Thank you for all your comments,
Sigalit.
On 11/15/07, Johannes Hüsing <[EMAIL PROTECTED]> wrote:
>
> Excuse me, but I think your code deserves some comments. Unfortunately,
> the history of postings is in reverse order, so I'll address your
> first question first:
>
> > > >>> The simulation looks like this:
> > > >>>
> > > >>> z <- 0
> > > >>> x <- 0
> > > >>> y <- 0
> > > >>> aps <- 0
> > > >>> tiss <- 0
> > > >>> for (i in 1:500){
> > > >>> z[i] <- rbinom(1, 1, .6)
> > > >>> x[i] <- rbinom(1, 1, .95)
> > > >>> y[i] <- z[i]*x[i]
>
> If I'm getting this correctly, you don't need z and x later on?
> Then
> y <- rbinom(500, 1, .6*.95)
> should do the trick.
>
>
> > > >>> if (y[i]==1) aps[i] <- rnorm(1,mean=13.4, sd=7.09) else aps[i] <-
> > > >>> rnorm(1,mean=12.67, sd=6.82)
> > > >>> if (y[i]==1) tiss[i] <- rnorm(1,mean=20.731,sd=9.751)
> else tiss[i] <-
> > > >>> rnorm(1,mean=18.531,sd=9.499)
>
> tiss <- ifelse(y, rnorm(500, mean=20.731, sd=9.751), rnorm(500, mean=
> 18.531, sd=9.499))
> Likewise for aps.
>
> > > >>> }
> > > >>> v <- data.frame(y, aps, tiss)
> > > >>> log_v <- glm(y~., family=binomial, data=v)
> > > >>> summary(log_v)
>
> Makes me wonder what you need aps and tiss for. Let's assume for a moment
> that
> they are the coefficients. I do not see the necessity to put everything
> into a
> data frame, so
>
> log_v <- glm(y ~ aps+tiss, family=binomial)
>
> should be sufficient and
>
>
> summary(log_v)$coefficients[,"Pr(>|z|)"]
>
> extracts the p value.
>
> > how can I see all the P.Values (Pr(>|z|)) of the covariates from the
> 1000
> > iterations?
> > I tried names(log_v) and couldn'n find it.
>
> Try str(summary(log_v)) and you see the whole structure.
>
> --
> Johannes Hüsing There is something fascinating about
> science.
> One gets such wholesale returns of conjecture
> mailto:[EMAIL PROTECTED] from such a trifling investment of fact.
> http://derwisch.wikidot.com (Mark Twain, "Life on the
> Mississippi")
>
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