Last updated on 2020-08-07 01:49:54 CEST.
| Flavor | Version | Tinstall | Tcheck | Ttotal | Status | Flags |
|---|---|---|---|---|---|---|
| r-devel-linux-x86_64-debian-clang | 1.0.12 | 24.60 | 688.79 | 713.39 | OK | |
| r-devel-linux-x86_64-debian-gcc | 1.0.12 | 17.74 | 607.87 | 625.61 | OK | |
| r-devel-linux-x86_64-fedora-clang | 1.0.12 | 775.99 | OK | |||
| r-devel-linux-x86_64-fedora-gcc | 1.0.12 | 431.71 | OK | |||
| r-devel-windows-ix86+x86_64 | 1.0.12 | 69.00 | 378.00 | 447.00 | OK | |
| r-patched-linux-x86_64 | 1.0.12 | 18.58 | 734.11 | 752.69 | OK | |
| r-patched-solaris-x86 | 1.0.12 | 1366.80 | OK | |||
| r-release-linux-x86_64 | 1.0.12 | 20.88 | 726.47 | 747.35 | OK | |
| r-release-macos-x86_64 | 1.0.12 | OK | ||||
| r-release-windows-ix86+x86_64 | 1.0.12 | 70.00 | 429.00 | 499.00 | OK | |
| r-oldrel-macos-x86_64 | 1.0.12 | ERROR | ||||
| r-oldrel-windows-ix86+x86_64 | 1.0.12 | 47.00 | 365.00 | 412.00 | ERROR |
Version: 1.0.12
Check: tests
Result: ERROR
Running ‘tests.R’ [11s/11s]
Running the tests in ‘tests/tests.R’ failed.
Last 13 lines of output:
\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
Flavor: r-oldrel-macos-x86_64
Version: 1.0.12
Check: running tests for arch ‘i386’
Result: ERROR
Running 'tests.R' [10s]
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
+ }
>
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>
> data.IBM <- structure(c(176.668606104443, 175.947896814914, 175.113391321774,
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>
> 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
Flavor: r-oldrel-windows-ix86+x86_64
Version: 1.0.12
Check: running tests for arch ‘x64’
Result: ERROR
Running 'tests.R' [12s]
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, 40.1792790775297, 40.1300035329166,
+ 40.3172506024464, 40.1694239686071, 40.40594658275, 40.0511626615356,
+ 39.5288418886367, 39.1346375317318, 38.5433309963745, 38.1688368573148,
+ 37.7647773914873, 38.3955043625351, 38.6320269766781, 38.6517371945233,
+ 38.7995638283626, 38.6517371945233, 39.0853619871187, 38.2690477622191,
+ 38.3874972265335, 37.8643454258119, 37.8051206936547, 38.0025364675119,
+ 39.0192277028765, 39.0488400689551, 39.3548345184338, 39.0093569141837,
+ 39.1574187445766, 38.7231040420907, 39.196901899348, 39.9372110513125,
+ 40.183980768634, 40.3419133877198, 40.3813965424912, 39.3252221523552,
+ 39.552250292291, 38.8119411403264, 38.8316827177122, 39.335092941048,
+ 39.621345813141, 40.3813965424912, 40.4801044294198, 40.8058404562842,
+ 40.0655313043197, 39.976694206084, 39.6805705452981, 39.4338008279766,
+ 39.8286323756911, 39.5719918696767, 40.3715257537984, 40.1642391912483,
+ 40.1938515573269, 40.4899752181127, 40.4603628520341, 40.0260481495483,
+ 39.9470818400054, 39.7792784322267, 39.7792784322267, 40.4603628520341,
+ 41.1611888492272, 39.0290984915694, 39.0784524350337, 38.9402613933336,
+ 38.9501321820265, 39.8286323756911, 39.8977278965411, 40.0556605156268,
+ 39.9372110513125, 39.9470818400054, 39.9865649947768, 39.9372110513125,
+ 39.9668234173911, 39.9174694739268, 39.9964357834697, 39.9569526286982,
+ 40.0852728817054, 39.9668234173911, 39.9174694739268, 39.2561266315052,
+ 39.7101829113767, 39.8977278965411, 39.7003121226839, 39.9108897674813,
+ 39.7922544746377, 40.1877054507832, 40.3261132924341, 40.464521134085,
+ 40.464521134085, 40.7116779941759, 40.7413368173868, 41.008266226285,
+ 40.9094034822486, 41.8387132761905, 42.204505429125, 41.8090544529796,
+ 41.5717838672923, 41.2455368119723, 40.9687211286705, 40.9588348542668,
+ 41.0379250494959, 40.7709956405977, 40.4941799572959, 40.8105407382123,
+ 40.790768189405, 41.0774701471105, 41.0576975983032, 40.8204270126159,
+ 41.4828073976596, 41.4828073976596, 41.6014426905032, 41.3246270072014,
+ 41.0774701471105, 41.1367877935323, 41.008266226285, 41.2158779887614,
+ 41.6212152393105, 42.204505429125, 42.6790466004996, 42.0265524898596,
+ 41.9672348434378, 41.334513281605, 41.5421250440814, 41.9178034714196,
+ 41.9079171970159, 41.7991681785759, 42.4318897404087, 41.6805328857323,
+ 41.6904191601359, 41.8485995505941, 40.7314505429832, 40.1580466275722,
+ 40.6622466221577, 40.5238387805068, 40.1481603531686, 39.4660074193177,
+ 39.3770309496849, 40.2569093716086, 40.3755446644523, 40.2667956460122,
+ 40.3755446644523, 40.8303132870195, 40.6227015245432, 40.2766819204159,
+ 40.4479793605475, 40.5667983941647, 40.4776841189518, 40.3192587407954,
+ 40.5469952218952, 40.6559126693777, 40.8737475643427, 40.8737475643427,
+ 40.6361094971081, 41.0915824593077, 41.0321729424991, 40.5866015664343,
+ 40.249947637852, 40.6262079109734, 41.2302046651945, 40.7648301168602,
+ 40.8341412198036, 40.4974872912213, 40.339061913065, 40.1311286042347,
+ 40.0915222596956, 39.7845730895177, 40.3489634991997, 41.121287217712,
+ 41.2995157681379, 41.646071282855, 41.6955792135288, 41.646071282855,
+ 41.6955792135288, 42.3391823122891, 42.2302648648066, 42.1213474173241,
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+ 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,
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+ 15790, 15791, 15792, 15796, 15797, 15798, 15799, 15800, 15803,
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+ 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
Flavor: r-oldrel-windows-ix86+x86_64