[R] glm with binomial errors in R and GLIM

[Ronaldo Reis Jr.]

Hi all,

In my continuous transition of GLIM to R I try to make a glm with binomial 
errors.

The data file have 3 vectors:
h -> the factor that is ajusted (have 3 levels)
d -> number of animais alive (the response)
n -> total number of animals

To test proportion of alive, make d/n.

In GLIM:

$yvar d$

$error binomial n$

$fit +h$

scale deviance = 25.730 (change = -9.138) at cycle 4
          d.f. = 15     (change = -2)

factor h is significant by chisq² with 2df fron tables = 5.99

$disp e$

   estimate  se       parameter
1  -0.1054   0.2055   1
2   0.7985   0.2961   h(2)
3   0.08827  0.26764  h(3)
Scale parameter taken as 1.000

In R:

> modelo.glex24.1 <- glm((d/n)~h,family=binomial)
Warning message: 
non-integer #successes in a binomial glm! in: eval(expr, envir, enclos) 
> summary(modelo.glex24.1)

Call:
glm(formula = (d/n) ~ h, family = binomial)

Deviance Residuals: 
     Min        1Q    Median        3Q       Max  
-0.66227  -0.09918  -0.06041   0.18103   0.64740  

Coefficients:
            Estimate Std. Error z value Pr(>|z|)
(Intercept) -0.06119    0.81688  -0.075    0.940
h2h          0.57994    1.17433   0.494    0.621
h3h          0.07761    1.15499   0.067    0.946

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 2.0737  on 17  degrees of freedom
Residual deviance: 1.7843  on 15  degrees of freedom
AIC: 31.147

Number of Fisher Scoring iterations: 2

> anova.glm(modelo.glex24.1,test="Chisq")
Analysis of Deviance Table

Model: binomial, link: logit

Response: (d/n)

Terms added sequentially (first to last)


     Df Deviance Resid. Df Resid. Dev P(>|Chi|)
NULL                    17    2.07368          
h     2  0.28935        15    1.78433   0.86530
> 

The values calculate by GLIM and R is very different, in GLIM h is 
significant and in R no.

What is my error????

Another question is:

I make a barplot graphic with mean of (d/n) by h and I need to plot a SE bar. 
The SE used is the same value returned by summary or need to make 
transformations like means (1/(1+1/odds ratio))?

Thanks for all
Ronaldo
-- 
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