R Under development (unstable) (2024-11-07 r87302 ucrt) -- "Unsuffered Consequences"
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> library("model4you")
Loading required package: partykit
Loading required package: grid
Loading required package: libcoin
Loading required package: mvtnorm
> 
> set.seed(2018)
> n <- 160
> trt <- factor(rep(0:1, each = n/2))
> y <- 4 + (trt == 1) + rnorm(n)
> z <- matrix(rnorm(n * 2), ncol = 2)
> 
> dat <- data.frame(y, trt, z)
> 
> mod <- lm(y ~ trt, data = dat)
> 
> ## Note that ntree should usually be higher
> frst <- pmforest(mod, ntree = 20) 
No data given. I'm using data set dat from the current environment parent.frame(). Please check if that is what you want.
> pmods <- pmodel(frst, fun = identity)
> 
> sum(objfun(pmods))
[1] -179.7791
> 
> ## Note that B should be at least 100
> ## The low B is just for demonstration 
> ## purposes.
> tst <- pmtest(forest = frst, 
+               pmodels = pmods, 
+               B = 5) 
No data given. I'm using data set dat from the current environment parent.frame(). Please check if that is what you want.
> tst
$pvalue
[1] 0

$objfun
      base_model personalised_models   sim hypothesis      diff
1      -161.3734           -179.7791 FALSE       H[1] -18.40569
sim_1  -155.1617           -182.4686  TRUE       H[0] -27.30692
sim_2  -180.4936           -220.7145  TRUE       H[0] -40.22091
sim_3  -153.3232           -173.0736  TRUE       H[0] -19.75034
sim_4  -135.1752           -157.5817  TRUE       H[0] -22.40648
sim_5  -112.2658           -133.9999  TRUE       H[0] -21.73409

attr(,"class")
[1] "heterogeneity_test"
> 
> proc.time()
   user  system elapsed 
  13.67    0.70   14.35