[R-sig-ME] glmmPQL inquiry

[Ken Beath]

On 07/12/2007, at 7:58 PM, kennedy otwombe wrote:

> Hi Sundar/Ken,
> I would like to clarify my inquiry. The data structure i gave was  
> just a sample of my data. I actually have 50 subjects each with  
> three binary observations (hence 150 observations). So the error i  
> initially gave emanates from the analysis involving all the 50  
> subjects. It just turns out that for the sample i gave, the first  
> two subjects have only 0 as their entries but this varies as you  
> look through the data.
> I am using the latest version of R i.e 2.6.1 and i downloaded the  
> nlme version currently available on the CRAN website. I am aslo  
> assuming my random part is the intercept.
> Hope this clarifies my inquiry.
>

If I modify your code to include better values for y, using code

library(MASS)
navs<-data.frame(matrix(c(1,1,1,0,0,1,1,
2,1,0,0,1,0,1,
3,1,0,0,1,0,1,
1,2,1,0,0,1,0,
2,2,1,1,1,1,0,
3,2,0,1,1,1,0),nrow=6,byrow=T))

names(navs) <- c("t","id","y","x0","x1","x2","x3")

fit<-glmmPQL(y~x1+x2+x3, random=~1|id, family=binomial, data=navs)
summary(fit)

the results are fine, except for some problems due to not having  
enough subjects, as in the following output.

Ken

 > library(MASS)
 > navs<-data.frame(matrix(c(1,1,1,0,0,1,1,
+ 2,1,0,0,1,0,1,
+ 3,1,0,0,1,0,1,
+ 1,2,1,0,0,1,0,
+ 2,2,1,1,1,1,0,
+ 3,2,0,1,1,1,0),nrow=6,byrow=T))
 >
 > names(navs) <- c("t","id","y","x0","x1","x2","x3")
 >
 > fit<-glmmPQL(y~x1+x2+x3, random=~1|id, family=binomial, data=navs)
iteration 1
iteration 2
iteration 3
iteration 4
iteration 5
iteration 6
iteration 7
iteration 8
iteration 9
iteration 10
 > summary(fit)
Linear mixed-effects model fit by maximum likelihood
  Data: navs
   AIC BIC logLik
    NA  NA     NA

Random effects:
  Formula: ~1 | id
          (Intercept)  Residual
StdDev: 8.009601e-05 0.5773503

Variance function:
  Structure: fixed weights
  Formula: ~invwt
Fixed effects: y ~ x1 + x2 + x3
                  Value Std.Error DF       t-value p-value
(Intercept)   0.000353   6171979  2  5.700000e-11       1
x1          -30.566351   2631792  2 -1.161427e-05       1
x2           30.565997   4161051  2  7.345739e-06       1
x3           -0.000071   3721849  0 -1.900000e-11     NaN
  Correlation:
    (Intr) x1     x2
x1 -0.853
x2 -0.944  0.632
x3 -0.905  0.707  0.894

Standardized Within-Group Residuals:
           Min            Q1           Med            Q3           Max
-1.732051e+00 -5.451298e-17  8.078053e-16  1.506878e-15  1.732051e+00

Number of Observations: 6
Number of Groups: 2
Warning message:
In pt(q, df, lower.tail, log.p) : NaNs produced