Comments (5)
Sorry, can't reproduce this -- I get:
library(RLRsim)
library(lme4)
#> Loading required package: Matrix
m0 <- lmer(Reaction ~ I(Days-4.5) + (1|Subject), data = sleepstudy)
rlr <- exactRLRT(m0)
str(rlr$sample)
#> num [1:10000] 4.101 0 0.256 0.7 0 ...
#> - attr(*, "lambda")= num [1:10000] 0.913 0 0.195 0.336 0 ...
Created on 2019-04-23 by the reprex package (v0.2.1)
Session info
devtools::session_info()
#> ─ Session info ──────────────────────────────────────────────────────────
#> setting value
#> version R version 3.5.3 (2019-03-11)
#> os Linux Mint 19.1
#> system x86_64, linux-gnu
#> ui X11
#> language en_GB
#> collate en_GB.UTF-8
#> ctype en_GB.UTF-8
#> tz Europe/Berlin
#> date 2019-04-23
#>
#> ─ Packages ──────────────────────────────────────────────────────────────
#> package * version date lib source
#> assertthat 0.2.1 2019-03-21 [1] CRAN (R 3.5.3)
#> backports 1.1.4 2019-04-10 [1] CRAN (R 3.5.3)
#> boot 1.3-20 2017-07-30 [1] CRAN (R 3.5.3)
#> callr 3.2.0 2019-03-15 [1] CRAN (R 3.5.3)
#> cli 1.1.0 2019-03-19 [1] CRAN (R 3.5.3)
#> crayon 1.3.4 2017-09-16 [1] CRAN (R 3.5.3)
#> desc 1.2.0 2018-05-01 [1] CRAN (R 3.5.3)
#> devtools 2.0.2 2019-04-08 [1] CRAN (R 3.5.3)
#> digest 0.6.18 2018-10-10 [1] CRAN (R 3.5.3)
#> evaluate 0.13 2019-02-12 [1] CRAN (R 3.5.3)
#> fs 1.2.7 2019-03-19 [1] CRAN (R 3.5.3)
#> glue 1.3.1 2019-03-12 [1] CRAN (R 3.5.3)
#> highr 0.8 2019-03-20 [1] CRAN (R 3.5.3)
#> htmltools 0.3.6 2017-04-28 [1] CRAN (R 3.5.3)
#> knitr 1.22 2019-03-08 [1] CRAN (R 3.5.3)
#> lattice 0.20-38 2018-11-04 [4] CRAN (R 3.5.1)
#> lme4 * 1.1-21 2019-03-05 [1] CRAN (R 3.5.3)
#> magrittr 1.5 2014-11-22 [1] CRAN (R 3.5.3)
#> MASS 7.3-51.1 2018-11-01 [4] CRAN (R 3.5.1)
#> Matrix * 1.2-17 2019-03-22 [4] CRAN (R 3.5.3)
#> memoise 1.1.0 2017-04-21 [1] CRAN (R 3.5.3)
#> mgcv 1.8-28 2019-03-21 [4] CRAN (R 3.5.3)
#> minqa 1.2.4 2014-10-09 [1] CRAN (R 3.5.3)
#> nlme 3.1-139 2019-04-09 [1] CRAN (R 3.5.3)
#> nloptr 1.2.1 2018-10-03 [1] CRAN (R 3.5.3)
#> pkgbuild 1.0.3 2019-03-20 [1] CRAN (R 3.5.3)
#> pkgload 1.0.2 2018-10-29 [1] CRAN (R 3.5.3)
#> prettyunits 1.0.2 2015-07-13 [1] CRAN (R 3.5.3)
#> processx 3.3.0 2019-03-10 [1] CRAN (R 3.5.3)
#> ps 1.3.0 2018-12-21 [1] CRAN (R 3.5.3)
#> R6 2.4.0 2019-02-14 [1] CRAN (R 3.5.3)
#> Rcpp 1.0.1 2019-03-17 [1] CRAN (R 3.5.3)
#> remotes 2.0.4 2019-04-10 [1] CRAN (R 3.5.3)
#> rlang 0.3.4 2019-04-07 [1] CRAN (R 3.5.3)
#> RLRsim * 3.1-3 2016-11-04 [1] CRAN (R 3.5.3)
#> rmarkdown 1.12 2019-03-14 [1] CRAN (R 3.5.3)
#> rprojroot 1.3-2 2018-01-03 [1] CRAN (R 3.5.3)
#> sessioninfo 1.1.1 2018-11-05 [1] CRAN (R 3.5.3)
#> stringi 1.4.3 2019-03-12 [1] CRAN (R 3.5.3)
#> stringr 1.4.0 2019-02-10 [1] CRAN (R 3.5.3)
#> testthat 2.0.1 2018-10-13 [1] CRAN (R 3.5.3)
#> usethis 1.5.0 2019-04-07 [1] CRAN (R 3.5.3)
#> withr 2.1.2 2018-03-15 [1] CRAN (R 3.5.3)
#> xfun 0.6 2019-04-02 [1] CRAN (R 3.5.3)
#> yaml 2.2.0 2018-07-25 [1] CRAN (R 3.5.3)
#>
#> [1] /home/lmmista-wap218/R/x86_64-pc-linux-gnu-library/3.5
#> [2] /usr/local/lib/R/site-library
#> [3] /usr/lib/R/site-library
#> [4] /usr/lib/R/library
from rlrsim.
Please include a reproducible example of the problem.
from rlrsim.
yes!
library(RLRsim)
library(nlme)
library(lme4)
form <- as.formula(c("~env+(env|ID)"))
beta <- c(1, -1)
names(beta) <- c("(Intercept)", "env")
N_ind <- 10
N_obs <- 100
simdat <- data.frame(ID = factor(rep(1:N_ind, each = N_obs)),
env = rnorm(N_ind * N_obs, 0, 1))
V_err0 <- 1
vcov <- matrix(c(0, 0, 0, 0), 2, 2)
theta <- c(0, 0, 0)
names(theta) <- c("ID.(Intercept)", "ID.env.(Intercept)", "ID.env")
response <- simulate(form, newdata = simdat, family = gaussian,
newparams = list(theta = theta, beta = beta, sigma = sqrt(V_err0)))
simdat$resp <- as.vector(response[, 1])
m0 <- lmer(respenv+(1|ID), data = simdat). + (0 + env|ID))
mA <- update(m0, .
mSlope <- update(mA, .~. - (1|ID))
rlrt_cr <- as.matrix(exactRLRT(mSlope, mA, m0, nsim = 1e5)$sample, ncol = 1)
Sometimes it works but sometimes exactRLRT(mSlope, mA, m0, nsim = 1e5)$sample is a function.
from rlrsim.
Thx for the example -- this is due to a shortcut I take in case when the observed test statistic is 0.
The p-value in that case is always 1, so there's no need to simulate anything.
If you do need a sample of the test statistic for such cases, I'm afraid you'll have to use the RLRsim function directly, sorry.
from rlrsim.
Gotcha, thank you so much!
from rlrsim.
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from rlrsim.