Group-specific predictors in nlmefit

Does anyone have a simple example of estimating group-specific predictors (non-empty VFUN model parameter) for the nlmefit function?

Answer 1

[Tom Lane]

If you are happy with a toy illustration rather than a realistic example, here's one. Set up some fake data following a linear model:

subject =  kron((1:6)',ones(4,1));   % 6 subjects measure at 4 times
time = kron(ones(6,1),(1:4)');
weight = [100 150 140 200 190 140]'; % weight of each subject
effect = [1 -1 0 -2 2 0]';           % a "random" effect of each subject
y = 5 + effect(subject) - time + .03*weight(subject) + randn(size(subject));

Fit a linear function of time and weight by including weight in the first input as a column. Since weight is measured only once per subject, we have to expand it to the right length.

model = @(phi,t) phi(1) + phi(2)*t(:,1) + phi(3)*t(:,2);
phi0 = [1 1 1];
[beta,PSI,stats,br] = nlmefit([time weight(subject)],y,subject,[],model,phi0,'reparam',1);
beta
br

Now move weight to the separate V input where it does not have to be expanded to have one value per observation:

model = @(phi,t,v) phi(1) + phi(2)*t + phi(3)*v;
phi0 = [1 1 1];
[beta,PSI,stats,br] = nlmefit(time,y,subject,weight,model,phi0,'reparam',1);
beta
br

[the cyclist]

Thanks, Tom. I often prefer toy examples to "real" examples, because it is easier to make the connection between the data and the output.

[yu zhang]

Thanks. I have one more question. [beta,PSI,stats,br] = nlmefit(time,y,subject,weight,model,phi0,'reparam',1); what does the 'reparam',1 means?

mag if my Group vaules , I define the group-specific predictors group, and VFUN like this.

[group, gN,VFUN] = grp2idx(mag)

model = @(c,R,M)(c(1)+c(2)*(M-6)+c(3)*log(M/6)+(c(4)*M+c(5)).*log(sqrt(R(:,1).^2+c(6)^2))+c(7)*R(:,2));

beta0 = [3.08 6.39 -16 -0.61 1.75 10 -0.012];

[BETA,PSI,STATS,B] = nlmefit([rup,RA],lnarias,Mv,gL,model,beta0);

It runs a long time, and gives the waring information "stop calculation because the maximum number of iterations is reached"

if i use [BETA,PSI,STATS,B] = nlmefit([rup,RA],lnarias,Mv,gL,model,beta0,'reparam',1);

It seems woring well. But I don't konw why?

[the cyclist]

'reparam' is short-hand for specifying the name-value pair 'REParamsSelect' (which you can think of as short-hand for "random effects parameter selection"). That input is specifying which of your parameters has a random effect.

By default, all elements PHI vector include a random effect. Using

'reparam',1

specifies that only the first element should include a random effect.

See the documentation for nlmefit, for details.