% Identify the columns that contain at least one 999999
isKey = matriz_media == key;
colIdx = find(any(isKey,1)); % turn into the column IDs from logical vector
% Replace key value with mean each side of missing value
for c=colIdx
ix=find(isKey(:,c)); % missing rows
matriz_media(ix,c)=interp1(matriz_media(:,c),matriz_media(ix,c)); % fill in with linear interpolation
end
Alternatively, w/ R2016b and later...
matriz_media(ismissing(matriz_media,key))=nan; % create missing values
matriz_media=fillmissing(matriz_media,'movmean',2); % fill in 2-pt movinvg mean (same as linear interp)
ERRATUM/ADDENDUM:
Oh, yeah...one oversight -- the column vector (:,c) contains all the values including the NaN and interp1 will end up with the same index...you've got to build an x vector without those elements to fill in with the wanted...let's see...
key=999999;
isKey = matriz_media == key;
colIdx = find(any(isKey,1)); % turn into the column IDs from logical vector
nRow=size(matriz_mdia,1); % number rows in array
% Replace key value with mean each side of missing value
for c=colIdx
ix=find(isKey(:,c)); % missing rows
x=1:nRow; % all x
x=x(~ix); % don't include missing ones
matriz_media(ix,c)=interp1(x,matriz_media(:,c),matriz_media(ix,c)); % fill in with linear interpolation
end
Z = zscore(matriz_media);
ALERT: Aircode; untested written in forum--check well. :)
The difference is using the x argument as well as y in interp1 which is all indices in array excepting the missing values to interpolate.
