Displaying 3 results from an estimated 3 matches for "model2b".
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model2
2012 Sep 14
2
when to use "I", "as is" caret
...e myself. Let's say we have
2 models:
model1 <- lm(vdep ~ log(v1) + v2 + v3 + I(v4^2) , data = mydata)
model2 <- lm(vdep ~ log(v1) + v2 + v3 + v4^2, data = mydata)
So in model1 you really square v4; and in model2, v4*^2 *doesn't do
anything, does it? Model2 could be rewritten:
model2b <- lm(vdep ~ log(v1) + v2 + v3 + v4, data = mydata) and nothing
changes, doesn't it?
This "I" caret is essential with powering or when including transformations
as I(1/(v2+v3)) but not with log transformation, isn't it?. Is there any
other transformation where I muss use als...
2011 Apr 21
1
Accounting for overdispersion in a mixed-effect model with a proportion response variable and categorical explanatory variables.
Dear R-help-list,
I have a problem in which the explanatory variables are categorical,
the response variable is a proportion, and experiment contains
technical replicates (pseudoreplicates) as well as biological
replicated. I am new to both generalized linear models and mixed-
effects models and would greatly appreciate the advice of experienced
analysts in this matter.
I analyzed the
2005 Jan 24
4
lme and varFunc()
Dear R users,
I am currently analyzing a dataset using lme(). The model I use has the
following structure:
model<-lme(response~Covariate+TreatmentA+TreatmentB,random=~1|Block/Plot,method="ML")
When I plot the residuals against the fitted values, I see a clear
positive trend (meaning that the variance increases with the mean).
I tried to solve this issue using weights=varPower(),