From: Felix Zajitschek - BEES - UNSW <felix.zajitschek_at_STUDENT.UNSW.EDU.AU>

Date: Tue, 10 Oct 2006 13:37:54 +1000

Date: Tue, 10 Oct 2006 13:37:54 +1000

Dear group members,

I’m trying to get a grip on a dataset where I measured calling effort in

males at specific ages during their adult life (5, 12, 19 days post

eclosion, and from then on every 4th day; maximum number of measurements

is 13).

My datafile contains my repeated measures as a single variable (‘call’),

and the age at which the measure was taken as another variable

(‘callage’).I measured 81 animals, and there are 631 missing values (out

of 1053 theoretical measurements from 81*13).

In the example below I want to start with a basic model (‘call’ as the

dependent variable, and ‘callage’ as the age at which the measurement

was taken).

Later on I want to include covariates (size at eclosion, ambient

temperature at time of measurement, eclosing date) in the model.

The model given below runs and the LL converges, but I’m not really sure

whether I got the model right, especially the !ASMV qualifier: when I

put ‘13’ (=number of levels) behind ‘!ASMV’ in the variance header, the

job doesn’t run.

The second point is that I’m still struggling to try out different

covariance structures, eg. antedependence. As in the model below, the

initial values are not given correctly. ASREML fixes them to 0.001 when

running the job (see .asr output below), but when I put ‘0.001’ between

‘AR1’ and ‘!GP’, the LL doesn’t converge. I don’t know how to correctly

specify them, and would appreciate any hint/help on that matter.

Cheers,

Felix

::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::

::::

JOBFILE:

Analysis of longitudinal data for males

ind !I

eclosed !DATE

size

temp

callage !I #13 levels of age at which CE was measured

(max=59)

call

males-readyFORasremlreduced.txt !SKIP1

call !POISSON !DISP ~ callage !r callage.ind !f mv

1 2 1 !ASMV

81

callage 0 AR1 !GP

13*0.1

callage.ind 2

callage 0 AR1 !GP

13*0.1

ind

::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::

:::::::

.ASR OUTPUT:

SReml 2.00a [01 Jul 2006] Analysis of longitudinal data for males

Build: h [ 9 Sep 2006] 32 bit

10 Oct 2006 12:44:12.255 32.00 Mbyte Windows

in\correl-longitudinal-reduced

Running under 30 day Demonstration License

***********************************************************

* SYNTAX change: A/B now means A A.B *

* *

* Contact support_at_asreml.co.uk for licensing and support *

***************************************************** ARG *

Folder: D:\academic\PhD\general\progs\ASREML\ASREML2\bin

ind !I

eclosed !DATE

callage !I

QUALIFIERS: !SKIP1

Reading males-readyFORasremlreduced.txt FREE FORMAT skipping 1

lines

Univariate analysis of call

Using 1053 records of 1053 read

Model term Size #miss #zero MinNon0 Mean

MaxNon0

1 ind 81 0 0 1 41.0000

81

2 eclosed 0 0 0.3876E+05 0.3877E+05

0.3879E+05

Warning: If eclosed is fitted as a covariate, it should be centred

first.

3 size 0 0 5.870 6.678

7.500

4 temp 0 0 16.20 21.43

24.60

5 callage 13 0 0 1 7.0000

13

6 call Variate 631 207 1.000 5.005

24.00

7 callage.ind 1053 5 callage : 13 1 ind

: 81

8 mv_estimates 631

81 identity

Warning: Invalid initial value for AR=AutoReg parameter 1 has been

changed

to 0.001 and fixed. The variance of the data is around

3.656

13 AR=AutoReg 0.0010

1053 records assumed pre-sorted 13 within 81

Warning: Invalid initial value for AR=AutoReg parameter 1 has been

changed

to 0.001 and fixed. The variance of the data is around

3.656

13 AR=AutoReg 0.0010

81 identity

Structure for callage.ind has 1053 levels defined

Forming 1697 equations: 13 dense.

Initial updates will be shrunk by factor 0.010

Distribution and link: Poisson; Log Mu=exp(XB) V=Mu

Warning: The LogL value is unsuitable for comparing GLM models

1 LogL=-709.968 S2= 1.5777 409 df 1.000 0.1000E-02

0.1000E-02

2 LogL=-628.968 S2= 1.7628 409 df 1.000 0.1000E-02

0.1000E-02

3 LogL=-624.167 S2= 1.7794 409 df 1.000 0.1000E-02

0.1000E-02

4 LogL=-624.163 S2= 1.7795 409 df 1.000 0.1000E-02

0.1000E-02

5 LogL=-624.163 S2= 1.7795 409 df 1.000 0.1000E-02

0.1000E-02

6 LogL=-624.163 S2= 1.7795 409 df 1.000 0.1000E-02

0.1000E-02

Final parameter values 1.0000

0.10000E-020.10000E-02

Deviance from GLM fit 409 404.96

Variance heterogenity factor [Deviance/DF] 0.99

Source Model terms Gamma Component Comp/SE

% C

Variance 1053 409 1.00000 1.77945 14.30

0 U

Residual AR=AutoR 13 0.100000E-02 0.100000E-02 0.00

0 F

callage.ind AR=AutoR 13 0.100000E-02 0.100000E-02 0.00

0 F

Analysis of Variance NumDF DenDF F_inc

Prob

5 callage 13 409.0 12.70

<.001

Notice: The DenDF values are calculated ignoring

fixed/boundary/singular

variance parameters using algebraic derivatives.

Warning: This Analysis of Variance based on the working variable is not

equivalent to the Analysis of Deviance. Standard errors are

scaled

by the variance of the working variable, not the residual

deviance.

8 mv_estimates 631 effects fitted

7 callage.ind 1053 effects fitted ( 401

are zero)

SLOPES FOR LOG(ABS(RES)) on LOG(PV) for Section 1

2.88

63 possible outliers: in section 1 (see .res file)

Finished: 10 Oct 2006 12:44:19.215 LogL Converged

::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::

:::::::::

______________________________________________________________

::Felix Zajitschek

::School of Biological, Earth and Environmental Sciences (BEES)

::University of New South Wales (UNSW) NSW 2052 - Australia

::Labtel +61 (0)2 9385 2124

::Fax +61 (0)2 9385 1558

::eMail felix.zajitschek_at_student.unsw.edu.au

::www.bees.unsw.edu.au/school/researchstudents/zajitschekfelix.html

Received on Sun Oct 10 2006 - 13:37:54 EST

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