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View Code? Open in Web Editor NEWR package for fast & exact (restricted) likelihood ratio tests for mixed and additive models
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No support for models of class "lm" so cannot compare "lmer" to "lm"
I cannot work out how to use exactRLRT to test a case with a single random effect. This is because lme4 will not let me create a model with no random effects and RLRsim does not support models of class lm.
set.seed(123)
group_intercepts <-
tibble(
group_id = 1:10,
group_effect = rnorm(10)
)
random_data <-
tibble(
group_id = rep(1:10, each = 10),
x = rnorm(100)
) |>
inner_join(group_intercepts, by = "group_id") |>
mutate(
y = 2 * x + group_effect + rnorm(100) / 2
)
m_full <- lmer(y ~ x - 1 + (1 | group_id), data = random_data)
summary(m_full)
m_restricted <- lm(y ~ x - 1, data = random_data)
summary(m_restricted)
exactRLRT(m = m_full, m0 = m_restricted)
exactRLRT(m = m_full, mA = m_full, m0 = m_restricted)The last 2 lines are me trying to get it to work, but I get errors about not being able to find a method getME for class NULL and lm respectively. Am I doing this right?
installation from source file failed
Dear Fabian,
When I tried to install from source file in my linux machine using
install.packages("PATH/RLRsim_3.0.tar.gz", repos = NULL, type="source")
Error message reported as
*** arch - i386
ERROR: compilation failed for package 'RLRsim'
However, intallation using the windows binary works for my windows machine. What might be the reason?
Thanks a lot.
Ni
interface for mgcv fits
just do it already!
extract.lmeDesign(lme4_object) not working
Hello,
is it possible to allow the extraction of a s4 class lme4 object?
Currently it gives an error:
library(lme4)
m<-lme4::lmer(Reaction ~ Days + (Days|Subject), sleepstudy)
extract.lmeDesign(m)
Error: $ operator not defined for this S4 class
change in anova() names breaks
This change breaks exactRLRT: I will try to fork / fix it/ submit a PR. Don't know when a new release of lme4 is happening, but it might be soon-ish.
sample of exactRLRT
exactRLRT(m0)$sample is a function instead of simulated test statistics. How should I fix it?
model.matrix warning about contrasts
### R code from vignette source 'Ch-LME.Rnw'
###################################################
### code chunk number 1: setup
###################################################
library("MVA")
set.seed(280875)
###################################################
### code chunk number 2: setup
###################################################
library("nlme")
###################################################
### code chunk number 6: ch:LME:timber:tab
###################################################
"timber" <-
matrix(c(0., 0., 0., 0., 0., 0., 0., 0., 2.3799999999999999, 2.6899999999999999, 2.8500000000000001, 2.46,
2.9700000000000002, 3.96, 3.1699999999999999, 3.3599999999999999, 4.3399999999999999, 4.75,
4.8899999999999997, 4.2800000000000002, 4.6799999999999997, 6.46, 5.3300000000000001, 5.4500000000000002,
6.6399999999999997, 7.04, 6.6100000000000003, 5.8799999999999999, 6.6600000000000001, 8.1400000000000006,
7.1399999999999997, 7.0800000000000001, 8.0500000000000007, 9.1999999999999993, 8.0899999999999999,
7.4299999999999997, 8.1099999999999994, 9.3499999999999996, 8.2899999999999991, 8.3200000000000003,
9.7799999999999994, 10.94, 9.7200000000000006, 8.3200000000000003, 9.6400000000000006, 10.720000000000001,
9.8599999999999994, 9.9100000000000001, 10.970000000000001, 12.23, 11.029999999999999, 9.9199999999999999,
11.06, 11.84, 11.07, 11.06, 12.050000000000001, 13.19, 12.140000000000001, 11.1, 12.25, 12.85,
12.130000000000001, 12.210000000000001, 12.98, 14.08, 13.18, 12.23, 13.35, 13.83, 13.15, 13.16, 13.94,
14.66, 14.119999999999999, 13.24, 14.539999999999999, 14.85, 14.09, 14.050000000000001, 14.74,
15.369999999999999, 15.09, 14.19, 15.529999999999999, 15.789999999999999, 15.109999999999999,
14.960000000000001, 16.129999999999999, 16.890000000000001, 16.68, 16.07, 17.379999999999999,
17.390000000000001, 16.690000000000001, 16.239999999999998, 17.98, 17.780000000000001, 17.940000000000001,
17.43, 18.760000000000002, 18.440000000000001, 17.690000000000001, 17.34, 19.52, 18.41, 18.219999999999999,
18.359999999999999, 19.809999999999999, 19.460000000000001, 18.710000000000001, 18.23, 19.969999999999999,
18.969999999999999, 19.399999999999999, 18.93, 20.620000000000001, 20.050000000000001, 19.539999999999999,
18.870000000000001)
, nrow = 8, ncol = 15)
slippage <- c((0:10)/10, seq(from = 1.2, to = 1.8, by = 0.2))
colnames(timber) <- paste("s", slippage, sep = "")
timber <- as.data.frame(timber)
timber$specimen <- factor(paste("spec", 1:nrow(timber), sep = ""))
timber.dat <- reshape(timber, direction = "long", idvar = "specimen",
varying = matrix(colnames(timber)[1:15], nr = 1),
timevar = "slippage")
names(timber.dat)[3] <- "loads"
timber.dat$slippage <- slippage[timber.dat$slippage]
timber <- timber.dat
###################################################
### code chunk number 11: ch:LME:timber:lme
###################################################
timber.lme <- lme(loads ~ slippage,
random = ~1 | specimen,
data = timber, method = "ML")
###################################################
### code chunk number 12: ch:LME:timber:LRT
###################################################
library("RLRsim")
exactRLRT(timber.lme)
#Using restricted likelihood evaluated at ML estimators.
#Refit with method="REML" for exact results.
# simulated finite sample distribution of RLRT.
# (p-value based on 10000 simulated values)
#data:
#RLRT = 3.2183e-07, p-value = 0.4419
#Warning message:
#In model.matrix.default(~m$groups[[n.levels - i + 1]] - 1, contrasts.arg = c("contr.treatment", :
# non-list contrasts argument ignoredtest in models with multiple VCs without redoing fit
Needs better formula parsing for nlme and lme4. May mean redoing the interface for exact(R)LRT - deprecate in CRAN version?
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