# varTestnlme

varTestnlme
implements the likelihood ratio test (LRT) for testing the presence of
random effects in linear, generalized linear and nonlinear mixed-effects
model. The test can be used to answer questions of the type:

- should a certain subset of random effects be in fact considered as
fixed effects?
- is there any random effects in the model?
- are there any correlation between two subsets of random
effects?

It is possible to compare two models with different random effects,
provided that the random structures of the two models are
**nested**.

The package works on models that were fitted using nlme, lme4, or saemix
packages.

## Reference

Baey C, Cournède P-H, Kuhn E, 2019. Asymptotic distribution of
likelihood ratio test statistics for variance components in nonlinear
mixed effects models. 135:107–122 (2019), https://doi.org/10.1016/j.csda.2019.01.014

## Installation

Install from CRAN:

`install.packages("varTestnlme")`

Or install the development version from Github:

```
install.packages("devtools")
devtools::install_github("baeyc/varTestnlme")
```

## Example

An example using the `nlme`

package.

**Since version 1.0.0, the name of the main function has been
changed from **`varTest`

to `varCompTest`

due to a
conflict with an existing function from package
`EnvStats`

.

```
library(nlme)
data("Orthodont")
# using nlme, with correlated slope and intercept
m1 <- lme(distance ~ 1 + Sex + age + age*Sex, random = pdSymm(Subject ~ 1 + age), data = Orthodont, method = "ML")
m0 <- lme(distance ~ 1 + Sex + age + age*Sex, random = ~ 1 | Subject, data = Orthodont, method = "ML")
vt <- varCompTest(m1,m0)
#> Variance components testing in mixed effects models
#> Testing that the variance of the random effect associated to age is equal to 0
#> Likelihood ratio test statistic:
#> LRT = 0.8331072
#>
#> p-value from exact weights: 0.5103454
#>
```

It works similarly with lme4 package or saemix.