
R Under development (unstable) (2024-08-14 r87014) -- "Unsuffered Consequences"
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> ## run reproduction scripts from the installed "mlbook" chapters
> testdir <- system.file("mlbook", package = "nlme", mustWork = TRUE)
> scripts <- dir(testdir, pattern = "^ch[0-9]*\\.R$")
> for(f in scripts) {
+     writeLines(c("", strrep("=", nchar(f)), basename(f), strrep("=", nchar(f))))
+     set.seed(1)
+     options(warn = 1, digits = 5)
+     source(file.path(testdir, f), echo = TRUE,
+            max.deparse.length = Inf, keep.source = TRUE)
+ }

======
ch04.R
======

> library(nlme)

> # data(bdf)
> ## Fit the null model
> ## Compare with Table 4.1, p. 47
> fm1 <- lme(langPOST ~ 1, data = bdf, random = ~ 1 | schoolNR)

> VarCorr(fm1)
schoolNR = pdLogChol(1) 
            Variance StdDev
(Intercept) 19.633   4.4309
Residual    64.564   8.0352

> -2*c(logLik(fm1))                       # deviance
[1] 16253

> ## Fit model with fixed IQ term and random intercept
> ## Compare with Table 4.2, p. 49
> ## From the results in Tables 4.2 and 4.4, it appears that
> ##  maximum likelihood fits are used, not REML fits.
> fm2 <- update(fm1, langPOST ~ IQ.ver.cen)

> summary(fm2)
Linear mixed-effects model fit by REML
  Data: bdf 
    AIC   BIC  logLik
  15264 15287 -7627.9

Random effects:
 Formula: ~1 | schoolNR
        (Intercept) Residual
StdDev:      3.0987   6.4996

Fixed effects:  langPOST ~ IQ.ver.cen 
             Value Std.Error   DF t-value p-value
(Intercept) 40.608  0.308186 2155 131.766       0
IQ.ver.cen   2.488  0.070081 2155  35.496       0
 Correlation: 
           (Intr)
IQ.ver.cen 0.018 

Standardized Within-Group Residuals:
      Min        Q1       Med        Q3       Max 
-4.093938 -0.637456  0.057947  0.706070  3.144829 

Number of Observations: 2287
Number of Groups: 131 

> VarCorr(fm2)
schoolNR = pdLogChol(1) 
            Variance StdDev
(Intercept)  9.6017  3.0987
Residual    42.2445  6.4996

> -2 * c(logLik(fm2))                     # deviance
[1] 15256

> ## Purely fixed-effects model for comparison
> ## Compare with Table 4.3, p. 51
> fm3 <- lm(langPOST ~ IQ.ver.cen, data = bdf)

> summary(fm3)

Call:
lm(formula = langPOST ~ IQ.ver.cen, data = bdf)

Residuals:
    Min      1Q  Median      3Q     Max 
-28.702  -4.394   0.606   5.260  26.221 

Coefficients:
            Estimate Std. Error t value Pr(>|t|)    
(Intercept)  40.9348     0.1492   274.3   <2e-16 ***
IQ.ver.cen    2.6539     0.0722    36.8   <2e-16 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 7.14 on 2285 degrees of freedom
Multiple R-squared:  0.372,	Adjusted R-squared:  0.372 
F-statistic: 1.35e+03 on 1 and 2285 DF,  p-value: <2e-16


> -2 * c(logLik(fm3))                     # deviance
[1] 15478

> ## Model with average IQ for the school
> ## Compare with Table 4.4, p. 55
> fm4 <- update(fm2, langPOST ~ IQ.ver.cen + avg.IQ.ver.cen)

> summary(fm4)
Linear mixed-effects model fit by REML
  Data: bdf 
    AIC   BIC  logLik
  15242 15271 -7616.1

Random effects:
 Formula: ~1 | schoolNR
        (Intercept) Residual
StdDev:      2.8082    6.494

Fixed effects:  langPOST ~ IQ.ver.cen + avg.IQ.ver.cen 
                Value Std.Error   DF t-value p-value
(Intercept)    40.741  0.286595 2155 142.155       0
IQ.ver.cen      2.415  0.071676 2155  33.690       0
avg.IQ.ver.cen  1.589  0.314772  129   5.049       0
 Correlation: 
               (Intr) IQ.vr.
IQ.ver.cen      0.000       
avg.IQ.ver.cen  0.077 -0.228

Standardized Within-Group Residuals:
      Min        Q1       Med        Q3       Max 
-4.130749 -0.642218  0.060944  0.702342  3.152993 

Number of Observations: 2287
Number of Groups: 131 

> VarCorr(fm4)
schoolNR = pdLogChol(1) 
            Variance StdDev
(Intercept)  7.8859  2.8082
Residual    42.1723  6.4940

> -2 * c(logLik(fm4))                     # deviance
[1] 15232

======
ch05.R
======

> library(nlme)

> # data(bdf)
> ## Model with random slope for IQ.ver.cen
> ## Compare with Table 5.1, p. 71.
> fm5 <- lme(langPOST ~ IQ.ver.cen + avg.IQ.ver.cen,
+            data = bdf, random = ~ IQ.ver.cen, method = "ML")

> summary(fm5)
Linear mixed-effects model fit by maximum likelihood
  Data: bdf 
    AIC   BIC  logLik
  15228 15268 -7606.8

Random effects:
 Formula: ~IQ.ver.cen | schoolNR
 Structure: General positive-definite, Log-Cholesky parametrization
            StdDev  Corr  
(Intercept) 2.81410 (Intr)
IQ.ver.cen  0.44728 -0.652
Residual    6.43043       

Fixed effects:  langPOST ~ IQ.ver.cen + avg.IQ.ver.cen 
                Value Std.Error   DF t-value p-value
(Intercept)    40.750   0.28610 2155 142.433       0
IQ.ver.cen      2.459   0.08324 2155  29.541       0
avg.IQ.ver.cen  1.405   0.32168  129   4.368       0
 Correlation: 
               (Intr) IQ.vr.
IQ.ver.cen     -0.274       
avg.IQ.ver.cen  0.028 -0.214

Standardized Within-Group Residuals:
     Min       Q1      Med       Q3      Max 
-4.17512 -0.63982  0.06693  0.70462  2.71089 

Number of Observations: 2287
Number of Groups: 131 

> VarCorr(fm5)
schoolNR = pdLogChol(IQ.ver.cen) 
            Variance StdDev  Corr  
(Intercept)  7.91916 2.81410 (Intr)
IQ.ver.cen   0.20006 0.44728 -0.652
Residual    41.35049 6.43043       

> -2 * c(logLik(fm5))                     # deviance
[1] 15214

> ## Add centered class size and interaction
> ## Compare with Table 5.2, p. 75
> fm6 <- update(fm5, langPOST ~ avg.IQ.ver.cen + IQ.ver.cen * grpSiz.cen)

> summary(fm6)
Linear mixed-effects model fit by maximum likelihood
  Data: bdf 
    AIC   BIC  logLik
  15226 15278 -7604.2

Random effects:
 Formula: ~IQ.ver.cen | schoolNR
 Structure: General positive-definite, Log-Cholesky parametrization
            StdDev  Corr  
(Intercept) 2.76882 (Intr)
IQ.ver.cen  0.42132 -0.658
Residual    6.43136       

Fixed effects:  langPOST ~ avg.IQ.ver.cen + IQ.ver.cen + grpSiz.cen + IQ.ver.cen:grpSiz.cen 
                       Value Std.Error   DF t-value p-value
(Intercept)           40.893   0.29249 2153 139.809  0.0000
avg.IQ.ver.cen         1.246   0.32642  129   3.818  0.0002
IQ.ver.cen             2.443   0.08233 2153  29.674  0.0000
grpSiz.cen             0.057   0.03691 2153   1.556  0.1198
IQ.ver.cen:grpSiz.cen -0.022   0.01091 2153  -1.989  0.0468
 Correlation: 
                      (Intr) a.IQ.. IQ.vr. grpSz.
avg.IQ.ver.cen        -0.024                     
IQ.ver.cen            -0.276 -0.195              
grpSiz.cen             0.249 -0.175 -0.086       
IQ.ver.cen:grpSiz.cen -0.118  0.169  0.032 -0.233

Standardized Within-Group Residuals:
      Min        Q1       Med        Q3       Max 
-4.163071 -0.639694  0.063419  0.710478  2.687724 

Number of Observations: 2287
Number of Groups: 131 

> VarCorr(fm6)
schoolNR = pdLogChol(IQ.ver.cen) 
            Variance StdDev  Corr  
(Intercept)  7.66639 2.76882 (Intr)
IQ.ver.cen   0.17751 0.42132 -0.658
Residual    41.36242 6.43136       

> -2 * c(logLik(fm6))                     # deviance
[1] 15208
> 
> proc.time()
   user  system elapsed 
  0.846   1.606   0.491 
