search for: ungrazed

Hi, Try either: res1 <- apply(mydata[,1:2],2,mean) ?res2 <- colMeans(mydata[,1:2]) ?identical(res1,res2) #[1] TRUE # Also if you need to find means for each group ("Ungrazed vs. "Grazed") by(mydata[,-3],mydata[,3],colMeans) #or if column names are "V1", "V2", "V3" aggregate(.~V3,mydata,mean) #or library(plyr) ?ddply(mydata,.(V3),numcolwise(mean)) A.K. I have a data set with two columns of data that I want to find the mean of...

I have a two part question one about statistical theory and the other about implementations in R. Thank you for all help in advance. (1) Am I correct in understanding that Heteroscedasticity is a problem for Generalized Additive Models as it is for standard linear models? I am asking particularly about the GAMs as implemented in the mgcv package. Based upon my online search it seems that some

...model4<-glm(Biomass~(Treatment+Time+Site)^2, data=bobB, family=quasi(link="log", variance="mu")) par(mfrow=c(2,2)) plot(model2) to test the effect of grazing exclusion of feral horses for a Phd with following factors: Treatment - 3 levels which are grazed controls (C), ungrazed horse exclosures (H)with 3 wire strand fence to allow other herbivores but not horses and ungrazed full netting exclosures to exclude all herbivores (A) Site - 6 levels for 6 Sites which are considered fixed Time - 6 levels also for 6 different monitoring or time periods which is where the repea...

...sely I have dependence in my data that should be allowed for in the permutation - I simulated the problem in the example of the vegan documentation p.24: library(vegan) data(varespec) ## Bray-Curtis distances between samples dis <- vegdist(varespec) ## First 16 sites grazed, remaining 8 sites ungrazed groups <- factor(c(rep(1,16), rep(2,8)), labels = c("grazed","ungrazed")) ## Calculate multivariate dispersions mod <- betadisper(dis, groups) ## Perform test anova(mod) ##simulation of dependence; blocks blocks<-factor(c(rep(1,4),rep(2,4),rep(3,4),rep(4,4))) ##the...

...model4<-glm(Biomass~(Treatment+Time+Site)^2, data=bobB, family=quasi(link="log", variance="mu")) par(mfrow=c(2,2)) plot(model2) to test the effect of grazing exclusion of feral horses for a Phd with following factors: Treatment - 3 levels which are grazed controls (C), ungrazed horse exclosures (H)with 3 wire strand fence to allow other herbivores but not horses and ungrazed full netting exclosures to exclude all herbivores (A) Site - 6 levels for 6 Sites which are considered fixed Time - 6 levels also for 6 different monitoring or time periods which is where the repea...

...t(wisconsin(varespec)) library(MASS) ## isoMDS mds.null <- isoMDS(vare.dist, tol=1e-7) mds.alt <- isoMDS(vare.dist, initMDS(vare.dist), maxit=200, tol=1e-7) vare.proc <- procrustes(mds.alt, mds.null) vare.proc groups <- factor(c(rep(1,16), rep(2,8)), labels = c("grazed","ungrazed")) source("xyplot.procrustes.R") xyplot(vare.proc, y ~ x | groups, data = as.data.frame(groups), kind = 1) The resulting plot has too many arrows on each panel - some points have multiple arrows emanating from they. panel.procrustes() is defined as: `panel.procrustes` <- functio...