search for: trtmnt

Hi all, I used bwplot in lattice to create a 6-panel boxplot grouped by a conditioning variable (param) that displays concentration (conc) in response to treatment (trtmnt). Here is the functional part of my code followed by my three questions: library(lattice); ww<-read.csv(file="c:/Rdata/lattice_boxplot_prep.csv",header=TRUE,sep=","); attach(ww); mylist<-list(c(0,200), c(0,60), c(0,1000000), c(0,80), c(0, 5), c(0,300)) print(ww<-bwplo...

...with the data below. Any help in understanding or overcoming this obstacle is appreciated. >library(dispmod) # required for dispersion fitting # Now the data >counts<-matrix(c(2,1,3,1,2715597,3296062,2945864,2215143), ncol=2) >temp<-c(25,20,25,20) # linear factor-Temperature >trtmnt<-c(0,0,1,1) #categorical factor #And the models >fit1<-glm(counts~temp+trtmnt, family="binomial") >fit2<-glm.binomial.disp(fit1) Binomial overdispersed logit model fitting... Iter. 1 phi: -3.615313e-07 Error in glm(formula = counts ~ temp + trtmnt, family = "bino...

...e (Intercept) 0.5311005 0.09369140 112 5.668615 0.0000 treatment 0.0495373 0.04616116 112 1.073138 0.2855 layingday -0.0488055 0.00991701 112 -4.921389 0.0000 treatment:layingday 0.0138449 0.00627207 112 2.207388 0.0293 Correlation: (Intr) trtmnt lyngdy treatment -0.739 layingday -0.688 0.838 treatment:layingday 0.653 -0.883 -0.949 Standardized Within-Group Residuals: Min Q1 Med Q3 Max -2.44529424 -0.68505388 0.01663401 0.59009515 3.53354000 Number o...

[corrected dataset below] Hello everyone, I am trying to do within subjects repeated measures anova followed by the test of sphericity (sample dataset below). I am able to get either mixed model or linear model anova and TukeyHSD, but have no luck with Repeated-Measures Assuming Sphericity or Separate Sphericity Tests. I am trying to follow example from "car" package, but it seems

...ar mixed-effects model fit by maximum likelihood Data: students AIC BIC logLik 17254.33 17361.37 -8610.163 Random effects: Formula: ~1 + Treatment | Name Structure: General positive-definite, Log-Cholesky parametrization StdDev Corr (Intercept) 0.08118364 (Intr) Trtmnt Treatmentvl 0.89891387 -0.059 Treatmentvs 0.54219504 -0.438 0.006 Residual 0.98920592 Variance function: Structure: fixed weights Formula: ~invwt Fixed effects: FreeChoice ~ PositionIndex + Treatment Value Std.Error DF t-value p-value (Intercept) 0.2329314 0.12311...