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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:500:69: runtime error: index 501 out of bounds for type 'int [*][*]' #0 0x7f9075aff911 in FMR_Norm_Surv_EM_MLE /data/gannet/ripley/R/packages/tests-gcc-SAN/fmrs/src/fmrs.c:500 #1 0x596b25 in do_dotCode /data/gannet/ripley/R/svn/R-devel/src/main/dotcode.c:1950 #2 0x66c1e8 in Rf_eval /data/gannet/ripley/R/svn/R-devel/src/main/eval.c:791 #3 0x678fe1 in do_set /data/gannet/ripley/R/svn/R-devel/src/main/eval.c:2920 #4 0x66bb1c in Rf_eval /data/gannet/ripley/R/svn/R-devel/src/main/eval.c:763 #5 0x675398 in do_begin /data/gannet/ripley/R/svn/R-devel/src/main/eval.c:2471 #6 0x66bb1c in Rf_eval /data/gannet/ripley/R/svn/R-devel/src/main/eval.c:763 #7 0x66bb1c in Rf_eval /data/gannet/ripley/R/svn/R-devel/src/main/eval.c:763 #8 0x66bb1c in Rf_eval /data/gannet/ripley/R/svn/R-devel/src/main/eval.c:763 #9 0x675398 in do_begin /data/gannet/ripley/R/svn/R-devel/src/main/eval.c:2471 #10 0x66bb1c in Rf_eval /data/gannet/ripley/R/svn/R-devel/src/main/eval.c:763 #11 0x6705d5 in R_execClosure /data/gannet/ripley/R/svn/R-devel/src/main/eval.c:1853 #12 0x672cf4 in Rf_applyClosure /data/gannet/ripley/R/svn/R-devel/src/main/eval.c:1779 #13 0x63d7a1 in bcEval /data/gannet/ripley/R/svn/R-devel/src/main/eval.c:7022 #14 0x66b1df in Rf_eval /data/gannet/ripley/R/svn/R-devel/src/main/eval.c:688 #15 0x6705d5 in R_execClosure /data/gannet/ripley/R/svn/R-devel/src/main/eval.c:1853 #16 0x673a56 in R_execMethod /data/gannet/ripley/R/svn/R-devel/src/main/eval.c:2029 #17 0x7f907aa63323 in R_dispatchGeneric /data/gannet/ripley/R/svn/R-devel/src/library/methods/src/methods_list_dispatch.c:1050 #18 0x7231e4 in do_standardGeneric /data/gannet/ripley/R/svn/R-devel/src/main/objects.c:1285 #19 0x63c605 in bcEval /data/gannet/ripley/R/svn/R-devel/src/main/eval.c:7011 #20 0x66b1df in Rf_eval /data/gannet/ripley/R/svn/R-devel/src/main/eval.c:688 #21 0x6705d5 in R_execClosure /data/gannet/ripley/R/svn/R-devel/src/main/eval.c:1853 #22 0x672cf4 in Rf_applyClosure /data/gannet/ripley/R/svn/R-devel/src/main/eval.c:1779 #23 0x66b628 in Rf_eval /data/gannet/ripley/R/svn/R-devel/src/main/eval.c:811 #24 0x678fe1 in do_set /data/gannet/ripley/R/svn/R-devel/src/main/eval.c:2920 #25 0x66bb1c in Rf_eval /data/gannet/ripley/R/svn/R-devel/src/main/eval.c:763 #26 0x6e9f3d in Rf_ReplIteration /data/gannet/ripley/R/svn/R-devel/src/main/main.c:264 #27 0x6e9f3d in Rf_ReplIteration /data/gannet/ripley/R/svn/R-devel/src/main/main.c:200 #28 0x6ea638 in R_ReplConsole /data/gannet/ripley/R/svn/R-devel/src/main/main.c:314 #29 0x6ea784 in run_Rmainloop /data/gannet/ripley/R/svn/R-devel/src/main/main.c:1113 #30 0x419388 in main /data/gannet/ripley/R/svn/R-devel/src/main/Rmain.c:29 #31 0x7f908605ff42 in __libc_start_main (/lib64/libc.so.6+0x23f42) #32 0x41bacd in _start (/data/gannet/ripley/R/gcc-SAN/bin/exec/R+0x41bacd) > 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 -1.023594646 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 > > > > 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:964:69: runtime error: index 501 out of bounds for type 'int [*][*]' #0 0x7f9075b13b43 in FMR_Norm_Surv_EM_VarSel /data/gannet/ripley/R/packages/tests-gcc-SAN/fmrs/src/fmrs.c:964 #1 0x58c38f in do_dotCode /data/gannet/ripley/R/svn/R-devel/src/main/dotcode.c:1986 #2 0x66c1e8 in Rf_eval /data/gannet/ripley/R/svn/R-devel/src/main/eval.c:791 #3 0x678fe1 in do_set /data/gannet/ripley/R/svn/R-devel/src/main/eval.c:2920 #4 0x66bb1c in Rf_eval /data/gannet/ripley/R/svn/R-devel/src/main/eval.c:763 #5 0x675398 in do_begin /data/gannet/ripley/R/svn/R-devel/src/main/eval.c:2471 #6 0x66bb1c in Rf_eval /data/gannet/ripley/R/svn/R-devel/src/main/eval.c:763 #7 0x66bb1c in Rf_eval /data/gannet/ripley/R/svn/R-devel/src/main/eval.c:763 #8 0x66bb1c in Rf_eval /data/gannet/ripley/R/svn/R-devel/src/main/eval.c:763 #9 0x675398 in do_begin /data/gannet/ripley/R/svn/R-devel/src/main/eval.c:2471 #10 0x66bb1c in Rf_eval /data/gannet/ripley/R/svn/R-devel/src/main/eval.c:763 #11 0x6705d5 in R_execClosure /data/gannet/ripley/R/svn/R-devel/src/main/eval.c:1853 #12 0x672cf4 in Rf_applyClosure /data/gannet/ripley/R/svn/R-devel/src/main/eval.c:1779 #13 0x63d7a1 in bcEval /data/gannet/ripley/R/svn/R-devel/src/main/eval.c:7022 #14 0x66b1df in Rf_eval /data/gannet/ripley/R/svn/R-devel/src/main/eval.c:688 #15 0x6705d5 in R_execClosure /data/gannet/ripley/R/svn/R-devel/src/main/eval.c:1853 #16 0x673a56 in R_execMethod /data/gannet/ripley/R/svn/R-devel/src/main/eval.c:2029 #17 0x7f907aa63323 in R_dispatchGeneric /data/gannet/ripley/R/svn/R-devel/src/library/methods/src/methods_list_dispatch.c:1050 #18 0x7231e4 in do_standardGeneric /data/gannet/ripley/R/svn/R-devel/src/main/objects.c:1285 #19 0x63c605 in bcEval /data/gannet/ripley/R/svn/R-devel/src/main/eval.c:7011 #20 0x66b1df in Rf_eval /data/gannet/ripley/R/svn/R-devel/src/main/eval.c:688 #21 0x6705d5 in R_execClosure /data/gannet/ripley/R/svn/R-devel/src/main/eval.c:1853 #22 0x672cf4 in Rf_applyClosure /data/gannet/ripley/R/svn/R-devel/src/main/eval.c:1779 #23 0x66b628 in Rf_eval /data/gannet/ripley/R/svn/R-devel/src/main/eval.c:811 #24 0x678fe1 in do_set /data/gannet/ripley/R/svn/R-devel/src/main/eval.c:2920 #25 0x66bb1c in Rf_eval /data/gannet/ripley/R/svn/R-devel/src/main/eval.c:763 #26 0x6e9f3d in Rf_ReplIteration /data/gannet/ripley/R/svn/R-devel/src/main/main.c:264 #27 0x6e9f3d in Rf_ReplIteration /data/gannet/ripley/R/svn/R-devel/src/main/main.c:200 #28 0x6ea638 in R_ReplConsole /data/gannet/ripley/R/svn/R-devel/src/main/main.c:314 #29 0x6ea784 in run_Rmainloop /data/gannet/ripley/R/svn/R-devel/src/main/main.c:1113 #30 0x419388 in main /data/gannet/ripley/R/svn/R-devel/src/main/Rmain.c:29 #31 0x7f908605ff42 in __libc_start_main (/lib64/libc.so.6+0x23f42) #32 0x41bacd in _start (/data/gannet/ripley/R/gcc-SAN/bin/exec/R+0x41bacd) > > coefficients(res.var)[-1,] Comp.1 Comp.2 X.1 -8.629639e-01 1.843140e+00 X.2 6.958970e-01 -8.463504e-01 X.3 2.150083e+00 -2.075233e+00 X.4 3.212254e-15 9.358465e-01 X.5 4.197092e-15 2.021650e+00 X.6 -1.663207e-13 1.344480e-01 X.7 -6.433389e-13 1.808080e-12 X.8 -8.610982e-01 6.267392e-13 X.9 1.852416e+00 -4.750981e-13 X.10 -2.003814e+00 1.372680e-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,] 0.01 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 > > > > ### *