Hello,
I am trying to use the cov.mve function on a set of variables to check for
outliers, before I perform PCA on them. I am using the code that I found on
Everitt (2005) An R ans S-Plus companion to multivariate analysis but its
doesn't seem to work. I wrote:
at.central-central[,7:17]
Hello,
I am using the package MuMI to run all the possible combinations of variables
in my full model, and select my best models. When I enter my variables in the
original model I write them like this
lm(y~ a +b +c +a:b)
However, MuMI will also use the variable b:a, which I do not want in my
Hello,
I am trying to see whether there has been a significant difference in whether
people experienced damages from wildlife in two different years. I therefore
have two columns:
year 1:
yes
no
no
no
yes
yes
no
year 2:
no
yes
no
yes
I wanted to do a chisq.test, but if I enter it this way:
Hello,
I'm using the glmulti package to run models of all the possible combinations
of my variables. However, I am only interested in a few interactions between my
variables.
I have tried the equivalent of:
mod1-lm(y~a+b+c+a:b)
glmulti(mod1, level=1)
mod2-lm(y~a+b+c+a:b)
glmulti(mod2,
Hello,
I'm using the glmulti package to run models of all the possible combinations
of my variables. However, I am only interested in a few interactions between my
variables.
I have tried the equivalent of:
mod1-lm(y~a+b+c+a:b)
glmulti(mod1, level=1)
mod2-lm(y~a+b+c+a:b)
glmulti(mod2,
Hello,
I'm using the glmulti package to run models of all the possible combinations
of my variables. However, I am only interested in a few interactions between
them.
I have tried the equivalent of:
1) mod1-lm(y~a+b+c+a:b)
glmulti(mod1, level=1)
2)mod2-lm(y~a+b+c+a:b)
Hello,
I am currently analyzing responses to questionnaires about general attitudes. I
have performed a PCA on my data, and have retained two Principal Components.
Now I would like to use the scores of both the principal comonents in a
multiple regression. I would like to know if it makes
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