Nothing

If pilot data is not available, `simr`

can be used to create `lme4`

objects from scratch as a starting point. This requires more paramters to be specified by the user. Values for these parameters might come from the literature or the user's own knowledge and experience.

```
library(simr)
```

simrOptions(nsim=100, progress=FALSE)

First set up some covariates with `expand.grid`

.

x <- 1:10 g <- letters[1:3] X <- expand.grid(x=x, g=g)

Specify some fixed and random parameters.

b <- c(2, -0.1) # fixed intercept and slope V1 <- 0.5 # random intercept variance V2 <- matrix(c(0.5,0.05,0.05,0.1), 2) # random intercept and slope variance-covariance matrix s <- 1 # residual standard deviation

Use the `makeLmer`

or `makeGlmer`

function to build an artificial `lme4`

object.

model1 <- makeLmer(y ~ x + (1|g), fixef=b, VarCorr=V1, sigma=s, data=X) print(model1) model2 <- makeGlmer(z ~ x + (x|g), family="poisson", fixef=b, VarCorr=V2, data=X) print(model2)

Now we have "pilot" models, which can be used with `simr`

.

powerSim(model1, nsim=20) powerSim(model2, nsim=20)

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