Examp2.5.1.1 from Duchateau, L. and Janssen, P. and Rowlands, G. J. (1998).Linear Mixed Models. An Introduction with applications in Veterinary Research. International Livestock Research Institute.

Source: R/Examp2.5.1.1.R

Examp2.5.1.1 is used for inspecting probability distribution and to define a plausible process through linear models and generalized linear models.

References

  1. Duchateau, L. and Janssen, P. and Rowlands, G. J. (1998).Linear Mixed Models. An Introduction with applications in Veterinary Research. International Livestock Research Institute.

See also

ex124

Examples

#-------------------------------------------------------------
## Example 2.5.1.1 p-67
#-------------------------------------------------------------
 # PROC MIXED DATA=ex125;
 # CLASS drug dose region;
 # MODEL pcv=drug dose drug*dose / solution covb;
 # RANDOM region drug*region;
 # RUN;

library(lme4)
str(ex125)
#> 'data.frame':	24 obs. of  4 variables:
#>  $ Region: int  1 1 1 1 2 2 2 2 3 3 ...
#>  $ Drug  : Factor w/ 2 levels "BERENIL","samorin": 1 1 2 2 1 1 2 2 1 1 ...
#>  $ dose  : Factor w/ 2 levels "h","l": 1 2 1 2 1 2 1 2 1 2 ...
#>  $ Pcv   : num  22.6 21.8 19.1 16.4 29 28.8 25.3 18.2 24 23.7 ...

fm2.9 <-
  lme4::lmer(
         formula    = Pcv ~ dose*Drug + (1|Region/Drug)
       , data       = ex125
       , REML       = TRUE
       , control    = lmerControl()
       , start      = NULL
       , verbose    = 0L
    #  , subset
    #  , weights
    #  , na.action
    #  , offset
       , contrasts  = list(dose = "contr.SAS", Drug = "contr.SAS")
       , devFunOnly = FALSE
    #  , ...
       )
 summary(fm2.9)
#> Linear mixed model fit by REML ['lmerMod']
#> Formula: Pcv ~ dose * Drug + (1 | Region/Drug)
#>    Data: ex125
#> 
#> REML criterion at convergence: 92.4
#> 
#> Scaled residuals: 
#>      Min       1Q   Median       3Q      Max 
#> -1.71077 -0.51628  0.08414  0.70276  1.16129 
#> 
#> Random effects:
#>  Groups      Name        Variance Std.Dev.
#>  Drug:Region (Intercept) 0.387    0.6221  
#>  Region      (Intercept) 5.151    2.2695  
#>  Residual                2.096    1.4479  
#> Number of obs: 24, groups:  Drug:Region, 12; Region, 6
#> 
#> Fixed effects:
#>                   Estimate Std. Error t value
#> (Intercept)        17.1333     1.1280  15.189
#> doseh               4.3500     0.8359   5.204
#> DrugBERENIL         7.1500     0.9098   7.859
#> doseh:DrugBERENIL  -3.1833     1.1822  -2.693
#> 
#> Correlation of Fixed Effects:
#>             (Intr) doseh  DBEREN
#> doseh       -0.371              
#> DrugBERENIL -0.403  0.459       
#> ds:DBERENIL  0.262 -0.707 -0.650
 anova(fm2.9)
#> Analysis of Variance Table
#>           Df Sum Sq Mean Sq F value
#> dose       1  45.65   45.65 21.7755
#> Drug       1 135.39  135.39 64.5796
#> dose:Drug  1  15.20   15.20  7.2507
 summary(fm2.9)$vcov
#> 4 x 4 Matrix of class "dpoMatrix"
#>                   (Intercept)      doseh DrugBERENIL doseh:DrugBERENIL
#> (Intercept)         1.2723750 -0.3494028  -0.4139028         0.3494028
#> doseh              -0.3494028  0.6988055   0.3494028        -0.6988055
#> DrugBERENIL        -0.4139028  0.3494028   0.8278056        -0.6988055
#> doseh:DrugBERENIL   0.3494028 -0.6988055  -0.6988055         1.3976111