% I can use fitgp to do linear regression without a problem.
m = 0.5;
b = 0.2;
x = [1,2,3,4,5,6,7,8,9,10];
y = m*x + b + 0.1*randn(1,length(x));
% Linear regression can easily be done by maximizing the log likelihood of N(y-(b2*x+b1),sigma)
% I can also use the fitrgp funtion to get the coefficients as well.
gpMod1 = fitrgp(x,y,'Basis','linear','Beta',[1;1],'Sigma',0.1);
% I can also use a custom function for fitrgp
hfcn = @(X) = X;
gpMod2 = fitrgp(x,y,'Basis',hfcn,'Beta',[1;1],'Sigma',0.1);
% However, if I have censored data, I am not sure how to impliment fitrgp or if it is possible. The normal way would be maximizing,
C = [1,1,1,1,1,0,0,0,0,0]; % are my classes for the right and left censored data
logL = @(g) -sum(C.*log(normcdf(y,b2*x+b1,sigma)) + (1-C)*log(1-normcdf(y,b2*x+b1,sigma)));
% or alternatively I could compact it since normcdf(t) = 1-normcdf(-t) so that,
C = [1,1,1,1,1,-1,-1,-1,-1,-1]; % are my classes for the right and left censored data
logL = @(g) -sum(C.*log(normcdf(y,b2*x+b1,sigma)));
% I do not know how to do this with fitrgp or if it is possible since I can't define the likelihood, only the basis. I am aware of other packages out there like GPML but I would like to know if I can do it with fitrgp. Thanks!
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