Calum-4 wrote: > > Hi I know asking which test to use is frowned upon on this list... so > please do read on for at least a couple on sentences... > > I have some multivariate data slit as follows > > Tumour Site (one of 5 categories) # > Chemo Schedule (one of 3 cats) ## > Cycle (one of 3 cats*) ## > Dose (one of 3 cats*) # > > *These are actually integers but for all our other analysis so far we > have grouped them into logical bands of categories. > > The dependant variable is "Reaction" or "No Reaction" > > I have individually analysed each of the independant variables against > Reaction/No Reaction using ChiSq and Fisher Tests. Those marked ## > produced p values less than 0.05, and those marked # produce p values > close to 0.05. > > We believe that Cycle is the crucial piece of data - the others just > appear to be different because there are more early cycles in certain > groups than others. > > SO - I believe what I need to do is a Linear Logistic Regression on the > 4 independant variables. And I'm expecting it to show that the tumour > site, schedule and dose don't matter, only the cycle matters. Done a lot > of reading and I'm clueless!! > > I think I want to do something like: > > glm (reaction ~ site + sched + cycle + dose, data=mydata, family=poisson) > ========================= > Comment 1: If you stick to Linear Logistic Regression, the family should > be "binomial" assuming that reaction has only two values (Yes/No). > "family=poisson" should be used when the response is a frequency count > such as the number of tumors. > ========================= > > I am then expecting to see some very long output with lots of numbers... > ...my question is TWO fold - > > 1. is glm the right thing to use before I waste my time > > and 2. how do I interpret the result! (I'm kind of expect a lecture here > as I'm really looking for a nice snappy 'p<0.05 means this variable is > the one having the influence' type answer and I suspect I'm going to be > told thats not possible...! > ================================================================ > Comment 2: The regression coefficients in binary logistic regression > models are called log-odds ratio. The interpretation of odds ratio can be > tricky but the p-value is interpreted in the usual way. > ================================================================ > To be clear the example given in the docs is: > >> library(MASS) > >> data(anorexia) > >> anorex.1<- glm(Postwt ~ Prewt + Treat + offset(Prewt), family = >> gaussian, data = anorexia) > > =================================== > Comment 3. Here Postwt is a continuous variable. The specification "family > = gaussian" assumes the that Postwt is a normal variable, therefore, the > fitted model is the ordinary normal linear regression model. > =================================== > > The output of anorex.1 is: > > Call: glm(formula = Postwt ~ Prewt + Treat + offset(Prewt), family = > gaussian, data = anorexia) > > Coefficients: > > (Intercept) Prewt TreatCont TreatFT > > 49.7711 -0.5655 -4.0971 4.5631 > > Degrees of Freedom: 71 Total (i.e. Null); 68 Residual > > Null Deviance: 4525 > > Residual Deviance: 3311 AIC: 490 > > > > and the output of summary(anorex.1) is: > > Call: > > glm(formula = Postwt ~ Prewt + Treat + offset(Prewt), family = gaussian, > > data = anorexia) > > Deviance Residuals: > > Min 1Q Median 3Q Max > > -14.1083 -4.2773 -0.5484 5.4838 15.2922 > > Coefficients: > > Estimate Std. Error t value Pr(>|t|) > > (Intercept) 49.7711 13.3910 3.717 0.000410 *** > > Prewt -0.5655 0.1612 -3.509 0.000803 *** > > TreatCont -4.0971 1.8935 -2.164 0.033999 * > > TreatFT 4.5631 2.1333 2.139 0.036035 * > > --- > > Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 > > (Dispersion parameter for gaussian family taken to be 48.69504) > > Null deviance: 4525.4 on 71 degrees of freedom > > Residual deviance: 3311.3 on 68 degrees of freedom > > AIC: 489.97 > > Number of Fisher Scoring iterations: 2 > > > > --- > Either can someone point me to a decent place that would explain what > the means or provide me some pointers? i.e. which of the variables has > the influence on the outcome in the anorexia data? > > Please don't shout!! happy to be pointed to a reference but would prefer > one in common english not some stats mumbo jumbo! > > Calum > > ______________________________________________ > [email protected] 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. > >
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