In a rather simple regression, I’d like to ask the question, for high trees,
whether it makes a difference (for volume) whether a three is thick.
If my interpretation is correct, for low trees, i.e. for which trees$isHigh
== FALSE, the answer is yes.
The problem is how to "merge" the standard er
Any ideas would be much appreciated; I suspect that this problem of
constructing the dummies applies not only to function coxph but to other
regression models in R as well. Effectively, my question is how to better
control for which dummies and interactions to include in the model and which
not.
T
The output is as follows. My question is how to include only the interaction
terms where x is equal to "Maintained".
(x has two possible values "Maintained" and "Nonmaintained".)
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I’m trying to set up Cox Proptional Hazard model with interactions between
time and the covariates (which are categorical). The problem that I face is
that how to define the interactions, i.e. “x+cutStart:x”, properly.
The code below illustrates the problem. R gives the error message ” X matrix
de
Thanks to all of you for your very helpful comments! As Terry suggested,
svykm is what I was looking for.
While testing that I get the same results with the package survey as with
the package survival, I encountered the issue of how to draw survival
curves. Apparently the implementations in the t
Say, that I have two observations, one from time 0 to time 50, and a second
from time 0 to time 100, both of which are known to have failed, i.e. no
censoring. I would like to give double the weight to the second observation.
This is what I’ve tried to implement in the both pieces of code. Both pi
As part of a research paper, I would like to draw both weighted and
unweighted Kaplan-Meier estimates, the weight being the ’importance’ of the
each project to the mass of projects whose survival I’m trying to estimate.
I know that the function survfit in the package survival accepts weights and
p
Hi Chris,
Yes, that is exactly the model I want to fit, i.e. a separate 'baseline'
hazard for each stratum (defined by cov1) and a coefficient for cov2 that is
different for each stratum.
Is suspect that there is a problem with the following line of your code.
sCox <- coxph(Surv(time, status)
I’m trying to set up proportional hazard model that is stratified with
respect to covariate 1 and has an interaction between covariate 1 and
another variable, covariate 2. Both variables are categorical. In the
following, I try to illustrate the two problems that I’ve encountered, using
the lung da
I’m trying to set up proportional hazard model that is stratified with
respect to covariate 1 and has an interaction between covariate 1 and
another variable, covariate 2. Both variables are categorical. In the
following, I try to illustrate the two problems that I’ve encountered, using
the lung da
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