只看到有matlab的脚本，没见到R的，求助转成R的[s:11]

function [x,nw,np,nt] = osccalc(x,y,nocomp,iter,tol)

%OSCCALC Calculates orthogonal signal correction

% The inputs are the matrix of predictor variables (x)

% and predicted variable(s) (y), scaled as desired, and

% the number of OSC components to calculate (nocomp).

% Optional input variables are the maximum number of

% iterations used in attempting to maximize the variance

% captured by orthogonal component (iter, default = 0),

% and the tolerance on percent of x variance to consider

% in formation of the final w vector (tol, default = 99.9).

% The outputs are the OSC corrected x matrix (nx) and

% the weights (nw), loads (np) and scores(nt) that were

% used in making the correction. Once the calibration is

% done, new (scaled) x data can be corrected by

% newx = x - x*nw*inv(np'*nw)*np';

%

%I/O: [nx,nw,np,nt] = osccalc(x,y,nocomp,iter,tol);

%

%See also: CROSSVAL

[m,n] = size(x);

nw = zeros(n,nocomp);

np = zeros(n,nocomp);

nt = zeros(m,nocomp);

if nargin < 4 | isempty(iter)

iter = 0;

end

if nargin < 5 | isempty(tol)

tol = 99.9;

end

for i = 1:nocomp

% Calculate the first score vector

[u,s,v] = svds(x,1);

p = v(:,1);

p = p*sign(sum(p));

told = u(:,1)*s(1);

dif = 1;

k = 0;

while dif > 1e-12

k = k+1;

% Calculate scores from loads

t = x*p/(p'*p);

% Othogonalize t to y

tnew = t - y*inv(y'*y)*y'*t;

% Compute a new loading

pnew = x'*tnew/(tnew'*tnew);

% Check for convergence

dif = norm(tnew-told)/norm(tnew);

% Assign pnew to p

told = tnew;

p = pnew;

if k > iter

dif = 0;

end

end

% Build PLS model relating x to t

nc = rank(x);

[w,ssq] = pls(x,tnew,nc,0);

% Include components as specified by tol on x variance

z = size(find(ssq(:,3)<tol));

nc = z(1)+1;

w = w(nc,:)';

w = w/norm(w);

% Calculate new scores vector

t = x*w;

% Othogonalize t to y

t = t - y*inv(y'*y)*y'*t;

% Compute new p

p = x'*t/(t'*t);

% Remove orthogonal signal from x

x = x - t*p';

np(:,i) = p;

nw(:,i) = w;

nt(:,i) = t;

end