glmmPQL: Fit Generalized Linear Mixed Models via PQL

Description Usage Arguments Details Value References See Also Examples

View source: R/glmmPQL.R

Description

Fit a GLMM model with multivariate normal random effects, using Penalized Quasi-Likelihood.

Usage

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glmmPQL(fixed, random, family, data, correlation, weights,
        control, niter = 10, verbose = TRUE, ...)

Arguments

fixed

a two-sided linear formula giving fixed-effects part of the model.

random

a formula or list of formulae describing the random effects.

family

a GLM family.

data

an optional data frame used as the first place to find variables in the formulae, weights and if present in ..., subset.

correlation

an optional correlation structure.

weights

optional case weights as in glm.

control

an optional argument to be passed to lme.

niter

maximum number of iterations.

verbose

logical: print out record of iterations?

...

Further arguments for lme.

Details

glmmPQL works by repeated calls to lme, so package nlme will be loaded at first use if necessary.

Value

A object of class "lme": see lmeObject.

References

Schall, R. (1991) Estimation in generalized linear models with random effects. Biometrika 78, 719–727.

Breslow, N. E. and Clayton, D. G. (1993) Approximate inference in generalized linear mixed models. Journal of the American Statistical Association 88, 9–25.

Wolfinger, R. and O'Connell, M. (1993) Generalized linear mixed models: a pseudo-likelihood approach. Journal of Statistical Computation and Simulation 48, 233–243.

Venables, W. N. and Ripley, B. D. (2002) Modern Applied Statistics with S. Fourth edition. Springer.

See Also

lme

Examples

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library(nlme) # will be loaded automatically if omitted
summary(glmmPQL(y ~ trt + I(week > 2), random = ~ 1 | ID,
                family = binomial, data = bacteria))

Example output

iteration 1
iteration 2
iteration 3
iteration 4
iteration 5
iteration 6
Linear mixed-effects model fit by maximum likelihood
 Data: bacteria 
  AIC BIC logLik
   NA  NA     NA

Random effects:
 Formula: ~1 | ID
        (Intercept)  Residual
StdDev:    1.410637 0.7800511

Variance function:
 Structure: fixed weights
 Formula: ~invwt 
Fixed effects: y ~ trt + I(week > 2) 
                    Value Std.Error  DF   t-value p-value
(Intercept)      3.412014 0.5185033 169  6.580506  0.0000
trtdrug         -1.247355 0.6440635  47 -1.936696  0.0588
trtdrug+        -0.754327 0.6453978  47 -1.168779  0.2484
I(week > 2)TRUE -1.607257 0.3583379 169 -4.485311  0.0000
 Correlation: 
                (Intr) trtdrg trtdr+
trtdrug         -0.598              
trtdrug+        -0.571  0.460       
I(week > 2)TRUE -0.537  0.047 -0.001

Standardized Within-Group Residuals:
       Min         Q1        Med         Q3        Max 
-5.1985361  0.1572336  0.3513075  0.4949482  1.7448845 

Number of Observations: 220
Number of Groups: 50 

MASS documentation built on May 29, 2017, 7:08 p.m.

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