## Do not edit this file manually. ## It has been automatically generated from *.org sources. test_that("fit_mixAR works", { prob <- exampleModels$WL_ibm@prob scale <- exampleModels$WL_ibm@scale arcoef <- exampleModels$WL_ibm@arcoef@a fi0 <- fit_mixAR(fma::ibmclose, exampleModels$WL_ibm, fix = "shift") fit_mixAR(fma::ibmclose, exampleModels$WL_ibm, fix = "shift", init = 2) tmp_mo <- fit_mixAR(fma::ibmclose, exampleModels$WL_ibm, fix = "shift", init = list(exampleModels$WL_ibm, 5)) ## the coefficients used for the fitting function below were obtained by first calling it with: ## ## mo_WLtf <- new("MixARgen", prob = prob, scale = scale, arcoef = arcoef, ## dist = list(generator = function(par) fn_stdt(par, fixed = FALSE), ## param = c(20, 30, 40))) fitmo_WLtf_prob <- c(0.578579797, 0.414862603, 0.006557599) fitmo_WLtf_scale <- c(5.505965, 5.911653, 6.988254) fitmo_WLtf_arcoef <- list(c(0.6991521, 0.3019527), c(1.6793752, -0.6819934), 0.9254728) fitmo_WLtf_dist_param <- c(6.243, 27.96, 8381944) mo_WLtf <- new("MixARgen", prob = fitmo_WLtf_prob, scale = fitmo_WLtf_scale, arcoef = fitmo_WLtf_arcoef, dist = list(generator = function(par) fn_stdt(par, fixed = FALSE), param = fitmo_WLtf_dist_param)) fitmo_WLtf <- fit_mixAR(fma::ibmclose, mo_WLtf, fix = "shift", minniter = 2, maxniter = 2) ## 2021-08-09: ## Fit a model with Gaussian components and AR order 0 for one of them. ## Before the fix today was giving the error below. ## ## simulate from a mar model ## mar <- new("MixARGaussian",prob = c(0.2,0.3,0.5), scale = c(1,4,2), arcoef = list(0.5,0.8,0)) ## simdata <- mixAR_sim(mar, n = 100, init=c(1,2)) ## mar110 <- fit_mixAR(simdata, c(1,1,0), fix="shift") ## Error in svd(a) : a dimension is zero mar110 <- fit_mixAR(diff(fma::ibmclose), c(1,1,0), fix="shift") if(covr::in_covr()){ mo_WLt30_111 <- new("MixARgen", prob = prob, scale = scale, arcoef = list(0.1, 0.5, 0.8), dist = list(fdist_stdt(30))) fi30wn_111 <- fit_mixAR(diff(fma::ibmclose), mo_WLt30_111, fix = "white.noise", method = "BBoptim") ## Estimating. Successful convergence. ## 0.kiki! 4.8227 6.0082 18.1716 ## ## Estimating. Successful convergence. ## 0.kiki! 5.128563 6.731221 17.89796 ## ## Estimating. Successful convergence. ## 0.kiki! 4.957974 6.549832 16.6071 ## ## Estimating. Successful convergence. ## 0.kiki! 4.941826 6.561235 23.35963 ## ## Estimating. Successful convergence. ## 0.kiki! 4.913312 6.576608 20.02167 ## ## Estimating. Successful convergence. ## 0.kiki! 4.875446 6.525766 19.55314 ## ## Estimating. Successful convergence. ## 0.kiki! 4.856034 6.514799 18.14091 ## ## Estimating. Successful convergence. ## 0.kiki! 4.839119 6.506982 17.21471 ## ## Estimating. Successful convergence. ## 0.kiki! 4.830101 6.510181 16.45221 ## ## Estimating. Successful convergence. ## 0.kiki! 4.824754 6.515123 15.8488 ## ## Estimating. Successful convergence. ## 0.kiki! 4.825015 6.523113 15.34611 ## ## Estimating. Successful convergence. ## 0.kiki! 4.830815 6.533221 14.87532 ## ## Estimating. Successful convergence. ## 0.kiki! 4.847118 6.549382 13.82671 ## ## Estimating. Successful convergence. ## 0.kiki! 4.864779 6.561899 12.12255 ## ## Estimating. Successful convergence. ## 0.kiki! 4.88103 6.571996 9.46088 ## ## Estimating. Successful convergence. ## 0.kiki! 4.8919 6.58133 5.991644 ## ## Estimating. Successful convergence. ## 0.kiki! 4.894542 6.59043 2.401979 ## ## Estimating. Successful convergence. ## 0.kiki! 4.889353 6.593083 1.075581 ## ## Estimating. Successful convergence. ## 0.kiki! 4.885277 6.598826 0.4455969 ## ## Estimating. Successful convergence. ## 0.kiki! 4.881645 6.60199 0.1199707 ## ## Estimating. Successful convergence. ## 0.kiki! 4.878514 6.606292 0.06918824 ## ## Estimating. Successful convergence. ## 0.kiki! 4.876538 6.611424 0.01 ## ## Estimating. Successful convergence. ## 0.kiki! 4.874768 6.613902 0.01 ## ## Estimating. Successful convergence. ## 0.kiki! 4.873536 6.615316 0.01 ## ## Estimating. Successful convergence. ## 0.kiki! 4.872717 6.616194 0.01 ## ## Estimating. Successful convergence. ## 0.kiki! 4.872181 6.616756 0.01 ## ## Estimating. Successful convergence. ## 0.kiki! 4.871831 6.61712 0.01 ## ## Estimating. Successful convergence. ## 0.kiki! 4.871603 6.617356 0.01 ## ## Estimating. Successful convergence. ## 0.kiki! 4.871455 6.61751 0.01 ## ## Estimating. Successful convergence. ## 0.kiki! 4.871358 6.61761 0.01 ## ## Estimating. Successful convergence. ## 0.kiki! 4.871295 6.617675 0.01 ## ## Estimating. Successful convergence. ## 0.kiki! 4.871255 6.617717 0.01 ## ## Estimating. Successful convergence. ## 0.kiki! 4.871228 6.617745 0.01 ## ## Estimating. Successful convergence. ## 0.kiki! 4.871211 6.617763 0.01 ## ## Estimating. Successful convergence. ## 0.kiki! 4.871199 6.617775 0.01 ## ## Estimating. Successful convergence. ## 0.kiki! 4.871192 6.617782 0.01 ## ## Estimating. Successful convergence. ## 0.kiki! 4.871187 6.617787 0.01 ## ## Estimating. Successful convergence. ## 0.kiki! 4.871184 6.61779 0.01 ## ## Estimating. Successful convergence. ## 0.kiki! 4.871182 6.617792 0.01 ## ## Estimating. Successful convergence. ## 0.kiki! 4.871181 6.617794 0.01 ## ## Estimating. Successful convergence. ## 0.kiki! 4.87118 6.617795 0.01 ## ## > fi30wn_111 ## $model ## An object of class "MixARgen" ## Number of components: 3 ## prob shift scale order ar_1 ## Comp_1 0.597337441 1.108563 4.871179 1 -0.3556190 ## Comp_2 0.395945242 -1.089003 6.617795 1 0.5977633 ## Comp_3 0.006717317 -34.388887 0.010000 1 -3.6111095 ## ## Distributions of the error components: ## [[1]] ## [[1]]$pdf ## function(x) dstd(x, nu = nu) ## ## ## ## [[1]]$cdf ## function(x) pstd(x, nu = nu) ## ## ## ## [[1]]$rand ## function(n) rstd(n, nu = nu) ## ## ## ## [[1]]$logpdf ## function(x) log(dstd(x, nu = nu)) ## ## ## ## [[1]]$Fscore ## function(x) - x*(1+nu)/(x^2+nu-2) ## ## ## ## [[1]]$xFscore ## function(x) - x^2*(1+nu)/(x^2+nu-2) ## ## ## ## [[1]]$Parscore ## function(x) param_score_stdt(x,nu) ## ## ## ## [[1]]$get_param ## function() nu ## ## ## ## [[1]]$set_param ## function(x) nu <<- x ## ## ## ## [[1]]$any_param ## function() param_flag ## ## ## ## ## ## ## $vallogf ## [1] -1197.189 ## ## $niter ## [1] 41 ## ## $all_vallogf ## [1] -1235.496 -1218.986 -1214.760 -1212.355 -1211.436 -1211.183 -1211.042 ## [8] -1210.952 -1210.891 -1210.845 -1210.808 -1210.767 -1210.690 -1210.531 ## [15] -1210.192 -1209.398 -1207.603 -1206.158 -1204.337 -1201.990 -1201.106 ## [22] -1197.189 -1197.189 -1197.189 -1197.189 -1197.189 -1197.189 -1197.189 ## [29] -1197.189 -1197.189 -1197.189 -1197.189 -1197.189 -1197.189 -1197.189 ## [36] -1197.189 -1197.189 -1197.189 -1197.189 -1197.189 -1197.189 -1197.189 ## ## $all_relchange ## [1] NA 3.467352e-03 1.979876e-03 7.577684e-04 2.092369e-04 ## [6] 1.163678e-04 7.362687e-05 5.070516e-05 3.764112e-05 3.102896e-05 ## [11] 3.366300e-05 6.366066e-05 1.311418e-04 2.805416e-04 6.556662e-04 ## [16] 1.484762e-03 1.196600e-03 1.509737e-03 1.948441e-03 7.355017e-04 ## [21] 3.261223e-03 1.225911e-07 2.561823e-08 1.083388e-08 4.587185e-09 ## [26] 1.942430e-09 8.225095e-10 3.482880e-10 1.474859e-10 6.245846e-11 ## [31] 2.645343e-11 1.120584e-11 4.759471e-12 2.011475e-12 8.504753e-13 ## [36] 3.600940e-13 1.525082e-13 6.476375e-14 2.753884e-14 1.177523e-14 ## [41] 5.127921e-15 } # end if in_covr() })