hi all-

I am doing some research, have never used R before until today and need to
understand the following program for a project.
if some one could PLEASE help me understand this program ASAP i would
GREATLY appreciate it (any syntax/ statistic comments would be great)

PLEASE PLEASE HELP!!  THANKYOU!!!
-on a side note, it seems to me that R doesnt include the pv, and it was
calculated seperatly, is this true?


 fit=gee(foci~as.factor(time)*cond,id=exper,data=drt,family=poisson(link =
"log"))
Beginning Cgee S-function, @(#) geeformula.q 4.13 98/01/27
running glm to get initial regression estimate
                  (Intercept)             as.factor(time)24
                     3.051177                     -2.705675
                  condHypoxia as.factor(time)24:condHypoxia
                    -0.402259                      1.429034
> pv=2*(1-pnorm(abs(summary(fit)$coef[,5])))
> data.frame(summary(fit)$coef,pv)
                               Estimate Naive.S.E.   Naive.z Robust.S.E.
Robust.z
(Intercept)                    3.051177 0.02221052 137.37527  0.04897055
62.306363
as.factor(time)24             -2.705675 0.10890056 -24.84537  0.19987174
-13.537057
condHypoxia                   -0.402259 0.03907961 -10.29332  0.10661248
-3.773095
as.factor(time)24:condHypoxia  1.429034 0.12549576  11.38711  0.17867421
7.997988
                                        pv
(Intercept)                   0.000000e+00
as.factor(time)24             0.000000e+00
condHypoxia                   1.612350e-04
as.factor(time)24:condHypoxia 1.332268e-15
> ftable(table(drt$cond,drt$time,predict(fit)))
             0.345501643340608 1.37227675004058 2.64891772174934
3.05117673373261


Oxia    0.5                  0                0
0              485
        24                 315                0
0                0
Hypoxia 0.5                  0                0
346                0
        24                   0              449
0                0
> ## 3-th term gives the difference between the Hypoxia/Oxia at time=0.5
> ## the difference between Hypoxia/Oxia at time=24
> L=matrix(c(0,0,1,1),nrow=1)
> fit$coef[L==1]
                  condHypoxia as.factor(time)24:condHypoxia
                    -0.402259                      1.429034
> L%*%fit$coef
         [,1]
[1,] 1.026775
> wald.test(fit$robust.variance,fit$coef,L=L)
Wald test:
----------

Chi-squared test:
X2 = 23.8, df = 1, P(> X2) = 1.1e-06
>

	[[alternative HTML version deleted]]

