[R] Can't find the error in a Binomial GLM I am doing, please help

[lincoln]

Hi all,

I can't find the error in the binomial GLM I have done. I want to use that
because there are more than one explanatory variables (all categorical) and
a binary response variable.
This is how my data set looks like:
> str(data)
'data.frame':	1004 obs. of  5 variables:
 $ site  : int  0 0 0 0 0 0 0 0 0 0 ...
 $ sex   : Factor w/ 2 levels "0","1": NA NA NA NA 1 NA NA NA NA NA ...
 $ age   : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
 $ cohort: Factor w/ 11 levels "1996","2000",..: 11 11 11 11 11 11 11 11 11
11 ...
 $ birth : Factor w/ 3 levels "5","6","7": 3 3 2 2 2 2 2 2 2 2 ...

I know that, particularly for one level of variable "cohort" (2004 value),
it should be a strong effect of variable "cohort" on variable "site" so I do
a Chi square test that confirms the null hypothesis there is a difference in
sites on the way "cohort" is distributed:

> (chisq.test(data$site,data$cohort))

	Pearson's Chi-squared test

data:  data$site and data$cohort 
X-squared = 82.6016, df = 10, *p-value = 1.549e-13*

Mensajes de aviso perdidos
In chisq.test(data$site, data$cohort) :
  Chi-squared approximation may be incorrect




After that, I have tried to use a binomial GLM with all the explanatory
variables but I couldn't find any significance of any variable, neither
cohort, and for this reason I tried to use only cohort as predictor and I
get this: 


> BinomialGlm <- glm(site ~  cohort, data=data,binomial)
> summary(BinomialGlm)

Call:
glm(formula = site ~ cohort, family = binomial, data = data)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-1.9239  -0.9365  -0.9365   1.3584   1.6651  

Coefficients:
            Estimate Std. Error z value Pr(>|z|)
(Intercept)   -12.57     324.74  -0.039    0.969
cohort2000     11.47     324.75   0.035    0.972
cohort2001     13.82     324.74   0.043    0.966
cohort2002     12.97     324.74   0.040    0.968
cohort2003     13.66     324.74   0.042    0.966
*cohort2004     14.25     324.74   0.044    0.965*
cohort2006     12.21     324.74   0.038    0.970
cohort2007     11.81     324.74   0.036    0.971
cohort2008     12.41     324.74   0.038    0.970
cohort2009     12.15     324.74   0.037    0.970
cohort2010     11.97     324.74   0.037    0.971

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 1369.3  on 1003  degrees of freedom
Residual deviance: 1283.7  on  993  degrees of freedom
AIC: 1305.7

Number of Fisher Scoring iterations: 11




I tired to use simple GLM (gaussian family) and I get results that are more
logicals:

> GaussGlm <- glm(site ~  cohort, data=data)
> summary(GaussGlm)

Call:
glm(formula = site ~ cohort, data = data)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-0.8429  -0.3550  -0.3550   0.6025   0.7500  

Coefficients:
             Estimate Std. Error t value Pr(>|t|)  
(Intercept) 5.740e-14  4.762e-01   0.000   1.0000  
cohort2000  2.500e-01  5.324e-01   0.470   0.6388  
cohort2001  7.778e-01  5.020e-01   1.549   0.1216  
cohort2002  6.000e-01  4.880e-01   1.230   0.2192  
cohort2003  7.500e-01  4.861e-01   1.543   0.1231  
*cohort2004  8.429e-01  4.796e-01   1.757   0.0792 .*
cohort2006  4.118e-01  4.832e-01   0.852   0.3943  
cohort2007  3.204e-01  4.785e-01   0.670   0.5033  
cohort2008  4.600e-01  4.786e-01   0.961   0.3367  
cohort2009  3.975e-01  4.772e-01   0.833   0.4051  
cohort2010  3.550e-01  4.768e-01   0.745   0.4567  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 

(Dispersion parameter for gaussian family taken to be 0.2267955)

    Null deviance: 245.40  on 1003  degrees of freedom
Residual deviance: 225.21  on  993  degrees of freedom
AIC: 1372.5

Number of Fisher Scoring iterations: 2
 


What is going on? Any suggestion/commentary?

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