278 lines
11 KiB
R
278 lines
11 KiB
R
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## Copyright (C) 2010 - 2023 Dirk Eddelbuettel and Romain Francois
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##
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## This file is part of Rcpp.
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##
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## Rcpp is free software: you can redistribute it and/or modify it
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## under the terms of the GNU General Public License as published by
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## the Free Software Foundation, either version 2 of the License, or
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## (at your option) any later version.
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##
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## Rcpp is distributed in the hope that it will be useful, but
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## WITHOUT ANY WARRANTY; without even the implied warranty of
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## MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
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## GNU General Public License for more details.
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##
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## You should have received a copy of the GNU General Public License
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## along with Rcpp. If not, see <http://www.gnu.org/licenses/>.
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if (Sys.getenv("RunAllRcppTests") != "yes") exit_file("Set 'RunAllRcppTests' to 'yes' to run.")
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Rcpp::sourceCpp("cpp/stats.cpp")
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# test.stats.dbeta <- function() {
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vv <- seq(0, 1, by = 0.1)
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a <- 0.5; b <- 2.5
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expect_equal(runit_dbeta(vv, a, b),
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list(NoLog = dbeta(vv, a, b), Log = dbeta(vv, a, b, log=TRUE)),
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info = " stats.qbeta")
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# test.stats.dbinom <- function( ){
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v <- 1:10
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expect_equal(runit_dbinom(v) ,
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list(false = dbinom(v, 10, .5), true = dbinom(v, 10, .5, TRUE )), info = "stats.dbinom" )
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# test.stats.dunif <- function() {
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vv <- seq(0, 1, by = 0.1)
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expect_equal(runit_dunif(vv),
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list(NoLog_noMin_noMax = dunif(vv),
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NoLog_noMax = dunif(vv, 0),
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NoLog = dunif(vv, 0, 1),
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Log = dunif(vv, 0, 1, log=TRUE),
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Log_noMax = dunif(vv, 0, log=TRUE)
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##,Log_noMin_noMax = dunif(vv, log=TRUE) ## wrong answer
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),
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info = " stats.dunif")
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# test.stats.dgamma <- function( ) {
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v <- 1:4
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expect_equal(runit_dgamma(v),
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list(NoLog = dgamma(v, 1.0, 1.0),
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Log = dgamma(v, 1.0, 1.0, log = TRUE ),
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Log_noRate = dgamma(v, 1.0, log = TRUE )),
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info = "stats.dgamma" )
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# test.stats.dpois <- function( ){
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v <- 0:5
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expect_equal(runit_dpois(v) ,
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list( false = dpois(v, .5), true = dpois(v, .5, TRUE )),
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info = "stats.dpois" )
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# test.stats.dnorm <- function( ) {
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v <- seq(0.0, 1.0, by=0.1)
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expect_equal(runit_dnorm(v),
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list(false_noMean_noSd = dnorm(v),
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false_noSd = dnorm(v, 0.0),
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false = dnorm(v, 0.0, 1.0),
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true = dnorm(v, 0.0, 1.0, log=TRUE ),
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true_noSd = dnorm(v, 0.0, log=TRUE ),
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true_noMean_noSd = dnorm(v, log=TRUE )),
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info = "stats.dnorm" )
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# test.stats.dt <- function( ) {
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v <- seq(0.0, 1.0, by=0.1)
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expect_equal(runit_dt(v),
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list(false = dt(v, 5),
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true = dt(v, 5, log=TRUE ) # NB: need log=TRUE here
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), info = "stats.dt" )
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# test.stats.pbeta <- function( ) {
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a <- 0.5; b <- 2.5
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v <- qbeta(seq(0.0, 1.0, by=0.1), a, b)
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expect_equal(runit_pbeta(v, a, b),
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list(lowerNoLog = pbeta(v, a, b),
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lowerLog = pbeta(v, a, b, log=TRUE),
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upperNoLog = pbeta(v, a, b, lower=FALSE),
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upperLog = pbeta(v, a, b, lower=FALSE, log=TRUE)), info = " stats.pbeta" )
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## Borrowed from R's d-p-q-r-tests.R
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x <- c(.01, .10, .25, .40, .55, .71, .98)
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pbval <- c(-0.04605755624088, -0.3182809860569, -0.7503593555585,
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-1.241555830932, -1.851527837938, -2.76044482378, -8.149862739881)
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expect_equal(runit_pbeta(x, 0.8, 2)$upperLog, pbval, info = " stats.pbeta")
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expect_equal(runit_pbeta(1-x, 2, 0.8)$lowerLog, pbval, info = " stats.pbeta")
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# test.stats.pbinom <- function( ) {
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n <- 20
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p <- 0.5
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vv <- 0:n
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expect_equal(runit_pbinom(vv, n, p),
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list(lowerNoLog = pbinom(vv, n, p),
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lowerLog = pbinom(vv, n, p, log=TRUE),
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upperNoLog = pbinom(vv, n, p, lower=FALSE),
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upperLog = pbinom(vv, n, p, lower=FALSE, log=TRUE)),
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info = " stats.pbinom")
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# test.stats.pcauchy <- function( ) {
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location <- 0.5
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scale <- 1.5
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vv <- 1:5
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expect_equal(runit_pcauchy(vv, location, scale),
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list(lowerNoLog = pcauchy(vv, location, scale),
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lowerLog = pcauchy(vv, location, scale, log=TRUE),
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upperNoLog = pcauchy(vv, location, scale, lower=FALSE),
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upperLog = pcauchy(vv, location, scale, lower=FALSE, log=TRUE)),
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info = " stats.pcauchy")
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# test.stats.punif <- function( ) {
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v <- qunif(seq(0.0, 1.0, by=0.1))
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expect_equal(runit_punif(v),
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list(lowerNoLog = punif(v),
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lowerLog = punif(v, log=TRUE ),
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upperNoLog = punif(v, lower=FALSE),
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upperLog = punif(v, lower=FALSE, log=TRUE)),
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info = "stats.punif" )
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# TODO: also borrow from R's d-p-q-r-tests.R
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# test.stats.pf <- function( ) {
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v <- (1:9)/10
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expect_equal(runit_pf(v),
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list(lowerNoLog = pf(v, 6, 8, lower=TRUE, log=FALSE),
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lowerLog = pf(v, 6, 8, log=TRUE ),
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upperNoLog = pf(v, 6, 8, lower=FALSE),
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upperLog = pf(v, 6, 8, lower=FALSE, log=TRUE)),
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info = "stats.pf" )
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# test.stats.pnf <- function( ) {
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v <- (1:9)/10
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expect_equal(runit_pnf(v),
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list(lowerNoLog = pf(v, 6, 8, ncp=2.5, lower=TRUE, log=FALSE),
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lowerLog = pf(v, 6, 8, ncp=2.5, log=TRUE ),
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upperNoLog = pf(v, 6, 8, ncp=2.5, lower=FALSE),
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upperLog = pf(v, 6, 8, ncp=2.5, lower=FALSE, log=TRUE)),
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info = "stats.pnf" )
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# test.stats.pchisq <- function( ) {
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v <- (1:9)/10
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expect_equal(runit_pchisq(v),
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list(lowerNoLog = pchisq(v, 6, lower=TRUE, log=FALSE),
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lowerLog = pchisq(v, 6, log=TRUE ),
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upperNoLog = pchisq(v, 6, lower=FALSE),
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upperLog = pchisq(v, 6, lower=FALSE, log=TRUE)),
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info = "stats.pchisq" )
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# test.stats.pnchisq <- function( ) {
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v <- (1:9)/10
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expect_equal(runit_pnchisq(v),
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list(lowerNoLog = pchisq(v, 6, ncp=2.5, lower=TRUE, log=FALSE),
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lowerLog = pchisq(v, 6, ncp=2.5, log=TRUE ),
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upperNoLog = pchisq(v, 6, ncp=2.5, lower=FALSE),
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upperLog = pchisq(v, 6, ncp=2.5, lower=FALSE, log=TRUE)),
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info = "stats.pnchisq" )
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# test.stats.pgamma <- function( ) {
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v <- (1:9)/10
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expect_equal(runit_pgamma(v),
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list(lowerNoLog = pgamma(v, shape = 2.0),
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lowerLog = pgamma(v, shape = 2.0, log=TRUE ),
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upperNoLog = pgamma(v, shape = 2.0, lower=FALSE),
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upperLog = pgamma(v, shape = 2.0, lower=FALSE, log=TRUE)),
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info = "stats.pgamma" )
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# test.stats.pnorm <- function( ) {
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v <- qnorm(seq(0.0, 1.0, by=0.1))
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expect_equal(runit_pnorm(v),
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list(lowerNoLog = pnorm(v),
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lowerLog = pnorm(v, log=TRUE ),
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upperNoLog = pnorm(v, lower=FALSE),
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upperLog = pnorm(v, lower=FALSE, log=TRUE)),
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info = "stats.pnorm" )
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## Borrowed from R's d-p-q-r-tests.R
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z <- c(-Inf,Inf,NA,NaN, rt(1000, df=2))
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z.ok <- z > -37.5 | !is.finite(z)
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pz <- runit_pnorm(z)
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expect_equal(pz$lowerNoLog, 1 - pz$upperNoLog, info = "stats.pnorm")
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expect_equal(pz$lowerNoLog, runit_pnorm(-z)$upperNoLog, info = "stats.pnorm")
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expect_equal(log(pz$lowerNoLog[z.ok]), pz$lowerLog[z.ok], info = "stats.pnorm")
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## FIXME: Add tests that use non-default mu and sigma
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# test.stats.ppois <- function( ) {
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vv <- 0:20
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expect_equal(runit_ppois(vv),
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list(lowerNoLog = ppois(vv, 0.5),
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lowerLog = ppois(vv, 0.5, log=TRUE),
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upperNoLog = ppois(vv, 0.5, lower=FALSE),
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upperLog = ppois(vv, 0.5, lower=FALSE, log=TRUE)),
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info = " stats.ppois")
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# test.stats.pt <- function( ) {
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v <- seq(0.0, 1.0, by=0.1)
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expect_equal(runit_pt(v),
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list(lowerNoLog = pt(v, 5),
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lowerLog = pt(v, 5, log=TRUE),
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upperNoLog = pt(v, 5, lower=FALSE),
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upperLog = pt(v, 5, lower=FALSE, log=TRUE) ),
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info = "stats.pt" )
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# test.stats.pnt <- function( ) {
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v <- seq(0.0, 1.0, by=0.1)
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expect_equal(runit_pnt(v),
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list(lowerNoLog = pt(v, 5, ncp=7),
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lowerLog = pt(v, 5, ncp=7, log=TRUE),
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upperNoLog = pt(v, 5, ncp=7, lower=FALSE),
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upperLog = pt(v, 5, ncp=7, lower=FALSE, log=TRUE) ),
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info = "stats.pnt" )
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# test.stats.qbinom <- function( ) {
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n <- 20
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p <- 0.5
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vv <- seq(0, 1, by = 0.1)
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expect_equal(runit_qbinom_prob(vv, n, p),
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list(lower = qbinom(vv, n, p),
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upper = qbinom(vv, n, p, lower=FALSE)),
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info = " stats.qbinom")
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# test.stats.qunif <- function( ) {
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expect_equal(runit_qunif_prob(c(0, 1, 1.1, -.1)),
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list(lower = c(0, 1, NaN, NaN),
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upper = c(1, 0, NaN, NaN)),
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info = "stats.qunif" )
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# TODO: also borrow from R's d-p-q-r-tests.R
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# test.stats.qnorm <- function( ) {
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expect_equal(runit_qnorm_prob(c(0, 1, 1.1, -.1)),
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list(lower = c(-Inf, Inf, NaN, NaN),
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upper = c(Inf, -Inf, NaN, NaN)),
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info = "stats.qnorm" )
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## Borrowed from R's d-p-q-r-tests.R and Wichura (1988)
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expect_equal(runit_qnorm_prob(c( 0.25, .001, 1e-20))$lower,
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c(-0.6744897501960817, -3.090232306167814, -9.262340089798408),
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info = "stats.qnorm",
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tol = 1e-15)
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expect_equal(runit_qnorm_log(c(-Inf, 0, 0.1)),
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list(lower = c(-Inf, Inf, NaN),
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upper = c(Inf, -Inf, NaN)),
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info = "stats.qnorm" )
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## newer high-precision code in R 4.3.0 has slightly different value
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## of -447.197893678525 so lowering tolerance a little
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expect_equal(runit_qnorm_log(-1e5)$lower, -447.1974945, tolerance=1e-6)
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# test.stats.qpois.prob <- function( ) {
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vv <- seq(0, 1, by = 0.1)
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expect_equal(runit_qpois_prob(vv),
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list(lower = qpois(vv, 0.5),
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upper = qpois(vv, 0.5, lower=FALSE)),
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info = " stats.qpois.prob")
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# test.stats.qt <- function( ) {
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v <- seq(0.05, 0.95, by=0.05)
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( x1 <- runit_qt(v, 5, FALSE, FALSE) )
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( x2 <- qt(v, df=5, lower=FALSE, log=FALSE) )
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expect_equal(x1, x2, info="stats.qt.f.f")
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( x1 <- runit_qt(v, 5, TRUE, FALSE) )
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( x2 <- qt(v, df=5, lower=TRUE, log=FALSE) )
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expect_equal(x1, x2, info="stats.qt.t.f")
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( x1 <- runit_qt(-v, 5, FALSE, TRUE) )
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( x2 <- qt(-v, df=5, lower=FALSE, log=TRUE) )
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expect_equal(x1, x2, info="stats.qt.f.t")
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( x1 <- runit_qt(-v, 5, TRUE, TRUE) )
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( x2 <- qt(-v, df=5, lower=TRUE, log=TRUE) )
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expect_equal(x1, x2, info="stats.qt.t.t")
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## TODO: test.stats.qgamma
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## TODO: test.stats.(dq)chisq
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