1, 2, 0, 0, 0, 0, -1, 2, -2) > umax = 40 > > dat <- fmrs.gendata(nObs = nObs, nComp = nComp, nCov = nCov, + coeff = c(coeff1, coeff2), dispersion = dispersion, + mixProp = mixProp, rho = rho, umax = umax, + disFamily = "lnorm") > > res.mle <- fmrs.mle(y = dat$y, x = dat$x, delta = dat$delta, + nComp = nComp, disFamily = "lnorm", + initCoeff = rnorm(nComp*nCov+nComp), + initDispersion = rep(1, nComp), + initmixProp = rep(1/nComp, nComp)) > head(weights(res.mle)) Comp.1 Comp.2 [1,] 1.481408e-04 9.998519e-01 [2,] 1.000000e+00 1.628800e-08 [3,] 9.999741e-01 2.589725e-05 [4,] 3.489706e-08 1.000000e+00 [5,] 7.744602e-01 2.255398e-01 [6,] 9.964880e-01 3.511983e-03 > > > > ### *