R Under development (unstable) (2024-11-03 r87286 ucrt) -- "Unsuffered Consequences"
Copyright (C) 2024 The R Foundation for Statistical Computing
Platform: x86_64-w64-mingw32/x64

R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.

R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.

Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.

> library("glmertree")
Loading required package: lme4
Loading required package: Matrix
Loading required package: partykit
Loading required package: grid
Loading required package: libcoin
Loading required package: mvtnorm
> options(width = 70, prompt = "R> ", continue = "+  ", digits = 4)
R> 
R> data("DepressionDemo", package = "glmertree")
R> summary(DepressionDemo)
   depression          treatment     cluster        age    
 Min.   : 3.00   Treatment 1:78   1      :15   Min.   :18  
 1st Qu.: 7.00   Treatment 2:72   2      :15   1st Qu.:39  
 Median : 9.00                    3      :15   Median :45  
 Mean   : 9.12                    4      :15   Mean   :45  
 3rd Qu.:11.00                    5      :15   3rd Qu.:52  
 Max.   :16.00                    6      :15   Max.   :69  
                                  (Other):60               
    anxiety        duration     depression_bin
 Min.   : 3.0   Min.   : 1.00   0:78          
 1st Qu.: 8.0   1st Qu.: 5.00   1:72          
 Median :10.0   Median : 7.00                 
 Mean   :10.3   Mean   : 6.97                 
 3rd Qu.:12.0   3rd Qu.: 9.00                 
 Max.   :18.0   Max.   :17.00                 
                                              
R> 
R> lmm_tree <- lmertree(depression ~ treatment | cluster | age + duration + anxiety,
+    data = DepressionDemo)
R> plot(lmm_tree, which = "tree")
R> plot(lmm_tree, which = "ranef")
$cluster

R> coef(lmm_tree)
  (Intercept) treatmentTreatment 2
3       7.500               4.1221
4       8.591               0.5213
5      11.088              -4.5469
R> ranef(lmm_tree)$cluster
   (Intercept)
1     -0.30964
2     -0.34155
3     -0.06755
4     -0.57676
5     -0.15247
6     -0.08762
7      0.12906
8      0.22501
9      0.26126
10     0.92027
R> predict(lmm_tree, newdata = DepressionDemo[1:7, ])
     1      2      3      4      5      6      7 
10.778 11.555  7.159  9.045 11.281  8.816 11.883 
R> predict(lmm_tree, newdata = DepressionDemo[1:7, -3], re.form = NA)
     1      2      3      4      5      6      7 
11.088 11.622  7.500  9.113 11.622  8.591 11.622 
R> residuals(lmm_tree)[1:10]
      1       2       3       4       5       6       7       8 
 2.2220  2.4453 -0.1586  0.9549 -1.2807  1.1836 -1.8835 -1.7711 
      9      10 
 0.8008  4.0766 
R> predict(lmm_tree)[1:10]
     1      2      3      4      5      6      7      8      9     10 
10.778 11.555  7.159  9.045 11.281  8.816 11.883  8.771  6.199  6.923 
R> 
R> glmm_tree <- glmertree(depression_bin ~ treatment | cluster | age + duration + anxiety,
+    data = DepressionDemo, family = binomial)
R> plot(glmm_tree, which = "tree")
Loading required namespace: vcd
R> plot(glmm_tree, which = "ranef")
$cluster

R> coef(glmm_tree)
  (Intercept) treatmentTreatment 2
3     -2.0406               3.0486
4      0.8951              -0.4461
5      1.9203              -4.8921
R> ranef(glmm_tree)$cluster
   (Intercept)
1     -0.28669
2     -0.27530
3      0.04737
4      0.05603
5     -0.10327
6     -0.18038
7      0.26866
8      0.23237
9      0.04446
10     0.17721
R> predict(glmm_tree, newdata = DepressionDemo[1:7, ])
      1       2       3       4       5       6       7 
0.83666 0.74181 0.08982 0.62160 0.67541 0.75537 0.74126 
R> predict(glmm_tree, newdata = DepressionDemo[1:7, -3], re.form = NA)
     1      2      3      4      5      6      7 
0.8722 0.7326 0.1150 0.6104 0.7326 0.7099 0.7326 
R> residuals(glmm_tree)[1:10]
      1       2       3       4       5       6       7       8 
 0.5972  0.7729 -0.4338  0.9751  0.8859  0.7491  0.7738 -1.5001 
      9      10 
-0.2762  2.0559 
R> predict(glmm_tree)[1:10]
      1       2       3       4       5       6       7       8 
0.83666 0.74181 0.08982 0.62160 0.67541 0.75537 0.74126 0.67541 
      9      10 
0.03743 0.12084 
R> 
> proc.time()
   user  system elapsed 
   2.20    0.20    2.39