R help - Apr 2011 - 1 continuous non-normal variable ~ 4 factors + 1 continuous covariate (with interactions)

Hello

Im struggling on something... I have one continuous variable (A), and I need
to explain it with 4 factors, and maybe one continuous covariate. 
And of course, my variable A is not normal at all (it's a duration in
seconds, whole numbers). 

What can I do? I would know how do deal with it if I had one factor, maybe
two, but in that case Im really not sure what I am supposed to do to write a
model that is statistically correct.

Thanks,

Al

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>
> Im struggling on something... I have one continuous variable (A), and I 
> need
> to explain it with 4 factors, and maybe one continuous covariate.
> And of course, my variable A is not normal at all (it's a duration in
> seconds, whole numbers).
>
> What can I do? I would know how do deal with it if I had one factor, maybe
> two, but in that case Im really not sure what I am supposed to do to write 
> a
> model that is statistically correct.
If you are working with a positive outcome variable, one approach is to use 
glm( ..., family ="Gamma"),
other approach is to use survreg() in the package survival.

Cheers,

Pablo

>
> Thanks,
>
> Al
>
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>
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Thanks

Im not sure about the gamma, but a survival analysis seems appropriate, but
does it work for factors and continuous covariates? Do you have to verify
some conditions beforehand? 

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Zitat von Alal <bohk at voila.fr>:
> Thanks
>
> Im not sure about the gamma, but a survival analysis seems appropriate, but
> does it work for factors and continuous covariates? Do you have to verify
> some conditions beforehand?
Here is an example:

# test data...
library(survival)

set.seed(1007)
x <- runif(50)
mu <- c(rep(1, 25), rep(2, 25))

test1 <- data.frame(Time = qsurvreg(x, mean = mu, scale= 0.5, distribution
"weibull"),
                    Status = rbinom(50,1,0.7),
                    f1 = gl(2, 25),
                    f2 = factor(rbinom(50, 1, 0.5)),
                    f3 = factor(rbinom(50, 1, 0.5)),
                    f4 = factor(rbinom(50, 1, 0.5)),
                    z = rnorm(50)
                    )

mod1 <- survreg(Surv(Time, Status) ~  f1*(f2 + f2 + f4) + z, data = test1)
summary(mod1)

Cheers,

Pablo








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> PLEASE do read the posting guide
http://www.R-project.org/posting-guide.html
> and provide commented, minimal, self-contained, reproducible code.
>

Thanks, I guess I can do that, and it actually seem appropriate for one of my
variable. 

But can you do post-hoc tests on a survival analysis? Use contrasts or
something? 

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