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test-extract.R
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context("extract methods")
suppressPackageStartupMessages(library("texreg"))
# Arima (stats) ----
test_that("extract Arima objects from the stats package", {
testthat::skip_on_cran()
set.seed(12345)
m <- arima(USAccDeaths,
order = c(0, 1, 1),
seasonal = list(order = c(0, 1, 1)))
tr <- extract(m)
expect_length(tr@coef.names, 2)
expect_length(tr@coef, 2)
expect_length(tr@se, 2)
expect_length(tr@pvalues, 2)
expect_length(tr@ci.low, 0)
expect_length(tr@ci.up, 0)
expect_length(tr@gof, 4)
expect_length(tr@gof.names, 4)
expect_length(tr@gof.decimal, 4)
expect_equivalent(which(tr@gof.decimal), 1:3)
expect_equivalent(which(tr@pvalues < 0.05), 1:2)
expect_equivalent(dim(matrixreg(m)), c(9, 2))
})
# forecast_ARIMA (forecast) ----
test_that("extract forecast_ARIMA objects from the forecast package", {
testthat::skip_on_cran()
skip_if_not_installed("forecast")
require("forecast")
set.seed(12345)
air.model <- Arima(window(AirPassengers, end = 1956 + 11 / 12),
order = c(0, 1, 1),
seasonal = list(order = c(0, 1, 1), period = 12),
lambda = 0)
tr <- extract(air.model)
expect_length(tr@coef.names, 2)
expect_length(tr@coef, 2)
expect_length(tr@se, 2)
expect_length(tr@pvalues, 2)
expect_length(tr@ci.low, 0)
expect_length(tr@ci.up, 0)
expect_length(tr@gof, 5)
expect_length(tr@gof.names, 5)
expect_length(tr@gof.decimal, 5)
expect_equivalent(which(tr@gof.decimal), 1:4)
expect_equivalent(which(tr@pvalues < 0.05), 1:2)
expect_equivalent(dim(matrixreg(air.model)), c(10, 2))
m1 <- arima(USAccDeaths,
order = c(0, 1, 1),
seasonal = list(order = c(0, 1, 1)))
m2 <- Arima(USAccDeaths,
order = c(0, 1, 1),
seasonal = list(order = c(0, 1, 1)))
expect_s3_class(m1, "Arima")
expect_s3_class(m2, "Arima")
expect_s3_class(m2, "forecast_ARIMA")
m <- matrixreg(list(m1, m2))
expect_equivalent(dim(m), c(10, 3))
expect_equivalent(m[2:9, 2], m[2:9, 3])
expect_equivalent(m[10, 1], "AICc")
})
# bergm (Bergm) ----
test_that("extract bergm objects from the Bergm package", {
testthat::skip_on_cran()
suppressWarnings(skip_if_not_installed("Bergm", minimum_version = "5.0.2"))
require("Bergm")
set.seed(12345)
data(florentine)
suppressWarnings(suppressMessages(
p.flo <- bergm(flomarriage ~ edges + kstar(2),
burn.in = 10,
aux.iters = 30,
main.iters = 30,
gamma = 1.2)))
tr <- extract(p.flo)
expect_length(tr@se, 0)
expect_length(tr@pvalues, 0)
expect_length(tr@ci.low, 2)
expect_length(tr@ci.up, 2)
expect_length(tr@gof, 0)
expect_length(tr@coef, 2)
expect_equivalent(dim(matrixreg(p.flo)), c(5, 2))
})
# bife (bife) ----
test_that("extract bife objects from the bife package", {
testthat::skip_on_cran()
skip_if_not_installed("bife", minimum_version = "0.7")
require("bife")
set.seed(12345)
mod <- bife(LFP ~ I(AGE^2) + log(INCH) + KID1 + KID2 + KID3 + factor(TIME) | ID, psid)
tr <- extract(mod)
expect_length(tr@coef.names, 13)
expect_length(tr@coef, 13)
expect_length(tr@se, 13)
expect_length(tr@pvalues, 13)
expect_length(tr@ci.low, 0)
expect_length(tr@ci.up, 0)
expect_length(tr@gof, 3)
expect_length(tr@gof.names, 3)
expect_length(tr@gof.decimal, 3)
expect_equivalent(which(tr@gof.decimal), 1:2)
expect_equivalent(which(tr@pvalues < 0.05), c(1:4, 8:13))
expect_equivalent(dim(matrixreg(mod)), c(30, 2))
})
## commented out because it takes long and causes segfault in combination with other tests
# # brmsfit (brms) ----
# test_that("extract brmsfit objects from the brms package", {
# testthat::skip_on_cran()
# skip_if_not_installed("brms", minimum_version = "2.8.8")
# skip_if_not_installed("coda", minimum_version = "0.19.2")
# require("brms")
# require("coda")
#
# # example 2 from brm help page; see ?brm
# sink(nullfile())
# suppressMessages(
# fit2 <- brm(rating ~ period + carry + cs(treat),
# data = inhaler, family = sratio("logit"),
# prior = set_prior("normal(0,5)"), chains = 1))
# sink()
#
# suppressWarnings(tr <- extract(fit2))
# expect_length(tr@gof.names, 4)
# expect_length(tr@coef, 8)
# expect_length(tr@se, 8)
# expect_length(tr@pvalues, 0)
# expect_length(tr@ci.low, 8)
# expect_length(tr@ci.up, 8)
# expect_equivalent(which(tr@gof.decimal), c(1, 3, 4))
# suppressWarnings(expect_equivalent(dim(matrixreg(fit2)), c(21, 2)))
#
# # example 1 from brm help page; see ?brm
# bprior1 <- prior(student_t(5, 0, 10), class = b) + prior(cauchy(0, 2), class = sd)
# sink(nullfile())
# suppressMessages(
# fit1 <- brm(count ~ zAge + zBase * Trt + (1|patient),
# data = epilepsy,
# family = poisson(),
# prior = bprior1))
# sink()
#
# expect_warning(suppressMessages(tr <- extract(fit1, use.HDI = TRUE, reloo = TRUE)))
# expect_length(tr@gof.names, 5)
# expect_length(tr@coef, 5)
# expect_length(tr@se, 5)
# expect_length(tr@pvalues, 0)
# expect_length(tr@ci.low, 5)
# expect_length(tr@ci.up, 5)
# expect_equivalent(which(tr@gof.decimal), c(1:2, 4:5))
# expect_equivalent(suppressWarnings(dim(matrixreg(fit1))), c(16, 2))
# })
# btergm (btergm) ----
test_that("extract btergm objects from the btergm package", {
testthat::skip_on_cran()
skip_if_not_installed("btergm", minimum_version = "1.10.10")
set.seed(5)
networks <- list()
for (i in 1:10) { # create 10 random networks with 10 actors
mat <- matrix(rbinom(100, 1, .25), nrow = 10, ncol = 10)
diag(mat) <- 0 # loops are excluded
networks[[i]] <- mat # add network to the list
}
covariates <- list()
for (i in 1:10) { # create 10 matrices as covariate
mat <- matrix(rnorm(100), nrow = 10, ncol = 10)
covariates[[i]] <- mat # add matrix to the list
}
suppressWarnings(fit <- btergm::btergm(networks ~ edges + istar(2) + edgecov(covariates), R = 100, verbose = FALSE))
tr <- extract(fit)
expect_length(tr@se, 0)
expect_length(tr@pvalues, 0)
expect_length(tr@ci.low, 3)
expect_length(tr@ci.up, 3)
expect_length(tr@gof, 1)
expect_length(tr@coef, 3)
expect_equivalent(dim(matrixreg(fit)), c(8, 2))
expect_true(all(tr@ci.low < tr@coef))
expect_true(all(tr@coef < tr@ci.up))
})
# clm (ordinal) ----
test_that("extract clm objects from the ordinal package", {
testthat::skip_on_cran()
skip_if_not_installed("ordinal", minimum_version = "2019.12.10")
set.seed(12345)
fit <- ordinal::clm(Species ~ Sepal.Length, data = iris)
tr <- extract(fit)
expect_length(tr@coef.names, 3)
expect_length(tr@coef, 3)
expect_length(tr@se, 3)
expect_length(tr@pvalues, 3)
expect_length(tr@ci.low, 0)
expect_length(tr@ci.up, 0)
expect_length(tr@gof, 4)
expect_length(tr@gof.names, 4)
expect_length(tr@gof.decimal, 4)
expect_equivalent(which(tr@gof.decimal), 1:3)
expect_equivalent(which(tr@pvalues < 0.05), 1:3)
expect_equivalent(dim(matrixreg(fit)), c(11, 2))
})
# dynlm (dynlm) ----
test_that("extract dynlm objects from the dynlm package", {
testthat::skip_on_cran()
skip_if_not_installed("dynlm")
skip_if_not_installed("datasets")
require("dynlm")
set.seed(12345)
data("UKDriverDeaths", package = "datasets")
uk <- log10(UKDriverDeaths)
dfm <- dynlm(uk ~ L(uk, 1) + L(uk, 12))
tr <- extract(dfm, include.rmse = TRUE)
expect_length(tr@coef.names, 3)
expect_length(tr@coef, 3)
expect_length(tr@se, 3)
expect_length(tr@pvalues, 3)
expect_length(tr@ci.low, 0)
expect_length(tr@ci.up, 0)
expect_length(tr@gof, 4)
expect_length(tr@gof.names, 4)
expect_length(tr@gof.decimal, 4)
expect_equivalent(which(tr@gof.decimal), c(1, 2, 4))
expect_equivalent(which(tr@pvalues < 0.05), 2:3)
expect_equivalent(dim(matrixreg(dfm)), c(10, 2))
})
# ergm (ergm) ----
test_that("extract ergm objects from the ergm package", {
testthat::skip_on_cran()
skip_if_not_installed("ergm", minimum_version = "4.1.2")
require("ergm")
set.seed(12345)
data(florentine)
suppressMessages(gest <- ergm(flomarriage ~ edges + absdiff("wealth")))
tr1 <- extract(gest)
expect_length(tr1@coef.names, 2)
expect_length(tr1@coef, 2)
expect_length(tr1@se, 2)
expect_length(tr1@pvalues, 2)
expect_length(tr1@ci.low, 0)
expect_length(tr1@ci.up, 0)
expect_length(tr1@gof, 3)
expect_length(tr1@gof.names, 3)
expect_length(tr1@gof.decimal, 3)
expect_equivalent(which(tr1@gof.decimal), 1:3)
expect_equivalent(dim(matrixreg(gest)), c(8, 2))
data(molecule)
molecule %v% "atomic type" <- c(1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 3, 3,
3, 3, 3, 3, 3)
suppressMessages(gest <- ergm(molecule ~ edges + kstar(2) + triangle +
nodematch("atomic type")))
tr2 <- extract(gest)
expect_length(tr2@coef.names, 4)
expect_length(tr2@coef, 4)
expect_length(tr2@se, 4)
expect_length(tr2@pvalues, 4)
expect_length(tr2@ci.low, 0)
expect_length(tr2@ci.up, 0)
expect_length(tr2@gof, 3)
expect_length(tr2@gof.names, 3)
expect_length(tr2@gof.decimal, 3)
expect_equivalent(which(tr2@gof.decimal), 1:3)
expect_equivalent(dim(matrixreg(gest)), c(12, 2))
})
# feglm (alpaca) ----
test_that("extract feglm objects from the alpaca package", {
testthat::skip_on_cran()
skip_if_not_installed("alpaca", minimum_version = "0.3.2")
require("alpaca")
set.seed(12345)
data <- simGLM(1000L, 20L, 1805L, model = "logit")
mod <- feglm(y ~ x1 + x2 + x3 | i + t, data)
tr <- extract(mod)
expect_length(tr@coef.names, 3)
expect_length(tr@coef, 3)
expect_length(tr@se, 3)
expect_length(tr@pvalues, 3)
expect_length(tr@ci.low, 0)
expect_length(tr@ci.up, 0)
expect_length(tr@gof, 4)
expect_length(tr@gof.names, 4)
expect_length(tr@gof.decimal, 4)
expect_equivalent(which(tr@gof.decimal), 1)
expect_equivalent(which(tr@pvalues < 0.05), 1:3)
expect_equivalent(dim(matrixreg(mod)), c(11, 2))
})
# feis (feisr) ----
test_that("extract feis objects from the feisr package", {
testthat::skip_on_cran()
skip_if_not_installed("feisr", minimum_version = "1.0.1")
require("feisr")
set.seed(12345)
data("mwp", package = "feisr")
feis1.mod <- feis(lnw ~ marry | exp, data = mwp, id = "id")
feis2.mod <- feis(lnw ~ marry + enrol + as.factor(yeargr) | exp,
data = mwp,
id = "id")
tr <- extract(feis1.mod)
expect_equivalent(tr@coef, 0.056, tolerance = 1e-3)
expect_equivalent(tr@se, 0.0234, tolerance = 1e-3)
expect_equivalent(tr@pvalues, 0.0165, tolerance = 1e-3)
expect_equivalent(tr@gof, c(0.002, 0.002, 3100, 268, 0.312), tolerance = 1e-3)
expect_length(tr@gof.names, 5)
tr2 <- extract(feis2.mod)
expect_length(tr2@coef, 6)
expect_length(which(tr2@pvalues < 0.05), 2)
expect_length(which(tr2@gof.decimal), 3)
})
# felm (lfe) ----
test_that("extract felm objects from the lfe package", {
testthat::skip_on_cran()
skip_if_not_installed("lfe", minimum_version = "2.8.5")
require("lfe")
set.seed(12345)
x <- rnorm(1000)
x2 <- rnorm(length(x))
id <- factor(sample(20, length(x), replace = TRUE))
firm <- factor(sample(13, length(x),replace = TRUE))
id.eff <- rnorm(nlevels(id))
firm.eff <- rnorm(nlevels(firm))
u <- rnorm(length(x))
y <- x + 0.5 * x2 + id.eff[id] + firm.eff[firm] + u
est <- felm(y ~ x + x2 | id + firm)
tr <- extract(est)
expect_equivalent(tr@coef, c(1.0188, 0.5182), tolerance = 1e-2)
expect_equivalent(tr@se, c(0.032, 0.032), tolerance = 1e-2)
expect_equivalent(tr@pvalues, c(0.00, 0.00), tolerance = 1e-2)
expect_equivalent(tr@gof, c(1000, 0.7985, 0.575, 0.792, 0.560, 20, 13), tolerance = 1e-2)
expect_length(tr@gof.names, 7)
expect_length(tr@coef, 2)
expect_equivalent(which(tr@pvalues < 0.05), 1:2)
expect_equivalent(which(tr@gof.decimal), 2:5)
# check exclusion of projected model statistics
tr <- extract(est, include.proj.stats = FALSE)
expect_length(tr@gof.names, 5)
expect_false(any(grepl('proj model', tr@gof.names, fixed = TRUE)))
# without fixed effects
OLS1 <- felm(Sepal.Length ~ Sepal.Width |0|0|0, data = iris)
tr1 <- extract(OLS1)
expect_length(tr1@gof, 5)
})
# fixest (fixest) ----
test_that("extract fixest objects created with the fixest package", {
testthat::skip_on_cran()
skip_if_not_installed("fixest", minimum_version = "0.10.5")
require("fixest")
# test ordinary least squares with multiple fixed effects
set.seed(12345)
x <- rnorm(1000)
data <- data.frame(
x = x,
x2 = rnorm(length(x)),
id = factor(sample(20, length(x), replace = TRUE)),
firm = factor(sample(13, length(x),replace = TRUE))
)
id.eff <- rnorm(nlevels(data$id))
firm.eff <- rnorm(nlevels(data$firm))
u <- rnorm(length(x))
data$y <- with(data, x + 0.5 * x2 + id.eff[id] + firm.eff[firm] + u)
est <- feols(y ~ x + x2 | id + firm, data = data)
tr <- extract(est)
expect_equivalent(tr@coef, c(1.0188, 0.5182), tolerance = 1e-2)
# NOTE: standard errors differ from default produced by lfe (tested above)
# see https://cran.r-project.org/web/packages/fixest/vignettes/standard_errors.html
expect_equivalent(tr@se, c(0.021, 0.032), tolerance = 1e-2)
expect_equivalent(tr@pvalues, c(0.00, 0.00), tolerance = 1e-2)
expect_equivalent(tr@gof, c(1000, 20, 13, 0.7985, 0.575, 0.792, 0.57), tolerance = 1e-2)
expect_lte(length(tr@gof.names), 7)
expect_gte(length(tr@gof.names), 5)
expect_length(tr@coef, 2)
expect_equivalent(which(tr@pvalues < 0.05), 1:2)
# test generalized linear model
data$y <- rpois(length(data$x), exp(data$x + data$x2 + id.eff[data$id]))
est <- fepois(y ~ x + x2 | id, data = data)
tr <- extract(est)
expect_equivalent(tr@coef, c(1.00, 1.00), tolerance = 1e-2)
expect_equivalent(tr@se, c(0.01, 0.02), tolerance = 1e-2)
expect_equivalent(tr@pvalues, c(0.00, 0.00), tolerance = 1e-2)
expect_equivalent(tr@gof, c(1000, 20, 955.4, -1479.6, 0.83), tolerance = 1e-2)
expect_length(tr@gof.names, 5)
expect_length(tr@coef, 2)
expect_equivalent(which(!tr@gof.decimal), 1:2)
})
# gamlssZadj (gamlss.inf) ----
test_that("extract gamlssZadj objects from the gamlss.inf package", {
testthat::skip_on_cran()
skip_if_not_installed("gamlss.inf", minimum_version = "1.0.1")
require("gamlss.inf")
set.seed(12345)
sink(nullfile())
y0 <- rZAGA(1000, mu = .3, sigma = .4, nu = .15)
g0 <- gamlss(y0 ~ 1, family = ZAGA)
t0 <- gamlssZadj(y = y0, mu.formula = ~1, family = GA, trace = TRUE)
sink()
tr <- extract(t0)
expect_length(tr@gof.names, 2)
expect_length(tr@coef, 3)
expect_length(tr@se, 3)
expect_length(tr@pvalues, 3)
expect_length(tr@ci.low, 0)
expect_length(tr@ci.up, 0)
expect_equivalent(which(tr@gof.decimal), 2)
expect_equivalent(tr@coef.names, c("$\\mu$ (Intercept)",
"$\\sigma$ (Intercept)",
"$\\nu$ (Intercept)"))
})
# glm.cluster (miceadds) ----
test_that("extract glm.cluster objects from the miceadds package", {
testthat::skip_on_cran()
skip_if_not_installed("miceadds", minimum_version = "3.8.9")
require("miceadds")
data(data.ma01)
dat <- data.ma01
dat$highmath <- 1 * (dat$math > 600)
mod2 <- miceadds::glm.cluster(data = dat,
formula = highmath ~ hisei + female,
cluster = "idschool",
family = "binomial")
tr <- extract(mod2)
expect_equivalent(tr@coef, c(-2.76, 0.03, -0.15), tolerance = 1e-2)
expect_equivalent(tr@se, c(0.25, 0.00, 0.10), tolerance = 1e-2)
expect_equivalent(tr@pvalues, c(0.00, 0.00, 0.13), tolerance = 1e-2)
expect_equivalent(tr@gof, c(3108.095, 3126.432, -1551.047, 3102.095, 3336.000), tolerance = 1e-2)
expect_length(tr@gof.names, 5)
expect_length(tr@coef, 3)
expect_equivalent(which(tr@pvalues < 0.05), 1:2)
expect_equivalent(which(tr@gof.decimal), 1:4)
})
# glmerMod (lme4) ----
test_that("extract glmerMod objects from the lme4 package", {
testthat::skip_on_cran()
testthat::skip_on_ci()
skip_if_not_installed("lme4", minimum_version = "1.1.34")
skip_if_not_installed("Matrix", minimum_version = "1.6.1")
require("lme4")
set.seed(12345)
gm1 <- glmer(cbind(incidence, size - incidence) ~ period + (1 | herd),
data = cbpp,
family = binomial)
expect_equivalent(class(gm1)[1], "glmerMod")
tr <- extract(gm1, include.dic = TRUE, include.deviance = TRUE)
expect_equivalent(tr@coef, c(-1.40, -0.99, -1.13, -1.58), tolerance = 1e-2)
expect_equivalent(tr@se, c(0.23, 0.30, 0.32, 0.42), tolerance = 1e-2)
expect_equivalent(tr@pvalues, c(0, 0, 0, 0), tolerance = 1e-2)
expect_length(tr@gof.names, 8)
expect_equivalent(which(tr@gof.decimal), c(1:5, 8))
expect_length(which(grepl("Var", tr@gof.names)), 1)
expect_length(which(grepl("Cov", tr@gof.names)), 0)
tr_profile <- extract(gm1, method = "profile", nsim = 5)
tr_boot <- suppressWarnings(extract(gm1, method = "boot", nsim = 5))
tr_wald <- extract(gm1, method = "Wald")
expect_length(tr_profile@se, 0)
expect_length(tr_profile@ci.low, 4)
expect_length(tr_profile@ci.up, 4)
expect_length(tr_boot@se, 0)
expect_length(tr_boot@ci.low, 4)
expect_length(tr_boot@ci.up, 4)
expect_length(tr_wald@se, 0)
expect_length(tr_wald@ci.low, 4)
expect_length(tr_wald@ci.up, 4)
})
# glmmTMB (glmmTMB) ----
test_that("extract glmmTMB objects from the glmmTMB package", {
testthat::skip_on_cran()
skip_if_not_installed("glmmTMB", minimum_version = "1.0.1")
require("glmmTMB")
set.seed(12345)
m2 <- glmmTMB(count ~ spp + mined + (1|site),
zi = ~ spp + mined,
family = nbinom2, data = Salamanders)
tr <- extract(m2)
expect_length(tr@gof.names, 5)
expect_length(tr@coef, 16)
expect_length(tr@se, 16)
expect_length(tr@pvalues, 16)
expect_length(tr@ci.low, 0)
expect_length(tr@ci.up, 0)
expect_equivalent(which(tr@gof.decimal), c(1, 2, 5))
tr <- extract(m2, beside = TRUE)
expect_length(tr[[1]]@gof.names, 5)
expect_length(tr[[1]]@coef, 8)
expect_length(tr[[2]]@coef, 8)
expect_length(tr[[1]]@se, 8)
expect_length(tr[[2]]@se, 8)
expect_length(tr[[1]]@pvalues, 8)
expect_length(tr[[2]]@pvalues, 8)
expect_length(tr, 2)
expect_equivalent(which(tr[[2]]@gof.decimal), c(1, 2, 5))
data("mtcars")
cars <- glmmTMB(gear ~ mpg, data = mtcars)
tr_cars <- extract(cars)
expect_length(tr_cars@gof, 3)
expect_equal(tr_cars@gof.decimal, c(TRUE, TRUE, FALSE))
expect_equal(tr_cars@gof.names, c("AIC", "Log Likelihood", "Num. obs."))
expect_length(tr_cars@coef, 2)
expect_length(tr_cars@se, 2)
expect_length(tr_cars@pvalues, 2)
})
# ivreg (AER) ----
test_that("extract ivreg objects from the AER package", {
testthat::skip_on_cran()
skip_if_not_installed("AER")
require("AER")
set.seed(12345)
data("CigarettesSW", package = "AER")
CigarettesSW$rprice <- with(CigarettesSW, price / cpi)
CigarettesSW$rincome <- with(CigarettesSW, income/population / cpi)
CigarettesSW$tdiff <- with(CigarettesSW, (taxs - tax) / cpi)
fm <- ivreg(log(packs) ~ log(rprice) + log(rincome) | log(rincome) + tdiff + I(tax/cpi),
data = CigarettesSW,
subset = year == "1995")
tr1 <- extract(fm, vcov = sandwich, df = Inf, diagnostics = TRUE, include.rmse = TRUE)
fm2 <- ivreg(log(packs) ~ log(rprice) | tdiff, data = CigarettesSW,
subset = year == "1995")
tr2 <- extract(fm2)
expect_equivalent(tr1@coef, c(9.89, -1.28, 0.28), tolerance = 1e-2)
expect_equivalent(tr1@se, c(0.93, 0.24, 0.25), tolerance = 1e-2)
expect_equivalent(tr1@pvalues, c(0.00, 0.00, 0.25), tolerance = 1e-2)
expect_equivalent(tr1@gof, c(0.43, 0.40, 48, 0.19), tolerance = 1e-2)
expect_length(tr1@gof.names, 4)
expect_length(tr2@coef, 2)
expect_length(which(tr2@pvalues < 0.05), 2)
expect_equivalent(which(tr2@gof.decimal), 1:2)
})
# lm (stats) ----
test_that("extract lm objects from the stats package", {
set.seed(12345)
ctl <- c(4.17,5.58,5.18,6.11,4.50,4.61,5.17,4.53,5.33,5.14)
trt <- c(4.81,4.17,4.41,3.59,5.87,3.83,6.03,4.89,4.32,4.69)
group <- gl(2, 10, 20, labels = c("Ctl","Trt"))
weight <- c(ctl, trt)
lm.D9 <- lm(weight ~ group)
lm.D90 <- lm(weight ~ group - 1)
tr <- extract(lm.D9)
expect_equivalent(tr@coef, c(5.032, -0.371), tolerance = 1e-3)
expect_equivalent(tr@se, c(0.22, 0.31), tolerance = 1e-2)
expect_equivalent(tr@pvalues, c(0.00, 0.25), tolerance = 1e-2)
expect_equivalent(tr@gof, c(0.07, 0.02, 20), tolerance = 1e-2)
expect_length(tr@gof.names, 3)
tr2 <- extract(lm.D90, include.rmse = TRUE)
expect_length(tr2@coef, 2)
expect_length(which(tr2@pvalues < 0.05), 2)
expect_length(which(tr2@gof.decimal), 3)
})
# lm.cluster (miceadds) ----
test_that("extract lm.cluster objects from the miceadds package", {
testthat::skip_on_cran()
skip_if_not_installed("miceadds", minimum_version = "3.8.9")
require("miceadds")
data(data.ma01)
dat <- data.ma01
mod1 <- miceadds::lm.cluster(data = dat,
formula = read ~ hisei + female,
cluster = "idschool")
tr <- extract(mod1)
expect_equivalent(tr@coef, c(418.80, 1.54, 35.70), tolerance = 1e-2)
expect_equivalent(tr@se, c(6.45, 0.11, 3.81), tolerance = 1e-2)
expect_equivalent(tr@pvalues, c(0.00, 0.00, 0.00), tolerance = 1e-2)
expect_equivalent(tr@gof, c(0.15, 0.15, 3180), tolerance = 1e-2)
expect_length(tr@gof.names, 3)
expect_length(tr@coef, 3)
expect_equivalent(which(tr@pvalues < 0.05), 1:3)
expect_equivalent(which(tr@gof.decimal), 1:2)
})
# lmerMod (lme4) ----
test_that("extract lmerMod objects from the lme4 package", {
testthat::skip_on_cran()
testthat::skip_on_ci()
skip_if_not_installed("lme4", minimum_version = "1.1.34")
skip_if_not_installed("Matrix", minimum_version = "1.6.1")
require("lme4")
set.seed(12345)
fm1 <- lmer(Reaction ~ Days + (Days | Subject), sleepstudy)
fm1_ML <- update(fm1, REML = FALSE)
fm2 <- lmer(Reaction ~ Days + (Days || Subject), sleepstudy)
tr1 <- extract(fm1, include.dic = TRUE, include.deviance = TRUE)
tr1_ML <- extract(fm1_ML, include.dic = TRUE, include.deviance = TRUE)
tr2_profile <- extract(fm2, method = "profile", nsim = 5)
tr2_boot <- suppressWarnings(extract(fm2, method = "boot", nsim = 5))
tr2_wald <- extract(fm2, method = "Wald")
expect_equivalent(class(fm1)[1], "lmerMod")
expect_equivalent(tr1@coef, c(251.41, 10.47), tolerance = 1e-2)
expect_equivalent(tr1@coef, tr1_ML@coef, tolerance = 1e-2)
expect_equivalent(tr1@se, c(6.82, 1.55), tolerance = 1e-2)
expect_equivalent(tr1@pvalues, c(0, 0), tolerance = 1e-2)
expect_equivalent(tr1@gof, c(1755.63, 1774.79, 1760.25, 1751.94, -871.81, 180, 18, 611.90, 35.08, 9.61, 654.94), tolerance = 1e-2)
expect_length(tr1@gof.names, 11)
expect_equivalent(which(tr1@gof.decimal), c(1:5, 8:11))
expect_equivalent(tr1@coef, tr1_ML@coef)
expect_length(tr1_ML@gof, 11)
expect_length(tr2_profile@gof, 8)
expect_equivalent(tr1@coef, tr2_profile@coef, tolerance = 1e-2)
expect_equivalent(tr1@coef, tr2_boot@coef, tolerance = 1e-2)
expect_equivalent(tr1@coef, tr2_wald@coef, tolerance = 1e-2)
expect_length(which(grepl("Var", tr1@gof.names)), 3)
expect_length(which(grepl("Var", tr2_wald@gof.names)), 3)
expect_length(which(grepl("Cov", tr1@gof.names)), 1)
expect_length(which(grepl("Cov", tr2_wald@gof.names)), 0)
})
# maxLik (maxLik) ----
test_that("extract maxLik objects from the maxLik package", {
testthat::skip_on_cran()
testthat::skip_if_not_installed("maxLik", minimum_version = "1.4.8")
require("maxLik")
set.seed(12345)
# example 1 from help page
t <- rexp(100, 2)
loglik <- function(theta) log(theta) - theta * t
gradlik <- function(theta) 1 / theta - t
hesslik <- function(theta) -100 / theta^2
sink(nullfile())
a <- maxLik(loglik, start = 1, control = list(printLevel = 2))
sink()
tr1 <- extract(a)
expect_length(tr1@coef.names, 1)
expect_length(tr1@coef, 1)
expect_length(tr1@se, 1)
expect_length(tr1@pvalues, 1)
expect_length(tr1@ci.low, 0)
expect_length(tr1@ci.up, 0)
expect_true(!any(is.na(tr1@coef)))
expect_length(tr1@gof, 2)
expect_length(tr1@gof.names, 2)
expect_length(tr1@gof.decimal, 2)
expect_equivalent(which(tr1@gof.decimal), 1:2)
# example 2 from help page
b <- maxLik(loglik, gradlik, hesslik, start = 1,
control = list(tol = -1, reltol = 1e-12, gradtol = 1e-12))
tr2 <- extract(b)
expect_length(tr2@coef.names, 1)
expect_length(tr2@coef, 1)
expect_length(tr2@se, 1)
expect_length(tr2@pvalues, 1)
expect_length(tr2@ci.low, 0)
expect_length(tr2@ci.up, 0)
expect_true(!any(is.na(tr2@coef)))
expect_length(tr2@gof, 2)
expect_length(tr2@gof.names, 2)
expect_length(tr2@gof.decimal, 2)
expect_equivalent(which(tr2@gof.decimal), 1:2)
# example 3 from help page
loglik <- function(param) {
mu <- param[1]
sigma <- param[2]
ll <- -0.5 * N * log(2 * pi) - N * log(sigma) - sum(0.5 * (x - mu)^2 / sigma^2)
ll
}
x <- rnorm(100, 1, 2)
N <- length(x)
res <- maxLik(loglik, start = c(0, 1))
tr3 <- extract(res)
expect_length(tr3@coef.names, 2)
expect_length(tr3@coef, 2)
expect_length(tr3@se, 2)
expect_length(tr3@pvalues, 2)
expect_length(tr3@ci.low, 0)
expect_length(tr3@ci.up, 0)
expect_true(!any(is.na(tr3@coef)))
expect_length(tr3@gof, 2)
expect_length(tr3@gof.names, 2)
expect_length(tr3@gof.decimal, 2)
expect_equivalent(which(tr3@gof.decimal), 1:2)
# example 4 from help page
resFix <- maxLik(loglik, start = c(mu = 0, sigma = 1), fixed = "sigma")
tr4 <- extract(resFix)
expect_length(tr3@coef.names, 2)
expect_length(tr3@coef, 2)
expect_length(tr3@se, 2)
expect_length(tr3@pvalues, 2)
expect_length(tr3@ci.low, 0)
expect_length(tr3@ci.up, 0)
expect_true(!any(is.na(tr3@coef)))
expect_length(tr3@gof, 2)
expect_length(tr3@gof.names, 2)
expect_length(tr3@gof.decimal, 2)
expect_equivalent(which(tr3@gof.decimal), 1:2)
})
# mlogit (mlogit) ----
test_that("extract mlogit objects from the mlogit package", {
testthat::skip_on_cran()
testthat::skip_if_not_installed("mlogit", minimum_version = "1.1.0")
require("mlogit")
set.seed(12345)
data("Fishing", package = "mlogit")
Fish <- dfidx(Fishing, varying = 2:9, shape = "wide", choice = "mode")
m <- mlogit(mode ~ price + catch | income, data = Fish)
tr1 <- extract(m)
expect_equivalent(sum(abs(tr1@coef)), 3.382753, tolerance = 1e-2)
expect_equivalent(sum(tr1@se), 0.7789933, tolerance = 1e-2)
expect_equivalent(sum(tr1@pvalues), 0.6136796, tolerance = 1e-2)
expect_equivalent(sum(tr1@gof), 2417.138, tolerance = 1e-2)
expect_length(tr1@coef, 8)
expect_length(tr1@gof, 4)
expect_equivalent(which(tr1@gof.decimal), 1:2)
expect_equivalent(tr1@gof[4], 4)
expect_equal(dim(matrixreg(tr1)), c(21, 2))
expect_warning(extract(m, beside = TRUE), "choice-specific covariates")
})
# multinom (nnet) ----
test_that("extract multinom objects from the nnet package", {
testthat::skip_on_cran()
testthat::skip_if_not_installed("nnet", minimum_version = "7.3.12")
require("nnet")
# example from https://thomasleeper.com/Rcourse/Tutorials/nominalglm.html
set.seed(100)
y <- sort(sample(1:3, 600, TRUE))
x <- numeric(length = 600)
x[1:200] <- -1 * x[1:200] + rnorm(200, 4, 2)
x[201:400] <- 1 * x[201:400] + rnorm(200)
x[401:600] <- 2 * x[401:600] + rnorm(200, 2, 2)
sink(nullfile())
m1 <- multinom(y ~ x)
sink()
tr2 <- extract(m1, beside = FALSE)
tr3 <- extract(m1, beside = TRUE)
expect_equivalent(sum(abs(tr2@coef)), 6.845567, tolerance = 1e-2)
expect_equivalent(sum(tr2@se), 0.6671602, tolerance = 1e-2)
expect_equivalent(sum(tr2@pvalues), 1.677308e-16, tolerance = 1e-2)
expect_equivalent(sum(tr2@gof), 2852.451, tolerance = 1e-2)
expect_length(tr2@coef, 4)
expect_length(tr2@gof, 6)
expect_equivalent(which(tr2@gof.decimal), 1:4)
expect_equivalent(tr2@gof[6], 3)
expect_equal(dim(matrixreg(tr2)), c(15, 2))
expect_length(tr3, 2)
expect_length(tr3[[1]]@coef, 2)
expect_length(tr3[[2]]@coef, 2)
})
# nlmerMod (lme4) ----
test_that("extract nlmerMod objects from the lme4 package", {
testthat::skip_on_cran()
testthat::skip_on_ci()
skip_if_not_installed("lme4", minimum_version = "1.1.34")
skip_if_not_installed("Matrix", minimum_version = "1.6.1")
require("lme4")
set.seed(12345)
startvec <- c(Asym = 200, xmid = 725, scal = 350)
nm1 <- nlmer(circumference ~ SSlogis(age, Asym, xmid, scal) ~ Asym|Tree,
Orange,
start = startvec)
expect_equivalent(class(nm1)[1], "nlmerMod")
expect_warning(extract(nm1, include.dic = TRUE, include.deviance = TRUE),
"falling back to var-cov estimated from RX")
tr <- suppressWarnings(extract(nm1, include.dic = TRUE, include.deviance = TRUE))
expect_equivalent(tr@coef, c(192.05, 727.90, 348.07), tolerance = 1e-2)
expect_equivalent(tr@se, c(15.58, 34.44, 26.31), tolerance = 1e-2)
expect_equivalent(tr@pvalues, c(0, 0, 0), tolerance = 1e-2)
expect_length(tr@gof.names, 9)
expect_equivalent(which(tr@gof.decimal), c(1:5, 8, 9))
expect_length(which(grepl("Var", tr@gof.names)), 2)
expect_length(which(grepl("Cov", tr@gof.names)), 0)
tr_wald <- suppressWarnings(extract(nm1, method = "Wald"))
expect_length(tr_wald@se, 0)
expect_length(tr_wald@ci.low, 3)
expect_length(tr_wald@ci.up, 3)
})
# pcce (plm) ----
test_that("extract pcce objects from the plm package", {
testthat::skip_on_cran()
skip_if_not_installed("plm", minimum_version = "2.4.1")
require("plm")
set.seed(12345)
data("Produc", package = "plm")
ccepmod <- pcce(log(gsp) ~ log(pcap) + log(pc) + log(emp) + unemp, data = Produc, model="p")
tr <- extract(ccepmod)
expect_length(tr@coef.names, 4)
expect_length(tr@coef, 4)
expect_length(tr@se, 4)
expect_length(tr@pvalues, 4)
expect_length(tr@ci.low, 0)
expect_length(tr@ci.up, 0)
expect_length(tr@gof, 4)
expect_length(tr@gof.names, 4)
expect_length(tr@gof.decimal, 4)
expect_equivalent(which(tr@gof.decimal), 1:3)
ccemgmod <- pcce(log(gsp) ~ log(pcap) + log(pc) + log(emp) + unemp, data = Produc, model="mg")
tr2 <- extract(ccemgmod)
expect_length(tr2@coef.names, 4)
expect_length(tr2@coef, 4)
expect_length(tr2@se, 4)
expect_length(tr2@pvalues, 4)
expect_length(tr2@ci.low, 0)
expect_length(tr2@ci.up, 0)
expect_length(tr2@gof, 4)
expect_length(tr2@gof.names, 4)
expect_length(tr2@gof.decimal, 4)
expect_equivalent(which(tr2@gof.decimal), 1:3)
})
# prais (prais) ----
test_that("extract prais objects from the prais package", {
testthat::skip_on_cran()
skip_if_not_installed("prais", minimum_version = "1.1.3")
set.seed(12345)
data("barium", package = "prais")
output <- capture.output({
pwmod <- prais::prais_winsten(lchnimp ~ lchempi + lgas + lrtwex + befile6 + affile6 + afdec6, data = barium, index = "t")
}, file = NULL)
tr <- extract(pwmod)
expect_length(tr@coef.names, 8)
expect_length(tr@coef, 8)
expect_length(tr@se, 8)
expect_length(tr@pvalues, 8)
expect_length(tr@ci.low, 0)
expect_length(tr@ci.up, 0)
expect_length(tr@gof, 3)
expect_length(tr@gof.names, 3)
expect_length(tr@gof.decimal, 3)
expect_equivalent(which(tr@gof.decimal), 1:2)
})
# remstimate (remstimate) ----
test_that("extract remstimate objects from the remstimate package", {
testthat::skip_on_cran()
skip_if_not_installed("remstimate", minimum_version = "2.3.11")
skip_if_not_installed("remify", minimum_version = "3.2.6")
skip_if_not_installed("remstats", minimum_version = "3.2.2")
data(tie_data, package = "remstimate")
tie_reh <- remify::remify(edgelist = tie_data$edgelist, model = "tie")
tie_model <- ~ 1 +
remstats::indegreeSender() +
remstats::inertia() +
remstats::reciprocity()
tie_reh_stats <- remstats::remstats(reh = tie_reh, tie_effects = tie_model)
rem1 <- remstimate::remstimate(reh = tie_reh,
stats = tie_reh_stats,
method = "MLE",
ncores = 1)
rem2 <- remstimate::remstimate(reh = tie_reh,
stats = tie_reh_stats,
method = "HMC",
ncores = 1,
L = 5L)
rem3 <- remstimate::remstimate(reh = tie_reh,
stats = tie_reh_stats,
method = "GDADAMAX",
ncores = 1)
rem4 <- remstimate::remstimate(reh = tie_reh,
stats = tie_reh_stats,
method = "BSIR",
ncores = 1)
mr1 <- matrixreg(list(rem1, rem2, rem3, rem4))
expect_true("matrix" %in% class(mr1))
expect_equal(nrow(mr1), 14)
expect_equal(ncol(mr1), 5)
actor_reh <- remify::remify(edgelist = tie_data$edgelist, model = "actor")
sender_model <- ~ 1 + remstats::outdegreeSender()
receiver_model <- ~ 1 + remstats::otp()
actor_reh_stats <- remstats::remstats(reh = actor_reh, sender_effects = sender_model, receiver_effects = receiver_model)
rem5 <- remstimate::remstimate(reh = actor_reh,
stats = actor_reh_stats,
method = "MLE",
ncores = 1)
rem6 <- remstimate::remstimate(reh = actor_reh,
stats = actor_reh_stats,
method = "HMC",
ncores = 1,
L = 5L)
rem7 <- remstimate::remstimate(reh = actor_reh,
stats = actor_reh_stats,
method = "GDADAMAX",
ncores = 1)
tr5 <- extract(rem5)
expect_length(tr5, 2)
expect_length(tr5[[1]]@coef.names, 2)
expect_length(tr5[[1]]@gof, 5)
expect_equal(tr5[[1]]@model.name, "sender_model")
expect_length(tr5[[2]]@coef.names, 1)
expect_length(tr5[[2]]@gof, 5)
expect_equal(tr5[[2]]@model.name, "receiver_model")
mr2 <- matrixreg(list(rem5, rem6, rem7))
expect_true("matrix" %in% class(mr2))
expect_equal(nrow(mr2), 12)
expect_equal(ncol(mr2), 7)
})
# Sarlm (spatialreg) ----
test_that("extract Sarlm objects from the spatialreg package", {
testthat::skip_on_cran()
skip_if_not_installed("spatialreg", minimum_version = "1.2.1")
require("spatialreg")
set.seed(12345)
# first example from ?lagsarlm
data(oldcol, package = "spdep")
listw <- spdep::nb2listw(COL.nb, style = "W")
ev <- spatialreg::eigenw(listw)
W <- as(listw, "CsparseMatrix")
trMatc <- spatialreg::trW(W, type = "mult")
output <- capture.output({
COL.lag.eig <- spatialreg::lagsarlm(CRIME ~ INC + HOVAL,
data = COL.OLD,
listw = listw,
method = "eigen",
quiet = FALSE,
control = list(pre_eig = ev,
OrdVsign = 1))
}, file = NULL)
tr <- extract(COL.lag.eig)
expect_length(tr@coef.names, 4)
expect_length(tr@coef, 4)
expect_length(tr@se, 4)
expect_length(tr@pvalues, 4)
expect_length(tr@ci.low, 0)
expect_length(tr@ci.up, 0)
expect_length(tr@gof, 7)
expect_length(tr@gof.names, 7)
expect_length(tr@gof.decimal, 7)
expect_equivalent(which(tr@gof.decimal), 3:7)
# example from ?predict.Sarlm
lw <- spdep::nb2listw(COL.nb)
COL.lag.eig2 <- COL.mix.eig <- lagsarlm(CRIME ~ INC + HOVAL,
data = COL.OLD,
lw,
type = "mixed")
tr2 <- extract(COL.lag.eig2)
expect_length(tr2@coef.names, 6)
expect_length(tr2@coef, 6)
expect_length(tr2@se, 6)
expect_length(tr2@pvalues, 6)
expect_length(tr2@ci.low, 0)
expect_length(tr2@ci.up, 0)
expect_length(tr2@gof, 7)
expect_length(tr2@gof.names, 7)
expect_length(tr2@gof.decimal, 7)
expect_equivalent(which(tr2@gof.decimal), 3:7)