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Platform: x86_64-w64-mingw32/x64

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> ############################################################################
> #     MLwiN User Manual
> #
> # 6   Contextual Effects . . . . . . . . . . . . . . . . . . . . . . . . .79
> #
> #     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)
> 
> 
> data(tutorial, package = "R2MLwiN")
> 
> (mymodel1 <- runMLwiN(normexam ~ 1 + standlrt + (1 + standlrt | school) + (1 | student), 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 (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 : 0.1s 
Number of obs:  4059 (from total 4059)        The model converged after 4 iterations.
Log likelihood:      -4658.4 
Deviance statistic:  9316.9 
--------------------------------------------------------------------------------------------------- 
The model formula:
normexam ~ 1 + standlrt + (1 + standlrt | school) + (1 | student)
Level 2: school     Level 1: student      
--------------------------------------------------------------------------------------------------- 
The fixed part estimates:  
               Coef.   Std. Err.       z     Pr(>|z|)         [95% Conf.   Interval] 
Intercept   -0.01151     0.03978   -0.29       0.7724           -0.08948     0.06647 
standlrt     0.55673     0.01994   27.92   1.344e-171   ***      0.51765     0.59581 
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            0.09044     0.01792 
cov_Intercept_standlrt   0.01804     0.00672 
var_standlrt             0.01454     0.00441 
--------------------------------------------------------------------------------------------------- 
The random part estimates at the student level: 
                  Coef.   Std. Err. 
var_Intercept   0.55366     0.01248 
-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*- 
> 
> # 6.1 The impact of school gender on girls' achievement . . . . . . . . . 80
> 
> (mymodel2 <- runMLwiN(normexam ~ 1 + standlrt + sex + schgend + (1 + standlrt | school) + (1 | student), 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 (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 : 0.1s 
Number of obs:  4059 (from total 4059)        The model converged after 4 iterations.
Log likelihood:      -4640.6 
Deviance statistic:  9281.1 
--------------------------------------------------------------------------------------------------- 
The model formula:
normexam ~ 1 + standlrt + sex + schgend + (1 + standlrt | school) + 
    (1 | student)
Level 2: school     Level 1: student      
--------------------------------------------------------------------------------------------------- 
The fixed part estimates:  
                    Coef.   Std. Err.       z     Pr(>|z|)         [95% Conf.   Interval] 
Intercept        -0.18882     0.05135   -3.68     0.000236   ***     -0.28947    -0.08817 
standlrt          0.55443     0.01994   27.81   3.524e-170   ***      0.51535     0.59350 
sexgirl           0.16826     0.03382    4.98    6.524e-07   ***      0.10197     0.23455 
schgendboysch     0.17987     0.09914    1.81      0.06964   .       -0.01445     0.37418 
schgendgirlsch    0.17482     0.07876    2.22      0.02645   *        0.02045     0.32920 
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            0.07954     0.01597 
cov_Intercept_standlrt   0.02010     0.00649 
var_standlrt             0.01465     0.00441 
--------------------------------------------------------------------------------------------------- 
The random part estimates at the student level: 
                  Coef.   Std. Err. 
var_Intercept   0.55020     0.01240 
-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*- 
> 
> (mymodel3 <- runMLwiN(normexam ~ 1 + standlrt + sex + schgend + schgend:standlrt + (1 + standlrt | school) + (1 | student),
+  estoptions = list(startval = list(FP.b = mymodel2@FP, FP.v = mymodel2@FP.cov, RP.b = mymodel2@RP, RP.v = mymodel2@RP.cov)), 
+   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
JOIN -0.1888197756919 0.554426003109425 0.168263236565984 0.179866354527701 0.174823414870713 0 0 '_FP_b'
JOIN 0.00263695206715226 0.000327189405164753 0.000397525122485252 -0.000536443899325872 -2.59998235295558e-05 0.00114390721926941 -0.00235894200080809 1.05843244581469e-05 0.000514352069485757 0.00982881428441125 -0.00182798821608579 2.9913826620959e-05 -0.000629118882567792 0.0018534057412019 0.00620365697206955 0 0 0 0 0 0 0 0 0 0 0 0 0 '_FP_v'
JOIN 0.0795417396876042 0.0201008945830419 0.0146521072148694 0.550198312318614 '_RP_b'
JOIN 0.00025492576940079 5.84348555277209e-05 4.20881182530158e-05 1.30846738582449e-05 1.64017292133771e-05 1.94577130672965e-05 -2.89663433583334e-06 -2.36190338196134e-07 -2.64287337754297e-06 0.000153837417993462 '_RP_v'
TOLE 2
MAXI 20
NEXT
iteration 2
iteration 3

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 : 0.1s 
Number of obs:  4059 (from total 4059)        The model converged after 4 iterations.
Log likelihood:      -4640.4 
Deviance statistic:  9280.9 
--------------------------------------------------------------------------------------------------- 
The model formula:
normexam ~ 1 + standlrt + sex + schgend + schgend:standlrt + 
    (1 + standlrt | school) + (1 | student)
Level 2: school     Level 1: student      
--------------------------------------------------------------------------------------------------- 
The fixed part estimates:  
                             Coef.   Std. Err.       z    Pr(>|z|)         [95% Conf.   Interval] 
Intercept                 -0.18102     0.05357   -3.38   0.0007276   ***     -0.28602    -0.07602 
standlrt                   0.56390     0.02724   20.70   3.562e-95   ***      0.51050     0.61729 
sexgirl                    0.16764     0.03384    4.95   7.292e-07   ***      0.10131     0.23397 
schgendboysch              0.16658     0.11039    1.51      0.1313           -0.04977     0.38294 
schgendgirlsch             0.15687     0.08664    1.81     0.07022   .       -0.01295     0.32669 
standlrt:schgendboysch    -0.01593     0.05756   -0.28      0.7819           -0.12876     0.09689 
standlrt:schgendgirlsch   -0.02242     0.04486   -0.50      0.6173           -0.11035     0.06551 
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            0.07947     0.01598 
cov_Intercept_standlrt   0.02004     0.00649 
var_standlrt             0.01458     0.00441 
--------------------------------------------------------------------------------------------------- 
The random part estimates at the student level: 
                  Coef.   Std. Err. 
var_Intercept   0.55019     0.01240 
-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*- 
> 
> 
> # 6.2 Contextual effects of school intake ability averages . . . . . . . .83
> 
> (mymodel4 <- runMLwiN(normexam ~ 1 + standlrt + sex + schgend + schav + (1 + standlrt | school) + (1 | student), 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 (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 : 0.09s 
Number of obs:  4059 (from total 4059)        The model converged after 4 iterations.
Log likelihood:      -4639.2 
Deviance statistic:  9278.4 
--------------------------------------------------------------------------------------------------- 
The model formula:
normexam ~ 1 + standlrt + sex + schgend + schav + (1 + standlrt | 
    school) + (1 | student)
Level 2: school     Level 1: student      
--------------------------------------------------------------------------------------------------- 
The fixed part estimates:  
                    Coef.   Std. Err.       z     Pr(>|z|)         [95% Conf.   Interval] 
Intercept        -0.26481     0.08152   -3.25     0.001162   **      -0.42459    -0.10502 
standlrt          0.55155     0.02005   27.51   1.515e-166   ***      0.51225     0.59085 
sexgirl           0.16713     0.03382    4.94    7.751e-07   ***      0.10084     0.23342 
schgendboysch     0.18697     0.09769    1.91      0.05564   .       -0.00451     0.37844 
schgendgirlsch    0.15699     0.07774    2.02      0.04344   *        0.00463     0.30936 
schavmid          0.06694     0.08528    0.78       0.4325           -0.10021     0.23408 
schavhigh         0.17437     0.09868    1.77      0.07722   .       -0.01904     0.36777 
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            0.07076     0.01441 
cov_Intercept_standlrt   0.01609     0.00603 
var_standlrt             0.01470     0.00443 
--------------------------------------------------------------------------------------------------- 
The random part estimates at the student level: 
                  Coef.   Std. Err. 
var_Intercept   0.55016     0.01240 
-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*- 
> 
> (mymodel5 <- runMLwiN(normexam ~ 1 + standlrt + sex + schgend + schav + standlrt:schav + (1 + standlrt | school) + 
+   (1 | student), 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 (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 : 0.1s 
Number of obs:  4059 (from total 4059)        The model converged after 4 iterations.
Log likelihood:      -4634.2 
Deviance statistic:  9268.5 
--------------------------------------------------------------------------------------------------- 
The model formula:
normexam ~ 1 + standlrt + sex + schgend + schav + standlrt:schav + 
    (1 + standlrt | school) + (1 | student)
Level 2: school     Level 1: student      
--------------------------------------------------------------------------------------------------- 
The fixed part estimates:  
                        Coef.   Std. Err.       z    Pr(>|z|)         [95% Conf.   Interval] 
Intercept            -0.34725     0.08780   -3.96   7.651e-05   ***     -0.51933    -0.17517 
standlrt              0.45503     0.04181   10.88   1.385e-27   ***      0.37308     0.53697 
sexgirl               0.16760     0.03380    4.96   7.124e-07   ***      0.10135     0.23386 
schgendboysch         0.18928     0.09769    1.94     0.05267   .       -0.00218     0.38074 
schgendgirlsch        0.16086     0.07772    2.07     0.03848   *        0.00853     0.31320 
schavmid              0.14432     0.09421    1.53      0.1256           -0.04033     0.32898 
schavhigh             0.29044     0.10576    2.75     0.00603   **       0.08315     0.49773 
standlrt:schavmid     0.09222     0.04865    1.90     0.05801   .       -0.00313     0.18758 
standlrt:schavhigh    0.18046     0.05493    3.29    0.001019   **       0.07280     0.28812 
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            0.06931     0.01414 
cov_Intercept_standlrt   0.01359     0.00542 
var_standlrt             0.01072     0.00369 
--------------------------------------------------------------------------------------------------- 
The random part estimates at the student level: 
                  Coef.   Std. Err. 
var_Intercept   0.55028     0.01240 
-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*- 
> 
> pred <- predict(mymodel5, params = c("FP_schavhigh", "FP_standlrt:schavhigh"), se.fit = TRUE)
> 
> hilodiff <- pred$fit
> hilodiff_se <- pred$se.fit
> 
> hilodiff_lo <- hilodiff - 1.96 * hilodiff_se
> hilodiff_hi <- hilodiff + 1.96 * hilodiff_se
> 
> highdata <- as.data.frame(cbind(mymodel5@data$schavhigh, mymodel5@data[["standlrt:schavhigh"]], hilodiff, hilodiff_lo, 
+   hilodiff_hi)[order(mymodel5@data[["standlrt:schavhigh"]]), ])
> colnames(highdata) <- c("schavhigh", "standlrt:schavhigh", "hilodiff", "hilodiff_lo", "hilodiff_hi")
> highdata <- highdata[highdata$schavhigh == 1, ]
> 
> plot(highdata[["standlrt:schavhigh"]], highdata$hilodiff, type = "l")
> 
> if (!require(lattice)) {
+   warning("package lattice required to run this example")
+ } else {
+   xyplot(hilodiff ~ `standlrt:schavhigh`, panel = function(x, y, subscripts) {
+     panel.xyplot(x, y, type = "l")
+     panel.xyplot(x, highdata$hilodiff_hi, type = "l", lty = 2)
+     panel.xyplot(x, highdata$hilodiff_lo, type = "l", lty = 2)
+   }, data = highdata)
+ }
Loading required package: lattice
> 
> #     Chapter learning outcomes . . . . . . . . . . . . . . . . . . . . . 87
> 
> 
> 
> ############################################################################
> 
> proc.time()
   user  system elapsed 
   4.48    0.54    6.50