Hello R-users:

I am using "yags" for fitting GEE which is giving me the same result as "Proc GENMOD". Now I have couple of questions related to yags output. (By the way, someone told me to run the geeglm for the same analysis and I did run but did not get the same result as of genmod and don't know how to correct the geeglm codes so that all three will be same!) 
 
 
Questions:
 
1. How can I get the p-value from yags output ? 
2. How can I get the regression coefficients as a seperate row or column vector from the output for my simulation please? Also, how can I get the standard errors of these reg. coefficients as a seperate vector? Notice, as it is highlighted below, beta1=coef(wee) giving me "NULL" and also summary(wee) is not giving me nothing!

The following is the output from the yags analysis:
 
> yf=formula(Ddimer~newrace+steroid+treatment+SOFA+PSI)
> wee=yags(yf, id=Subject, data=final, cor.met=as.double(rep(0:6, 872)), family=gaussian, corstruct="exchangeable", control=yags.control(), weights=w, betainit=NULL, alphainit=.1, subset=NULL)
> wee
YAGS (yet another GEE solver) $Date: 2004/10/22 18:49:23 $
Call:
yags(formula = yf, id = Subject, cor.met = as.double(rep(0:6, 
    872)), family = gaussian, corstruct = "exchangeable", control = yags.control(), 
    weights = w, betainit = NULL, alphainit = 0.1, data = final, 
    subset = NULL)

Regression estimates:
                    est.  naive s.e.   naive z  sand. s.e.    sand. z    p-value <--How to generate it?
(Intercept)  6.972275093 0.122301393 57.008959 0.321211401 21.7061881    ?
newrace     -0.238497110 0.089208731 -2.673473 0.119576217 -1.9945196    ?    
steroid     -0.464207865 0.063099906 -7.356712 0.194455948 -2.3872135    ?
treatment    0.140764455 0.080611978  1.746198 0.192932560  0.7296045    ?
SOFA        -0.025986017 0.014140353 -1.837721 0.048131236 -0.5398992    ?    
PSI          0.007095163 0.001035622  6.851114 0.003543198  2.0024740    ?
 
Working correlation model: exchangeable
alpha est: 0.7344 
NULL
Pan QIC(R): 7534.732 
QLS: 56989.3 
Rotnitzky-Jewell: 9.477, 143.987
yags/R: $Id: yags.R,v 1.5 2004/10/22 18:49:23 stvjc Exp $

> beta1=coef(wee)
> beta1
NULL
> summary(wee)
    Length      Class       Mode 
         1 yagsResult         S4 
> summary(wee)

  
FYI, in the following geeglm analysis, I have gotten beta=coef(wgee) as a row vector(highlighted):

mf=formula(Ddimer~newrace+steroid+treatment+SOFA+PSI)
> wgee=geeglm(mf, id=Subject, data=na.omit(final), weights=w, family=gaussian("identity"), corstr="exchangeable") 
> beta=coef(wgee)
> beta
 (Intercept)      newrace      steroid    treatment         SOFA          PSI 
 6.904767685 -0.228246050 -0.425099489  0.160940654 -0.024995782  0.006562448 
> summary(wgee)
Call:
geeglm(formula = mf, family = gaussian("identity"), data = na.omit(final), 
    weights = w, id = Subject, corstr = "exchangeable")
 Coefficients:
                Estimate     Std.err        Wald      p(>W)
(Intercept)  6.904767685 0.275425965 628.4755438 0.00000000
newrace     -0.228246050 0.110604904   4.2585110 0.03905414
steroid     -0.425099489 0.181105281   5.5095856 0.01891253
treatment    0.160940654 0.174823465   0.8474851 0.35726476
SOFA        -0.024995782 0.044632439   0.3136406 0.57545474
PSI          0.006562448 0.003313452   3.9225669 0.04764208


If you could help me by answering these questions, I would really appreciate your help. 
 
Sincere thanks, 
 
Sattar


 
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