* using log directory 'd:/Rcompile/CRANpkg/local/3.6/partialAR.Rcheck' * using R version 3.6.3 (2020-02-29) * using platform: x86_64-w64-mingw32 (64-bit) * using session charset: ISO8859-1 * checking for file 'partialAR/DESCRIPTION' ... OK * checking extension type ... Package * this is package 'partialAR' version '1.0.12' * checking package namespace information ... OK * checking package dependencies ... OK * checking if this is a source package ... OK * checking if there is a namespace ... OK * checking for hidden files and directories ... OK * checking for portable file names ... OK * checking whether package 'partialAR' can be installed ... OK * checking installed package size ... OK * checking package directory ... OK * checking DESCRIPTION meta-information ... OK * checking top-level files ... OK * checking for left-over 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 * loading checks for arch 'i386' ** checking whether the package can be loaded ... OK ** checking whether the package can be loaded with stated dependencies ... OK ** checking whether the package can be unloaded cleanly ... OK ** checking whether the namespace can be loaded with stated dependencies ... OK ** checking whether the namespace can be unloaded cleanly ... OK ** checking loading without being on the library search path ... OK ** checking use of S3 registration ... OK * loading checks for arch 'x64' ** checking whether the package can be loaded ... OK ** checking whether the package can be loaded with stated dependencies ... OK ** checking whether the package can be unloaded cleanly ... OK ** checking whether the namespace can be loaded with stated dependencies ... OK ** checking whether the namespace can be unloaded cleanly ... OK ** checking loading without being on the library search path ... OK ** checking use of S3 registration ... OK * checking 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 ... [251s] OK * checking Rd files ... OK * checking Rd metadata ... OK * checking Rd cross-references ... OK * checking for missing documentation entries ... OK * checking for code/documentation mismatches ... OK * checking Rd \usage sections ... OK * checking Rd contents ... OK * checking for unstated dependencies in examples ... OK * checking line endings in C/C++/Fortran sources/headers ... OK * checking pragmas in C/C++ headers and code ... OK * checking compiled code ... OK * checking examples ... ** running examples for arch 'i386' ... [10s] OK ** running examples for arch 'x64' ... [11s] OK * checking for unstated dependencies in 'tests' ... OK * checking tests ... ** running tests for arch 'i386' ... [15s] ERROR Running 'tests.R' [15s] Running the tests in 'tests/tests.R' failed. Complete output: > all.tests.pass <- TRUE > all.tests.error.count <- 0 > > test <- function(expr, out="", val=eval.parent(parse(text=expr), 1), tol=1e-4) { + # expr is a string representing an R expression, and + # out is the output that is expected. Prints and evaluates + # expr. If out is given and it matches the output of + # evaluating expr, returns TRUE. Otherwise, returns FALSE. + + cat(expr, "-> ") + + p <- function (v) { + if (length(v) < 5) { + cat(v) + } else { + cat(class(v), "(", length(val), ")") + } + } + p(val) + + result <- all.equal(val, out, tolerance=tol) + if (!isTRUE(result)) { + if (!missing(out)) { + cat(" (Expecting ") + p(out) + cat(")") + } + cat("\nERROR: ", result, "\n") + all.tests.pass <<- FALSE + all.tests.error.count <<- all.tests.error.count + 1 + } else { + cat(" OK\n") + } + + isTRUE(result) + } > > assert <- function (expr, out) { + # expr is astring representing an R expression, + # and out is the output that is expected. Prints + # and evaluates expr. If out matches the output of + # evaluating expr, returns TRUE. Otherwise, stops + # the execution with an error message. + if (!test(expr, out)) { + stop("Expression ", deparse(substitute(expr)), + " does not evaluate to its expected value\n") + } + } > > build_par <- function (rho, eps_M, eps_R, R0=0, M0=0) { + R <- R0 + M <- M0 + X <- numeric() + for (i in 1:length(eps_M)) { + M <- rho * M + eps_M[i] + R <- R + eps_R[i] + X[i] <- M + R + } + X + } > > data.L <- structure(c(37.8517816659277, 37.3893346323175, 37.4385311252548, + 37.1138342718688, 37.2319058549183, 37.8616209645152, 37.7238707842909, + 37.900978158865, 37.6156384998289, 37.4188525280799, 37.7632279786407, + 37.9108174574525, 37.9403353532148, 38.314228699538, 37.8222637701654, + 37.5664420068916, 37.3401381393802, 37.0252805845818, 36.7202623283708, + 36.7104230297833, 37.2417451535057, 37.3893346323175, 37.9895318461521, + 37.7632279786407, 37.7435493814658, 37.8714602631026, 37.5861206040665, + 37.487727618192, 37.8025851729905, 37.5369241111293, 36.985923390232, + 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15971, + 15972, 15973, 15974, 15975, 15978, 15979, 15980, 15981, 15982, + 15985, 15986, 15987, 15988, 15989, 15992, 15993, 15994, 15995, + 15996, 15999, 16000, 16001, 16002, 16003, 16006, 16007, 16008, + 16009, 16010, 16013, 16014, 16015, 16016, 16017, 16020, 16021, + 16022, 16023, 16024, 16027, 16028, 16029, 16030, 16031, 16034, + 16035, 16036, 16038, 16041, 16042, 16043, 16044, 16045, 16048, + 16049, 16050, 16051, 16052, 16055, 16056, 16057, 16058, 16059, + 16062, 16063, 16065, 16066, 16069, 16070), class = "Date"), class = "zoo") > > test_cfit <- function (fast_only=FALSE) { + test("partialAR:::estimate_rho_par_c(numeric())", NA_real_) + test("partialAR:::estimate_rho_par_c(rep(0,5))", NaN) + x1 <- build_par(0.95, rep(0,10), rep(0,10), M0=1) + test("partialAR:::estimate_rho_par_c(x1)", 0.8497954230236) + x1na <- x1 + x1na[1] <- NA + test("partialAR:::estimate_rho_par_c(x1na)", NA_real_) + + test("partialAR:::estimate_par_c(numeric())", c(NA_real_, NA_real_, NA_real_)) + test("partialAR:::estimate_par_c(rep(0,5))", c(NaN, NaN, NaN)) + test("partialAR:::estimate_par_c(x1)", c(0.849795423024, 0, 0.00624752527433)) + test("partialAR:::estimate_par_c(x1na)", c(NA_real_, NA_real_, NA_real_)) + + test("partialAR:::pvmr_par_c(0,0,0)", NA_real_) + test("partialAR:::pvmr_par_c(-1,1,0)", 1) + test("partialAR:::pvmr_par_c(1,-1,0)", NA_real_) + test("partialAR:::pvmr_par_c(1,1,-1)", NA_real_) + test("partialAR:::pvmr_par_c(0,0,1)", 0) + test("partialAR:::pvmr_par_c(0,1,0)", 1) + test("partialAR:::pvmr_par_c(0,1,1)", 2/3) + test("partialAR:::pvmr_par_c(0.5,1,1)", 0.571428571429) + test("partialAR:::pvmr_par_c(0.5,1,2)", 0.25) + test("partialAR:::pvmr_par_c(0.5,0.5,1)", 0.25) + + test("partialAR:::kalman_gain_par_mr(0,0,0)", NA_real_) + test("partialAR:::kalman_gain_par_mr(0,1,0)", 1) + test("partialAR:::kalman_gain_par_mr(0,0,1)", 0) + test("partialAR:::kalman_gain_par_mr(0.5,1,1)", 1/3) + + test("partialAR:::loglik_par_c(numeric(),0,0,1,0,0)", NA_real_) + test("partialAR:::loglik_par_c(0,0,0,1,0,0)", 0.918938533205) + test("partialAR:::loglik_par_c(c(0,0,0),0,0,1,0,0)", 2.75681559961) + test("partialAR:::loglik_par_c(1,0,0,1,0,0)", 1.4189385332) + test("partialAR:::loglik_par_c(0,0,1,0,0,0)", 0.918938533205) + test("partialAR:::loglik_par_c(c(0,0,0),0,1,0,0,0)", 2.75681559961) + test("partialAR:::loglik_par_c(c(0,0,0),0.5,1,0,0,0)", 2.75681559961) + test("partialAR:::loglik_par_c(c(0,1,2),0,0,1,0,1)", 4.25681559961) + test("partialAR:::loglik_par_c(0.5,0.5,1,0,1,0)", 0.918938533205) + test("partialAR:::loglik_par_c(data.L, 0.8720, 0.3385, 0.1927, 0, data.L[1])", 238.533361432) + test("partialAR:::loglik_par_c(data.IBM, 0.9764, 2.0136, 0.4719, 0, data.IBM[1])", 1076.5235347) + + test("partialAR:::loglik_par_t_c(numeric(),0,0,1,0,0)", NA_real_) + test("partialAR:::loglik_par_t_c(0,0,0,1,0,0)", 0.968619589055) + test("partialAR:::loglik_par_t_c(c(0,0,0),0,0,1,0,0)", 2.90585876716) + test("partialAR:::loglik_par_t_c(1,0,0,1,0,0)", 1.51558425944) + test("partialAR:::loglik_par_t_c(0,0,1,0,0,0)", 0.968619589055) + test("partialAR:::loglik_par_t_c(c(0,0,0),0,1,0,0,0)", 2.90585876716) + test("partialAR:::loglik_par_t_c(c(0,0,0),0.5,1,0,0,0)", 2.90585876716) + test("partialAR:::loglik_par_t_c(c(0,1,2),0,0,1,0,1)", 4.54675277831) + test("partialAR:::loglik_par_t_c(0.5,0.5,1,0,1,0)", 0.968619589055) + test("partialAR:::loglik_par_t_c(0,0,0,1,0,0,6)", 0.960418255752) + test("partialAR:::loglik_par_t_c(data.L, 0.8958, 0.2612, 0.1768, 0, data.L[1])", 229.807616531) + test("partialAR:::loglik_par_t_c(data.IBM, 0.9829, 1.3072, 0.6901, 0, data.IBM[1])", 1020.88295106) + + } > > > test_lr <- function (fast_only=FALSE) { + test("partialAR:::loglik.par.kfas(numeric(),0,0,1,0,0)", NA_real_) + test("partialAR:::loglik.par.kfas(0,0,0,1,0,0)", 0.918938533205) + test("partialAR:::loglik.par.kfas(c(0,0,0),0,0,1,0,0)", 2.75681559961) + test("partialAR:::loglik.par.kfas(1,0,0,1,0,0)", 1.4189385332) + test("partialAR:::loglik.par.kfas(0,0,1,0,0,0)", 0.918938533205) + test("partialAR:::loglik.par.kfas(c(0,0,0),0,1,0,0,0)", 2.75681559961) + test("partialAR:::loglik.par.kfas(c(0,0,0),0.5,1,0,0,0)", 2.75681559961) + test("partialAR:::loglik.par.kfas(c(0,1,2),0,0,1,0,1)", 4.25681559961) + test("partialAR:::loglik.par.kfas(0.5,0.5,1,0,1,0)", 1.0439385332) # Note difference + test("partialAR:::loglik.par.kfas(data.L, 0.8720, 0.3385, 0.1927)", 238.53374143) + test("partialAR:::loglik.par.kfas(data.IBM, 0.9764, 2.0136, 0.4719, 0, data.IBM[1])", 1077.02787353) + + test("partialAR:::loglik.par.ss(numeric(),0,0,1,0,0)", NA_real_) + test("partialAR:::loglik.par.ss(0,0,0,1,0,0)", 0.918938533205) + test("partialAR:::loglik.par.ss(c(0,0,0),0,0,1,0,0)", 2.75681559961) + test("partialAR:::loglik.par.ss(1,0,0,1,0,0)", 1.4189385332) + test("partialAR:::loglik.par.ss(0,0,1,0,0,0)", 0.918938533205) + test("partialAR:::loglik.par.ss(c(0,0,0),0,1,0,0,0)", 2.75681559961) + test("partialAR:::loglik.par.ss(c(0,0,0),0.5,1,0,0,0)", 2.75681559961) + test("partialAR:::loglik.par.ss(c(0,1,2),0,0,1,0,1)", 4.25681559961) + test("partialAR:::loglik.par.ss(0.5,0.5,1,0,1,0)", 0.918938533205) + test("partialAR:::loglik.par.ss(data.L, 0.8720, 0.3385, 0.1927, 0, data.L[1])", 238.533361432) + test("partialAR:::loglik.par.ss(data.IBM, 0.9764, 2.0136, 0.4719)", 1076.5235347) + + test("partialAR:::loglik.par.ss.t(numeric(),0,0,1,0,0)", NA_real_) + test("partialAR:::loglik.par.ss.t(0,0,0,1,0,0)", 0.968619589055) + test("partialAR:::loglik.par.ss.t(c(0,0,0),0,0,1,0,0)", 2.90585876716) + test("partialAR:::loglik.par.ss.t(1,0,0,1,0,0)", 1.51558425944) + test("partialAR:::loglik.par.ss.t(0,0,1,0,0,0)", 0.968619589055) + test("partialAR:::loglik.par.ss.t(c(0,0,0),0,1,0,0,0)", 2.90585876716) + test("partialAR:::loglik.par.ss.t(c(0,0,0),0.5,1,0,0,0)", 2.90585876716) + test("partialAR:::loglik.par.ss.t(c(0,1,2),0,0,1,0,1)", 4.54675277831) + test("partialAR:::loglik.par.ss.t(0.5,0.5,1,0,1,0)", 0.968619589055) + test("partialAR:::loglik.par.ss.t(0,0,0,1,0,0,6)", 0.960418255752) + test("partialAR:::loglik.par.ss.t(data.L, 0.8958, 0.2612, 0.1768, 0, data.L[1])", 229.807616531) + test("partialAR:::loglik.par.ss.t(data.IBM, 0.9829, 1.3072, 0.6901, 0, data.IBM[1])", 1020.88295106) + + test("partialAR:::loglik.par(data.L, 0.8720, 0.3385, 0.1927)", 238.533361432) + test("partialAR:::loglik.par(data.L, 0.8720, 0.3385, 0.1927, calc_method=\"css\")", 238.533361432) + test("partialAR:::loglik.par(data.L, 0.8720, 0.3385, 0.1927, calc_method=\"kfas\")", 238.53374143) + test("partialAR:::loglik.par(data.L, 0.8720, 0.3385, 0.1927, calc_method=\"ss\")", 238.533361432) + test("partialAR:::loglik.par(data.L, 0.8958, 0.2612, 0.1768, calc_method=\"sst\")", 229.807616531) + test("partialAR:::loglik.par(data.L, 0.8958, 0.2612, 0.1768, calc_method=\"csst\")", 229.807616531) + } > > test.likelihood_ratio.par <- function (fast_only=FALSE) { + test("partialAR:::likelihood_ratio.par(data.L)", -4.44824727945) + test("partialAR:::likelihood_ratio.par(data.L, robust=TRUE)", -2.64805301476) + test("partialAR:::likelihood_ratio.par(data.L, null_model='rw')", -4.44824727945) + test("partialAR:::likelihood_ratio.par(data.L, null_model='rw', robust=TRUE)", -2.64805301476) + test("partialAR:::likelihood_ratio.par(data.L, null_model='ar1')", -4.44824693057) + test("partialAR:::likelihood_ratio.par(data.L, null_model='ar1', robust=TRUE)", -2.6480522184) + + if (!fast_only) test("partialAR:::likelihood_ratio.par(data.L, opt_method='ss')", -4.44824727945) + if (!fast_only) test("partialAR:::likelihood_ratio.par(data.L, robust=TRUE, opt_method='ss')", -2.64805301476) + if (!fast_only) test("partialAR:::likelihood_ratio.par(data.L, null_model='rw', opt_method='ss')", -4.44824727945) + if (!fast_only) test("partialAR:::likelihood_ratio.par(data.L, null_model='rw', robust=TRUE, opt_method='ss')", -2.64805301476) + if (!fast_only) test("partialAR:::likelihood_ratio.par(data.L, null_model='ar1', opt_method='ss')", -4.44824693057) + if (!fast_only) test("partialAR:::likelihood_ratio.par(data.L, null_model='ar1', robust=TRUE, opt_method='ss')", -2.6480522184) + + test("partialAR:::likelihood_ratio.par(data.L, opt_method='css')", -4.44824727945) + test("partialAR:::likelihood_ratio.par(data.L, robust=TRUE, opt_method='css')", -2.64805301476) + test("partialAR:::likelihood_ratio.par(data.L, null_model='rw', opt_method='css')", -4.44824727945) + test("partialAR:::likelihood_ratio.par(data.L, null_model='rw', robust=TRUE, opt_method='css')", -2.64805301476) + test("partialAR:::likelihood_ratio.par(data.L, null_model='ar1', opt_method='css')", -4.44824693057) + test("partialAR:::likelihood_ratio.par(data.L, null_model='ar1', robust=TRUE, opt_method='css')", -2.6480522184) + + if (!fast_only) test("partialAR:::likelihood_ratio.par(data.L, opt_method='kfas')", -4.59676088358) + if (!fast_only) test("partialAR:::likelihood_ratio.par(data.L, null_model='rw', opt_method='kfas')", -4.59676088358) + if (!fast_only) test("partialAR:::likelihood_ratio.par(data.L, null_model='ar1', opt_method='kfas')", -4.5967605347) + + SAMPLES <- partialAR:::sample.likelihood_ratio.par(nrep=10, use.multicore=FALSE) + test("nrow(SAMPLES)", 10) + test("sum(SAMPLES$seed)", 55) + test("mean(SAMPLES$rw_lrt)", -4.43576369917) + test("mean(SAMPLES$mr_lrt)", -3.8960913155) + test("mean(SAMPLES$kpss_stat)", 3.7269871366) + } > > test_lr2 <- function(fast_only=FALSE) { + test.likelihood_ratio.par(fast_only) + + test("partialAR:::par.rw.pvalue(-3.5,400) < 0.05", TRUE) + test("partialAR:::par.rw.pvalue(-1,500) > 0.10", TRUE) + test("partialAR:::par.mr.pvalue(-1,600) < 0.05", TRUE) + test("partialAR:::par.mr.pvalue(-0.1, 700) > 0.05", TRUE) + test("partialAR:::par.rw.pvalue(-3.5,400, robust=TRUE) < 0.05", TRUE) + test("partialAR:::par.rw.pvalue(-1,500, robust=TRUE) > 0.10", TRUE) + test("partialAR:::par.mr.pvalue(-1,600, robust=TRUE) < 0.05", TRUE) + test("partialAR:::par.mr.pvalue(-0.1, 700, robust=TRUE) > 0.05", TRUE) + + test("partialAR:::par.mr.pvalue(-2,400,ar1test='kpss') < 0.05", TRUE) + test("partialAR:::par.mr.pvalue(-0.5, 500,ar1test='kpss') > 0.05", TRUE) + test("partialAR:::par.mr.pvalue(-2,600, robust=TRUE,ar1test='kpss') < 0.05", TRUE) + test("partialAR:::par.mr.pvalue(-0.5, 700, robust=TRUE,ar1test='kpss') > 0.05", TRUE) + + test("partialAR:::par.joint.pvalue(-4,-0.5,500) < 0.05", TRUE) + test("partialAR:::par.joint.pvalue(-1,-0.25,500) > 0.05", TRUE) + test("partialAR:::par.joint.pvalue(-5,-0.8,500, robust=TRUE) < 0.05", TRUE) + test("partialAR:::par.joint.pvalue(-3,-0.1,500, robust=TRUE) > 0.05", TRUE) + test("partialAR:::par.joint.pvalue(-5,-2,500, ar1test='kpss') < 0.05", TRUE) + test("partialAR:::par.joint.pvalue(-3,-1,500, ar1test='kpss') > 0.05", TRUE) + test("partialAR:::par.joint.pvalue(-4,-0.5,50000)", 0.03) + test("partialAR:::par.joint.pvalue(-4,-0.5,50)", 0.10) + test("partialAR:::par.joint.pvalue(4,-0.5,50)", 1) + test("partialAR:::par.joint.pvalue(-4,-0.5,49)", 1) + + test("partialAR:::test.par.nullrw(data.L)$p.value < 0.05", TRUE) + test("partialAR:::test.par.nullrw(data.IBM)$p.value > 0.05", TRUE) + test("partialAR:::test.par.nullrw(data.L, robust=TRUE)$p.value < 0.10", TRUE) + test("partialAR:::test.par.nullrw(data.IBM, robust=TRUE)$p.value > 0.10", TRUE) + + test("partialAR:::test.par.nullmr(data.L)$p.value <= 0.01", TRUE) + test("partialAR:::test.par.nullmr(data.L, robust=TRUE)$p.value <= 0.01", TRUE) + test("partialAR:::test.par.nullmr(data.L, ar1test='kpss')$p.value <= 0.01", TRUE) + test("partialAR:::test.par.nullmr(data.L, robust=TRUE, ar1test='kpss')$p.value <= 0.01", TRUE) + + test("partialAR:::test.par.nullmr(data.IBM)$p.value < 0.05", TRUE) + test("partialAR:::test.par.nullmr(data.IBM, robust=TRUE)$p.value < 0.10", TRUE) + test("partialAR:::test.par.nullmr(data.IBM, ar1test='kpss')$p.value > 0.10", TRUE) + test("partialAR:::test.par.nullmr(data.IBM, ar1test='kpss', robust=TRUE)$p.value > 0.10", TRUE) + + test("partialAR:::test.par(data.L, null_hyp='rw')$p.value == partialAR:::test.par.nullrw(data.L)$p.value", TRUE) + test("partialAR:::test.par(data.IBM, null_hyp='rw')$p.value == partialAR:::test.par.nullrw(data.IBM)$p.value", TRUE) + test("partialAR:::test.par(data.L, null_hyp='mr')$p.value == partialAR:::test.par.nullmr(data.L)$p.value", TRUE) + test("partialAR:::test.par(data.IBM, null_hyp='mr')$p.value == partialAR:::test.par.nullmr(data.IBM)$p.value", TRUE) + + test("partialAR:::test.par(data.L)$p.value['PAR'] <= 0.01", c(PAR=TRUE)) + test("partialAR:::test.par(data.L, robust=TRUE)$p.value['PAR'] <= 0.10", c(PAR=TRUE)) + test("partialAR:::test.par(data.IBM)$p.value['PAR'] > 0.10", c(PAR=TRUE)) + test("partialAR:::test.par(data.IBM, robust=TRUE)$p.value['PAR'] > 0.10", c(PAR=TRUE)) + test("partialAR:::test.par(data.L, ar1test='kpss')$p.value['PAR'] <= 0.01", c(PAR=TRUE)) + test("partialAR:::test.par(data.L, ar1test='kpss',robust=TRUE)$p.value['PAR'] <= 0.10", c(PAR=TRUE)) + test("partialAR:::test.par(data.IBM, ar1test='kpss')$p.value['PAR'] > 0.10", c(PAR=TRUE)) + + print(partialAR:::test.par(data.L)) + print(partialAR:::test.par(data.L, robust=TRUE)) + + test("partialAR:::which.hypothesis.partest(partialAR:::test.par(data.L))", "PAR") + test("partialAR:::which.hypothesis.partest(partialAR:::test.par(data.L, robust=TRUE))", "RRW") + test("partialAR:::which.hypothesis.partest(partialAR:::test.par(data.IBM))", "RW") + + partialAR:::print.par.lrt(); cat("\n\n") + partialAR:::print.par.lrt(robust=TRUE); cat("\n\n") + partialAR:::print.par.lrt(latex=TRUE); cat("\n\n") + + # partialAR:::print.par.lrt.mr(); cat("\n\n") + # partialAR:::print.par.lrt.mr(robust=TRUE); cat("\n\n") + # partialAR:::print.par.lrt.mr(latex=TRUE); cat("\n\n") + + partialAR:::print.par.lrt.rw(); cat("\n\n") + partialAR:::print.par.lrt.rw(robust=TRUE); cat("\n\n") + partialAR:::print.par.lrt.rw(latex=TRUE); cat("\n\n") + + } > > test_fit.par.both <- function (fast_only=FALSE) { + test("partialAR:::fit.par.both(data.L)$par", + structure(c(0.871991364792238, 0.338198849510798, 0.192519577779812, + 0, 37.8348806008997), .Names = c("rho", "sigma_M", "sigma_R", + "M0", "R0")) ) + test("partialAR:::fit.par.both(data.L)$stderr", + structure(c(0.0493755130952366, 0.0306037545403534, 0.0507506043059735, + NA, 0.382843915239426), .Names = c("rho.se", "sigma_M.se", "sigma_R.se", + "M0.se", "R0.se")) ) + if (!fast_only) test("partialAR:::fit.par.both(data.L, opt_method='ss')$par", + structure(c(0.871991364792238, 0.338198849510798, 0.192519577779812, + 0, 37.8348806008997), .Names = c("rho", "sigma_M", "sigma_R", + "M0", "R0")) ) + if (!fast_only) test("partialAR:::fit.par.both(data.L, opt_method='ss')$stderr", + structure(c(0.0493755130952366, 0.0306037545403534, 0.0507506043059735, + NA, 0.382843915239426), .Names = c("rho.se", "sigma_M.se", "sigma_R.se", + "M0.se", "R0.se")) ) + if (!fast_only) test("partialAR:::fit.par.both(data.L, opt_method='kfas')$par", + structure(c(0.873239025413773, 0.334187559078876, 0.187013759524079, + 0, 37.8228485852872), .Names = c("rho", "sigma_M", "sigma_R", + "M0", "R0")) ) + if (!fast_only) test("partialAR:::fit.par.both(data.L, opt_method='kfas')$stderr", + structure(c(0.0480869790579741, 0.0299959210912542, 0.0482633848885082, + NA, 0.366440477748884), .Names = c("rho.se", "sigma_M.se", "sigma_R.se", + "M0.se", "R0.se")) ) + test("partialAR:::fit.par.both(data.IBM)$par", + structure(c(0.976388651908034, 2.01216604959705, 0.467711046901045, + 0, 177.472892129038), .Names = c("rho", "sigma_M", "sigma_R", + "M0", "R0")) ) + test("partialAR:::fit.par.both(data.IBM)$stderr", + structure(c(0.018222371388718, 0.153130468131214, 0.599803359236283, + NA, 2.12284254607983), .Names = c("rho.se", "sigma_M.se", "sigma_R.se", + "M0.se", "R0.se")) ) + test("partialAR:::fit.par.both(data.IBM, robust=TRUE)$par", + structure(c(0.982921831279379, 1.30721045019958, 0.690103593777354, + 0, 176.743925850553), .Names = c("rho", "sigma_M", "sigma_R", + "M0", "R0")) ) + if (!fast_only) test("partialAR:::fit.par.both(data.IBM, robust=TRUE, opt_method='ss')$par", + structure(c(0.982921831279379, 1.30721045019958, 0.690103593777354, + 0, 176.743925850553), .Names = c("rho", "sigma_M", "sigma_R", + "M0", "R0")) ) + test("partialAR:::fit.par.both(data.IBM, robust=TRUE, nu=3)$par", + structure(c(0.985936838750558, 1.20382984003629, 0.587584874718192, + 0, 176.716597228655), .Names = c("rho", "sigma_M", "sigma_R", + "M0", "R0")) ) + test("partialAR:::fit.par.both(data.IBM, rho.max=0.95)$par", + structure(c(0.95, 1.8101310703133, 0.998701976498605, 0, 176.958377474755 + ), .Names = c("rho", "sigma_M", "sigma_R", "M0", "R0")) ) + test("partialAR:::fit.par.both(data.IBM, lambda=2)$pvmr", c(pvmr=1)) + test("partialAR:::fit.par.both(data.IBM, lambda=-2)$pvmr", c(pvmr=0.0442039289027)) + } > > test_fit.par.mr <- function (fast_only=FALSE) { + test("partialAR:::fit.par.mr(data.L)$par", + structure(c(1, 0.392621113046972, 0, 0, 37.8517816705337), .Names = c("rho", + "sigma_M", "sigma_R", "M0", "R0")) ) + test("partialAR:::fit.par.mr(data.L)$stderr", + structure(c(1.55086108092093e-05, 0.0123907243901383, NA, NA, + 0.392621124942204), .Names = c("rho.se", "sigma_M.se", "sigma_R.se", + "M0.se", "R0.se")) ) + if (!fast_only) test("partialAR:::fit.par.mr(data.L, opt_method='ss')$par", + structure(c(1, 0.392621113046972, 0, 0, 37.8517816705337), .Names = c("rho", + "sigma_M", "sigma_R", "M0", "R0")) ) + if (!fast_only) test("partialAR:::fit.par.mr(data.L, opt_method='ss')$stderr", + structure(c(1.55086108092093e-05, 0.0123907243901383, NA, NA, + 0.392621124942204), .Names = c("rho.se", "sigma_M.se", "sigma_R.se", + "M0.se", "R0.se")) ) + if (!fast_only) test("partialAR:::fit.par.mr(data.L, opt_method='kfas')$par", + structure(c(1, 0.392621113047498, 0, 0, 37.8517816705312), .Names = c("rho", + "sigma_M", "sigma_R", "M0", "R0")) ) + if (!fast_only) test("partialAR:::fit.par.mr(data.L, opt_method='kfas')$stderr", + structure(c(1.55086108092093e-05, 0.0123907243901654, NA, NA, + 0.392621124727183), .Names = c("rho.se", "sigma_M.se", "sigma_R.se", + "M0.se", "R0.se")) ) + test("partialAR:::fit.par.mr(data.IBM)$par", + structure(c(0.989394562548544, 2.06766254187052, 0, 0, 177.378135957708 + ), .Names = c("rho", "sigma_M", "sigma_R", "M0", "R0")) ) + test("partialAR:::fit.par.mr(data.IBM)$stderr", + structure(c(0.00711953959492437, 0.0652545415824236, NA, NA, + 2.18393834163026), .Names = c("rho.se", "sigma_M.se", "sigma_R.se", + "M0.se", "R0.se")) ) + test("partialAR:::fit.par.mr(data.IBM, robust=TRUE)$par", + structure(c(0.996850903105148, 1.47881632988678, 0, 0, 176.742922370692 + ), .Names = c("rho", "sigma_M", "sigma_R", "M0", "R0")) ) + if (!fast_only) test("partialAR:::fit.par.mr(data.IBM, robust=TRUE, opt_method='ss')$par", + structure(c(0.996850903105148, 1.47881632988678, 0, 0, 176.742922370692 + ), .Names = c("rho", "sigma_M", "sigma_R", "M0", "R0")) ) + test("partialAR:::fit.par.mr(data.IBM, robust=TRUE, nu=3)$par", + structure(c(0.996784426974733, 1.33994364448777, 0, 0, 176.717640850721 + ), .Names = c("rho", "sigma_M", "sigma_R", "M0", "R0")) ) + test("partialAR:::fit.par.mr(data.IBM, rho.max=0.95)$par", + structure(c(0.95, 2.10195614607977, 0, 0, 183.429724544732), .Names = c("rho", + "sigma_M", "sigma_R", "M0", "R0")) ) + test("partialAR:::fit.par.mr(data.IBM)$pvmr", c(pvmr=1)) + + } > > test_fit.par.rw <- function (fast_only=FALSE) { + test("partialAR:::fit.par.rw(data.L)$par", + structure(c(0, 0, 0.392609091324016, 0, 37.8517816659277), .Names = c("rho", + "sigma_M", "sigma_R", "M0", "R0")) ) + test("partialAR:::fit.par.rw(data.L)$stderr", + structure(c(NA, NA, 0.0175230013091655, NA, 0), .Names = c("rho.se", + "sigma_M.se", "sigma_R.se", "M0.se", "R0.se")) ) + if (!fast_only) test("partialAR:::fit.par.rw(data.L, opt_method='ss')$par", + structure(c(0, 0, 0.392609091324016, 0, 37.8517816659277), .Names = c("rho", + "sigma_M", "sigma_R", "M0", "R0")) ) + if (!fast_only) test("partialAR:::fit.par.rw(data.L, opt_method='kfas')$par", + structure(c(0, 0, 0.392609091324016, 0, 37.8517816659277), .Names = c("rho", + "sigma_M", "sigma_R", "M0", "R0")) ) + test("partialAR:::fit.par.rw(data.IBM)$par", + structure(c(0, 0, 2.07281796275108, 0, 176.668606104443), .Names = c("rho", + "sigma_M", "sigma_R", "M0", "R0")) ) + test("partialAR:::fit.par.rw(data.IBM)$stderr", + structure(c(NA, NA, 0.0925143932669985, NA, 0), .Names = c("rho.se", + "sigma_M.se", "sigma_R.se", "M0.se", "R0.se")) ) + test("partialAR:::fit.par.rw(data.IBM, robust=TRUE)$par", + structure(c(0, 0, 1.47924935869178, 0, 176.668606104443), .Names = c("rho", + "sigma_M", "sigma_R", "M0", "R0")) ) + if (!fast_only) test("partialAR:::fit.par.rw(data.IBM, robust=TRUE, opt_method='ss')$par", + structure(c(0, 0, 1.47924935869178, 0, 176.668606104443), .Names = c("rho", + "sigma_M", "sigma_R", "M0", "R0")) ) + test("partialAR:::fit.par.rw(data.IBM, robust=TRUE, nu=3)$par", + structure(c(0, 0, 1.34077692991459, 0, 176.668606104443), .Names = c("rho", + "sigma_M", "sigma_R", "M0", "R0")) ) + test("partialAR:::fit.par.rw(data.IBM)$pvmr", c(pvmr=0)) + } > > test_fit.par <- function (fast_only=FALSE) { + test("partialAR:::fit.par(data.L)$par", + structure(c(0.871991364792238, 0.338198849510798, 0.192519577779812, + 0, 37.8348806008997), .Names = c("rho", "sigma_M", "sigma_R", + "M0", "R0")) ) + test("partialAR:::fit.par(data.L)$stderr", + structure(c(0.0493755130952366, 0.0306037545403534, 0.0507506043059735, + NA, 0.382843915239426), .Names = c("rho.se", "sigma_M.se", "sigma_R.se", + "M0.se", "R0.se")) ) + if (!fast_only) test("partialAR:::fit.par(data.L, opt_method='kfas')$par", + structure(c(0.873239025413773, 0.334187559078876, 0.187013759524079, + 0, 37.8228485852872), .Names = c("rho", "sigma_M", "sigma_R", + "M0", "R0")) ) + test("partialAR:::fit.par(data.IBM)$par", + structure(c(0.976388651908034, 2.01216604959705, 0.467711046901045, + 0, 177.472892129038), .Names = c("rho", "sigma_M", "sigma_R", + "M0", "R0")) ) + test("partialAR:::fit.par(data.IBM)$stderr", + structure(c(0.018222371388718, 0.153130468131214, 0.599803359236283, + NA, 2.12284254607983), .Names = c("rho.se", "sigma_M.se", "sigma_R.se", + "M0.se", "R0.se")) ) + test("partialAR:::fit.par(data.IBM, robust=TRUE)$par", + structure(c(0.982921831279379, 1.30721045019958, 0.690103593777354, + 0, 176.743925850553), .Names = c("rho", "sigma_M", "sigma_R", + "M0", "R0")) ) + test("partialAR:::fit.par(data.IBM, robust=TRUE, nu=3)$par", + structure(c(0.985936838750558, 1.20382984003629, 0.587584874718192, + 0, 176.716597228655), .Names = c("rho", "sigma_M", "sigma_R", + "M0", "R0")) ) + test("partialAR:::fit.par(data.IBM, rho.max=0.95)$par", + structure(c(0.95, 1.8101310703133, 0.998701976498605, 0, 176.958377474755 + ), .Names = c("rho", "sigma_M", "sigma_R", "M0", "R0")) ) + test("partialAR:::fit.par(data.IBM, lambda=2)$pvmr", c(pvmr=1)) + test("partialAR:::fit.par(data.IBM, lambda=-2)$pvmr", c(pvmr=0.0442039289027)) + test("partialAR:::fit.par(data.L, model='ar1')$par", + structure(c(1, 0.392621113046972, 0, 0, 37.8517816705337), .Names = c("rho", + "sigma_M", "sigma_R", "M0", "R0")) ) + test("partialAR:::fit.par(data.L, model='ar1')$stderr", + structure(c(1.55086108092093e-05, 0.0123907243901383, NA, NA, + 0.392621124942204), .Names = c("rho.se", "sigma_M.se", "sigma_R.se", + "M0.se", "R0.se")) ) + test("partialAR:::fit.par(data.L, model='rw')$par", + structure(c(0, 0, 0.392609091324016, 0, 37.8517816659277), .Names = c("rho", + "sigma_M", "sigma_R", "M0", "R0")) ) + test("partialAR:::fit.par(data.L, model='rw')$stderr", + structure(c(NA, NA, 0.0175230013091655, NA, 0), .Names = c("rho.se", + "sigma_M.se", "sigma_R.se", "M0.se", "R0.se")) ) + } > > test_fit <- function (fast_only=FALSE) { + test("partialAR:::par.rho.cutoff(25)", NA_real_) + test("partialAR:::par.rho.cutoff(50)", 0.724) + test("partialAR:::par.rho.cutoff(50,0.01)", 0.594) + test("partialAR:::par.rho.cutoff(50,.00001)", 0.438) + + test("partialAR:::estimate.rho.par(numeric())", NA_real_) + test("partialAR:::estimate.rho.par(rep(0,5))", NaN) + x1 <- build_par(0.95, rep(0,10), rep(0,10), M0=1) + test("partialAR:::estimate.rho.par(x1)", 0.8497954230236) + x1na <- x1 + x1na[1] <- NA + test("partialAR:::estimate.rho.par(x1na)", NA_real_) + + test("partialAR:::estimate.par(numeric())", c(rho=NA_real_, sigma_M=NA_real_, sigma_R=NA_real_)) + test("partialAR:::estimate.par(rep(0,5))", c(rho=NaN, sigma_M=NaN, sigma_R=NaN)) + test("partialAR:::estimate.par(x1)", c(rho=0.849795423024, sigma_M=0, sigma_R=0.00624752527433)) + test("partialAR:::estimate.par(x1na)", c(rho=NA_real_, sigma_M=NA_real_, sigma_R=NA_real_)) + + test("partialAR:::pvmr.par(0,0,0)", c(pvmr=NA_real_)) + test("partialAR:::pvmr.par(-1,1,0)", c(pvmr=1)) + test("partialAR:::pvmr.par(1,-1,0)", c(pvmr=NA_real_)) + test("partialAR:::pvmr.par(1,1,-1)", c(pvmr=NA_real_)) + test("partialAR:::pvmr.par(0,0,1)", c(pvmr=0)) + test("partialAR:::pvmr.par(0,1,0)", c(pvmr=1)) + test("partialAR:::pvmr.par(0,1,1)", c(pvmr=2/3)) + test("partialAR:::pvmr.par(0.5,1,1)", c(pvmr=0.571428571429)) + test("partialAR:::pvmr.par(0.5,1,2)", c(pvmr=0.25)) + test("partialAR:::pvmr.par(0.5,0.5,1)", c(pvmr=0.25)) + + test("partialAR:::kalman.gain.par(0,0,0)", c(NA_real_, NA_real_)) + test("partialAR:::kalman.gain.par(0,1,0)", c(1,0)) + test("partialAR:::kalman.gain.par(0,0,1)", c(0,1)) + test("partialAR:::kalman.gain.par(0.5,1,1)", c(1/3,2/3)) + + test("partialAR:::kalman.gain.from.pvmr(0,0)", c(0,1)) + test("partialAR:::kalman.gain.from.pvmr(1,0)", c(0,1)) + test("partialAR:::kalman.gain.from.pvmr(0,1)", c(1,0)) + test("partialAR:::kalman.gain.from.pvmr(0,0)", c(0,1)) + test("partialAR:::kalman.gain.from.pvmr(0,0)", c(0,1)) + test("partialAR:::kalman.gain.from.pvmr(0.8,0.8)", c(0.545454545455, 0.454545454545)) + + test_fit.par.both (fast_only) + test_fit.par.mr(fast_only) + test_fit.par.rw(fast_only) + test_fit.par(fast_only) + + test("partialAR:::statehistory.par(partialAR:::fit.par(data.L))[1,]", + structure(list(X = 37.8517816659277, M = 0.00867470536387833, + R = 37.8431069605638, eps_M = 0.00867470536387833, eps_R = 0.00822635966417289), + .Names = c("X", + "M", "R", "eps_M", "eps_R"), row.names = 1L, class = "data.frame") ) + test("partialAR:::statehistory.par(partialAR:::fit.par(data.L))[length(data.L),]", + structure(list(X = 48.0305776082708, M = 0.379272544771068, R = 47.6513050634997, + eps_M = 0.159638785630931, eps_R = 0.151387973638877), .Names = c("X", + "M", "R", "eps_M", "eps_R"), row.names = 502L, class = "data.frame") ) + + print(partialAR:::fit.par(data.L)) + print(partialAR:::fit.par(data.IBM)) + + test("as.data.frame(partialAR:::fit.par(data.L))", + structure(list(robust = FALSE, nu = 5, + opt_method = "css", + n = 502L, rho = 0.871991364792238, sigma_M = 0.338198849510798, + sigma_R = 0.192519577779812, M0 = 0, R0 = 37.8348806008997, + rho.se = 0.0493755130952366, sigma_M.se = 0.0306037545403534, + sigma_R.se = 0.0507506043059735, M0.se = NA_real_, R0.se = 0.382843915239426, + lambda = 0, pvmr = 0.767280179062111, negloglik = 238.531977143138), .Names = c("robust", + "nu", "opt_method", "n", "rho", "sigma_M", "sigma_R", "M0", "R0", + "rho.se", "sigma_M.se", "sigma_R.se", "M0.se", "R0.se", "lambda", + "pvmr", "negloglik"), row.names = c(NA, -1L), class = "data.frame") ) + } > > test_par <- function (fast_only=FALSE) { + # Comprehensive unit testing for PAR package + + options(warn=1) + + test_cfit(fast_only) + test_lr(fast_only) + test_fit(fast_only) + test_lr2(fast_only) + + if (all.tests.pass) { + cat("SUCCESS! All tests passed.\n") + } else { + stop("ERRORS! ", all.tests.error.count," tests failed\n") + } + } > > test_par(TRUE) partialAR:::estimate_rho_par_c(numeric()) -> NA OK partialAR:::estimate_rho_par_c(rep(0,5)) -> NA OK partialAR:::estimate_rho_par_c(x1) -> 0.8497954 OK partialAR:::estimate_rho_par_c(x1na) -> NA OK partialAR:::estimate_par_c(numeric()) -> NA NA NA OK partialAR:::estimate_par_c(rep(0,5)) -> NA NaN NaN OK partialAR:::estimate_par_c(x1) -> 0.8497954 0 0.006247525 OK partialAR:::estimate_par_c(x1na) -> NA NaN NaN OK partialAR:::pvmr_par_c(0,0,0) -> NA OK partialAR:::pvmr_par_c(-1,1,0) -> 1 OK partialAR:::pvmr_par_c(1,-1,0) -> NA OK partialAR:::pvmr_par_c(1,1,-1) -> NA OK partialAR:::pvmr_par_c(0,0,1) -> 0 OK partialAR:::pvmr_par_c(0,1,0) -> 1 OK partialAR:::pvmr_par_c(0,1,1) -> 0.6666667 OK partialAR:::pvmr_par_c(0.5,1,1) -> 0.5714286 OK partialAR:::pvmr_par_c(0.5,1,2) -> 0.25 OK partialAR:::pvmr_par_c(0.5,0.5,1) -> 0.25 OK partialAR:::kalman_gain_par_mr(0,0,0) -> NA OK partialAR:::kalman_gain_par_mr(0,1,0) -> 1 OK partialAR:::kalman_gain_par_mr(0,0,1) -> 0 OK partialAR:::kalman_gain_par_mr(0.5,1,1) -> 0.3333333 OK partialAR:::loglik_par_c(numeric(),0,0,1,0,0) -> NA OK partialAR:::loglik_par_c(0,0,0,1,0,0) -> 0.9189385 OK partialAR:::loglik_par_c(c(0,0,0),0,0,1,0,0) -> 2.756816 OK partialAR:::loglik_par_c(1,0,0,1,0,0) -> 1.418939 OK partialAR:::loglik_par_c(0,0,1,0,0,0) -> 0.9189385 OK partialAR:::loglik_par_c(c(0,0,0),0,1,0,0,0) -> 2.756816 OK partialAR:::loglik_par_c(c(0,0,0),0.5,1,0,0,0) -> 2.756816 OK partialAR:::loglik_par_c(c(0,1,2),0,0,1,0,1) -> 4.256816 OK partialAR:::loglik_par_c(0.5,0.5,1,0,1,0) -> 0.9189385 OK partialAR:::loglik_par_c(data.L, 0.8720, 0.3385, 0.1927, 0, data.L[1]) -> 238.5334 OK partialAR:::loglik_par_c(data.IBM, 0.9764, 2.0136, 0.4719, 0, data.IBM[1]) -> 1076.524 OK partialAR:::loglik_par_t_c(numeric(),0,0,1,0,0) -> NA OK partialAR:::loglik_par_t_c(0,0,0,1,0,0) -> 0.9686196 OK partialAR:::loglik_par_t_c(c(0,0,0),0,0,1,0,0) -> 2.905859 OK partialAR:::loglik_par_t_c(1,0,0,1,0,0) -> 1.515584 OK partialAR:::loglik_par_t_c(0,0,1,0,0,0) -> 0.9686196 OK partialAR:::loglik_par_t_c(c(0,0,0),0,1,0,0,0) -> 2.905859 OK partialAR:::loglik_par_t_c(c(0,0,0),0.5,1,0,0,0) -> 2.905859 OK partialAR:::loglik_par_t_c(c(0,1,2),0,0,1,0,1) -> 4.546753 OK partialAR:::loglik_par_t_c(0.5,0.5,1,0,1,0) -> 0.9686196 OK partialAR:::loglik_par_t_c(0,0,0,1,0,0,6) -> 0.9604183 OK partialAR:::loglik_par_t_c(data.L, 0.8958, 0.2612, 0.1768, 0, data.L[1]) -> 229.8076 OK partialAR:::loglik_par_t_c(data.IBM, 0.9829, 1.3072, 0.6901, 0, data.IBM[1]) -> 1020.883 OK partialAR:::loglik.par.kfas(numeric(),0,0,1,0,0) -> NA OK partialAR:::loglik.par.kfas(0,0,0,1,0,0) -> 0.9189385 OK partialAR:::loglik.par.kfas(c(0,0,0),0,0,1,0,0) -> 2.756816 OK partialAR:::loglik.par.kfas(1,0,0,1,0,0) -> 1.418939 OK partialAR:::loglik.par.kfas(0,0,1,0,0,0) -> 0.9189385 OK partialAR:::loglik.par.kfas(c(0,0,0),0,1,0,0,0) -> 2.756816 OK partialAR:::loglik.par.kfas(c(0,0,0),0.5,1,0,0,0) -> 2.756816 OK partialAR:::loglik.par.kfas(c(0,1,2),0,0,1,0,1) -> 4.256816 OK partialAR:::loglik.par.kfas(0.5,0.5,1,0,1,0) -> 1.043939 OK partialAR:::loglik.par.kfas(data.L, 0.8720, 0.3385, 0.1927) -> 238.5337 OK partialAR:::loglik.par.kfas(data.IBM, 0.9764, 2.0136, 0.4719, 0, data.IBM[1]) -> 1077.028 OK partialAR:::loglik.par.ss(numeric(),0,0,1,0,0) -> NA OK partialAR:::loglik.par.ss(0,0,0,1,0,0) -> 0.9189385 OK partialAR:::loglik.par.ss(c(0,0,0),0,0,1,0,0) -> 2.756816 OK partialAR:::loglik.par.ss(1,0,0,1,0,0) -> 1.418939 OK partialAR:::loglik.par.ss(0,0,1,0,0,0) -> 0.9189385 OK partialAR:::loglik.par.ss(c(0,0,0),0,1,0,0,0) -> 2.756816 OK partialAR:::loglik.par.ss(c(0,0,0),0.5,1,0,0,0) -> 2.756816 OK partialAR:::loglik.par.ss(c(0,1,2),0,0,1,0,1) -> 4.256816 OK partialAR:::loglik.par.ss(0.5,0.5,1,0,1,0) -> 0.9189385 OK partialAR:::loglik.par.ss(data.L, 0.8720, 0.3385, 0.1927, 0, data.L[1]) -> 238.5334 OK partialAR:::loglik.par.ss(data.IBM, 0.9764, 2.0136, 0.4719) -> 1076.524 OK partialAR:::loglik.par.ss.t(numeric(),0,0,1,0,0) -> NA OK partialAR:::loglik.par.ss.t(0,0,0,1,0,0) -> 0.9686196 OK partialAR:::loglik.par.ss.t(c(0,0,0),0,0,1,0,0) -> 2.905859 OK partialAR:::loglik.par.ss.t(1,0,0,1,0,0) -> 1.515584 OK partialAR:::loglik.par.ss.t(0,0,1,0,0,0) -> 0.9686196 OK partialAR:::loglik.par.ss.t(c(0,0,0),0,1,0,0,0) -> 2.905859 OK partialAR:::loglik.par.ss.t(c(0,0,0),0.5,1,0,0,0) -> 2.905859 OK partialAR:::loglik.par.ss.t(c(0,1,2),0,0,1,0,1) -> 4.546753 OK partialAR:::loglik.par.ss.t(0.5,0.5,1,0,1,0) -> 0.9686196 OK partialAR:::loglik.par.ss.t(0,0,0,1,0,0,6) -> 0.9604183 OK partialAR:::loglik.par.ss.t(data.L, 0.8958, 0.2612, 0.1768, 0, data.L[1]) -> 229.8076 OK partialAR:::loglik.par.ss.t(data.IBM, 0.9829, 1.3072, 0.6901, 0, data.IBM[1]) -> 1020.883 OK partialAR:::loglik.par(data.L, 0.8720, 0.3385, 0.1927) -> 238.5334 OK partialAR:::loglik.par(data.L, 0.8720, 0.3385, 0.1927, calc_method="css") -> 238.5334 OK partialAR:::loglik.par(data.L, 0.8720, 0.3385, 0.1927, calc_method="kfas") -> 238.5337 OK partialAR:::loglik.par(data.L, 0.8720, 0.3385, 0.1927, calc_method="ss") -> 238.5334 OK partialAR:::loglik.par(data.L, 0.8958, 0.2612, 0.1768, calc_method="sst") -> 229.8076 OK partialAR:::loglik.par(data.L, 0.8958, 0.2612, 0.1768, calc_method="csst") -> 229.8076 OK partialAR:::par.rho.cutoff(25) -> NA OK partialAR:::par.rho.cutoff(50) -> 0.724 OK partialAR:::par.rho.cutoff(50,0.01) -> 0.594 OK partialAR:::par.rho.cutoff(50,.00001) -> 0.438 OK partialAR:::estimate.rho.par(numeric()) -> NA OK partialAR:::estimate.rho.par(rep(0,5)) -> NA OK partialAR:::estimate.rho.par(x1) -> 0.8497954 OK partialAR:::estimate.rho.par(x1na) -> NA OK partialAR:::estimate.par(numeric()) -> NA NA NA OK partialAR:::estimate.par(rep(0,5)) -> NA NaN NaN OK partialAR:::estimate.par(x1) -> 0.8497954 0 0.006247525 OK partialAR:::estimate.par(x1na) -> NA NaN NaN OK partialAR:::pvmr.par(0,0,0) -> NaN OK partialAR:::pvmr.par(-1,1,0) -> 1 OK partialAR:::pvmr.par(1,-1,0) -> NA OK partialAR:::pvmr.par(1,1,-1) -> NA OK partialAR:::pvmr.par(0,0,1) -> 0 OK partialAR:::pvmr.par(0,1,0) -> 1 OK partialAR:::pvmr.par(0,1,1) -> 0.6666667 OK partialAR:::pvmr.par(0.5,1,1) -> 0.5714286 OK partialAR:::pvmr.par(0.5,1,2) -> 0.25 OK partialAR:::pvmr.par(0.5,0.5,1) -> 0.25 OK partialAR:::kalman.gain.par(0,0,0) -> NA NA OK partialAR:::kalman.gain.par(0,1,0) -> 1 0 OK partialAR:::kalman.gain.par(0,0,1) -> 0 1 OK partialAR:::kalman.gain.par(0.5,1,1) -> 0.3333333 0.6666667 OK partialAR:::kalman.gain.from.pvmr(0,0) -> 0 1 OK partialAR:::kalman.gain.from.pvmr(1,0) -> 0 1 OK partialAR:::kalman.gain.from.pvmr(0,1) -> 1 0 OK partialAR:::kalman.gain.from.pvmr(0,0) -> 0 1 OK partialAR:::kalman.gain.from.pvmr(0,0) -> 0 1 OK partialAR:::kalman.gain.from.pvmr(0.8,0.8) -> 0.5454545 0.4545455 OK partialAR:::fit.par.both(data.L)$par -> numeric ( 5 ) OK partialAR:::fit.par.both(data.L)$stderr -> numeric ( 5 ) OK partialAR:::fit.par.both(data.IBM)$par -> numeric ( 5 ) OK partialAR:::fit.par.both(data.IBM)$stderr -> numeric ( 5 ) OK partialAR:::fit.par.both(data.IBM, robust=TRUE)$par -> numeric ( 5 ) OK partialAR:::fit.par.both(data.IBM, robust=TRUE, nu=3)$par -> numeric ( 5 ) OK partialAR:::fit.par.both(data.IBM, rho.max=0.95)$par -> numeric ( 5 ) OK partialAR:::fit.par.both(data.IBM, lambda=2)$pvmr -> 1 OK partialAR:::fit.par.both(data.IBM, lambda=-2)$pvmr -> 0.04420384 OK partialAR:::fit.par.mr(data.L)$par -> numeric ( 5 ) OK partialAR:::fit.par.mr(data.L)$stderr -> numeric ( 5 ) OK partialAR:::fit.par.mr(data.IBM)$par -> numeric ( 5 ) OK partialAR:::fit.par.mr(data.IBM)$stderr -> numeric ( 5 ) OK partialAR:::fit.par.mr(data.IBM, robust=TRUE)$par -> numeric ( 5 ) OK partialAR:::fit.par.mr(data.IBM, robust=TRUE, nu=3)$par -> numeric ( 5 ) OK partialAR:::fit.par.mr(data.IBM, rho.max=0.95)$par -> numeric ( 5 ) OK partialAR:::fit.par.mr(data.IBM)$pvmr -> 1 OK partialAR:::fit.par.rw(data.L)$par -> numeric ( 5 ) OK partialAR:::fit.par.rw(data.L)$stderr -> numeric ( 5 ) OK partialAR:::fit.par.rw(data.IBM)$par -> numeric ( 5 ) OK partialAR:::fit.par.rw(data.IBM)$stderr -> numeric ( 5 ) OK partialAR:::fit.par.rw(data.IBM, robust=TRUE)$par -> numeric ( 5 ) OK partialAR:::fit.par.rw(data.IBM, robust=TRUE, nu=3)$par -> numeric ( 5 ) OK partialAR:::fit.par.rw(data.IBM)$pvmr -> 0 OK partialAR:::fit.par(data.L)$par -> numeric ( 5 ) OK partialAR:::fit.par(data.L)$stderr -> numeric ( 5 ) OK partialAR:::fit.par(data.IBM)$par -> numeric ( 5 ) OK partialAR:::fit.par(data.IBM)$stderr -> numeric ( 5 ) OK partialAR:::fit.par(data.IBM, robust=TRUE)$par -> numeric ( 5 ) OK partialAR:::fit.par(data.IBM, robust=TRUE, nu=3)$par -> numeric ( 5 ) OK partialAR:::fit.par(data.IBM, rho.max=0.95)$par -> numeric ( 5 ) OK partialAR:::fit.par(data.IBM, lambda=2)$pvmr -> 1 OK partialAR:::fit.par(data.IBM, lambda=-2)$pvmr -> 0.04420384 OK partialAR:::fit.par(data.L, model='ar1')$par -> numeric ( 5 ) OK partialAR:::fit.par(data.L, model='ar1')$stderr -> numeric ( 5 ) OK partialAR:::fit.par(data.L, model='rw')$par -> numeric ( 5 ) OK partialAR:::fit.par(data.L, model='rw')$stderr -> numeric ( 5 ) OK partialAR:::statehistory.par(partialAR:::fit.par(data.L))[1,] -> data.frame ( 5 ) OK partialAR:::statehistory.par(partialAR:::fit.par(data.L))[length(data.L),] -> data.frame ( 5 ) OK Fitted model: X[t] = M[t] + R[t] M[t] = 0.8720 M[t-1] + eps_M,t, eps_M,t ~ N(0, 0.3382^2) (0.0494) (0.0306) R[t] = R[t-1] + eps_R,t, eps_R,t ~ N(0, 0.1925^2) (0.0508) M_0 = 0.0000, R_0 = 37.8349 (NA) (0.3828) Proportion of variance attributable to mean reversion (pvmr) = 0.7673 Negative log likelihood = 238.53 Fitted model: X[t] = M[t] + R[t] M[t] = 0.9764 M[t-1] + eps_M,t, eps_M,t ~ N(0, 2.0122^2) (0.0182) (0.1531) R[t] = R[t-1] + eps_R,t, eps_R,t ~ N(0, 0.4677^2) (0.5998) M_0 = 0.0000, R_0 = 177.4729 (NA) (2.1228) Proportion of variance attributable to mean reversion (pvmr) = 0.9493 Negative log likelihood = 1076.49 as.data.frame(partialAR:::fit.par(data.L)) -> data.frame ( 17 ) (Expecting data.frame ( 17 )) ERROR: Component "opt_method": 'current' is not a factor partialAR:::likelihood_ratio.par(data.L) -> -4.448247 OK partialAR:::likelihood_ratio.par(data.L, robust=TRUE) -> -2.648053 OK partialAR:::likelihood_ratio.par(data.L, null_model='rw') -> -4.448247 OK partialAR:::likelihood_ratio.par(data.L, null_model='rw', robust=TRUE) -> -2.648053 OK partialAR:::likelihood_ratio.par(data.L, null_model='ar1') -> -4.448247 OK partialAR:::likelihood_ratio.par(data.L, null_model='ar1', robust=TRUE) -> -2.648052 OK partialAR:::likelihood_ratio.par(data.L, opt_method='css') -> -4.448247 OK partialAR:::likelihood_ratio.par(data.L, robust=TRUE, opt_method='css') -> -2.648053 OK partialAR:::likelihood_ratio.par(data.L, null_model='rw', opt_method='css') -> -4.448247 OK partialAR:::likelihood_ratio.par(data.L, null_model='rw', robust=TRUE, opt_method='css') -> -2.648053 OK partialAR:::likelihood_ratio.par(data.L, null_model='ar1', opt_method='css') -> -4.448247 OK partialAR:::likelihood_ratio.par(data.L, null_model='ar1', robust=TRUE, opt_method='css') -> -2.648052 OK nrow(SAMPLES) -> 10 OK sum(SAMPLES$seed) -> 55 OK mean(SAMPLES$rw_lrt) -> -4.435764 OK mean(SAMPLES$mr_lrt) -> -3.896091 OK mean(SAMPLES$kpss_stat) -> 3.726987 OK partialAR:::par.rw.pvalue(-3.5,400) < 0.05 -> TRUE OK partialAR:::par.rw.pvalue(-1,500) > 0.10 -> TRUE OK partialAR:::par.mr.pvalue(-1,600) < 0.05 -> Warning in regularize.values(x, y, ties, missing(ties)) : collapsing to unique 'x' values Warning in regularize.values(x, y, ties, missing(ties)) : collapsing to unique 'x' values TRUE OK partialAR:::par.mr.pvalue(-0.1, 700) > 0.05 -> Warning in regularize.values(x, y, ties, missing(ties)) : collapsing to unique 'x' values Warning in regularize.values(x, y, ties, missing(ties)) : collapsing to unique 'x' values TRUE OK partialAR:::par.rw.pvalue(-3.5,400, robust=TRUE) < 0.05 -> TRUE OK partialAR:::par.rw.pvalue(-1,500, robust=TRUE) > 0.10 -> TRUE OK partialAR:::par.mr.pvalue(-1,600, robust=TRUE) < 0.05 -> Warning in regularize.values(x, y, ties, missing(ties)) : collapsing to unique 'x' values Warning in regularize.values(x, y, ties, missing(ties)) : collapsing to unique 'x' values TRUE OK partialAR:::par.mr.pvalue(-0.1, 700, robust=TRUE) > 0.05 -> Warning in regularize.values(x, y, ties, missing(ties)) : collapsing to unique 'x' values Warning in regularize.values(x, y, ties, missing(ties)) : collapsing to unique 'x' values TRUE OK partialAR:::par.mr.pvalue(-2,400,ar1test='kpss') < 0.05 -> TRUE OK partialAR:::par.mr.pvalue(-0.5, 500,ar1test='kpss') > 0.05 -> TRUE OK partialAR:::par.mr.pvalue(-2,600, robust=TRUE,ar1test='kpss') < 0.05 -> TRUE OK partialAR:::par.mr.pvalue(-0.5, 700, robust=TRUE,ar1test='kpss') > 0.05 -> TRUE OK partialAR:::par.joint.pvalue(-4,-0.5,500) < 0.05 -> TRUE OK partialAR:::par.joint.pvalue(-1,-0.25,500) > 0.05 -> TRUE OK partialAR:::par.joint.pvalue(-5,-0.8,500, robust=TRUE) < 0.05 -> TRUE OK partialAR:::par.joint.pvalue(-3,-0.1,500, robust=TRUE) > 0.05 -> TRUE OK partialAR:::par.joint.pvalue(-5,-2,500, ar1test='kpss') < 0.05 -> TRUE OK partialAR:::par.joint.pvalue(-3,-1,500, ar1test='kpss') > 0.05 -> TRUE OK partialAR:::par.joint.pvalue(-4,-0.5,50000) -> 0.03 OK partialAR:::par.joint.pvalue(-4,-0.5,50) -> 0.1 OK partialAR:::par.joint.pvalue(4,-0.5,50) -> 1 OK partialAR:::par.joint.pvalue(-4,-0.5,49) -> Warning in partialAR:::par.joint.pvalue(-4, -0.5, 49) : Sample size too small (49) to provide accurate p-value 1 OK partialAR:::test.par.nullrw(data.L)$p.value < 0.05 -> TRUE OK partialAR:::test.par.nullrw(data.IBM)$p.value > 0.05 -> TRUE OK partialAR:::test.par.nullrw(data.L, robust=TRUE)$p.value < 0.10 -> TRUE OK partialAR:::test.par.nullrw(data.IBM, robust=TRUE)$p.value > 0.10 -> TRUE OK partialAR:::test.par.nullmr(data.L)$p.value <= 0.01 -> Warning in regularize.values(x, y, ties, missing(ties)) : collapsing to unique 'x' values Warning in regularize.values(x, y, ties, missing(ties)) : collapsing to unique 'x' values TRUE OK partialAR:::test.par.nullmr(data.L, robust=TRUE)$p.value <= 0.01 -> Warning in regularize.values(x, y, ties, missing(ties)) : collapsing to unique 'x' values Warning in regularize.values(x, y, ties, missing(ties)) : collapsing to unique 'x' values TRUE OK partialAR:::test.par.nullmr(data.L, ar1test='kpss')$p.value <= 0.01 -> TRUE OK partialAR:::test.par.nullmr(data.L, robust=TRUE, ar1test='kpss')$p.value <= 0.01 -> TRUE OK partialAR:::test.par.nullmr(data.IBM)$p.value < 0.05 -> Warning in regularize.values(x, y, ties, missing(ties)) : collapsing to unique 'x' values Warning in regularize.values(x, y, ties, missing(ties)) : collapsing to unique 'x' values TRUE OK partialAR:::test.par.nullmr(data.IBM, robust=TRUE)$p.value < 0.10 -> Warning in regularize.values(x, y, ties, missing(ties)) : collapsing to unique 'x' values Warning in regularize.values(x, y, ties, missing(ties)) : collapsing to unique 'x' values TRUE OK partialAR:::test.par.nullmr(data.IBM, ar1test='kpss')$p.value > 0.10 -> TRUE OK partialAR:::test.par.nullmr(data.IBM, ar1test='kpss', robust=TRUE)$p.value > 0.10 -> TRUE OK partialAR:::test.par(data.L, null_hyp='rw')$p.value == partialAR:::test.par.nullrw(data.L)$p.value -> TRUE OK partialAR:::test.par(data.IBM, null_hyp='rw')$p.value == partialAR:::test.par.nullrw(data.IBM)$p.value -> TRUE OK partialAR:::test.par(data.L, null_hyp='mr')$p.value == partialAR:::test.par.nullmr(data.L)$p.value -> Warning in regularize.values(x, y, ties, missing(ties)) : collapsing to unique 'x' values Warning in regularize.values(x, y, ties, missing(ties)) : collapsing to unique 'x' values Warning in regularize.values(x, y, ties, missing(ties)) : collapsing to unique 'x' values Warning in regularize.values(x, y, ties, missing(ties)) : collapsing to unique 'x' values TRUE OK partialAR:::test.par(data.IBM, null_hyp='mr')$p.value == partialAR:::test.par.nullmr(data.IBM)$p.value -> Warning in regularize.values(x, y, ties, missing(ties)) : collapsing to unique 'x' values Warning in regularize.values(x, y, ties, missing(ties)) : collapsing to unique 'x' values Warning in regularize.values(x, y, ties, missing(ties)) : collapsing to unique 'x' values Warning in regularize.values(x, y, ties, missing(ties)) : collapsing to unique 'x' values TRUE OK partialAR:::test.par(data.L)$p.value['PAR'] <= 0.01 -> Warning in regularize.values(x, y, ties, missing(ties)) : collapsing to unique 'x' values Warning in regularize.values(x, y, ties, missing(ties)) : collapsing to unique 'x' values TRUE OK partialAR:::test.par(data.L, robust=TRUE)$p.value['PAR'] <= 0.10 -> Warning in regularize.values(x, y, ties, missing(ties)) : collapsing to unique 'x' values Warning in regularize.values(x, y, ties, missing(ties)) : collapsing to unique 'x' values TRUE OK partialAR:::test.par(data.IBM)$p.value['PAR'] > 0.10 -> Warning in regularize.values(x, y, ties, missing(ties)) : collapsing to unique 'x' values Warning in regularize.values(x, y, ties, missing(ties)) : collapsing to unique 'x' values TRUE OK partialAR:::test.par(data.IBM, robust=TRUE)$p.value['PAR'] > 0.10 -> Warning in regularize.values(x, y, ties, missing(ties)) : collapsing to unique 'x' values Warning in regularize.values(x, y, ties, missing(ties)) : collapsing to unique 'x' values TRUE OK partialAR:::test.par(data.L, ar1test='kpss')$p.value['PAR'] <= 0.01 -> TRUE OK partialAR:::test.par(data.L, ar1test='kpss',robust=TRUE)$p.value['PAR'] <= 0.10 -> TRUE OK partialAR:::test.par(data.IBM, ar1test='kpss')$p.value['PAR'] > 0.10 -> TRUE OK Warning in regularize.values(x, y, ties, missing(ties)) : collapsing to unique 'x' values Warning in regularize.values(x, y, ties, missing(ties)) : collapsing to unique 'x' values Test of [Random Walk or AR(1)] vs Almost AR(1) [LR test for AR1] data: data.L Hypothesis Statistic p-value Random Walk -4.45 0.014 AR(1) -4.45 0.010 Combined 0.010 Warning in regularize.values(x, y, ties, missing(ties)) : collapsing to unique 'x' values Warning in regularize.values(x, y, ties, missing(ties)) : collapsing to unique 'x' values Test of [Robust Random Walk or Robust AR(1)] vs Robust Almost AR(1) [LR test for AR1] data: data.L Hypothesis Statistic p-value Robust RW -2.65 0.071 Robust AR(1) -2.65 0.010 Combined 0.060 partialAR:::which.hypothesis.partest(partialAR:::test.par(data.L)) -> Warning in regularize.values(x, y, ties, missing(ties)) : collapsing to unique 'x' values Warning in regularize.values(x, y, ties, missing(ties)) : collapsing to unique 'x' values PAR OK partialAR:::which.hypothesis.partest(partialAR:::test.par(data.L, robust=TRUE)) -> Warning in regularize.values(x, y, ties, missing(ties)) : collapsing to unique 'x' values Warning in regularize.values(x, y, ties, missing(ties)) : collapsing to unique 'x' values RRW OK partialAR:::which.hypothesis.partest(partialAR:::test.par(data.IBM)) -> Warning in regularize.values(x, y, ties, missing(ties)) : collapsing to unique 'x' values Warning in regularize.values(x, y, ties, missing(ties)) : collapsing to unique 'x' values RW OK Critical Values for Likelihood Ratio Tests Single Hypothesis Test NULL: Random Walk | NULL: AR(1) p=0.01 p=0.05 p=0.10 | p=0.01 p=0.05 p=0.10 ------------------------------------------------------------ n=50 -4.7 -2.9 -2.2 | -2.6 -1.2 -0.7 n=100 -4.7 -3.0 -2.2 | -2.4 -1.0 -0.4 n=250 -4.6 -3.0 -2.2 | -1.9 -0.5 -0.1 n=500 -4.7 -3.2 -2.4 | -1.6 -0.3 -0.0 n=1000 -4.8 -3.1 -2.4 | -1.4 -0.1 -0.0 n=2500 -4.8 -3.1 -2.4 | -1.3 -0.0 -0.0 Critical Values for Likelihood Ratio Tests Single Hypothesis Test Robust Model NULL: Random Walk | NULL: AR(1) p=0.01 p=0.05 p=0.10 | p=0.01 p=0.05 p=0.10 ------------------------------------------------------------ n=50 -4.5 -2.9 -2.2 | -2.9 -1.4 -0.8 n=100 -4.6 -2.9 -2.2 | -2.8 -1.2 -0.6 n=250 -4.6 -2.9 -2.3 | -2.2 -0.8 -0.3 n=500 -4.6 -3.0 -2.3 | -1.9 -0.6 -0.1 n=1000 -4.5 -3.0 -2.4 | -1.6 -0.3 -0.0 n=2500 -4.7 -3.1 -2.4 | -1.3 -0.2 -0.0 \begin{table} \begin{tabular}{crrr|rrr} & \multicolumn{3}{c}{NULL: Random Walk} & \multicolumn{3}{c}{NULL: AR(1)} \\ & \multicolumn{1}{c}{p=0.01} & \multicolumn{1}{c}{p=0.05} & \multicolumn{1}{c}{p=0.10} & p=0.01 & p=0.05 & p=0.10\\ \hline n=50 & -4.7 & -2.9 & -2.2 & -2.6 & -1.2 & -0.7 \\ n=100 & -4.7 & -3.0 & -2.2 & -2.4 & -1.0 & -0.4 \\ n=250 & -4.6 & -3.0 & -2.2 & -1.9 & -0.5 & -0.1 \\ n=500 & -4.7 & -3.2 & -2.4 & -1.6 & -0.3 & -0.0 \\ n=1000 & -4.8 & -3.1 & -2.4 & -1.4 & -0.1 & -0.0 \\ n=2500 & -4.8 & -3.1 & -2.4 & -1.3 & -0.0 & -0.0 \\ \end{tabular} \caption{Critical Values for Likelihood Ratio Tests} \caption*{For each sample size, 40,000 random walks were generated, and then the likelihood ratios were calculated under the hypothesis of a random walk (left panel) and under the hypothesis of an AR(1) series (right panel). For the hypothesis of an AR(1) series, it was found that the critical values depend upon the value of $\rho$, and that as $\rho$ increases, the critical values for a given quantile decrease. Thus, by using the limiting case of a random walk when computing critical values for the AR(1) case, a conservative estimate is obtained.} \end{table} Critical Values for Likelihood Ratio Tests Null hypothesis: Random Walk p=0.01 p=0.05 p=0.10 ---------------------------- n=50 -4.7 -2.9 -2.2 n=100 -4.7 -3.0 -2.2 n=250 -4.6 -3.0 -2.2 n=500 -4.7 -3.2 -2.4 n=1000 -4.8 -3.1 -2.4 n=2500 -4.8 -3.1 -2.4 Critical Values for Likelihood Ratio Tests Robust Model Null hypothesis: Random Walk p=0.01 p=0.05 p=0.10 ---------------------------- n=50 -4.5 -2.9 -2.2 n=100 -4.6 -2.9 -2.2 n=250 -4.6 -2.9 -2.3 n=500 -4.6 -3.0 -2.3 n=1000 -4.5 -3.0 -2.4 n=2500 -4.7 -3.1 -2.4 \begin{tabular}{crrr} & \multicolumn{3}{c}{NULL: Random Walk} \\ & \multicolumn{1}{c}{p=0.01} & \multicolumn{1}{c}{p=0.05} & \multicolumn{1}{c}{p=0.10}\\ \hline n=50 & -4.7 & -2.9 & -2.2 \\ n=100 & -4.7 & -3.0 & -2.2 \\ n=250 & -4.6 & -3.0 & -2.2 \\ n=500 & -4.7 & -3.2 & -2.4 \\ n=1000 & -4.8 & -3.1 & -2.4 \\ n=2500 & -4.8 & -3.1 & -2.4 \\ \end{tabular} Error in test_par(TRUE) : ERRORS! 1 tests failed Execution halted ** running tests for arch 'x64' ... [18s] ERROR Running 'tests.R' [17s] Running the tests in 'tests/tests.R' failed. Complete output: > all.tests.pass <- TRUE > all.tests.error.count <- 0 > > test <- function(expr, out="", val=eval.parent(parse(text=expr), 1), tol=1e-4) { + # expr is a string representing an R expression, and + # out is the output that is expected. Prints and evaluates + # expr. If out is given and it matches the output of + # evaluating expr, returns TRUE. Otherwise, returns FALSE. + + cat(expr, "-> ") + + p <- function (v) { + if (length(v) < 5) { + cat(v) + } else { + cat(class(v), "(", length(val), ")") + } + } + p(val) + + result <- all.equal(val, out, tolerance=tol) + if (!isTRUE(result)) { + if (!missing(out)) { + cat(" (Expecting ") + p(out) + cat(")") + } + cat("\nERROR: ", result, "\n") + all.tests.pass <<- FALSE + all.tests.error.count <<- all.tests.error.count + 1 + } else { + cat(" OK\n") + } + + isTRUE(result) + } > > assert <- function (expr, out) { + # expr is astring representing an R expression, + # and out is the output that is expected. Prints + # and evaluates expr. If out matches the output of + # evaluating expr, returns TRUE. Otherwise, stops + # the execution with an error message. + if (!test(expr, out)) { + stop("Expression ", deparse(substitute(expr)), + " does not evaluate to its expected value\n") + } + } > > build_par <- function (rho, eps_M, eps_R, R0=0, M0=0) { + R <- R0 + M <- M0 + X <- numeric() + for (i in 1:length(eps_M)) { + M <- rho * M + eps_M[i] + R <- R + eps_R[i] + X[i] <- M + R + } + X + } > > data.L <- structure(c(37.8517816659277, 37.3893346323175, 37.4385311252548, + 37.1138342718688, 37.2319058549183, 37.8616209645152, 37.7238707842909, + 37.900978158865, 37.6156384998289, 37.4188525280799, 37.7632279786407, + 37.9108174574525, 37.9403353532148, 38.314228699538, 37.8222637701654, + 37.5664420068916, 37.3401381393802, 37.0252805845818, 36.7202623283708, + 36.7104230297833, 37.2417451535057, 37.3893346323175, 37.9895318461521, + 37.7632279786407, 37.7435493814658, 37.8714602631026, 37.5861206040665, + 37.487727618192, 37.8025851729905, 37.5369241111293, 36.985923390232, + 37.4582097224297, 37.6845135899411, 38.1076034292015, 38.0879248320266, + 38.5405325670494, 38.511014671287, 38.6389255529239, 38.7798536105174, + 38.5728963231423, 38.6615923034459, 38.3068083822315, 38.2870981643863, + 37.6070956487254, 37.6563711933385, 37.7647773914873, 38.0899959859339, + 38.0111551145529, 38.7305780659043, 38.4546350160709, 38.9868108978925, + 38.9079700265115, 39.1050722049639, 39.1247824228092, 38.7699985015948, + 38.2378226197732, 38.6221718677554, 39.2824641655711, 39.1149273138865, + 39.0557966603508, 38.8981149175889, 39.2923192744937, 39.7850747206248, + 39.4795663440236, 39.1346375317318, 38.9966660068151, 38.4349247982256, + 37.8337631539457, 38.2279675108506, 38.8586944818984, 38.346228817922, + 38.6813025212912, 39.3415948191068, 39.0755068781961, 38.9769557889698, + 39.2627539477259, 39.0459415514282, 39.6569583046307, 40.0511626615356, + 40.4552221273631, 40.4158016916726, 40.5340629987441, 40.8888469199585, + 40.6720345236608, 40.5439181076667, 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176.137481416927, 175.218418233291, 175.237972769113, 176.254808631859, + 173.888709797393, 176.139728143555, 176.935939872984, 176.926110098546, + 179.766914910951, 179.953680625262, 180.425509798257, 179.108320023647, + 180.071637918511, 181.329849046496, 182.096571452612, 182.037592805988, + 180.995636715625, 178.213810549844, 175.893983782621, 174.291730549326, + 175.923473105933, 176.621387090987, 174.458836714762, 173.082668293528, + 172.748455962656, 173.082668293528, 174.645602429072, 174.439177165887, + 174.104964835016, 172.217648143038, 170.418799420996, 169.858502278064, + 174.822538368945, 172.768115511531, 175.658069196123, 177.152194910606, + 176.955599421859, 179.127979572521, 180.101127241823, 182.194869196986, + 181.929465287177, 183.236825287349, 184.377079122086), .Dim = c(502L, + 1L), .Dimnames = list(NULL, "IBM"), index = structure(c(15342, + 15343, 15344, 15345, 15348, 15349, 15350, 15351, 15352, 15356, + 15357, 15358, 15359, 15362, 15363, 15364, 15365, 15366, 15369, + 15370, 15371, 15372, 15373, 15376, 15377, 15378, 15379, 15380, + 15383, 15384, 15385, 15386, 15387, 15391, 15392, 15393, 15394, + 15397, 15398, 15399, 15400, 15401, 15404, 15405, 15406, 15407, + 15408, 15411, 15412, 15413, 15414, 15415, 15418, 15419, 15420, + 15421, 15422, 15425, 15426, 15427, 15428, 15429, 15432, 15433, + 15434, 15435, 15439, 15440, 15441, 15442, 15443, 15446, 15447, + 15448, 15449, 15450, 15453, 15454, 15455, 15456, 15457, 15460, + 15461, 15462, 15463, 15464, 15467, 15468, 15469, 15470, 15471, + 15474, 15475, 15476, 15477, 15478, 15481, 15482, 15483, 15484, + 15485, 15489, 15490, 15491, 15492, 15495, 15496, 15497, 15498, + 15499, 15502, 15503, 15504, 15505, 15506, 15509, 15510, 15511, + 15512, 15513, 15516, 15517, 15518, 15519, 15520, 15523, 15524, + 15526, 15527, 15530, 15531, 15532, 15533, 15534, 15537, 15538, + 15539, 15540, 15541, 15544, 15545, 15546, 15547, 15548, 15551, + 15552, 15553, 15554, 15555, 15558, 15559, 15560, 15561, 15562, + 15565, 15566, 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15771, 15772, 15775, 15776, + 15777, 15778, 15779, 15782, 15783, 15784, 15785, 15786, 15789, + 15790, 15791, 15792, 15796, 15797, 15798, 15799, 15800, 15803, + 15804, 15805, 15806, 15807, 15810, 15811, 15812, 15813, 15814, + 15817, 15818, 15819, 15820, 15821, 15824, 15825, 15826, 15827, + 15828, 15831, 15832, 15833, 15834, 15835, 15838, 15839, 15840, + 15841, 15842, 15845, 15846, 15847, 15848, 15849, 15853, 15854, + 15855, 15856, 15859, 15860, 15861, 15862, 15863, 15866, 15867, + 15868, 15869, 15870, 15873, 15874, 15875, 15876, 15877, 15880, + 15881, 15882, 15883, 15884, 15887, 15888, 15889, 15891, 15894, + 15895, 15896, 15897, 15898, 15901, 15902, 15903, 15904, 15905, + 15908, 15909, 15910, 15911, 15912, 15915, 15916, 15917, 15918, + 15919, 15922, 15923, 15924, 15925, 15926, 15929, 15930, 15931, + 15932, 15933, 15936, 15937, 15938, 15939, 15940, 15943, 15944, + 15945, 15946, 15947, 15951, 15952, 15953, 15954, 15957, 15958, + 15959, 15960, 15961, 15964, 15965, 15966, 15967, 15968, 15971, + 15972, 15973, 15974, 15975, 15978, 15979, 15980, 15981, 15982, + 15985, 15986, 15987, 15988, 15989, 15992, 15993, 15994, 15995, + 15996, 15999, 16000, 16001, 16002, 16003, 16006, 16007, 16008, + 16009, 16010, 16013, 16014, 16015, 16016, 16017, 16020, 16021, + 16022, 16023, 16024, 16027, 16028, 16029, 16030, 16031, 16034, + 16035, 16036, 16038, 16041, 16042, 16043, 16044, 16045, 16048, + 16049, 16050, 16051, 16052, 16055, 16056, 16057, 16058, 16059, + 16062, 16063, 16065, 16066, 16069, 16070), class = "Date"), class = "zoo") > > test_cfit <- function (fast_only=FALSE) { + test("partialAR:::estimate_rho_par_c(numeric())", NA_real_) + test("partialAR:::estimate_rho_par_c(rep(0,5))", NaN) + x1 <- build_par(0.95, rep(0,10), rep(0,10), M0=1) + test("partialAR:::estimate_rho_par_c(x1)", 0.8497954230236) + x1na <- x1 + x1na[1] <- NA + test("partialAR:::estimate_rho_par_c(x1na)", NA_real_) + + test("partialAR:::estimate_par_c(numeric())", c(NA_real_, NA_real_, NA_real_)) + test("partialAR:::estimate_par_c(rep(0,5))", c(NaN, NaN, NaN)) + test("partialAR:::estimate_par_c(x1)", c(0.849795423024, 0, 0.00624752527433)) + test("partialAR:::estimate_par_c(x1na)", c(NA_real_, NA_real_, NA_real_)) + + test("partialAR:::pvmr_par_c(0,0,0)", NA_real_) + test("partialAR:::pvmr_par_c(-1,1,0)", 1) + test("partialAR:::pvmr_par_c(1,-1,0)", NA_real_) + test("partialAR:::pvmr_par_c(1,1,-1)", NA_real_) + test("partialAR:::pvmr_par_c(0,0,1)", 0) + test("partialAR:::pvmr_par_c(0,1,0)", 1) + test("partialAR:::pvmr_par_c(0,1,1)", 2/3) + test("partialAR:::pvmr_par_c(0.5,1,1)", 0.571428571429) + test("partialAR:::pvmr_par_c(0.5,1,2)", 0.25) + test("partialAR:::pvmr_par_c(0.5,0.5,1)", 0.25) + + test("partialAR:::kalman_gain_par_mr(0,0,0)", NA_real_) + test("partialAR:::kalman_gain_par_mr(0,1,0)", 1) + test("partialAR:::kalman_gain_par_mr(0,0,1)", 0) + test("partialAR:::kalman_gain_par_mr(0.5,1,1)", 1/3) + + test("partialAR:::loglik_par_c(numeric(),0,0,1,0,0)", NA_real_) + test("partialAR:::loglik_par_c(0,0,0,1,0,0)", 0.918938533205) + test("partialAR:::loglik_par_c(c(0,0,0),0,0,1,0,0)", 2.75681559961) + test("partialAR:::loglik_par_c(1,0,0,1,0,0)", 1.4189385332) + test("partialAR:::loglik_par_c(0,0,1,0,0,0)", 0.918938533205) + test("partialAR:::loglik_par_c(c(0,0,0),0,1,0,0,0)", 2.75681559961) + test("partialAR:::loglik_par_c(c(0,0,0),0.5,1,0,0,0)", 2.75681559961) + test("partialAR:::loglik_par_c(c(0,1,2),0,0,1,0,1)", 4.25681559961) + test("partialAR:::loglik_par_c(0.5,0.5,1,0,1,0)", 0.918938533205) + test("partialAR:::loglik_par_c(data.L, 0.8720, 0.3385, 0.1927, 0, data.L[1])", 238.533361432) + test("partialAR:::loglik_par_c(data.IBM, 0.9764, 2.0136, 0.4719, 0, data.IBM[1])", 1076.5235347) + + test("partialAR:::loglik_par_t_c(numeric(),0,0,1,0,0)", NA_real_) + test("partialAR:::loglik_par_t_c(0,0,0,1,0,0)", 0.968619589055) + test("partialAR:::loglik_par_t_c(c(0,0,0),0,0,1,0,0)", 2.90585876716) + test("partialAR:::loglik_par_t_c(1,0,0,1,0,0)", 1.51558425944) + test("partialAR:::loglik_par_t_c(0,0,1,0,0,0)", 0.968619589055) + test("partialAR:::loglik_par_t_c(c(0,0,0),0,1,0,0,0)", 2.90585876716) + test("partialAR:::loglik_par_t_c(c(0,0,0),0.5,1,0,0,0)", 2.90585876716) + test("partialAR:::loglik_par_t_c(c(0,1,2),0,0,1,0,1)", 4.54675277831) + test("partialAR:::loglik_par_t_c(0.5,0.5,1,0,1,0)", 0.968619589055) + test("partialAR:::loglik_par_t_c(0,0,0,1,0,0,6)", 0.960418255752) + test("partialAR:::loglik_par_t_c(data.L, 0.8958, 0.2612, 0.1768, 0, data.L[1])", 229.807616531) + test("partialAR:::loglik_par_t_c(data.IBM, 0.9829, 1.3072, 0.6901, 0, data.IBM[1])", 1020.88295106) + + } > > > test_lr <- function (fast_only=FALSE) { + test("partialAR:::loglik.par.kfas(numeric(),0,0,1,0,0)", NA_real_) + test("partialAR:::loglik.par.kfas(0,0,0,1,0,0)", 0.918938533205) + test("partialAR:::loglik.par.kfas(c(0,0,0),0,0,1,0,0)", 2.75681559961) + test("partialAR:::loglik.par.kfas(1,0,0,1,0,0)", 1.4189385332) + test("partialAR:::loglik.par.kfas(0,0,1,0,0,0)", 0.918938533205) + test("partialAR:::loglik.par.kfas(c(0,0,0),0,1,0,0,0)", 2.75681559961) + test("partialAR:::loglik.par.kfas(c(0,0,0),0.5,1,0,0,0)", 2.75681559961) + test("partialAR:::loglik.par.kfas(c(0,1,2),0,0,1,0,1)", 4.25681559961) + test("partialAR:::loglik.par.kfas(0.5,0.5,1,0,1,0)", 1.0439385332) # Note difference + test("partialAR:::loglik.par.kfas(data.L, 0.8720, 0.3385, 0.1927)", 238.53374143) + test("partialAR:::loglik.par.kfas(data.IBM, 0.9764, 2.0136, 0.4719, 0, data.IBM[1])", 1077.02787353) + + test("partialAR:::loglik.par.ss(numeric(),0,0,1,0,0)", NA_real_) + test("partialAR:::loglik.par.ss(0,0,0,1,0,0)", 0.918938533205) + test("partialAR:::loglik.par.ss(c(0,0,0),0,0,1,0,0)", 2.75681559961) + test("partialAR:::loglik.par.ss(1,0,0,1,0,0)", 1.4189385332) + test("partialAR:::loglik.par.ss(0,0,1,0,0,0)", 0.918938533205) + test("partialAR:::loglik.par.ss(c(0,0,0),0,1,0,0,0)", 2.75681559961) + test("partialAR:::loglik.par.ss(c(0,0,0),0.5,1,0,0,0)", 2.75681559961) + test("partialAR:::loglik.par.ss(c(0,1,2),0,0,1,0,1)", 4.25681559961) + test("partialAR:::loglik.par.ss(0.5,0.5,1,0,1,0)", 0.918938533205) + test("partialAR:::loglik.par.ss(data.L, 0.8720, 0.3385, 0.1927, 0, data.L[1])", 238.533361432) + test("partialAR:::loglik.par.ss(data.IBM, 0.9764, 2.0136, 0.4719)", 1076.5235347) + + test("partialAR:::loglik.par.ss.t(numeric(),0,0,1,0,0)", NA_real_) + test("partialAR:::loglik.par.ss.t(0,0,0,1,0,0)", 0.968619589055) + test("partialAR:::loglik.par.ss.t(c(0,0,0),0,0,1,0,0)", 2.90585876716) + test("partialAR:::loglik.par.ss.t(1,0,0,1,0,0)", 1.51558425944) + test("partialAR:::loglik.par.ss.t(0,0,1,0,0,0)", 0.968619589055) + test("partialAR:::loglik.par.ss.t(c(0,0,0),0,1,0,0,0)", 2.90585876716) + test("partialAR:::loglik.par.ss.t(c(0,0,0),0.5,1,0,0,0)", 2.90585876716) + test("partialAR:::loglik.par.ss.t(c(0,1,2),0,0,1,0,1)", 4.54675277831) + test("partialAR:::loglik.par.ss.t(0.5,0.5,1,0,1,0)", 0.968619589055) + test("partialAR:::loglik.par.ss.t(0,0,0,1,0,0,6)", 0.960418255752) + test("partialAR:::loglik.par.ss.t(data.L, 0.8958, 0.2612, 0.1768, 0, data.L[1])", 229.807616531) + test("partialAR:::loglik.par.ss.t(data.IBM, 0.9829, 1.3072, 0.6901, 0, data.IBM[1])", 1020.88295106) + + test("partialAR:::loglik.par(data.L, 0.8720, 0.3385, 0.1927)", 238.533361432) + test("partialAR:::loglik.par(data.L, 0.8720, 0.3385, 0.1927, calc_method=\"css\")", 238.533361432) + test("partialAR:::loglik.par(data.L, 0.8720, 0.3385, 0.1927, calc_method=\"kfas\")", 238.53374143) + test("partialAR:::loglik.par(data.L, 0.8720, 0.3385, 0.1927, calc_method=\"ss\")", 238.533361432) + test("partialAR:::loglik.par(data.L, 0.8958, 0.2612, 0.1768, calc_method=\"sst\")", 229.807616531) + test("partialAR:::loglik.par(data.L, 0.8958, 0.2612, 0.1768, calc_method=\"csst\")", 229.807616531) + } > > test.likelihood_ratio.par <- function (fast_only=FALSE) { + test("partialAR:::likelihood_ratio.par(data.L)", -4.44824727945) + test("partialAR:::likelihood_ratio.par(data.L, robust=TRUE)", -2.64805301476) + test("partialAR:::likelihood_ratio.par(data.L, null_model='rw')", -4.44824727945) + test("partialAR:::likelihood_ratio.par(data.L, null_model='rw', robust=TRUE)", -2.64805301476) + test("partialAR:::likelihood_ratio.par(data.L, null_model='ar1')", -4.44824693057) + test("partialAR:::likelihood_ratio.par(data.L, null_model='ar1', robust=TRUE)", -2.6480522184) + + if (!fast_only) test("partialAR:::likelihood_ratio.par(data.L, opt_method='ss')", -4.44824727945) + if (!fast_only) test("partialAR:::likelihood_ratio.par(data.L, robust=TRUE, opt_method='ss')", -2.64805301476) + if (!fast_only) test("partialAR:::likelihood_ratio.par(data.L, null_model='rw', opt_method='ss')", -4.44824727945) + if (!fast_only) test("partialAR:::likelihood_ratio.par(data.L, null_model='rw', robust=TRUE, opt_method='ss')", -2.64805301476) + if (!fast_only) test("partialAR:::likelihood_ratio.par(data.L, null_model='ar1', opt_method='ss')", -4.44824693057) + if (!fast_only) test("partialAR:::likelihood_ratio.par(data.L, null_model='ar1', robust=TRUE, opt_method='ss')", -2.6480522184) + + test("partialAR:::likelihood_ratio.par(data.L, opt_method='css')", -4.44824727945) + test("partialAR:::likelihood_ratio.par(data.L, robust=TRUE, opt_method='css')", -2.64805301476) + test("partialAR:::likelihood_ratio.par(data.L, null_model='rw', opt_method='css')", -4.44824727945) + test("partialAR:::likelihood_ratio.par(data.L, null_model='rw', robust=TRUE, opt_method='css')", -2.64805301476) + test("partialAR:::likelihood_ratio.par(data.L, null_model='ar1', opt_method='css')", -4.44824693057) + test("partialAR:::likelihood_ratio.par(data.L, null_model='ar1', robust=TRUE, opt_method='css')", -2.6480522184) + + if (!fast_only) test("partialAR:::likelihood_ratio.par(data.L, opt_method='kfas')", -4.59676088358) + if (!fast_only) test("partialAR:::likelihood_ratio.par(data.L, null_model='rw', opt_method='kfas')", -4.59676088358) + if (!fast_only) test("partialAR:::likelihood_ratio.par(data.L, null_model='ar1', opt_method='kfas')", -4.5967605347) + + SAMPLES <- partialAR:::sample.likelihood_ratio.par(nrep=10, use.multicore=FALSE) + test("nrow(SAMPLES)", 10) + test("sum(SAMPLES$seed)", 55) + test("mean(SAMPLES$rw_lrt)", -4.43576369917) + test("mean(SAMPLES$mr_lrt)", -3.8960913155) + test("mean(SAMPLES$kpss_stat)", 3.7269871366) + } > > test_lr2 <- function(fast_only=FALSE) { + test.likelihood_ratio.par(fast_only) + + test("partialAR:::par.rw.pvalue(-3.5,400) < 0.05", TRUE) + test("partialAR:::par.rw.pvalue(-1,500) > 0.10", TRUE) + test("partialAR:::par.mr.pvalue(-1,600) < 0.05", TRUE) + test("partialAR:::par.mr.pvalue(-0.1, 700) > 0.05", TRUE) + test("partialAR:::par.rw.pvalue(-3.5,400, robust=TRUE) < 0.05", TRUE) + test("partialAR:::par.rw.pvalue(-1,500, robust=TRUE) > 0.10", TRUE) + test("partialAR:::par.mr.pvalue(-1,600, robust=TRUE) < 0.05", TRUE) + test("partialAR:::par.mr.pvalue(-0.1, 700, robust=TRUE) > 0.05", TRUE) + + test("partialAR:::par.mr.pvalue(-2,400,ar1test='kpss') < 0.05", TRUE) + test("partialAR:::par.mr.pvalue(-0.5, 500,ar1test='kpss') > 0.05", TRUE) + test("partialAR:::par.mr.pvalue(-2,600, robust=TRUE,ar1test='kpss') < 0.05", TRUE) + test("partialAR:::par.mr.pvalue(-0.5, 700, robust=TRUE,ar1test='kpss') > 0.05", TRUE) + + test("partialAR:::par.joint.pvalue(-4,-0.5,500) < 0.05", TRUE) + test("partialAR:::par.joint.pvalue(-1,-0.25,500) > 0.05", TRUE) + test("partialAR:::par.joint.pvalue(-5,-0.8,500, robust=TRUE) < 0.05", TRUE) + test("partialAR:::par.joint.pvalue(-3,-0.1,500, robust=TRUE) > 0.05", TRUE) + test("partialAR:::par.joint.pvalue(-5,-2,500, ar1test='kpss') < 0.05", TRUE) + test("partialAR:::par.joint.pvalue(-3,-1,500, ar1test='kpss') > 0.05", TRUE) + test("partialAR:::par.joint.pvalue(-4,-0.5,50000)", 0.03) + test("partialAR:::par.joint.pvalue(-4,-0.5,50)", 0.10) + test("partialAR:::par.joint.pvalue(4,-0.5,50)", 1) + test("partialAR:::par.joint.pvalue(-4,-0.5,49)", 1) + + test("partialAR:::test.par.nullrw(data.L)$p.value < 0.05", TRUE) + test("partialAR:::test.par.nullrw(data.IBM)$p.value > 0.05", TRUE) + test("partialAR:::test.par.nullrw(data.L, robust=TRUE)$p.value < 0.10", TRUE) + test("partialAR:::test.par.nullrw(data.IBM, robust=TRUE)$p.value > 0.10", TRUE) + + test("partialAR:::test.par.nullmr(data.L)$p.value <= 0.01", TRUE) + test("partialAR:::test.par.nullmr(data.L, robust=TRUE)$p.value <= 0.01", TRUE) + test("partialAR:::test.par.nullmr(data.L, ar1test='kpss')$p.value <= 0.01", TRUE) + test("partialAR:::test.par.nullmr(data.L, robust=TRUE, ar1test='kpss')$p.value <= 0.01", TRUE) + + test("partialAR:::test.par.nullmr(data.IBM)$p.value < 0.05", TRUE) + test("partialAR:::test.par.nullmr(data.IBM, robust=TRUE)$p.value < 0.10", TRUE) + test("partialAR:::test.par.nullmr(data.IBM, ar1test='kpss')$p.value > 0.10", TRUE) + test("partialAR:::test.par.nullmr(data.IBM, ar1test='kpss', robust=TRUE)$p.value > 0.10", TRUE) + + test("partialAR:::test.par(data.L, null_hyp='rw')$p.value == partialAR:::test.par.nullrw(data.L)$p.value", TRUE) + test("partialAR:::test.par(data.IBM, null_hyp='rw')$p.value == partialAR:::test.par.nullrw(data.IBM)$p.value", TRUE) + test("partialAR:::test.par(data.L, null_hyp='mr')$p.value == partialAR:::test.par.nullmr(data.L)$p.value", TRUE) + test("partialAR:::test.par(data.IBM, null_hyp='mr')$p.value == partialAR:::test.par.nullmr(data.IBM)$p.value", TRUE) + + test("partialAR:::test.par(data.L)$p.value['PAR'] <= 0.01", c(PAR=TRUE)) + test("partialAR:::test.par(data.L, robust=TRUE)$p.value['PAR'] <= 0.10", c(PAR=TRUE)) + test("partialAR:::test.par(data.IBM)$p.value['PAR'] > 0.10", c(PAR=TRUE)) + test("partialAR:::test.par(data.IBM, robust=TRUE)$p.value['PAR'] > 0.10", c(PAR=TRUE)) + test("partialAR:::test.par(data.L, ar1test='kpss')$p.value['PAR'] <= 0.01", c(PAR=TRUE)) + test("partialAR:::test.par(data.L, ar1test='kpss',robust=TRUE)$p.value['PAR'] <= 0.10", c(PAR=TRUE)) + test("partialAR:::test.par(data.IBM, ar1test='kpss')$p.value['PAR'] > 0.10", c(PAR=TRUE)) + + print(partialAR:::test.par(data.L)) + print(partialAR:::test.par(data.L, robust=TRUE)) + + test("partialAR:::which.hypothesis.partest(partialAR:::test.par(data.L))", "PAR") + test("partialAR:::which.hypothesis.partest(partialAR:::test.par(data.L, robust=TRUE))", "RRW") + test("partialAR:::which.hypothesis.partest(partialAR:::test.par(data.IBM))", "RW") + + partialAR:::print.par.lrt(); cat("\n\n") + partialAR:::print.par.lrt(robust=TRUE); cat("\n\n") + partialAR:::print.par.lrt(latex=TRUE); cat("\n\n") + + # partialAR:::print.par.lrt.mr(); cat("\n\n") + # partialAR:::print.par.lrt.mr(robust=TRUE); cat("\n\n") + # partialAR:::print.par.lrt.mr(latex=TRUE); cat("\n\n") + + partialAR:::print.par.lrt.rw(); cat("\n\n") + partialAR:::print.par.lrt.rw(robust=TRUE); cat("\n\n") + partialAR:::print.par.lrt.rw(latex=TRUE); cat("\n\n") + + } > > test_fit.par.both <- function (fast_only=FALSE) { + test("partialAR:::fit.par.both(data.L)$par", + structure(c(0.871991364792238, 0.338198849510798, 0.192519577779812, + 0, 37.8348806008997), .Names = c("rho", "sigma_M", "sigma_R", + "M0", "R0")) ) + test("partialAR:::fit.par.both(data.L)$stderr", + structure(c(0.0493755130952366, 0.0306037545403534, 0.0507506043059735, + NA, 0.382843915239426), .Names = c("rho.se", "sigma_M.se", "sigma_R.se", + "M0.se", "R0.se")) ) + if (!fast_only) test("partialAR:::fit.par.both(data.L, opt_method='ss')$par", + structure(c(0.871991364792238, 0.338198849510798, 0.192519577779812, + 0, 37.8348806008997), .Names = c("rho", "sigma_M", "sigma_R", + "M0", "R0")) ) + if (!fast_only) test("partialAR:::fit.par.both(data.L, opt_method='ss')$stderr", + structure(c(0.0493755130952366, 0.0306037545403534, 0.0507506043059735, + NA, 0.382843915239426), .Names = c("rho.se", "sigma_M.se", "sigma_R.se", + "M0.se", "R0.se")) ) + if (!fast_only) test("partialAR:::fit.par.both(data.L, opt_method='kfas')$par", + structure(c(0.873239025413773, 0.334187559078876, 0.187013759524079, + 0, 37.8228485852872), .Names = c("rho", "sigma_M", "sigma_R", + "M0", "R0")) ) + if (!fast_only) test("partialAR:::fit.par.both(data.L, opt_method='kfas')$stderr", + structure(c(0.0480869790579741, 0.0299959210912542, 0.0482633848885082, + NA, 0.366440477748884), .Names = c("rho.se", "sigma_M.se", "sigma_R.se", + "M0.se", "R0.se")) ) + test("partialAR:::fit.par.both(data.IBM)$par", + structure(c(0.976388651908034, 2.01216604959705, 0.467711046901045, + 0, 177.472892129038), .Names = c("rho", "sigma_M", "sigma_R", + "M0", "R0")) ) + test("partialAR:::fit.par.both(data.IBM)$stderr", + structure(c(0.018222371388718, 0.153130468131214, 0.599803359236283, + NA, 2.12284254607983), .Names = c("rho.se", "sigma_M.se", "sigma_R.se", + "M0.se", "R0.se")) ) + test("partialAR:::fit.par.both(data.IBM, robust=TRUE)$par", + structure(c(0.982921831279379, 1.30721045019958, 0.690103593777354, + 0, 176.743925850553), .Names = c("rho", "sigma_M", "sigma_R", + "M0", "R0")) ) + if (!fast_only) test("partialAR:::fit.par.both(data.IBM, robust=TRUE, opt_method='ss')$par", + structure(c(0.982921831279379, 1.30721045019958, 0.690103593777354, + 0, 176.743925850553), .Names = c("rho", "sigma_M", "sigma_R", + "M0", "R0")) ) + test("partialAR:::fit.par.both(data.IBM, robust=TRUE, nu=3)$par", + structure(c(0.985936838750558, 1.20382984003629, 0.587584874718192, + 0, 176.716597228655), .Names = c("rho", "sigma_M", "sigma_R", + "M0", "R0")) ) + test("partialAR:::fit.par.both(data.IBM, rho.max=0.95)$par", + structure(c(0.95, 1.8101310703133, 0.998701976498605, 0, 176.958377474755 + ), .Names = c("rho", "sigma_M", "sigma_R", "M0", "R0")) ) + test("partialAR:::fit.par.both(data.IBM, lambda=2)$pvmr", c(pvmr=1)) + test("partialAR:::fit.par.both(data.IBM, lambda=-2)$pvmr", c(pvmr=0.0442039289027)) + } > > test_fit.par.mr <- function (fast_only=FALSE) { + test("partialAR:::fit.par.mr(data.L)$par", + structure(c(1, 0.392621113046972, 0, 0, 37.8517816705337), .Names = c("rho", + "sigma_M", "sigma_R", "M0", "R0")) ) + test("partialAR:::fit.par.mr(data.L)$stderr", + structure(c(1.55086108092093e-05, 0.0123907243901383, NA, NA, + 0.392621124942204), .Names = c("rho.se", "sigma_M.se", "sigma_R.se", + "M0.se", "R0.se")) ) + if (!fast_only) test("partialAR:::fit.par.mr(data.L, opt_method='ss')$par", + structure(c(1, 0.392621113046972, 0, 0, 37.8517816705337), .Names = c("rho", + "sigma_M", "sigma_R", "M0", "R0")) ) + if (!fast_only) test("partialAR:::fit.par.mr(data.L, opt_method='ss')$stderr", + structure(c(1.55086108092093e-05, 0.0123907243901383, NA, NA, + 0.392621124942204), .Names = c("rho.se", "sigma_M.se", "sigma_R.se", + "M0.se", "R0.se")) ) + if (!fast_only) test("partialAR:::fit.par.mr(data.L, opt_method='kfas')$par", + structure(c(1, 0.392621113047498, 0, 0, 37.8517816705312), .Names = c("rho", + "sigma_M", "sigma_R", "M0", "R0")) ) + if (!fast_only) test("partialAR:::fit.par.mr(data.L, opt_method='kfas')$stderr", + structure(c(1.55086108092093e-05, 0.0123907243901654, NA, NA, + 0.392621124727183), .Names = c("rho.se", "sigma_M.se", "sigma_R.se", + "M0.se", "R0.se")) ) + test("partialAR:::fit.par.mr(data.IBM)$par", + structure(c(0.989394562548544, 2.06766254187052, 0, 0, 177.378135957708 + ), .Names = c("rho", "sigma_M", "sigma_R", "M0", "R0")) ) + test("partialAR:::fit.par.mr(data.IBM)$stderr", + structure(c(0.00711953959492437, 0.0652545415824236, NA, NA, + 2.18393834163026), .Names = c("rho.se", "sigma_M.se", "sigma_R.se", + "M0.se", "R0.se")) ) + test("partialAR:::fit.par.mr(data.IBM, robust=TRUE)$par", + structure(c(0.996850903105148, 1.47881632988678, 0, 0, 176.742922370692 + ), .Names = c("rho", "sigma_M", "sigma_R", "M0", "R0")) ) + if (!fast_only) test("partialAR:::fit.par.mr(data.IBM, robust=TRUE, opt_method='ss')$par", + structure(c(0.996850903105148, 1.47881632988678, 0, 0, 176.742922370692 + ), .Names = c("rho", "sigma_M", "sigma_R", "M0", "R0")) ) + test("partialAR:::fit.par.mr(data.IBM, robust=TRUE, nu=3)$par", + structure(c(0.996784426974733, 1.33994364448777, 0, 0, 176.717640850721 + ), .Names = c("rho", "sigma_M", "sigma_R", "M0", "R0")) ) + test("partialAR:::fit.par.mr(data.IBM, rho.max=0.95)$par", + structure(c(0.95, 2.10195614607977, 0, 0, 183.429724544732), .Names = c("rho", + "sigma_M", "sigma_R", "M0", "R0")) ) + test("partialAR:::fit.par.mr(data.IBM)$pvmr", c(pvmr=1)) + + } > > test_fit.par.rw <- function (fast_only=FALSE) { + test("partialAR:::fit.par.rw(data.L)$par", + structure(c(0, 0, 0.392609091324016, 0, 37.8517816659277), .Names = c("rho", + "sigma_M", "sigma_R", "M0", "R0")) ) + test("partialAR:::fit.par.rw(data.L)$stderr", + structure(c(NA, NA, 0.0175230013091655, NA, 0), .Names = c("rho.se", + "sigma_M.se", "sigma_R.se", "M0.se", "R0.se")) ) + if (!fast_only) test("partialAR:::fit.par.rw(data.L, opt_method='ss')$par", + structure(c(0, 0, 0.392609091324016, 0, 37.8517816659277), .Names = c("rho", + "sigma_M", "sigma_R", "M0", "R0")) ) + if (!fast_only) test("partialAR:::fit.par.rw(data.L, opt_method='kfas')$par", + structure(c(0, 0, 0.392609091324016, 0, 37.8517816659277), .Names = c("rho", + "sigma_M", "sigma_R", "M0", "R0")) ) + test("partialAR:::fit.par.rw(data.IBM)$par", + structure(c(0, 0, 2.07281796275108, 0, 176.668606104443), .Names = c("rho", + "sigma_M", "sigma_R", "M0", "R0")) ) + test("partialAR:::fit.par.rw(data.IBM)$stderr", + structure(c(NA, NA, 0.0925143932669985, NA, 0), .Names = c("rho.se", + "sigma_M.se", "sigma_R.se", "M0.se", "R0.se")) ) + test("partialAR:::fit.par.rw(data.IBM, robust=TRUE)$par", + structure(c(0, 0, 1.47924935869178, 0, 176.668606104443), .Names = c("rho", + "sigma_M", "sigma_R", "M0", "R0")) ) + if (!fast_only) test("partialAR:::fit.par.rw(data.IBM, robust=TRUE, opt_method='ss')$par", + structure(c(0, 0, 1.47924935869178, 0, 176.668606104443), .Names = c("rho", + "sigma_M", "sigma_R", "M0", "R0")) ) + test("partialAR:::fit.par.rw(data.IBM, robust=TRUE, nu=3)$par", + structure(c(0, 0, 1.34077692991459, 0, 176.668606104443), .Names = c("rho", + "sigma_M", "sigma_R", "M0", "R0")) ) + test("partialAR:::fit.par.rw(data.IBM)$pvmr", c(pvmr=0)) + } > > test_fit.par <- function (fast_only=FALSE) { + test("partialAR:::fit.par(data.L)$par", + structure(c(0.871991364792238, 0.338198849510798, 0.192519577779812, + 0, 37.8348806008997), .Names = c("rho", "sigma_M", "sigma_R", + "M0", "R0")) ) + test("partialAR:::fit.par(data.L)$stderr", + structure(c(0.0493755130952366, 0.0306037545403534, 0.0507506043059735, + NA, 0.382843915239426), .Names = c("rho.se", "sigma_M.se", "sigma_R.se", + "M0.se", "R0.se")) ) + if (!fast_only) test("partialAR:::fit.par(data.L, opt_method='kfas')$par", + structure(c(0.873239025413773, 0.334187559078876, 0.187013759524079, + 0, 37.8228485852872), .Names = c("rho", "sigma_M", "sigma_R", + "M0", "R0")) ) + test("partialAR:::fit.par(data.IBM)$par", + structure(c(0.976388651908034, 2.01216604959705, 0.467711046901045, + 0, 177.472892129038), .Names = c("rho", "sigma_M", "sigma_R", + "M0", "R0")) ) + test("partialAR:::fit.par(data.IBM)$stderr", + structure(c(0.018222371388718, 0.153130468131214, 0.599803359236283, + NA, 2.12284254607983), .Names = c("rho.se", "sigma_M.se", "sigma_R.se", + "M0.se", "R0.se")) ) + test("partialAR:::fit.par(data.IBM, robust=TRUE)$par", + structure(c(0.982921831279379, 1.30721045019958, 0.690103593777354, + 0, 176.743925850553), .Names = c("rho", "sigma_M", "sigma_R", + "M0", "R0")) ) + test("partialAR:::fit.par(data.IBM, robust=TRUE, nu=3)$par", + structure(c(0.985936838750558, 1.20382984003629, 0.587584874718192, + 0, 176.716597228655), .Names = c("rho", "sigma_M", "sigma_R", + "M0", "R0")) ) + test("partialAR:::fit.par(data.IBM, rho.max=0.95)$par", + structure(c(0.95, 1.8101310703133, 0.998701976498605, 0, 176.958377474755 + ), .Names = c("rho", "sigma_M", "sigma_R", "M0", "R0")) ) + test("partialAR:::fit.par(data.IBM, lambda=2)$pvmr", c(pvmr=1)) + test("partialAR:::fit.par(data.IBM, lambda=-2)$pvmr", c(pvmr=0.0442039289027)) + test("partialAR:::fit.par(data.L, model='ar1')$par", + structure(c(1, 0.392621113046972, 0, 0, 37.8517816705337), .Names = c("rho", + "sigma_M", "sigma_R", "M0", "R0")) ) + test("partialAR:::fit.par(data.L, model='ar1')$stderr", + structure(c(1.55086108092093e-05, 0.0123907243901383, NA, NA, + 0.392621124942204), .Names = c("rho.se", "sigma_M.se", "sigma_R.se", + "M0.se", "R0.se")) ) + test("partialAR:::fit.par(data.L, model='rw')$par", + structure(c(0, 0, 0.392609091324016, 0, 37.8517816659277), .Names = c("rho", + "sigma_M", "sigma_R", "M0", "R0")) ) + test("partialAR:::fit.par(data.L, model='rw')$stderr", + structure(c(NA, NA, 0.0175230013091655, NA, 0), .Names = c("rho.se", + "sigma_M.se", "sigma_R.se", "M0.se", "R0.se")) ) + } > > test_fit <- function (fast_only=FALSE) { + test("partialAR:::par.rho.cutoff(25)", NA_real_) + test("partialAR:::par.rho.cutoff(50)", 0.724) + test("partialAR:::par.rho.cutoff(50,0.01)", 0.594) + test("partialAR:::par.rho.cutoff(50,.00001)", 0.438) + + test("partialAR:::estimate.rho.par(numeric())", NA_real_) + test("partialAR:::estimate.rho.par(rep(0,5))", NaN) + x1 <- build_par(0.95, rep(0,10), rep(0,10), M0=1) + test("partialAR:::estimate.rho.par(x1)", 0.8497954230236) + x1na <- x1 + x1na[1] <- NA + test("partialAR:::estimate.rho.par(x1na)", NA_real_) + + test("partialAR:::estimate.par(numeric())", c(rho=NA_real_, sigma_M=NA_real_, sigma_R=NA_real_)) + test("partialAR:::estimate.par(rep(0,5))", c(rho=NaN, sigma_M=NaN, sigma_R=NaN)) + test("partialAR:::estimate.par(x1)", c(rho=0.849795423024, sigma_M=0, sigma_R=0.00624752527433)) + test("partialAR:::estimate.par(x1na)", c(rho=NA_real_, sigma_M=NA_real_, sigma_R=NA_real_)) + + test("partialAR:::pvmr.par(0,0,0)", c(pvmr=NA_real_)) + test("partialAR:::pvmr.par(-1,1,0)", c(pvmr=1)) + test("partialAR:::pvmr.par(1,-1,0)", c(pvmr=NA_real_)) + test("partialAR:::pvmr.par(1,1,-1)", c(pvmr=NA_real_)) + test("partialAR:::pvmr.par(0,0,1)", c(pvmr=0)) + test("partialAR:::pvmr.par(0,1,0)", c(pvmr=1)) + test("partialAR:::pvmr.par(0,1,1)", c(pvmr=2/3)) + test("partialAR:::pvmr.par(0.5,1,1)", c(pvmr=0.571428571429)) + test("partialAR:::pvmr.par(0.5,1,2)", c(pvmr=0.25)) + test("partialAR:::pvmr.par(0.5,0.5,1)", c(pvmr=0.25)) + + test("partialAR:::kalman.gain.par(0,0,0)", c(NA_real_, NA_real_)) + test("partialAR:::kalman.gain.par(0,1,0)", c(1,0)) + test("partialAR:::kalman.gain.par(0,0,1)", c(0,1)) + test("partialAR:::kalman.gain.par(0.5,1,1)", c(1/3,2/3)) + + test("partialAR:::kalman.gain.from.pvmr(0,0)", c(0,1)) + test("partialAR:::kalman.gain.from.pvmr(1,0)", c(0,1)) + test("partialAR:::kalman.gain.from.pvmr(0,1)", c(1,0)) + test("partialAR:::kalman.gain.from.pvmr(0,0)", c(0,1)) + test("partialAR:::kalman.gain.from.pvmr(0,0)", c(0,1)) + test("partialAR:::kalman.gain.from.pvmr(0.8,0.8)", c(0.545454545455, 0.454545454545)) + + test_fit.par.both (fast_only) + test_fit.par.mr(fast_only) + test_fit.par.rw(fast_only) + test_fit.par(fast_only) + + test("partialAR:::statehistory.par(partialAR:::fit.par(data.L))[1,]", + structure(list(X = 37.8517816659277, M = 0.00867470536387833, + R = 37.8431069605638, eps_M = 0.00867470536387833, eps_R = 0.00822635966417289), + .Names = c("X", + "M", "R", "eps_M", "eps_R"), row.names = 1L, class = "data.frame") ) + test("partialAR:::statehistory.par(partialAR:::fit.par(data.L))[length(data.L),]", + structure(list(X = 48.0305776082708, M = 0.379272544771068, R = 47.6513050634997, + eps_M = 0.159638785630931, eps_R = 0.151387973638877), .Names = c("X", + "M", "R", "eps_M", "eps_R"), row.names = 502L, class = "data.frame") ) + + print(partialAR:::fit.par(data.L)) + print(partialAR:::fit.par(data.IBM)) + + test("as.data.frame(partialAR:::fit.par(data.L))", + structure(list(robust = FALSE, nu = 5, + opt_method = "css", + n = 502L, rho = 0.871991364792238, sigma_M = 0.338198849510798, + sigma_R = 0.192519577779812, M0 = 0, R0 = 37.8348806008997, + rho.se = 0.0493755130952366, sigma_M.se = 0.0306037545403534, + sigma_R.se = 0.0507506043059735, M0.se = NA_real_, R0.se = 0.382843915239426, + lambda = 0, pvmr = 0.767280179062111, negloglik = 238.531977143138), .Names = c("robust", + "nu", "opt_method", "n", "rho", "sigma_M", "sigma_R", "M0", "R0", + "rho.se", "sigma_M.se", "sigma_R.se", "M0.se", "R0.se", "lambda", + "pvmr", "negloglik"), row.names = c(NA, -1L), class = "data.frame") ) + } > > test_par <- function (fast_only=FALSE) { + # Comprehensive unit testing for PAR package + + options(warn=1) + + test_cfit(fast_only) + test_lr(fast_only) + test_fit(fast_only) + test_lr2(fast_only) + + if (all.tests.pass) { + cat("SUCCESS! All tests passed.\n") + } else { + stop("ERRORS! ", all.tests.error.count," tests failed\n") + } + } > > test_par(TRUE) partialAR:::estimate_rho_par_c(numeric()) -> NA OK partialAR:::estimate_rho_par_c(rep(0,5)) -> NA OK partialAR:::estimate_rho_par_c(x1) -> 0.8497954 OK partialAR:::estimate_rho_par_c(x1na) -> NA OK partialAR:::estimate_par_c(numeric()) -> NA NA NA OK partialAR:::estimate_par_c(rep(0,5)) -> NA NaN NaN OK partialAR:::estimate_par_c(x1) -> 0.8497954 0 0.006247525 OK partialAR:::estimate_par_c(x1na) -> NA NaN NaN OK partialAR:::pvmr_par_c(0,0,0) -> NA OK partialAR:::pvmr_par_c(-1,1,0) -> 1 OK partialAR:::pvmr_par_c(1,-1,0) -> NA OK partialAR:::pvmr_par_c(1,1,-1) -> NA OK partialAR:::pvmr_par_c(0,0,1) -> 0 OK partialAR:::pvmr_par_c(0,1,0) -> 1 OK partialAR:::pvmr_par_c(0,1,1) -> 0.6666667 OK partialAR:::pvmr_par_c(0.5,1,1) -> 0.5714286 OK partialAR:::pvmr_par_c(0.5,1,2) -> 0.25 OK partialAR:::pvmr_par_c(0.5,0.5,1) -> 0.25 OK partialAR:::kalman_gain_par_mr(0,0,0) -> NA OK partialAR:::kalman_gain_par_mr(0,1,0) -> 1 OK partialAR:::kalman_gain_par_mr(0,0,1) -> 0 OK partialAR:::kalman_gain_par_mr(0.5,1,1) -> 0.3333333 OK partialAR:::loglik_par_c(numeric(),0,0,1,0,0) -> NA OK partialAR:::loglik_par_c(0,0,0,1,0,0) -> 0.9189385 OK partialAR:::loglik_par_c(c(0,0,0),0,0,1,0,0) -> 2.756816 OK partialAR:::loglik_par_c(1,0,0,1,0,0) -> 1.418939 OK partialAR:::loglik_par_c(0,0,1,0,0,0) -> 0.9189385 OK partialAR:::loglik_par_c(c(0,0,0),0,1,0,0,0) -> 2.756816 OK partialAR:::loglik_par_c(c(0,0,0),0.5,1,0,0,0) -> 2.756816 OK partialAR:::loglik_par_c(c(0,1,2),0,0,1,0,1) -> 4.256816 OK partialAR:::loglik_par_c(0.5,0.5,1,0,1,0) -> 0.9189385 OK partialAR:::loglik_par_c(data.L, 0.8720, 0.3385, 0.1927, 0, data.L[1]) -> 238.5334 OK partialAR:::loglik_par_c(data.IBM, 0.9764, 2.0136, 0.4719, 0, data.IBM[1]) -> 1076.524 OK partialAR:::loglik_par_t_c(numeric(),0,0,1,0,0) -> NA OK partialAR:::loglik_par_t_c(0,0,0,1,0,0) -> 0.9686196 OK partialAR:::loglik_par_t_c(c(0,0,0),0,0,1,0,0) -> 2.905859 OK partialAR:::loglik_par_t_c(1,0,0,1,0,0) -> 1.515584 OK partialAR:::loglik_par_t_c(0,0,1,0,0,0) -> 0.9686196 OK partialAR:::loglik_par_t_c(c(0,0,0),0,1,0,0,0) -> 2.905859 OK partialAR:::loglik_par_t_c(c(0,0,0),0.5,1,0,0,0) -> 2.905859 OK partialAR:::loglik_par_t_c(c(0,1,2),0,0,1,0,1) -> 4.546753 OK partialAR:::loglik_par_t_c(0.5,0.5,1,0,1,0) -> 0.9686196 OK partialAR:::loglik_par_t_c(0,0,0,1,0,0,6) -> 0.9604183 OK partialAR:::loglik_par_t_c(data.L, 0.8958, 0.2612, 0.1768, 0, data.L[1]) -> 229.8076 OK partialAR:::loglik_par_t_c(data.IBM, 0.9829, 1.3072, 0.6901, 0, data.IBM[1]) -> 1020.883 OK partialAR:::loglik.par.kfas(numeric(),0,0,1,0,0) -> NA OK partialAR:::loglik.par.kfas(0,0,0,1,0,0) -> 0.9189385 OK partialAR:::loglik.par.kfas(c(0,0,0),0,0,1,0,0) -> 2.756816 OK partialAR:::loglik.par.kfas(1,0,0,1,0,0) -> 1.418939 OK partialAR:::loglik.par.kfas(0,0,1,0,0,0) -> 0.9189385 OK partialAR:::loglik.par.kfas(c(0,0,0),0,1,0,0,0) -> 2.756816 OK partialAR:::loglik.par.kfas(c(0,0,0),0.5,1,0,0,0) -> 2.756816 OK partialAR:::loglik.par.kfas(c(0,1,2),0,0,1,0,1) -> 4.256816 OK partialAR:::loglik.par.kfas(0.5,0.5,1,0,1,0) -> 1.043939 OK partialAR:::loglik.par.kfas(data.L, 0.8720, 0.3385, 0.1927) -> 238.5337 OK partialAR:::loglik.par.kfas(data.IBM, 0.9764, 2.0136, 0.4719, 0, data.IBM[1]) -> 1077.028 OK partialAR:::loglik.par.ss(numeric(),0,0,1,0,0) -> NA OK partialAR:::loglik.par.ss(0,0,0,1,0,0) -> 0.9189385 OK partialAR:::loglik.par.ss(c(0,0,0),0,0,1,0,0) -> 2.756816 OK partialAR:::loglik.par.ss(1,0,0,1,0,0) -> 1.418939 OK partialAR:::loglik.par.ss(0,0,1,0,0,0) -> 0.9189385 OK partialAR:::loglik.par.ss(c(0,0,0),0,1,0,0,0) -> 2.756816 OK partialAR:::loglik.par.ss(c(0,0,0),0.5,1,0,0,0) -> 2.756816 OK partialAR:::loglik.par.ss(c(0,1,2),0,0,1,0,1) -> 4.256816 OK partialAR:::loglik.par.ss(0.5,0.5,1,0,1,0) -> 0.9189385 OK partialAR:::loglik.par.ss(data.L, 0.8720, 0.3385, 0.1927, 0, data.L[1]) -> 238.5334 OK partialAR:::loglik.par.ss(data.IBM, 0.9764, 2.0136, 0.4719) -> 1076.524 OK partialAR:::loglik.par.ss.t(numeric(),0,0,1,0,0) -> NA OK partialAR:::loglik.par.ss.t(0,0,0,1,0,0) -> 0.9686196 OK partialAR:::loglik.par.ss.t(c(0,0,0),0,0,1,0,0) -> 2.905859 OK partialAR:::loglik.par.ss.t(1,0,0,1,0,0) -> 1.515584 OK partialAR:::loglik.par.ss.t(0,0,1,0,0,0) -> 0.9686196 OK partialAR:::loglik.par.ss.t(c(0,0,0),0,1,0,0,0) -> 2.905859 OK partialAR:::loglik.par.ss.t(c(0,0,0),0.5,1,0,0,0) -> 2.905859 OK partialAR:::loglik.par.ss.t(c(0,1,2),0,0,1,0,1) -> 4.546753 OK partialAR:::loglik.par.ss.t(0.5,0.5,1,0,1,0) -> 0.9686196 OK partialAR:::loglik.par.ss.t(0,0,0,1,0,0,6) -> 0.9604183 OK partialAR:::loglik.par.ss.t(data.L, 0.8958, 0.2612, 0.1768, 0, data.L[1]) -> 229.8076 OK partialAR:::loglik.par.ss.t(data.IBM, 0.9829, 1.3072, 0.6901, 0, data.IBM[1]) -> 1020.883 OK partialAR:::loglik.par(data.L, 0.8720, 0.3385, 0.1927) -> 238.5334 OK partialAR:::loglik.par(data.L, 0.8720, 0.3385, 0.1927, calc_method="css") -> 238.5334 OK partialAR:::loglik.par(data.L, 0.8720, 0.3385, 0.1927, calc_method="kfas") -> 238.5337 OK partialAR:::loglik.par(data.L, 0.8720, 0.3385, 0.1927, calc_method="ss") -> 238.5334 OK partialAR:::loglik.par(data.L, 0.8958, 0.2612, 0.1768, calc_method="sst") -> 229.8076 OK partialAR:::loglik.par(data.L, 0.8958, 0.2612, 0.1768, calc_method="csst") -> 229.8076 OK partialAR:::par.rho.cutoff(25) -> NA OK partialAR:::par.rho.cutoff(50) -> 0.724 OK partialAR:::par.rho.cutoff(50,0.01) -> 0.594 OK partialAR:::par.rho.cutoff(50,.00001) -> 0.438 OK partialAR:::estimate.rho.par(numeric()) -> NA OK partialAR:::estimate.rho.par(rep(0,5)) -> NA OK partialAR:::estimate.rho.par(x1) -> 0.8497954 OK partialAR:::estimate.rho.par(x1na) -> NA OK partialAR:::estimate.par(numeric()) -> NA NA NA OK partialAR:::estimate.par(rep(0,5)) -> NA NaN NaN OK partialAR:::estimate.par(x1) -> 0.8497954 0 0.006247525 OK partialAR:::estimate.par(x1na) -> NA NaN NaN OK partialAR:::pvmr.par(0,0,0) -> NaN OK partialAR:::pvmr.par(-1,1,0) -> 1 OK partialAR:::pvmr.par(1,-1,0) -> NA OK partialAR:::pvmr.par(1,1,-1) -> NA OK partialAR:::pvmr.par(0,0,1) -> 0 OK partialAR:::pvmr.par(0,1,0) -> 1 OK partialAR:::pvmr.par(0,1,1) -> 0.6666667 OK partialAR:::pvmr.par(0.5,1,1) -> 0.5714286 OK partialAR:::pvmr.par(0.5,1,2) -> 0.25 OK partialAR:::pvmr.par(0.5,0.5,1) -> 0.25 OK partialAR:::kalman.gain.par(0,0,0) -> NA NA OK partialAR:::kalman.gain.par(0,1,0) -> 1 0 OK partialAR:::kalman.gain.par(0,0,1) -> 0 1 OK partialAR:::kalman.gain.par(0.5,1,1) -> 0.3333333 0.6666667 OK partialAR:::kalman.gain.from.pvmr(0,0) -> 0 1 OK partialAR:::kalman.gain.from.pvmr(1,0) -> 0 1 OK partialAR:::kalman.gain.from.pvmr(0,1) -> 1 0 OK partialAR:::kalman.gain.from.pvmr(0,0) -> 0 1 OK partialAR:::kalman.gain.from.pvmr(0,0) -> 0 1 OK partialAR:::kalman.gain.from.pvmr(0.8,0.8) -> 0.5454545 0.4545455 OK partialAR:::fit.par.both(data.L)$par -> numeric ( 5 ) OK partialAR:::fit.par.both(data.L)$stderr -> numeric ( 5 ) OK partialAR:::fit.par.both(data.IBM)$par -> numeric ( 5 ) OK partialAR:::fit.par.both(data.IBM)$stderr -> numeric ( 5 ) OK partialAR:::fit.par.both(data.IBM, robust=TRUE)$par -> numeric ( 5 ) OK partialAR:::fit.par.both(data.IBM, robust=TRUE, nu=3)$par -> numeric ( 5 ) OK partialAR:::fit.par.both(data.IBM, rho.max=0.95)$par -> numeric ( 5 ) OK partialAR:::fit.par.both(data.IBM, lambda=2)$pvmr -> 1 OK partialAR:::fit.par.both(data.IBM, lambda=-2)$pvmr -> 0.04420393 OK partialAR:::fit.par.mr(data.L)$par -> numeric ( 5 ) OK partialAR:::fit.par.mr(data.L)$stderr -> numeric ( 5 ) OK partialAR:::fit.par.mr(data.IBM)$par -> numeric ( 5 ) OK partialAR:::fit.par.mr(data.IBM)$stderr -> numeric ( 5 ) OK partialAR:::fit.par.mr(data.IBM, robust=TRUE)$par -> numeric ( 5 ) OK partialAR:::fit.par.mr(data.IBM, robust=TRUE, nu=3)$par -> numeric ( 5 ) OK partialAR:::fit.par.mr(data.IBM, rho.max=0.95)$par -> numeric ( 5 ) OK partialAR:::fit.par.mr(data.IBM)$pvmr -> 1 OK partialAR:::fit.par.rw(data.L)$par -> numeric ( 5 ) OK partialAR:::fit.par.rw(data.L)$stderr -> numeric ( 5 ) OK partialAR:::fit.par.rw(data.IBM)$par -> numeric ( 5 ) OK partialAR:::fit.par.rw(data.IBM)$stderr -> numeric ( 5 ) OK partialAR:::fit.par.rw(data.IBM, robust=TRUE)$par -> numeric ( 5 ) OK partialAR:::fit.par.rw(data.IBM, robust=TRUE, nu=3)$par -> numeric ( 5 ) OK partialAR:::fit.par.rw(data.IBM)$pvmr -> 0 OK partialAR:::fit.par(data.L)$par -> numeric ( 5 ) OK partialAR:::fit.par(data.L)$stderr -> numeric ( 5 ) OK partialAR:::fit.par(data.IBM)$par -> numeric ( 5 ) OK partialAR:::fit.par(data.IBM)$stderr -> numeric ( 5 ) OK partialAR:::fit.par(data.IBM, robust=TRUE)$par -> numeric ( 5 ) OK partialAR:::fit.par(data.IBM, robust=TRUE, nu=3)$par -> numeric ( 5 ) OK partialAR:::fit.par(data.IBM, rho.max=0.95)$par -> numeric ( 5 ) OK partialAR:::fit.par(data.IBM, lambda=2)$pvmr -> 1 OK partialAR:::fit.par(data.IBM, lambda=-2)$pvmr -> 0.04420393 OK partialAR:::fit.par(data.L, model='ar1')$par -> numeric ( 5 ) OK partialAR:::fit.par(data.L, model='ar1')$stderr -> numeric ( 5 ) OK partialAR:::fit.par(data.L, model='rw')$par -> numeric ( 5 ) OK partialAR:::fit.par(data.L, model='rw')$stderr -> numeric ( 5 ) OK partialAR:::statehistory.par(partialAR:::fit.par(data.L))[1,] -> data.frame ( 5 ) OK partialAR:::statehistory.par(partialAR:::fit.par(data.L))[length(data.L),] -> data.frame ( 5 ) OK Fitted model: X[t] = M[t] + R[t] M[t] = 0.8720 M[t-1] + eps_M,t, eps_M,t ~ N(0, 0.3382^2) (0.0494) (0.0306) R[t] = R[t-1] + eps_R,t, eps_R,t ~ N(0, 0.1925^2) (0.0508) M_0 = 0.0000, R_0 = 37.8349 (NA) (0.3828) Proportion of variance attributable to mean reversion (pvmr) = 0.7673 Negative log likelihood = 238.53 Fitted model: X[t] = M[t] + R[t] M[t] = 0.9764 M[t-1] + eps_M,t, eps_M,t ~ N(0, 2.0122^2) (0.0182) (0.1531) R[t] = R[t-1] + eps_R,t, eps_R,t ~ N(0, 0.4677^2) (0.5998) M_0 = 0.0000, R_0 = 177.4729 (NA) (2.1228) Proportion of variance attributable to mean reversion (pvmr) = 0.9493 Negative log likelihood = 1076.49 as.data.frame(partialAR:::fit.par(data.L)) -> data.frame ( 17 ) (Expecting data.frame ( 17 )) ERROR: Component "opt_method": 'current' is not a factor partialAR:::likelihood_ratio.par(data.L) -> -4.448247 OK partialAR:::likelihood_ratio.par(data.L, robust=TRUE) -> -2.648053 OK partialAR:::likelihood_ratio.par(data.L, null_model='rw') -> -4.448247 OK partialAR:::likelihood_ratio.par(data.L, null_model='rw', robust=TRUE) -> -2.648053 OK partialAR:::likelihood_ratio.par(data.L, null_model='ar1') -> -4.448247 OK partialAR:::likelihood_ratio.par(data.L, null_model='ar1', robust=TRUE) -> -2.648052 OK partialAR:::likelihood_ratio.par(data.L, opt_method='css') -> -4.448247 OK partialAR:::likelihood_ratio.par(data.L, robust=TRUE, opt_method='css') -> -2.648053 OK partialAR:::likelihood_ratio.par(data.L, null_model='rw', opt_method='css') -> -4.448247 OK partialAR:::likelihood_ratio.par(data.L, null_model='rw', robust=TRUE, opt_method='css') -> -2.648053 OK partialAR:::likelihood_ratio.par(data.L, null_model='ar1', opt_method='css') -> -4.448247 OK partialAR:::likelihood_ratio.par(data.L, null_model='ar1', robust=TRUE, opt_method='css') -> -2.648052 OK nrow(SAMPLES) -> 10 OK sum(SAMPLES$seed) -> 55 OK mean(SAMPLES$rw_lrt) -> -4.435764 OK mean(SAMPLES$mr_lrt) -> -3.896091 OK mean(SAMPLES$kpss_stat) -> 3.726987 OK partialAR:::par.rw.pvalue(-3.5,400) < 0.05 -> TRUE OK partialAR:::par.rw.pvalue(-1,500) > 0.10 -> TRUE OK partialAR:::par.mr.pvalue(-1,600) < 0.05 -> Warning in regularize.values(x, y, ties, missing(ties)) : collapsing to unique 'x' values Warning in regularize.values(x, y, ties, missing(ties)) : collapsing to unique 'x' values TRUE OK partialAR:::par.mr.pvalue(-0.1, 700) > 0.05 -> Warning in regularize.values(x, y, ties, missing(ties)) : collapsing to unique 'x' values Warning in regularize.values(x, y, ties, missing(ties)) : collapsing to unique 'x' values TRUE OK partialAR:::par.rw.pvalue(-3.5,400, robust=TRUE) < 0.05 -> TRUE OK partialAR:::par.rw.pvalue(-1,500, robust=TRUE) > 0.10 -> TRUE OK partialAR:::par.mr.pvalue(-1,600, robust=TRUE) < 0.05 -> Warning in regularize.values(x, y, ties, missing(ties)) : collapsing to unique 'x' values Warning in regularize.values(x, y, ties, missing(ties)) : collapsing to unique 'x' values TRUE OK partialAR:::par.mr.pvalue(-0.1, 700, robust=TRUE) > 0.05 -> Warning in regularize.values(x, y, ties, missing(ties)) : collapsing to unique 'x' values Warning in regularize.values(x, y, ties, missing(ties)) : collapsing to unique 'x' values TRUE OK partialAR:::par.mr.pvalue(-2,400,ar1test='kpss') < 0.05 -> TRUE OK partialAR:::par.mr.pvalue(-0.5, 500,ar1test='kpss') > 0.05 -> TRUE OK partialAR:::par.mr.pvalue(-2,600, robust=TRUE,ar1test='kpss') < 0.05 -> TRUE OK partialAR:::par.mr.pvalue(-0.5, 700, robust=TRUE,ar1test='kpss') > 0.05 -> TRUE OK partialAR:::par.joint.pvalue(-4,-0.5,500) < 0.05 -> TRUE OK partialAR:::par.joint.pvalue(-1,-0.25,500) > 0.05 -> TRUE OK partialAR:::par.joint.pvalue(-5,-0.8,500, robust=TRUE) < 0.05 -> TRUE OK partialAR:::par.joint.pvalue(-3,-0.1,500, robust=TRUE) > 0.05 -> TRUE OK partialAR:::par.joint.pvalue(-5,-2,500, ar1test='kpss') < 0.05 -> TRUE OK partialAR:::par.joint.pvalue(-3,-1,500, ar1test='kpss') > 0.05 -> TRUE OK partialAR:::par.joint.pvalue(-4,-0.5,50000) -> 0.03 OK partialAR:::par.joint.pvalue(-4,-0.5,50) -> 0.1 OK partialAR:::par.joint.pvalue(4,-0.5,50) -> 1 OK partialAR:::par.joint.pvalue(-4,-0.5,49) -> Warning in partialAR:::par.joint.pvalue(-4, -0.5, 49) : Sample size too small (49) to provide accurate p-value 1 OK partialAR:::test.par.nullrw(data.L)$p.value < 0.05 -> TRUE OK partialAR:::test.par.nullrw(data.IBM)$p.value > 0.05 -> TRUE OK partialAR:::test.par.nullrw(data.L, robust=TRUE)$p.value < 0.10 -> TRUE OK partialAR:::test.par.nullrw(data.IBM, robust=TRUE)$p.value > 0.10 -> TRUE OK partialAR:::test.par.nullmr(data.L)$p.value <= 0.01 -> Warning in regularize.values(x, y, ties, missing(ties)) : collapsing to unique 'x' values Warning in regularize.values(x, y, ties, missing(ties)) : collapsing to unique 'x' values TRUE OK partialAR:::test.par.nullmr(data.L, robust=TRUE)$p.value <= 0.01 -> Warning in regularize.values(x, y, ties, missing(ties)) : collapsing to unique 'x' values Warning in regularize.values(x, y, ties, missing(ties)) : collapsing to unique 'x' values TRUE OK partialAR:::test.par.nullmr(data.L, ar1test='kpss')$p.value <= 0.01 -> TRUE OK partialAR:::test.par.nullmr(data.L, robust=TRUE, ar1test='kpss')$p.value <= 0.01 -> TRUE OK partialAR:::test.par.nullmr(data.IBM)$p.value < 0.05 -> Warning in regularize.values(x, y, ties, missing(ties)) : collapsing to unique 'x' values Warning in regularize.values(x, y, ties, missing(ties)) : collapsing to unique 'x' values TRUE OK partialAR:::test.par.nullmr(data.IBM, robust=TRUE)$p.value < 0.10 -> Warning in regularize.values(x, y, ties, missing(ties)) : collapsing to unique 'x' values Warning in regularize.values(x, y, ties, missing(ties)) : collapsing to unique 'x' values TRUE OK partialAR:::test.par.nullmr(data.IBM, ar1test='kpss')$p.value > 0.10 -> TRUE OK partialAR:::test.par.nullmr(data.IBM, ar1test='kpss', robust=TRUE)$p.value > 0.10 -> TRUE OK partialAR:::test.par(data.L, null_hyp='rw')$p.value == partialAR:::test.par.nullrw(data.L)$p.value -> TRUE OK partialAR:::test.par(data.IBM, null_hyp='rw')$p.value == partialAR:::test.par.nullrw(data.IBM)$p.value -> TRUE OK partialAR:::test.par(data.L, null_hyp='mr')$p.value == partialAR:::test.par.nullmr(data.L)$p.value -> Warning in regularize.values(x, y, ties, missing(ties)) : collapsing to unique 'x' values Warning in regularize.values(x, y, ties, missing(ties)) : collapsing to unique 'x' values Warning in regularize.values(x, y, ties, missing(ties)) : collapsing to unique 'x' values Warning in regularize.values(x, y, ties, missing(ties)) : collapsing to unique 'x' values TRUE OK partialAR:::test.par(data.IBM, null_hyp='mr')$p.value == partialAR:::test.par.nullmr(data.IBM)$p.value -> Warning in regularize.values(x, y, ties, missing(ties)) : collapsing to unique 'x' values Warning in regularize.values(x, y, ties, missing(ties)) : collapsing to unique 'x' values Warning in regularize.values(x, y, ties, missing(ties)) : collapsing to unique 'x' values Warning in regularize.values(x, y, ties, missing(ties)) : collapsing to unique 'x' values TRUE OK partialAR:::test.par(data.L)$p.value['PAR'] <= 0.01 -> Warning in regularize.values(x, y, ties, missing(ties)) : collapsing to unique 'x' values Warning in regularize.values(x, y, ties, missing(ties)) : collapsing to unique 'x' values TRUE OK partialAR:::test.par(data.L, robust=TRUE)$p.value['PAR'] <= 0.10 -> Warning in regularize.values(x, y, ties, missing(ties)) : collapsing to unique 'x' values Warning in regularize.values(x, y, ties, missing(ties)) : collapsing to unique 'x' values TRUE OK partialAR:::test.par(data.IBM)$p.value['PAR'] > 0.10 -> Warning in regularize.values(x, y, ties, missing(ties)) : collapsing to unique 'x' values Warning in regularize.values(x, y, ties, missing(ties)) : collapsing to unique 'x' values TRUE OK partialAR:::test.par(data.IBM, robust=TRUE)$p.value['PAR'] > 0.10 -> Warning in regularize.values(x, y, ties, missing(ties)) : collapsing to unique 'x' values Warning in regularize.values(x, y, ties, missing(ties)) : collapsing to unique 'x' values TRUE OK partialAR:::test.par(data.L, ar1test='kpss')$p.value['PAR'] <= 0.01 -> TRUE OK partialAR:::test.par(data.L, ar1test='kpss',robust=TRUE)$p.value['PAR'] <= 0.10 -> TRUE OK partialAR:::test.par(data.IBM, ar1test='kpss')$p.value['PAR'] > 0.10 -> TRUE OK Warning in regularize.values(x, y, ties, missing(ties)) : collapsing to unique 'x' values Warning in regularize.values(x, y, ties, missing(ties)) : collapsing to unique 'x' values Test of [Random Walk or AR(1)] vs Almost AR(1) [LR test for AR1] data: data.L Hypothesis Statistic p-value Random Walk -4.45 0.014 AR(1) -4.45 0.010 Combined 0.010 Warning in regularize.values(x, y, ties, missing(ties)) : collapsing to unique 'x' values Warning in regularize.values(x, y, ties, missing(ties)) : collapsing to unique 'x' values Test of [Robust Random Walk or Robust AR(1)] vs Robust Almost AR(1) [LR test for AR1] data: data.L Hypothesis Statistic p-value Robust RW -2.65 0.071 Robust AR(1) -2.65 0.010 Combined 0.060 partialAR:::which.hypothesis.partest(partialAR:::test.par(data.L)) -> Warning in regularize.values(x, y, ties, missing(ties)) : collapsing to unique 'x' values Warning in regularize.values(x, y, ties, missing(ties)) : collapsing to unique 'x' values PAR OK partialAR:::which.hypothesis.partest(partialAR:::test.par(data.L, robust=TRUE)) -> Warning in regularize.values(x, y, ties, missing(ties)) : collapsing to unique 'x' values Warning in regularize.values(x, y, ties, missing(ties)) : collapsing to unique 'x' values RRW OK partialAR:::which.hypothesis.partest(partialAR:::test.par(data.IBM)) -> Warning in regularize.values(x, y, ties, missing(ties)) : collapsing to unique 'x' values Warning in regularize.values(x, y, ties, missing(ties)) : collapsing to unique 'x' values RW OK Critical Values for Likelihood Ratio Tests Single Hypothesis Test NULL: Random Walk | NULL: AR(1) p=0.01 p=0.05 p=0.10 | p=0.01 p=0.05 p=0.10 ------------------------------------------------------------ n=50 -4.7 -2.9 -2.2 | -2.6 -1.2 -0.7 n=100 -4.7 -3.0 -2.2 | -2.4 -1.0 -0.4 n=250 -4.6 -3.0 -2.2 | -1.9 -0.5 -0.1 n=500 -4.7 -3.2 -2.4 | -1.6 -0.3 -0.0 n=1000 -4.8 -3.1 -2.4 | -1.4 -0.1 -0.0 n=2500 -4.8 -3.1 -2.4 | -1.3 -0.0 -0.0 Critical Values for Likelihood Ratio Tests Single Hypothesis Test Robust Model NULL: Random Walk | NULL: AR(1) p=0.01 p=0.05 p=0.10 | p=0.01 p=0.05 p=0.10 ------------------------------------------------------------ n=50 -4.5 -2.9 -2.2 | -2.9 -1.4 -0.8 n=100 -4.6 -2.9 -2.2 | -2.8 -1.2 -0.6 n=250 -4.6 -2.9 -2.3 | -2.2 -0.8 -0.3 n=500 -4.6 -3.0 -2.3 | -1.9 -0.6 -0.1 n=1000 -4.5 -3.0 -2.4 | -1.6 -0.3 -0.0 n=2500 -4.7 -3.1 -2.4 | -1.3 -0.2 -0.0 \begin{table} \begin{tabular}{crrr|rrr} & \multicolumn{3}{c}{NULL: Random Walk} & \multicolumn{3}{c}{NULL: AR(1)} \\ & \multicolumn{1}{c}{p=0.01} & \multicolumn{1}{c}{p=0.05} & \multicolumn{1}{c}{p=0.10} & p=0.01 & p=0.05 & p=0.10\\ \hline n=50 & -4.7 & -2.9 & -2.2 & -2.6 & -1.2 & -0.7 \\ n=100 & -4.7 & -3.0 & -2.2 & -2.4 & -1.0 & -0.4 \\ n=250 & -4.6 & -3.0 & -2.2 & -1.9 & -0.5 & -0.1 \\ n=500 & -4.7 & -3.2 & -2.4 & -1.6 & -0.3 & -0.0 \\ n=1000 & -4.8 & -3.1 & -2.4 & -1.4 & -0.1 & -0.0 \\ n=2500 & -4.8 & -3.1 & -2.4 & -1.3 & -0.0 & -0.0 \\ \end{tabular} \caption{Critical Values for Likelihood Ratio Tests} \caption*{For each sample size, 40,000 random walks were generated, and then the likelihood ratios were calculated under the hypothesis of a random walk (left panel) and under the hypothesis of an AR(1) series (right panel). For the hypothesis of an AR(1) series, it was found that the critical values depend upon the value of $\rho$, and that as $\rho$ increases, the critical values for a given quantile decrease. Thus, by using the limiting case of a random walk when computing critical values for the AR(1) case, a conservative estimate is obtained.} \end{table} Critical Values for Likelihood Ratio Tests Null hypothesis: Random Walk p=0.01 p=0.05 p=0.10 ---------------------------- n=50 -4.7 -2.9 -2.2 n=100 -4.7 -3.0 -2.2 n=250 -4.6 -3.0 -2.2 n=500 -4.7 -3.2 -2.4 n=1000 -4.8 -3.1 -2.4 n=2500 -4.8 -3.1 -2.4 Critical Values for Likelihood Ratio Tests Robust Model Null hypothesis: Random Walk p=0.01 p=0.05 p=0.10 ---------------------------- n=50 -4.5 -2.9 -2.2 n=100 -4.6 -2.9 -2.2 n=250 -4.6 -2.9 -2.3 n=500 -4.6 -3.0 -2.3 n=1000 -4.5 -3.0 -2.4 n=2500 -4.7 -3.1 -2.4 \begin{tabular}{crrr} & \multicolumn{3}{c}{NULL: Random Walk} \\ & \multicolumn{1}{c}{p=0.01} & \multicolumn{1}{c}{p=0.05} & \multicolumn{1}{c}{p=0.10}\\ \hline n=50 & -4.7 & -2.9 & -2.2 \\ n=100 & -4.7 & -3.0 & -2.2 \\ n=250 & -4.6 & -3.0 & -2.2 \\ n=500 & -4.7 & -3.2 & -2.4 \\ n=1000 & -4.8 & -3.1 & -2.4 \\ n=2500 & -4.8 & -3.1 & -2.4 \\ \end{tabular} Error in test_par(TRUE) : ERRORS! 1 tests failed Execution halted * checking PDF version of manual ... OK * DONE Status: 2 ERRORs