R version 4.4.1 (2024-06-14 ucrt) -- "Race for Your Life"
Copyright (C) 2024 The R Foundation for Statistical Computing
Platform: x86_64-w64-mingw32/x64

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> ############################################################################
> #     MLwiN User Manual
> #
> # 3   Residuals . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37
> #
> #     Rasbash, J., Steele, F., Browne, W. J. and Goldstein, H. (2012).
> #     A User's Guide to MLwiN, v2.26. Centre for Multilevel Modelling,
> #     University of Bristol.
> ############################################################################
> #     R script to replicate all analyses using R2MLwiN
> #
> #     Zhang, Z., Charlton, C., Parker, R, Leckie, G., and Browne, W.J.
> #     Centre for Multilevel Modelling, 2012
> #     http://www.bristol.ac.uk/cmm/software/R2MLwiN/
> ############################################################################
> 
> library(R2MLwiN)

R2MLwiN: A package to run models implemented in MLwiN from R
Copyright 2013-2024 Zhengzheng Zhang, Christopher M. J. Charlton, Richard M. A. Parker, William J. Browne and George Leckie

Support provided by the Economic and Social Research Council (ESRC)
(Grants RES-149-25-1084, RES-576-25-0032 and ES/K007246/1)

To cite R2MLwiN in publications use:

  Zhengzheng Zhang, Richard M. A. Parker, Christopher M. J. Charlton,
  George Leckie, William J. Browne (2016). R2MLwiN: A Package to Run
  MLwiN from within R. Journal of Statistical Software, 72(10), 1-43.
  doi:10.18637/jss.v072.i10

A BibTeX entry for LaTeX users is

  @Article{,
    title = {{R2MLwiN}: A Package to Run {MLwiN} from within {R}},
    author = {Zhengzheng Zhang and Richard M. A. Parker and Christopher M. J. Charlton and George Leckie and William J. Browne},
    journal = {Journal of Statistical Software},
    year = {2016},
    volume = {72},
    number = {10},
    pages = {1--43},
    doi = {10.18637/jss.v072.i10},
  }

The MLwiN_path option is currently set to C:/Program Files/MLwiN v3.11/
To change this use: options(MLwiN_path="<path to MLwiN>")
> # MLwiN folder
> mlwin <- getOption("MLwiN_path")
> while (!file.access(mlwin, mode = 1) == 0) {
+   cat("Please specify the root MLwiN folder or the full path to the MLwiN executable:\n")
+   mlwin <- scan(what = character(0), sep = "\n")
+   mlwin <- gsub("\\", "/", mlwin, fixed = TRUE)
+ }
> options(MLwiN_path = mlwin)
> 
> 
> # 3.1 What are multilevel residuals? . . . . . . . . . . . . . . . . . . .37
> 
> data(tutorial, package = "R2MLwiN")
> 
> (mymodel1 <- runMLwiN(normexam ~ 1 + (1 | school) + (1 | student), data = tutorial, estoptions = list(resi.store = TRUE)))
/nogui option ignored
ECHO    0


Echoing is ON
BATC 1

Batch mode is ON
MAXI 2
STAR
iteration 0
iteration 1

Convergence not achieved
TOLE 2
MAXI 20
NEXT
iteration 2

Convergence achieved
ECHO 0
Execution completed


-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*- 
MLwiN (version: 3.11)  multilevel model (Normal) 
        N min     mean max N_complete min_complete mean_complete max_complete
school 65   2 62.44615 198         65            2      62.44615          198
Estimation algorithm:  IGLS        Elapsed time : 0.11s 
Number of obs:  4059 (from total 4059)        The model converged after 3 iterations.
Log likelihood:      -5505.3 
Deviance statistic:  11010.6 
--------------------------------------------------------------------------------------------------- 
The model formula:
normexam ~ 1 + (1 | school) + (1 | student)
Level 2: school     Level 1: student      
--------------------------------------------------------------------------------------------------- 
The fixed part estimates:  
               Coef.   Std. Err.       z   Pr(>|z|)         [95% Conf.   Interval] 
Intercept   -0.01317     0.05363   -0.25      0.806           -0.11827     0.09194 
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1  
--------------------------------------------------------------------------------------------------- 
The random part estimates at the school level: 
                  Coef.   Std. Err. 
var_Intercept   0.16863     0.03245 
--------------------------------------------------------------------------------------------------- 
The random part estimates at the student level: 
                  Coef.   Std. Err. 
var_Intercept   0.84776     0.01897 
-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*- 
> 
> 
> 
> # 3.2 Calculating residuals in MLwiN . . . . . . . . . . . . . . . . . . .40
> 
> residuals <- mymodel1@residual$lev_2_resi_est_Intercept
> residualsCI <- 1.96 * sqrt(mymodel1@residual$lev_2_resi_var_Intercept)
> residualsRank <- rank(residuals)
> rankno <- order(residualsRank)
> 
> caterpillar(y = residuals[rankno], x = 1:65, qtlow = (residuals - residualsCI)[rankno],
+            qtup = (residuals + residualsCI)[rankno], xlab = 'Rank', ylab = 'Intercept')
> 
> # 3.3 Normal plots . . . . . . . . . . . . . . . . . . . . . . . . . . . .43
> 
> e0 <- mymodel1@residual$lev_1_resi_est_Intercept
> 
> e0std <- (e0 - mean(e0))/sd(e0)
> 
> e0rank <- rank(e0)
> 
> e0uniform <- (e0rank - 0.5)/length(e0rank)
> 
> e0nscore <- qnorm(e0uniform)
> 
> plot(e0nscore, e0std, asp = 1)
> 
> 
> u0 <- mymodel1@residual$lev_2_resi_est_Intercept
> 
> u0std <- (u0 - mean(u0))/sd(u0)
> 
> u0rank <- rank(u0)
> 
> u0uniform <- (u0rank - 0.5)/length(u0rank)
> 
> u0nscore <- qnorm(u0uniform)
> 
> plot(u0nscore, u0std, asp = 1)
> 
> # Chapter learning outcomes . . . . . . . . . . . . . . . . . . . . . . . 45
> 
> 
> ############################################################################
> 
> proc.time()
   user  system elapsed 
   4.39    0.34    5.12