similar to: Specification of factorial random-effects model

Dear professors and collegues I need to perform a analysis of dates from a nested experimental design. From "Bioestatical Analysis" of Zar "Bimetry of Sokal" & Rohlf "Design and Analysis of Experiments" of Montgomery I have: Sum (mean(x)_i - mean(x)_T)2 / (a-1) -> var(epsilon) + n sigma2_B + n b (sum alfa_i)2 / (a-1) Sum (mean(x)_ij - mean(x)_i)2 /

Dispongo de un experimento en el que cinco tratamientos ha sido aplicados a cinco grupos de voluntarios. En cada grupo había tres personas y a cada persona se le tomaron 3 medidas, En total dispongo de 45 medidas, pero evidentemente no son independientes entre sí. Si no tomo en cuenta que las medidas de la misma persona son más parecidas entre sí (bloques anidados) estaría incurriendo en

I'm new using R im trying to do a tukey test, but when i see the results the p value results in NA im guessing its because i have missing values but im not sure how to fix it AnovaModel.2 <- aov(area ~ trat, data=apilados) > summary(AnovaModel.2) Df Sum Sq Mean Sq F value Pr(>F) trat 4 11847 2961.76 9.9905 1.500e-06 *** Residuals 76 22531 296.46

Dear all How do I access individual elements of a "summary.aov" object? > data(iris) > AnovaModel.1 <- aov(Sepal.Length ~ Species, data=iris) > tmp <- summary(AnovaModel.1) > tmp Df Sum Sq Mean Sq F value Pr(>F) Species 2 63.2 31.6 119 <2e-16 *** Residuals 147 39.0 0.3 --- Signif. codes: 0 '***' 0.001 '**' 0.01

Dear all I have found that the two "equivalent" commands do not produce the same results. 1. (I wrote this command by hand, this is what I would do usually) >summary(aov(eduyrs ~ cntry * edf, data=ESS1)) Df Sum Sq Mean Sq F value Pr(>F) cntry 1 257 256.65 21.2251 4.243e-06 *** edf 4 11010 2752.42 227.6296 <

The syntax below works, and gives the expected results, in R version 2.0.0, provided that I have loaded packages Matrix, latticeExtra and lme4. However, I cannot get it to work in version 2.2.1. Can anyone tell me how to fit this model in the more recent version? Thanks in anticipation, Nick Galwey barleyprogeny.model1lme <- lme(yield_g_m2 ~ 1, data =

In the output of the code below, I want to do the following: - get hats over some of the betas - get the polygons stippled, not coloured grey - remove the tick marks at the ends of the axes. If I put tick = false, the whole axis disappears. betahat <- c(0.04*0:150) betahatdens <- dnorm(betahat, 3, 1) plot(betahat, betahatdens, xlim = c(-0.2, 6.2),

Dear R user I have 2 problems with lmer. The statistical consultance service of my university has recomended to me to expose those problems here. Sorry for this quite long message. Your help will be greatly appreciated... Gilles San Martin 1) anova() I fit a first model : model1 <- lmer(eclw~1 + density + landsc + temp + landsc:temp + (1|region) + (1|region:pop) + (1|region:pop:family),

The code below is intended to analyse a textbook example of a balanced incomplete block design: # # Data taken from pp. 219-230 in # Cox, D.R. (1958) Planning of Experiments. John Wiley and Son, Inc. New York. 308 pp. # day <- factor(rep(1:10, each = 3)) T <-