Assume we have spectral data xcal, ycal, xval, yval where
xcal is mxn : m spectra, or observations, of a sample, n wavelengths per spectrum
ycal is mx1 : m concentrations of the sample corresponding to the m observations in xcal
xval is 1xn : 1 new spetrum or new observation of the sample (ie, not a member of xcal)
yval is 1×1 : 1 new concentration of the sample corresponding to the observation in xval
assuming m>n and ncomp<n and xcal0 is xcal with its mean subtracted,
xcal0 = xcal – ones(m,1)*mean(xcal)
[XL,YL,XS,YS,BETA,PCTVAR,MSE,STATS] = PLSREGRESS(xcal,ycal,ncomp);
Can be used to compute Q residuals, or the rowwise sum of squares of the STATS.XResiduals matrix
and
STATS.T2, is the Hotelling T^2 value
for each of the m observations in xcal
Q residuals and T2 values can be used to determine if the observations in xcal are outliers
Can the outputs of plsregress as described above be used to compute a Q residual and a T^2 value for the single observation in xval to determine if it seems to be an outlier with respect to xcal?Assume we have spectral data xcal, ycal, xval, yval where
xcal is mxn : m spectra, or observations, of a sample, n wavelengths per spectrum
ycal is mx1 : m concentrations of the sample corresponding to the m observations in xcal
xval is 1xn : 1 new spetrum or new observation of the sample (ie, not a member of xcal)
yval is 1×1 : 1 new concentration of the sample corresponding to the observation in xval
assuming m>n and ncomp<n and xcal0 is xcal with its mean subtracted,
xcal0 = xcal – ones(m,1)*mean(xcal)
[XL,YL,XS,YS,BETA,PCTVAR,MSE,STATS] = PLSREGRESS(xcal,ycal,ncomp);
Can be used to compute Q residuals, or the rowwise sum of squares of the STATS.XResiduals matrix
and
STATS.T2, is the Hotelling T^2 value
for each of the m observations in xcal
Q residuals and T2 values can be used to determine if the observations in xcal are outliers
Can the outputs of plsregress as described above be used to compute a Q residual and a T^2 value for the single observation in xval to determine if it seems to be an outlier with respect to xcal? Assume we have spectral data xcal, ycal, xval, yval where
xcal is mxn : m spectra, or observations, of a sample, n wavelengths per spectrum
ycal is mx1 : m concentrations of the sample corresponding to the m observations in xcal
xval is 1xn : 1 new spetrum or new observation of the sample (ie, not a member of xcal)
yval is 1×1 : 1 new concentration of the sample corresponding to the observation in xval
assuming m>n and ncomp<n and xcal0 is xcal with its mean subtracted,
xcal0 = xcal – ones(m,1)*mean(xcal)
[XL,YL,XS,YS,BETA,PCTVAR,MSE,STATS] = PLSREGRESS(xcal,ycal,ncomp);
Can be used to compute Q residuals, or the rowwise sum of squares of the STATS.XResiduals matrix
and
STATS.T2, is the Hotelling T^2 value
for each of the m observations in xcal
Q residuals and T2 values can be used to determine if the observations in xcal are outliers
Can the outputs of plsregress as described above be used to compute a Q residual and a T^2 value for the single observation in xval to determine if it seems to be an outlier with respect to xcal? plsregress q residuals, plsregress t2, plsregress outlier new data MATLAB Answers — New Questions

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