naginterfaces.library.correg.pls_​pred

naginterfaces.library.correg.pls_pred(orig, xbar, ybar, iscale, xstd, ystd, b, isz, z)

pls_pred calculates predictions given the output from an orthogonal scores PLS regression (pls_svd() or pls_wold()) and pls_fit().

For full information please refer to the NAG Library document for g02ld

https://support.nag.com/numeric/nl/nagdoc_30/flhtml/g02/g02ldf.html

Parameters

origint

Indicates how parameter estimates are supplied.

$orig=1$

Parameter estimates are for the original data.

$orig=-1$

Parameter estimates are for the centred, and possibly scaled, data.

xbarfloat, array-like, shape $\left(\text{ip}\right)$

If $orig=-1$, $xbar$ must contain mean values of predictor variables in the model; otherwise $xbar$ is not referenced.

ybarfloat, array-like, shape $\left(\text{my}\right)$

If $orig=-1$, $ybar$ must contain the mean value of each response variable in the model; otherwise $ybar$ is not referenced.

iscaleint

If $orig=-1$, $iscale$ must take the value supplied to either pls_svd() or pls_wold(); otherwise $iscale$ is not referenced.

xstdfloat, array-like, shape $\left(\text{ip}\right)$

If $orig=-1$ and $iscale\ne -1$, $xstd$ must contain the scalings of predictor variables in the model as returned from either pls_svd() or pls_wold(); otherwise $xstd$ is not referenced.

ystdfloat, array-like, shape $\left(\text{my}\right)$

If $orig=-1$ and $iscale\ne -1$, $ystd$ must contain the scalings of response variables as returned from either pls_svd() or pls_wold(); otherwise $ystd$ is not referenced.

bfloat, array-like, shape $(:,\text{my})$

Note: the required extent for this argument in dimension 1 is determined as follows: if $orig=-1$: $\text{ip}$; if $orig=1$: $1+\text{ip}$; otherwise: $0$.

If $orig=-1$, $b$ must contain the parameter estimate for the centred, and possibly scaled, data as returned by pls_fit(); otherwise $b$ must contain the parameter estimates for the original data as returned by pls_fit().

iszint, array-like, shape $\left(\text{mz}\right)$

Indicates which predictor variables are to be included in the model. Predictor variables included from $z$ must be in the same order as those included in the fitted model.

If $isz[\text{j}-1]=1$, the $\text{j}$th predictor variable is included in the model, for $\text{j}=1,2,\dots ,\text{mz}$, otherwise $isz[j-1]=0$.

zfloat, array-like, shape $(n,\text{mz})$

$z[\text{i}-1,\text{j}-1]$ contains the $\text{i}$th observation on the $\text{j}$th available predictor variable, for $\text{j}=1,2,\dots ,\text{mz}$, for $\text{i}=1,2,\dots ,n$.

Returns

yhatfloat, ndarray, shape $(n,\text{my})$

$yhat[i-1,j-1]$ contains the $i$th predicted value of the $j$th $y$-variable in the model.

Raises

NagValueError

(errno $1$)

On entry, $\text{ip}=⟨value⟩$.

Constraint: $\text{ip}>1$.

(errno $1$)

On entry, $\text{my}=⟨value⟩$.

Constraint: $\text{my}\ge 1$.

(errno $1$)

On entry, $orig=⟨value⟩$.

Constraint: $orig=-1$ or $1$.

(errno $1$)

On entry, $iscale=⟨value⟩$.

Constraint: if $orig=-1$, $iscale=-1$, $1$ or $2$.

(errno $1$)

On entry, $n=⟨value⟩$.

Constraint: $n\ge 1$.

(errno $1$)

On entry, $isz[⟨value⟩]=⟨value⟩$.

Constraint: $isz[j-1]=0$ or $1$.

(errno $2$)

On entry, $\text{ldb}=⟨value⟩$ and $\text{ip}+1=⟨value⟩$.

Constraint: if $orig=1$, $\text{ldb}\ge 1+\text{ip}$.

(errno $2$)

On entry, $\text{ldb}=⟨value⟩$ and $\text{ip}=⟨value⟩$.

Constraint: if $orig=-1$, $\text{ldb}\ge \text{ip}$.

(errno $2$)

On entry, $\text{mz}=⟨value⟩$ and $\text{ip}=⟨value⟩$.

Constraint: $\text{mz}\ge \text{ip}$.

(errno $3$)

On entry, the number of elements of $isz$ equal to $1$ is not $\text{ip}$.

Notes

pls_pred calculates the predictions $\hat{Y}$ of a PLS model given a set $Z$ of test data and a set $B$ of parameter estimates as returned by pls_fit().

If pls_fit() returns parameter estimates for the original data scale, no further information is required.

If pls_fit() returns parameter estimates for the centred, and possibly scaled, data, further information is required. The means of variables in the fitted model must be supplied. In the case of a PLS model fitted by using scaled data, the means and standard deviations of variables in the fitted model must also be supplied. These means and standard deviations are those returned by either pls_svd() and pls_wold().

See also

naginterfaces.library.examples.correg.pls_ex.main()