To get a confidence interval on lambda, you need to have measures of variability in the elements of the transition matrix. If you have that, you can use a parametric bootstrap to get approximate confidence intervals. I have done this, and it seems to work. Alternatively, you could calculate a Bayesian posterior density for lambda using the Bayesian melding methods developed by Adrian Raftery et al., and calculate an HPD interval from that. I've done that too. It's slightly more difficult, however.

Simon.

Simon Blomberg, BSc (Hons), PhD, MAppStat.

Lecturer and Consultant Statistician

Faculty of Biological and Chemical Sciences

The University of Queensland

St. Lucia Queensland 4072

Australia

T: +61 7 3365 2506

email: S.Blomberg1_at_uq.edu.au

Policies:

1. I will NOT analyse your data for you.

2. Your deadline is your problem.

The combination of some data and an aching desire for

an answer does not ensure that a reasonable answer can

be extracted from a given body of data. - John Tukey.

-----Original Message-----

From:

[hidden email] on behalf of Anouk Simard

Sent: Wed 29/08/2007 1:17 AM

To:

[hidden email]
Subject: [R] Interpreting the eigen value of a population matrix (2nd try)

Thanks for telling me that you could not get my message, I hope this work

better...

so my question was:

I built a population matrix to which I applied the fonction eigen in order

to find the main parameters about my population. I know that the first

eigen value correspond to lambda or exponential growth rate of my

population. My problem is that I want to have the 95% confidence interval

of the specific lambda (1.056 in the case). Is there a way to do that? Are

the other eigen value shown in the output could help me doing it.

I would very appreciate any help.

Thanks for your time

$values

[1] 1.0561867+0.0000000i 0.0749653+0.5249157i 0.0749653-0.5249157i

[4] 0.4498348+0.0795373i 0.4498348-0.0795373i -0.3357868+0.0000000i

$vectors

[1,] -0.72849129+0i -0.11058308+0.3293511i -0.11058308-0.3293511i

0.00244042+0.03012017i 0.00244042-0.03012017i

[2,] -0.41384232+0i 0.35124594+0.1765638i 0.35124594-0.1765638i

0.01004458+0.03839895i 0.01004458-0.03839895i

[3,] -0.27427879+0i 0.29630718-0.4260863i 0.29630718+0.4260863i

0.02540181+0.05526223i 0.02540181-0.05526223i

[4,] -0.34274458+0i -0.62502691+0.0000000i -0.62502691+0.0000000i

0.55688585-0.17705587i 0.55688585+0.17705587i

[5,] -0.31754610+0i 0.19351247+0.1625154i 0.19351247-0.1625154i

-0.73460380+0.00000000i -0.73460380+0.00000000i

[6,] -0.06705781+0i -0.00340804-0.0295753i -0.00340804+0.0295753i

0.30711075+0.13557984i 0.30711075-0.13557984i

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