Re: [R] producing a graph with glm poisson distributed respons count data and categorical independant variables
Hi, I do have replicates, but not many. The offsets are my the number of replicates actually, i misled myself by thinking i could add the counts of the replicates all up and then go further with the offset function. I decided to split up my experiments in order to interpret them, they were actually from the beginning split up experiments. Because i thought i could see if the bees reacted in a same manner to the different experiments i thought i could analyse it with an interaction factor to reveal whether this was true. But i want now want to analyse them apart so i can draw conclusions from the both experiments apart. I did the following with the counts from p1, including the replicates so i have only the type of field-margin as a variable, as i was only interested in this from the beginning: mengsel count C 39 C 38 A 79 A 96 A 278 D 15 D 15 B 322 B 449 B 262 a.data.p1-read.table(a.data.p1.txt,header=TRUE,sep=) a.data.p1 fit.sat.a.p1-glm(count~mengsel,data=a.data.p1,family=poisson) anova(fit.sat.a.p1,test=Chisq) fit.main.a.p1-glm(count~1,data=a.data.p1,family=poisson) anova(fit.sat.a.p1,fit.main.a.p1,test=Chisq) extractAIC(fit.sat.a.p1) extractAIC(fit.main.a.p1) summary(fit.sat.a.p1) This tells me that the saturated model is better explaning then the other so: Call: glm(formula = count ~ mengsel, family = poisson, data = a.data.p1) Deviance Residuals: Min 1Q Median 3Q Max -6.4531 -3.7799 -0.0404 0.0603 9.2385 Coefficients: Estimate Std. Error z value Pr(|z|) (Intercept) 5.017280.04698 106.79 2e-16 *** mengselB 0.824330.05635 14.63 2e-16 *** mengselC-1.366620.12327 -11.09 2e-16 *** mengselD-2.309230.18852 -12.25 2e-16 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 (Dispersion parameter for poisson family taken to be 1) Null deviance: 1385.58 on 9 degrees of freedom Residual deviance: 202.01 on 6 degrees of freedom AIC: 273.15 Number of Fisher Scoring iterations: 5 How can i produce a graph for this? I am worried that i still do not have enough replicates to actually draw descent conclusions or conclusions at all...in my second experiment i do not have replicates for 2 out of four types of field margins, for the other two i only have 2 replicates. this being the counts from the second experiment: C 3 p2 C 1 p2 A 90 p2 A 29 p2 D 0 p2 B 157 p2 thanks, babs -- View this message in context: http://r.789695.n4.nabble.com/producing-a-graph-with-glm-poisson-distributed-respons-count-data-and-categorical-independant-variabs-tp4638110p4638192.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.
[R] producing a graph with glm poisson distributed respons count data and categorical independant variables
Hello, I am working on my thesis and can't really figure out how to produce a reasonable graph from the output from my glm., I could just give the R-output in my results and then discuss them, but it would be more interesting if I could visualise what is going on. My research is how bees react to different fieldmargins, for this I have 4 different types of field margin (A,B,C D) and two different experiments, one where the field margins are adjecent and one where they are seperated. I wanted to know if the bees react differently on the different types of field margin and whether there were differences between the two experiments... I also used an offset function to correct for the different number of field margins of the same type were the counts have been going on. I counted the counts of the same fied margins together and then put in the offset function. So i used the model that is underneath: mengsel A, B, C D=type of field margin and proefopzet 1 and 2= experiment p1 and p2 I already checked if this saturated model is better then that without an interaction effect: so I think i have a good model for my data Call: glm(formula = count ~ mengsel * proefopzet, family = poisson, data = a.data, offset = log(opp)) Deviance Residuals: [1] 0 0 0 0 0 0 0 0 Coefficients: Estimate Std. Error z value Pr(|z|) (Intercept)5.015070.04704 106.622 2e-16 *** mengselB 0.826540.05640 14.656 2e-16 *** mengselC -1.364410.12329 -11.067 2e-16 *** mengselD -2.307020.18854 -12.237 2e-16 *** proefopzetp2 -0.929090.10303 -9.017 2e-16 *** mengselB:proefopzetp2 0.143730.13399 1.073 0.283411 mengselC:proefopzetp2 -2.028420.52307 -3.878 0.000105 *** mengselD:proefopzetp2 3.033230.22821 13.291 2e-16 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 (Dispersion parameter for poisson family taken to be 1) Null deviance: 1.8216e+03 on 7 degrees of freedom Residual deviance: -1.2212e-14 on 0 degrees of freedom AIC: 67.589 Number of Fisher Scoring iterations: 3 Now...how can i visualise this? I don't seem to find how i could do this... The data on which this is computed is the following: a.data count proefopzet mengsel opp 1 1033p1 B3 277 p1 C 2 3 452 p1 A 3 430 p1 D 2 5 157 p2 B 1 6 4p2 C 2 7 119 p2 A 2 8 123 p2 D 1 Thanks, Babs -- View this message in context: http://r.789695.n4.nabble.com/producing-a-graph-with-glm-poisson-distributed-respons-count-data-and-categorical-independant-variabs-tp4638110.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.
