Smoothing with lowess

Question

I'm trying to convert a matlab script to R, and am having some trouble with smoothing.

The matlab code I want to convert is the following:

for i = 1:size(spike_sum,2)
        smooth_sum(1:Ne,i)=smooth(double(spike_sum(1:Ne,i)),spanNe,'lowess');
end

for i = 1:Ne
        smoother_sum(i,:)=smooth(double(smooth_sum(i,:)),spanT,'lowess');
end

where spike_sum is a matrix that is Ne x 4000. I want to first smooth in Dim 1, with span spanNe, and do that for all 4000 slices. Then, I want to smooth in Dim 2 with span spanT, and do that for all Ne slices.

I've looked at the lowess function in R, but it seems like it takes in 2 dimensions as lowess(x,y,span,iter,delta). So to get the results of the code above in R, would I just take a slice of the matrix for y and replicate a constant value for x?

Answer 1

Well, although the answer provided by joran did not work as intended, and joran spent quite a bit of time trying to debug it, which I appreciate, ultimately I was unable to get that solution to work, and I don't really know why, but it was not producing results comparable to the matlab code. After messing around I found out this solution (which might not be optimal, but works as expected)

loesscontrol=loess.control(surface="interpolate", statistics="approximate", trace.hat="exact", iterations=1)
spanNe=100/Ne
spanT=50/nsteps
spanNi=30/Ni

for (i in 1:nsteps){
        x<-1:Ne
        y<-spike_sum[1:Ne,i]
        smoothingNe<-loess.smooth(x, y, span=spanNe, degree=1, family="gaussian", evaluation=Ne)
        smooth_sumNe[1:Ne,i]<-smoothingNe$y
}

for (i in 1:Ne){
        x<-1:nsteps
        y<-smooth_sumNe[i,1:nsteps]
        smoothingT<-loess.smooth(x, y, span=spanT, degree=1, control=loesscontrol, family="gaussian", evaluation=nsteps)
        smoother_sumNe[i,1:nsteps]<-smoothingT$y        
}

I want to mention that one of the keys here was to set evaluation=Ne in the first smoothing, because otherwise the result was null. I don't know why that was, but perhaps because the data was sparse and highly discontinuous.