Dear R-users,
I have a data set where each observation consists of a number of trials
(n.trials) that varies between 5 and 7, 6 being most common. Each trial
can take either of two outcomes, success or failure.
A dummy data set:
n.trials <- sample(5:7, 50, replace=T, prob=c(0.2, 0.6, 0.2))
succes
Dear all,
Given the discussions and issues of d.f., p-values and mcmcsamp-CIs in
mixed models, I wonder if anyone could recommend one or two papers (or
other citable sources for that sake) that summarizes the arguments
for/against P-values/mcCIs.
I have followed the discussions on R-help and h
Dear all,
I run a linear model with three significant explanatory variabels
x1: a factor with 4 levels
x2 and x3: factors with two levels each
x4: continuous
model <- lm(y ~ x1 + x2 * x3 + x4)
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The data is not perfectly balanced between the different
factor-combinations and I use treatment con
Dear all,
I wish to thank Christoph Buser and John Wilkinson for their input, and
especially John his examples and for for pointing me to the thread
'Doubt about nested aov output' where the rat-example was hiding...:
http://search.gmane.org/?query=Doubt+about+nested+aov+output&email=&group=gma
Dear all,
During my pre-R era I tried (yes, tried) to understand mixed models by
working through the 'rat example' in Sokal and Rohlfs Biometry (2000)
3ed p 288-292. The same example was later used by Crawley (2002) in his
Statistical Computing p 363-373 and I have seen the same data being used
Dear all,
I have a question on handling of missing values in lmList. My data set
have continuous predictor and response, x and y, and a grouping variable
group.id. All these variables have NAs and the data set also has several
other variables that also contains NAs.
To create the lmList-object
Dear all,
I am running a mixed model using lmer. In order to obtain CI of
individual coefficients I use mcmcsamp. However, I need advice which
values that are most appropriate to present in result section of a
paper. I have not used mixed models and lmer so much before so my
question is probab
Dear all,
I have received some data arranged like this:
# original data
id <- rep(letters[1:6], each=2)
time <- as.factor(rep(1:2, 6))
y <- as.vector(replicate(6, c(rnorm(n=1, mean=1), rnorm(n=1, mean=2
test.data <- data.frame(id, time, y)
test.data
I would like to perform a paired t-test of
Dear all,
I have some csv-files (originating from Excel-files) containing empty
cells. In my example file I have four variables of different classes,
each with some empty cells in the original csv-file:
> test <- read.csv2("test.csv", dec=".")
> test
id id2 x y
1 a 1 NA
2 b e
Dear all,
I am trying to plot some data with differing range in y-values with
type="b", adding error bars and break the y-axis into two parts, one
lower part from 12 to 20, and one upper part from 34 to 40.
I have tried to follow the basic ideas from the script provided here by
Jim Lemon:
http
Dear Bill,
You might check Faraway's 'Extending the Linear Model with R:
Generalized Linear, Mixed Effects and Nonparametric Regression Models'
(2006). Without having read the book properly, at least I noticed that
package lme4 and the function lmer is used in the examples.
Best regards,
Henri
Dear all,
I have a data set with x and y positions of nests. Each nest can be
grouped according to a factor z. I have made a small dummy data set,
where the grouping variable z is 0 or 1:
x <- runif(6)
y <- runif(6)
z <- rep(c(0,1), each=3)
xyz <- cbind(x,y,z)
xyz
x y z
[
Dear R users,
I successfully installed SciViews the other day. However, when I try to
run it now, the command/script window does not appear. Strange. Well,
actually I see a tendency to a script window (in the lower part of the
sciview window where it is suppose to be) during start up, but when
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