On Mon, Oct 4, 2010 at 7:21 AM, klsk89 <karenkls...@yahoo.com> wrote: > > 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.:)
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/ > > Here's my data. > >> rat.toxic<-read.table(file="Rats.csv",header=T,row.names=NULL,sep=",") >> attach(rat.toxic) ^ why attach it? >> 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 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. > 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 > > > > > > > -- > View this message in context: > http://r.789695.n4.nabble.com/Plot-for-Binomial-GLM-tp2954406p2954406.html > Sent from the R help mailing list archive at Nabble.com. > > ______________________________________________ > R-help@r-project.org mailing list > https://stat.ethz.ch/mailman/listinfo/r-help > PLEASE do read the posting guide http://www.R-project.org/posting-guide.html > and provide commented, minimal, self-contained, reproducible code. > -- Joshua Wiley Ph.D. Student, Health Psychology University of California, Los Angeles http://www.joshuawiley.com/ ______________________________________________ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
