search for: tensionm

hi list, i have to ask you again, having tried and searched for several days... i want to do a TukeyHSD after an Anova, and want to get the adjusted p-values after the Tukey Correction. i found the p.adjust function, but it can only correct for "holm", "hochberg", bonferroni", but not "Tukey". Is it not possbile to get adjusted p-values after

Does anyone know how could I get the significant codes from mixed model output fitted with a GEE package? The output I got is the following: GEE: GENERALIZED LINEAR MODELS FOR DEPENDENT DATA gee S-function, version 4.13 modified 98/01/27 (1998) Model: Link: Logit Variance to Mean Relation: Binomial Correlation Structure: Exchangeable Call: gee(formula = bru

Hello, I am performing a BACI analysis with ANOVA using the following glm: fit1<-glm(log(Cucs_m+1)~(BA*Otter)+BA+Otter+ID+Primary, data=b1) The summary(aov(fit1)) shows significance in the interaction; however, now I would like to determine what combinations of BA and Otter are significantly different (each factor has two levels). ID and PRIMARY substrates are categorical and included in

...uot;B". However, if I also consider the tension variable in the model, I'm unable to figure out the meaning of the intercept value: > lm(breaks ~ wool + tension, data=warpbreaks) Call: lm(formula = breaks ~ wool + tension, data = warpbreaks) Coefficients: (Intercept) woolB tensionM tensionH 39.278 -5.778 -10.000 -14.722 I thought it would be the mean value of breaks when either wool equals "A" or tension equals "L", but that isn't true for this dataset. Any clues on interpreting the value of intercept? Thanks! -- Joao.

Hello, R-i-zens. I'm working on an data set with a factorial ANOVA that has a significant interaction. I'm interested in seeing whether the simple effects are different from 0, and I'm pondering how to do this. So, I have my.anova<-lm(response ~ trtA*trtB) The output for which gives me a number of coefficients and whether they are different from 0. However, I want the

...;- geeglm(breaks ~ tension, id=wool, data=warpbreaks, corstr="exchangeable", std.err = "san.se") mm3 <- yags(breaks ~ tension, id=wool, data=warpbreaks, corstr="exchangeable", alphainit=0.) # S.E. # gee: sqrt(diag(mm$robust.variance)) #(Intercept) tensionM tensionH # 5.77 7.46 3.73 # geeglm: sqrt(diag(mm2$geese$vbeta)) # [1] 8.17 10.56 5.28 # yags: sqrt(diag(slot(mm3, "robust.parmvar"))) # [1] 5.77 7.46 3.73 Any explanation of this behavior is welcome. Best wishes Torleif Markussen Lunde PhD candidate Centre for In...