Displaying 4 results from an estimated 4 matches for "interaction_average".
2012 Jan 12
0
glht (multicomparisons) with an interaction factor
...09541 3.037 0.0060 **
SUB - H == 0 -0.22610 0.22626 -0.999 0.5609
SUB - M == 0 -0.51586 0.21921 -2.353 0.0441 *
(Adjusted p values reported -- single-step method)
and with a single factor involved in an interaction
> summary(glht(mq5, linfct=mcp(estacion="Tukey", interaction_average=TRUE)))
Estimate Std. Error z value Pr(>|z|)
verano - primavera == 0 -1.352 0.151 -8.952 <2e-16 ***
(Adjusted p values reported -- single-step method)
But how can i test the interaction "zona:estacion", I've tried to do this
ways, but does...
2012 Feb 13
0
pairwise comparisons with multcomp package
....927e-06 ***
zona3 2 931.4 290 36569
3.0167 0.050510.
estacion:zona3 2 2421.7 288 34148
7.8438 0.000482 ***
> summary(glht(modezqM,linfct=mcp(zona3="Tukey",interaction_average=T)))
Estimate Std. Error z-value
Pr(>|z|)
norte - turist == 0 -0.05182 0.23701 -0.219 0.9727
occid - turist == 0 -1.05970 0.41618 -2.546 0.0271 *
occid - norte == 0 -1.00788 0.45830...
2012 Nov 19
0
glht function in multcomp gives unexpected p=1 for all comparisons
...Binomial GLM:
glm.out <- glm(survival~Site*Treatment, data=surv.data, family="binomial", weights=rep(10, nrow(surv.data)))
anova(glm.out, test="Chisq")??? # Treatment effect: p=0.001291
# Post-hoc test
library(multcomp)
summary(glht(glm.out, mcp(Treatment="Tukey", interaction_average=TRUE)))
This gives me the following results:
???????? Simultaneous Tests for General Linear Hypotheses
Multiple Comparisons of Means: Tukey Contrasts
Fit: glm(formula = survival ~ Site * Treatment, family = "binomial",
??? data = surv.small, weights = rep(10, nrow(surv.small)))
L...
2013 Mar 13
1
multi-comparison of means
Hi all:
I have a question about multi-comparison.
The data is in the attachment.
My purpose:
Compare the predicted means of the 3 methods(a,b,c) pairwisely.
I have 3 ideas:
#idea1
result_aov<-aov(y~ method + x1 + x2)
TukeyHSD(result_aov)
diff lwr upr p adj
b-a 0.845 0.5861098 1.1038902 0.0000001
c-a 0.790 0.5311098 1.0488902 0.0000002
c-b -0.055 -0.3138902