Displaying 5 results from an estimated 5 matches for "tensionh".
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tension
2005 May 15
3
adjusted p-values with TukeyHSD?
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
2011 Aug 15
1
Get significant codes from a model output fit with GEE package
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
2012 Jul 27
1
Understanding the intercept value in a multiple linear regression with categorical values
...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.
2008 Mar 04
2
Asking, are simple effects different from 0
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
2009 Dec 08
0
Difference in S.E. gee/yags and geeglm(/geese)
...eaks ~ 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 International...