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Plot for Binomial GLM

4 messages · klsk89, Peter Ehlers, Joshua Wiley

Original
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.
[1] "Dose"  "Sex"   "Dead"  "Alive"
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)
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
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
Joshua Wiley
#
On Mon, Oct 4, 2010 at 7:21 AM, klsk89 <karenklsk89 at yahoo.com> wrote:
What about xtabs?   For instance:

xtabs(deadalive ~ Dose + Sex, data = rat.toxic)

Regarding graphs, take a look at faceting in ggplot2 (or lattice).
You can get something close to the 3 way table but in graphical form
that way.  I am not sure if this is completely up and running yet, but
I know there has been work linking ggobi with R.  I have seen a few
demonstrations that looked quite promising, and it may work well for
you to visualize three variables at once (and interactively).  Here is
the link:  http://www.ggobi.org/rggobi/
^  why attach it?
Please tell me that after this, you converted the counts of dead and
alive into a single variable that had a 0 or 1 if dead and the
opposite as alive before you used it as the dependent variable in your
logistic regression.

  
    

      
      
    

  


  


      

    

    

      
      

        


  

        

      Peter Ehlers
    

    

      
      #
    

  


  

      
On 2010-10-04 8:21, klsk89 wrote:

            

      
      
      
    

        
This is pretty much the budworm example in MASS (the book).
I would produce a plot of Prob(dead) vs dose with separate
lines for M/F:

  dose <- seq(10, 60, length=51)
  ypF <- predict(glm2, data.frame(Dose=dose, Sex="F"), type="response")
  ypM <- predict(glm2, data.frame(Dose=dose, Sex="M"), type="response")

  plot(c(10,60), c(0,1), type="n")
  lines(dose, ypF, col=2)
  lines(dose, ypM, col=4)

  text(locator(1), "F", col=2)
  text(locator(1), "M", col=4)

See recent posts by Ben Bolker for confidence bands.

   -Peter Ehlers

  
  


  


      

    

    

      
      

        


  

        

      Joshua Wiley
    

    

      
      #
    

  


  

      
Hi,

Dennis was kind of enough to remind me that glm() can take a two
column matrix, which is probably what you did with deadalive.  He also
gave a rather elegant graphing solution using xyplot:

xyplot(Alive/20 ~ Dose, data = rat.toxic, groups = Sex, type = c('p', 'a'))

Josh

        
On Mon, Oct 4, 2010 at 8:23 AM, Joshua Wiley <jwiley.psych at gmail.com> wrote: