Hi all,
I am fitting a linear mixed model with lme4 in R. The model has a single
factor (des_days) with 4 levels (-1,1,14,48), and I am using random
intercept and slopes.
Fixed effects: data ~ des_days
Value Std.Error DF t-value p-value
(Intercept) 0.8274313 0.007937938 962
Hi all,
first of all, thanks a lot in advance for your help. I am running a
sequence of post-hoc tests with glht (mutcomp package), but the function
summary warns me that the algorithm ends with an error > abseps.
$ hr.ph <- glht(hr.lm, linfct = ph_conditional);
$ summary(hr.ph)
Warning messages
Hi all,
first of all, thanks a lot in advance for your help. I am running a
sequence of post-hoc tests with glht (mutcomp package), but the function
summary warns me that the algorithm ends with an error > abseps.
$ hr.ph <- glht(hr.lm, linfct = ph_conditional);
$ summary(hr.ph)
Warning messages
Dear all,
I am trying to visualize the regression coefficients of the linear model
that the function aov() implicitly fits. Unfortunately the function
summary.lm() throws an error I do not understand. Here is a toy example:
dv <- c(1,3,4,2,2,3,2,5,6,3,4,4,3,5,6);
subject <-
factor(c("s1","s1
' 0.01 '*' 0.05 '.' 0.1 ' ' 1
With just 3 distinct levels, however, you could just make myfactor_nc an
ordered factor, not defining the contrasts explicitly, and then you'd get both
linear and quadratic contrasts.
I hope this helps,
John
-
Hi all,
I am new to R, and I am trying to set up a repeated measure analysis
with a quantitative (as opposed to factorized/categorical)
within-subjects variable. For a variety of reasons I am not using
linear-mixed models, rather I am trying to fit a General Linear Model (I
am aware of assump
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