From: Felipe Carrillo <mazatlanmexico_at_yahoo.com>

Date: Sun, 16 Mar 2008 20:23:53 -0700 (PDT)

> > Be a better friend, newshound, and

R-help_at_r-project.org mailing list

https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code. Received on Mon 17 Mar 2008 - 03:30:05 GMT

Date: Sun, 16 Mar 2008 20:23:53 -0700 (PDT)

- jim holtman <jholtman_at_gmail.com> wrote:

*> Glad I could help. You might want to post it back
**> to R-Help so that
**> others can see what was done.
**>
**> On Sun, Mar 16, 2008 at 6:02 PM, Felipe Carrillo
**> <mazatlanmexico_at_yahoo.com> wrote:
**> > Jim: I owe you man, this is great,I never thought
**> that
**> > I could acomplish this task. Now I can estimate
**> > confidence intervals for mydf and I will be
**> > done,,Thanks again Jim..
**> >
**> >
**> > > This should do what you want: (you had td and pd
**> > > reversed in your example)
**> > >
*

> > > > xd <-

*> > >
**> c(2.2024,2.4216,1.4672,1.4817,1.4957,1.4431,1.5676)
**> > > > td <-
**> > >
**> >
*

>

c(0.017046,0.018504,0.012157,0.012253,0.012348,0.011997,0.012825)

*> > > > pd <-
**> > > c(160524,163565,143973,111956,89677,95269,81558)
**> > > > mydf<-data.frame(xd,pd,td)
**> > > > trans<-t(mydf)
*

> > > > trans

*> > > [,1] [,2] [,3]
**> [,4]
**> > > [,5]
**> > > [,6] [,7]
**> > > xd 2.20240e+00 2.42160e+00 1.46720e+00
**> 1.48170e+00
**> > > 1.4957e+00
**> > > 1.4431e+00 1.5676e+00
**> > > pd 1.60524e+05 1.63565e+05 1.43973e+05
**> 1.11956e+05
**> > > 8.9677e+04
**> > > 9.5269e+04 8.1558e+04
**> > > td 1.70460e-02 1.85040e-02 1.21570e-02
**> 1.22530e-02
**> > > 1.2348e-02
**> > > 1.1997e-02 1.2825e-02
**> > > > varA<- 0.0000036084
**> > > > covAB<- (-0.0000013046)
**> > > > varB<- 0.00000052628
**> > > >
**> > > > # create the sequences to test against
**> > > > i.seq <- lapply(seq(ncol(trans) - 1),
**> function(x)
**> > > x:(ncol(trans) - 1))
**> > > > x <- lapply(i.seq, function(.col){
**> > > + # compute the 3 columns of data
**> > > + cbind(xp=varA + trans[1, .col[1]] * covAB
**> +
**> > > trans[1, .col + 1] *
**> > > covAB + trans[1, .col[1]] * trans[1, .col + 1] *
**> > > varB,
**> > > + pd=trans[2, .col[1]] * trans[2, .col +
**> 1],
**> > > + td=trans[3, .col[1]] * trans[3, .col +
**> 1])
**> > > + })
**> > > > # rbind for the output
**> > > > z <- do.call(rbind, x)
**> > > > # add the covariance
**> > > > z <- cbind(z, cov=z[, 'xp'] * z[, 'pd'] / z[,
**> > > 'td'])
**> > > > z
**> >
**> >
**> >
**> > Felipe D. Carrillo
**> > Fishery Biologist
**> > Department of the Interior
**> > US Fish & Wildlife Service
**> > California, USA
**> >
**> >
**> >
**> >
*

>

> > Be a better friend, newshound, and

>

*> >
**> >
**>
**>
**>
**> --
*

> Jim Holtman

*> Cincinnati, OH
**> +1 513 646 9390
**>
**> What is the problem you are trying to solve?
**>
*

Felipe D. Carrillo

Fishery Biologist

Department of the Interior

US Fish & Wildlife Service

California, USA

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