[R] Plot for Binomial GLM

[klsk89]

Hi i would like to use some graphs or tables to explore the data and make
some sensible guesses of what  to expect to see in a glm model to assess if
toxin concentration and sex have a relationship with the kill rate of rats.
But i cant seem to work it out as i have two predictor
variables~help?Thanks.:)

Here's my data.

> rat.toxic<-read.table(file="Rats.csv",header=T,row.names=NULL,sep=",")
> attach(rat.toxic)
> names(rat.toxic)
[1] "Dose"  "Sex"   "Dead"  "Alive"
> rat.toxic
   Dose Sex Dead Alive
1    10   F    1    19
2    10   M    0    20
3    20   F    4    16
4    20   M    4    16
5    30   F    9    11
6    30   M    8    12
7    40   F   13     7
8    40   M   13     7
9    50   F   18     2
10   50   M   17     3
11   60   F   20     0
12   60   M   16     4
13   10   F    3    17
14   10   M    1    19
15   20   F    2    18
16   20   M    2    18
17   30   F   10    10
18   30   M    8    12
19   40   F   14     6
20   40   M   12     8
21   50   F   16     4
22   50   M   13     7
23   60   F   18     2
24   60   M   16     4

glm2<-glm(deadalive~Dose*Sex,family=binomial,data=rat.toxic)
> anova(glm2,test="Chi")
Analysis of Deviance Table

Model: binomial, link: logit

Response: deadalive

Terms added sequentially (first to last)


         Df Deviance Resid. Df Resid. Dev P(>|Chi|)    
NULL                        23    225.455              
Dose      1  202.366        22     23.090    <2e-16 ***
Sex       1    4.328        21     18.762    0.0375 *  
Dose:Sex  1    1.149        20     17.613    0.2838    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 
> summary(glm2)

Call:
glm(formula = deadalive ~ Dose * Sex, family = binomial, data = rat.toxic)

Deviance Residuals: 
     Min        1Q    Median        3Q       Max  
-1.82241  -0.85632   0.06675   0.61981   1.47874  

Coefficients:
            Estimate Std. Error z value Pr(>|z|)    
(Intercept) -3.47939    0.46167  -7.537 4.83e-14 ***
Dose         0.10597    0.01286   8.243  < 2e-16 ***
SexM         0.15501    0.63974   0.242    0.809    
Dose:SexM   -0.01821    0.01707  -1.067    0.286    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 225.455  on 23  degrees of freedom
Residual deviance:  17.613  on 20  degrees of freedom
AIC: 91.115

Number of Fisher Scoring iterations: 4






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