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

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.

> ############################################################################
> #     MLwiN User Manual
> #
> # 14  Multivariate Response Models . . . . . . . . . . . . . . . . . . . 211
> #
> #     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)
> 
> 
> # 14.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . .211
> 
> data(gcsemv1, package = "R2MLwiN")
> summary(gcsemv1)
     school         student        female        agemths         written      
 Min.   :20920   Min.   :   1   Male  : 777   Min.   :186.0   Min.   : 0.625  
 1st Qu.:60501   1st Qu.:  64   Female:1128   1st Qu.:205.0   1st Qu.:37.500  
 Median :68133   Median : 133                 Median :510.0   Median :46.875  
 Mean   :62128   Mean   :1037                 Mean   :389.8   Mean   :46.798  
 3rd Qu.:68411   3rd Qu.: 458                 3rd Qu.:510.0   3rd Qu.:55.625  
 Max.   :84772   Max.   :5521                 Max.   :510.0   Max.   :90.000  
                                                              NA's   :202     
    csework             cons  
 Min.   :  9.259   Min.   :1  
 1st Qu.: 62.963   1st Qu.:1  
 Median : 75.926   Median :1  
 Mean   : 73.435   Mean   :1  
 3rd Qu.: 86.111   3rd Qu.:1  
 Max.   :100.000   Max.   :1  
 NA's   :180                  
> 
> # 14.2 Specifying a multivariate model . . . . . . . . . . . . . . . . . 212
> 
> # 14.3 Setting up the basic model . . . . . . . . . . . . . . . . . . . .214
> 
> (mymodel1 <- runMLwiN(c(written, csework) ~ 1 + (1 | student), D = "Multivariate Normal", estoptions = list(sort.ignore = TRUE), 
+   data = gcsemv1))
/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 (Multivariate Normal) 
Estimation algorithm:  IGLS        Elapsed time : 0.25s 
Number of obs:  1905 (from total 1905)        The model converged after 3 iterations.
Log likelihood:      -13903.9 
Deviance statistic:  27807.9 
--------------------------------------------------------------------------------------------------- 
The model formula:
c(written, csework) ~ 1 + (1 | student)
Level 1: student      
--------------------------------------------------------------------------------------------------- 
The fixed part estimates:  
                       Coef.   Std. Err.        z   Pr(>|z|)         [95% Conf.   Interval] 
Intercept_written   46.80278     0.32017   146.18          0   ***     46.17527    47.43030 
Intercept_csework   73.36400     0.38821   188.98          0   ***     72.60313    74.12488 
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1  
--------------------------------------------------------------------------------------------------- 
The random part estimates at the student level: 
                                              Coef.   Std. Err. 
var_Intercept_written                     178.71008     6.10784 
cov_Intercept_written_Intercept_csework   102.31138     5.91751 
var_Intercept_csework                     265.44838     9.01669 
-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*- 
> 
> (mymodel2 <- runMLwiN(c(written, csework) ~ 1 + female + (1 | school) + (1 | student), D = "Multivariate Normal", 
+   data = gcsemv1))
/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
iteration 3

Convergence achieved
ECHO 0
Execution completed


-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*- 
MLwiN (version: 3.11)  multilevel model (Multivariate Normal) 
        N min     mean max N_complete min_complete mean_complete max_complete
school 73   2 26.09589 104         73            2      26.09589          104
Estimation algorithm:  IGLS        Elapsed time : 0.4s 
Number of obs:  1905 (from total 1905)        The model converged after 4 iterations.
Log likelihood:      -13400.2 
Deviance statistic:  26800.5 
--------------------------------------------------------------------------------------------------- 
The model formula:
c(written, csework) ~ 1 + female + (1 | school) + (1 | student)
Level 2: school     Level 1: student      
--------------------------------------------------------------------------------------------------- 
The fixed part estimates:  
                          Coef.   Std. Err.       z    Pr(>|z|)         [95% Conf.   Interval] 
Intercept_written      49.45213     0.93384   52.96           0   ***     47.62183    51.28243 
Intercept_csework      69.67166     1.17179   59.46           0   ***     67.37500    71.96831 
femaleFemale_written   -2.50295     0.56072   -4.46   8.052e-06   ***     -3.60194    -1.40396 
femaleFemale_csework    6.75139     0.67065   10.07   7.734e-24   ***      5.43694     8.06584 
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_written                     46.81298     9.18733 
cov_Intercept_written_Intercept_csework   24.87783     8.88036 
var_Intercept_csework                     75.16623    14.56485 
--------------------------------------------------------------------------------------------------- 
The random part estimates at the student level: 
                                              Coef.   Std. Err. 
var_Intercept_written                     124.63432     4.34983 
cov_Intercept_written_Intercept_csework    73.00323     4.17829 
var_Intercept_csework                     180.09817     6.24580 
-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*- 
> 
> mymodel2@RP["RP2_cov_Intercept_written_Intercept_csework"]/sqrt(mymodel2@RP["RP2_var_Intercept_written"] * mymodel2@RP["RP2_var_Intercept_csework"])
RP2_cov_Intercept_written_Intercept_csework 
                                  0.4193899 
> 
> mymodel2@RP["RP1_cov_Intercept_written_Intercept_csework"]/sqrt(mymodel2@RP["RP1_var_Intercept_written"] * mymodel2@RP["RP1_var_Intercept_csework"])
RP1_cov_Intercept_written_Intercept_csework 
                                  0.4872688 
> 
> # 14.4 A more elaborate model . . . . . . . . . . . . . . . . . . . . . .219
> 
> (mymodel3 <- runMLwiN(c(written, csework) ~ 1 + female + (1 + female | school) + (1 | student), D = "Multivariate Normal", 
+   data = gcsemv1))
/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
iteration 3
iteration 4
iteration 5
iteration 6

Convergence achieved
ECHO 0
Execution completed


-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*- 
MLwiN (version: 3.11)  multilevel model (Multivariate Normal) 
        N min     mean max N_complete min_complete mean_complete max_complete
school 73   2 26.09589 104         73            2      26.09589          104
Estimation algorithm:  IGLS        Elapsed time : 0.94s 
Number of obs:  1905 (from total 1905)        The model converged after 7 iterations.
Log likelihood:      -13378.1 
Deviance statistic:  26756.1 
--------------------------------------------------------------------------------------------------- 
The model formula:
c(written, csework) ~ 1 + female + (1 + female | school) + (1 | 
    student)
Level 2: school     Level 1: student      
--------------------------------------------------------------------------------------------------- 
The fixed part estimates:  
                          Coef.   Std. Err.       z    Pr(>|z|)         [95% Conf.   Interval] 
Intercept_written      49.40112     0.99594   49.60           0   ***     47.44912    51.35312 
Intercept_csework      69.30076     1.35678   51.08           0   ***     66.64152    71.96000 
femaleFemale_written   -2.47108     0.64394   -3.84   0.0001243   ***     -3.73318    -1.20898 
femaleFemale_csework    7.15671     1.13530    6.30   2.904e-10   ***      4.93157     9.38186 
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_written                            54.54177    11.77079 
cov_Intercept_written_Intercept_csework          38.89313    12.68564 
var_Intercept_csework                           104.69574    21.82904 
cov_Intercept_written_femaleFemale_written       -7.44933     5.68375 
cov_Intercept_csework_femaleFemale_written       -6.97482     7.29527 
var_femaleFemale_written                          5.28220     4.23883 
cov_Intercept_written_femaleFemale_csework      -21.10519     9.96561 
cov_Intercept_csework_femaleFemale_csework      -39.67547    14.69664 
cov_femaleFemale_written_femaleFemale_csework    11.00389     6.29634 
var_femaleFemale_csework                         49.90979    14.60603 
--------------------------------------------------------------------------------------------------- 
The random part estimates at the student level: 
                                              Coef.   Std. Err. 
var_Intercept_written                     123.40991     4.37104 
cov_Intercept_written_Intercept_csework    70.55722     4.10549 
var_Intercept_csework                     169.80446     5.99977 
-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*- 
> 
> (mymodel4 <- runMLwiN(c(written, csework) ~ 1 + female + (1 + female[1] | school) + (1 | student), D = "Multivariate Normal", 
+   estoptions = list(resi.store = TRUE), data = gcsemv1))
/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
iteration 3
iteration 4
iteration 5
iteration 6
iteration 7

Convergence achieved
ECHO 0
Execution completed


-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*- 
MLwiN (version: 3.11)  multilevel model (Multivariate Normal) 
        N min     mean max N_complete min_complete mean_complete max_complete
school 73   2 26.09589 104         73            2      26.09589          104
Estimation algorithm:  IGLS        Elapsed time : 0.94s 
Number of obs:  1905 (from total 1905)        The model converged after 8 iterations.
Log likelihood:      -13399.6 
Deviance statistic:  26799.2 
--------------------------------------------------------------------------------------------------- 
The model formula:
c(written, csework) ~ 1 + female + (1 + female[1] | school) + 
    (1 | student)
Level 2: school     Level 1: student      
--------------------------------------------------------------------------------------------------- 
The fixed part estimates:  
                       Coef.   Std. Err.       z    Pr(>|z|)         [95% Conf.   Interval] 
Intercept_written   49.51235     0.94927   52.16           0   ***     47.65181    51.37289 
Intercept_csework   69.65891     1.17223   59.42           0   ***     67.36139    71.95644 
femaleFemale_1      -2.58355     0.63117   -4.09   4.254e-05   ***     -3.82063    -1.34648 
femaleFemale_2       6.75772     0.67057   10.08   6.943e-24   ***      5.44342     8.07201 
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_written                     48.19051    10.44075 
cov_Intercept_written_Intercept_csework   23.72850     9.34337 
var_Intercept_csework                     75.23904    14.57476 
cov_Intercept_written_femaleFemale_1      -2.54470     4.60831 
cov_Intercept_csework_femaleFemale_1       2.00384     5.27357 
var_femaleFemale_1                         4.10047     3.41483 
--------------------------------------------------------------------------------------------------- 
The random part estimates at the student level: 
                                              Coef.   Std. Err. 
var_Intercept_written                     123.99295     4.37418 
cov_Intercept_written_Intercept_csework    73.30694     4.18134 
var_Intercept_csework                     180.08374     6.24510 
-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*- 
> 
> mymodel4@RP["RP2_cov_Intercept_written_Intercept_csework"]/sqrt(mymodel4@RP["RP2_var_Intercept_written"] * mymodel4@RP["RP2_var_Intercept_csework"])
RP2_cov_Intercept_written_Intercept_csework 
                                   0.394065 
> 
> mymodel4@RP["RP2_cov_Intercept_written_femaleFemale_1"]/sqrt(mymodel4@RP["RP2_var_Intercept_written"] * mymodel4@RP["RP2_var_femaleFemale_1"])
RP2_cov_Intercept_written_femaleFemale_1 
                              -0.1810253 
> 
> mymodel4@RP["RP2_cov_Intercept_csework_femaleFemale_1"]/sqrt(mymodel4@RP["RP2_var_Intercept_csework"] * mymodel4@RP["RP2_var_femaleFemale_1"])
RP2_cov_Intercept_csework_femaleFemale_1 
                                0.114084 
> 
> u0 <- mymodel4@residual$lev_2_resi_est_Intercept.written
> u1 <- mymodel4@residual$lev_2_resi_est_Intercept.csework
> u2 <- mymodel4@residual$lev_2_resi_est_femaleFemale.1
> 
> plot(u0, u0, asp = 1)
> plot(u0, u1, asp = 1)
> plot(u0, u2, asp = 1)
> plot(u1, u1, asp = 1)
> plot(u1, u2, asp = 1)
> plot(u2, u2, asp = 1)
> 
> # 14.5 Multivariate models for discrete responses . . . . . . . . . . . .222
> 
> data(tutorial, package = "R2MLwiN")
> 
> tutorial$binexam <- as.integer(tutorial$normexam > 0)
> tutorial$binlrt <- as.integer(tutorial$standlrt > 0)
> 
> (mymodel5 <- runMLwiN(c(logit(binexam), logit(binlrt)) ~ 1, D = c("Mixed", "Binomial", "Binomial"), estoptions = list(sort.ignore = TRUE), 
+   data = tutorial))
/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
iteration 3

Convergence achieved
ECHO 0
Execution completed


-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*- 
MLwiN (version: 3.11)  multilevel model (Mixed) 
Estimation algorithm:  IGLS MQL1        Elapsed time : 0.68s 
Number of obs:  4059 (from total 4059)        The model converged after 4 iterations.
Log likelihood:      NA 
Deviance statistic:  NA 
--------------------------------------------------------------------------------------------------- 
The model formula:
c(logit(binexam), logit(binlrt)) ~ 1
Level 1: l1id      
--------------------------------------------------------------------------------------------------- 
The fixed part estimates:  
                      Coef.   Std. Err.      z   Pr(>|z|)         [95% Conf.   Interval] 
Intercept_binexam   0.04879     0.03140   1.55     0.1202           -0.01276     0.11034 
Intercept_binlrt    0.06062     0.03141   1.93    0.05357   .       -0.00093     0.12218 
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1  
--------------------------------------------------------------------------------------------------- 
The random part estimates at the l1id level: 
                        Coef.   Std. Err. 
var_bcons_1           1.00000     0.00000 
cov_bcons_1_bcons_2   0.41914     0.01193 
var_bcons_2           1.00000     0.00000 
-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*- 
> 
> 
> # Chapter learning outcomes . . . . . . . . . . . . . . . . . . . . . . .224
> 
> ############################################################################
> 
> proc.time()
   user  system elapsed 
   3.75    0.46    8.34