NAG Library Routine Document

g02ldf  (pls_pred)

1

Purpose

2

Specification

3

Description

4

References

5

Arguments

1:     $\mathbf{ip}$ – IntegerInput

On entry: the number of predictor variables in the fitted model. ip must take the same value as that supplied to g02laf or g02lbf to fit the model.

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

2:     $\mathbf{my}$ – IntegerInput

On entry: the number of response variables in the fitted model. my must take the same value as that supplied to g02laf or g02lbf to fit the model.

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

3:     $\mathbf{orig}$ – IntegerInput

On entry: indicates how parameter estimates are supplied.

$\mathbf{orig}=1$

Parameter estimates are for the original data.

$\mathbf{orig}=-1$

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

Constraint: $\mathbf{orig}=-1$ or $1$.

4:     $\mathbf{xbar}\left(\mathbf{ip}\right)$ – Real (Kind=nag_wp) arrayInput

On entry: if $\mathbf{orig}=-1$, xbar must contain mean values of predictor variables in the model; otherwise xbar is not referenced.

5:     $\mathbf{ybar}\left(\mathbf{my}\right)$ – Real (Kind=nag_wp) arrayInput

On entry: if $\mathbf{orig}=-1$, ybar must contain the mean value of each response variable in the model; otherwise ybar is not referenced.

6:     $\mathbf{iscale}$ – IntegerInput

On entry: if $\mathbf{orig}=-1$, iscale must take the value supplied to either g02laf or g02lbf; otherwise iscale is not referenced.

Constraint: if $\mathbf{orig}=-1$, $\mathbf{iscale}=-1$, $1$ or $2$.

7:     $\mathbf{xstd}\left(\mathbf{ip}\right)$ – Real (Kind=nag_wp) arrayInput

On entry: if $\mathbf{orig}=-1$ and $\mathbf{iscale}\ne -1$, xstd must contain the scalings of predictor variables in the model as returned from either g02laf or g02lbf; otherwise xstd is not referenced.

8:     $\mathbf{ystd}\left(\mathbf{my}\right)$ – Real (Kind=nag_wp) arrayInput

On entry: if $\mathbf{orig}=-1$ and $\mathbf{iscale}\ne -1$, ystd must contain the scalings of response variables as returned from either g02laf or g02lbf; otherwise ystd is not referenced.

9:     $\mathbf{b}\left(\mathbf{ldb},\mathbf{my}\right)$ – Real (Kind=nag_wp) arrayInput

On entry: if $\mathbf{orig}=-1$, b must contain the parameter estimate for the centred, and possibly scaled, data as returned by g02lcf; otherwise b must contain the parameter estimates for the original data as returned by g02lcf.

10:   $\mathbf{ldb}$ – IntegerInput

On entry: the first dimension of the array b as declared in the (sub)program from which g02ldf is called. If $\mathbf{orig}=-1$, ldb must be at least ip; otherwise b also contains the estimate for the intercept parameter and consequently ldb must be at least $1+\mathbf{ip}$.

Constraints:

• if $\mathbf{orig}=-1$, $\mathbf{ldb}\ge \mathbf{ip}$;
• if $\mathbf{orig}=1$, $\mathbf{ldb}\ge 1+\mathbf{ip}$.

11:   $\mathbf{n}$ – IntegerInput

On entry: $n$, the number of observations in the test data $Z$.

Constraint: $\mathbf{n}\ge 1$.

12:   $\mathbf{mz}$ – IntegerInput

On entry: the number of available predictor variables in the test data.

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

13:   $\mathbf{isz}\left(\mathbf{mz}\right)$ – Integer arrayInput

On entry: 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 $\mathbf{isz}\left(\mathit{j}\right)=1$, the $\mathit{j}$th predictor variable is included in the model, for $\mathit{j}=1,2,\dots ,\mathbf{mz}$, otherwise $\mathbf{isz}\left(j\right)=0$.

Constraints:

• $\mathbf{isz}\left(\mathit{j}\right)=0\text{ or }1$, for $\mathit{j}=1,2,\dots ,\mathbf{mz}$;
• ${\sum }_j\mathbf{isz}\left(j\right)=\mathbf{ip}$.

14:   $\mathbf{z}\left(\mathbf{ldz},\mathbf{mz}\right)$ – Real (Kind=nag_wp) arrayInput

On entry: $\mathbf{z}\left(\mathit{i},\mathit{j}\right)$ contains the $\mathit{i}$th observation on the $\mathit{j}$th available predictor variable, for $\mathit{i}=1,2,\dots ,\mathbf{n}$ and $\mathit{j}=1,2,\dots ,\mathbf{mz}$.

15:   $\mathbf{ldz}$ – IntegerInput

On entry: the first dimension of the array z as declared in the (sub)program from which g02ldf is called.

Constraint: $\mathbf{ldz}\ge \mathbf{n}$.

16:   $\mathbf{yhat}\left(\mathbf{ldyhat},\mathbf{my}\right)$ – Real (Kind=nag_wp) arrayOutput

On exit: $\mathbf{yhat}\left(i,j\right)$ contains the $i$th predicted value of the $j$th $y$-variable in the model.

17:   $\mathbf{ldyhat}$ – IntegerInput

On entry: the first dimension of the array yhat as declared in the (sub)program from which g02ldf is called.

Constraint: $\mathbf{ldyhat}\ge \mathbf{n}$.

18:   $\mathbf{ifail}$ – IntegerInput/Output

On entry: ifail must be set to $0$, $-1\text{ or }1$. If you are unfamiliar with this argument you should refer to Section 3.4 in How to Use the NAG Library and its Documentation for details.

For environments where it might be inappropriate to halt program execution when an error is detected, the value $-1\text{ or }1$ is recommended. If the output of error messages is undesirable, then the value $1$ is recommended. Otherwise, if you are not familiar with this argument, the recommended value is $0$. When the value $-\mathbf{1}\text{ or }\mathbf{1}$ is used it is essential to test the value of ifail on exit.

On exit: $\mathbf{ifail}=\mathbf{0}$ unless the routine detects an error or a warning has been flagged (see Section 6).

6

Error Indicators and Warnings

$\mathbf{ifail}=1$

On entry, $\mathbf{ip}=〈\mathit{\text{value}}〉$.
Constraint: $\mathbf{ip}>1$.

On entry, $\mathbf{iscale}=〈\mathit{\text{value}}〉$.
Constraint: if $\mathbf{orig}=-1$, $\mathbf{iscale}=-1$, $1$ or $2$.

On entry, $\mathbf{isz}\left(j\right)=〈\mathit{\text{value}}〉$, $j=〈\mathit{\text{value}}〉$.
Constraint: $\mathbf{isz}\left(j\right)=0\text{ or }1$.

On entry, $\mathbf{my}=〈\mathit{\text{value}}〉$.
Constraint: $\mathbf{my}\ge 1$.

On entry, $\mathbf{n}=〈\mathit{\text{value}}〉$.
Constraint: $\mathbf{n}\ge 1$.

On entry, $\mathbf{orig}=〈\mathit{\text{value}}〉$.
Constraint: $\mathbf{orig}=-1$ or $1$.

$\mathbf{ifail}=2$

On entry, $\mathbf{ldb}=〈\mathit{\text{value}}〉$ and $\mathbf{ip}=〈\mathit{\text{value}}〉$.
Constraint: if $\mathbf{orig}=-1$, $\mathbf{ldb}\ge \mathbf{ip}$.

On entry, $\mathbf{ldb}=〈\mathit{\text{value}}〉$ and $\mathbf{ip}+1=〈\mathit{\text{value}}〉$.
Constraint: if $\mathbf{orig}=1$, $\mathbf{ldb}\ge 1+\mathbf{ip}$.

On entry, $\mathbf{ldyhat}=〈\mathit{\text{value}}〉$ and $\mathbf{n}=〈\mathit{\text{value}}〉$.
Constraint: $\mathbf{ldyhat}\ge \mathbf{n}$.

On entry, $\mathbf{ldz}=〈\mathit{\text{value}}〉$ and $\mathbf{n}=〈\mathit{\text{value}}〉$.
Constraint: $\mathbf{ldz}\ge \mathbf{n}$.

On entry, $\mathbf{mz}=〈\mathit{\text{value}}〉$ and $\mathbf{ip}=〈\mathit{\text{value}}〉$.
Constraint: $\mathbf{mz}\ge \mathbf{ip}$.

$\mathbf{ifail}=3$

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

$\mathbf{ifail}=-99$

An unexpected error has been triggered by this routine. Please contact NAG.

See Section 3.9 in How to Use the NAG Library and its Documentation for further information.

$\mathbf{ifail}=-399$

Your licence key may have expired or may not have been installed correctly.

See Section 3.8 in How to Use the NAG Library and its Documentation for further information.

$\mathbf{ifail}=-999$

Dynamic memory allocation failed.

See Section 3.7 in How to Use the NAG Library and its Documentation for further information.

7

Accuracy

8

Parallelism and Performance

9

Further Comments

10

Example

10.1

Program Text

Program Text (g02ldfe.f90)

10.2

Program Data

Program Data (g02ldfe.d)

10.3

Program Results

Program Results (g02ldfe.r)