NAG CL Interface
g05pwc (subsamp_​xyw)

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1 Purpose

g05pwc generates a dataset suitable for use with repeated random sub-sampling validation.

2 Specification

#include <nag.h>
void  g05pwc (Integer nt, Integer n, Integer m, Nag_DataByObsOrVar sordx, double x[], Integer pdx, double y[], double w[], Integer state[], NagError *fail)
The function may be called by the names: g05pwc or nag_rand_subsamp_xyw.

3 Description

Let Xo denote a matrix of n observations on m variables and yo and wo each denote a vector of length n. For example, Xo might represent a matrix of independent variables, yo the dependent variable and wo the associated weights in a weighted regression.
g05pwc generates a series of training datasets, denoted by the matrix, vector, vector triplet (Xt,yt,wt) of nt observations, and validation datasets, denoted (Xv,yv,wv) with nv observations. These training and validation datasets are generated by randomly assigning each observation to either the training dataset or the validation dataset.
The resulting datasets are suitable for use with repeated random sub-sampling validation.
One of the initialization functions g05kfc (for a repeatable sequence if computed sequentially) or g05kgc (for a non-repeatable sequence) must be called prior to the first call to g05pwc.

4 References

None.

5 Arguments

1: nt Integer Input
On entry: nt, the number of observations in the training dataset.
Constraint: 1ntn.
2: n Integer Input
On entry: n, the number of observations.
Constraint: n1.
3: m Integer Input
On entry: m, the number of variables.
Constraint: m1.
4: sordx Nag_DataByObsOrVar Input
On entry: determines how variables are stored in x.
Constraint: sordx=Nag_DataByVar or Nag_DataByObs.
5: x[dim] double Input/Output
Note: the dimension, dim, of the array x must be at least
  • pdx×m when sordx=Nag_DataByVar;
  • pdx×n when sordx=Nag_DataByObs.
The way the data is stored in x is defined by sordx.
If sordx=Nag_DataByVar, x[(j-1)×pdx+i-1] contains the ith observation for the jth variable, for i=1,2,,n and j=1,2,,m.
If sordx=Nag_DataByObs, x[(i-1)×pdx+j-1] contains the ith observation for the jth variable, for i=1,2,,n and j=1,2,,m.
On entry: x must hold Xo, the values of X for the original dataset. This may be the same x as updated by a previous call to g05pwc.
On exit: values of X for the training and validation datasets, with Xt held in observations 1 to nt and Xv in observations nt+1 to n.
6: pdx Integer Input
On entry: the stride separating row elements in the two-dimensional data stored in the array x.
Constraints:
  • if sordx=Nag_DataByObs, pdxm;
  • otherwise pdxn.
7: y[dim] double Input/Output
Note: the dimension, dim, of the array y must be at least
  • n, when yis notNULL;
  • otherwise y is not referenced and may be NULL.
If the original dataset does not include yo then y must be set to NULL.
On entry: y must hold yo, the values of y for the original dataset. This may be the same y as updated by a previous call to g05pwc.
On exit: values of y for the training and validation datasets, with yt held in elements 1 to nt and yv in elements nt+1 to n.
8: w[dim] double Input/Output
Note: the dimension, dim, of the array w must be at least
  • n, when wis notNULL;
  • otherwise w is not referenced and may be NULL.
If the original dataset does not include wo then w must be set to NULL.
On entry: w must hold wo, the values of w for the original dataset. This may be the same w as updated by a previous call to g05pwc.
On exit: values of w for the training and validation datasets, with wt held in elements 1 to nt and wv in elements nt+1 to n.
9: state[dim] Integer Communication Array
Note: the dimension, dim, of this array is dictated by the requirements of associated functions that must have been previously called. This array MUST be the same array passed as argument state in the previous call to nag_rand_init_repeatable (g05kfc) or nag_rand_init_nonrepeatable (g05kgc).
On entry: contains information on the selected base generator and its current state.
On exit: contains updated information on the state of the generator.
10: fail NagError * Input/Output
The NAG error argument (see Section 7 in the Introduction to the NAG Library CL Interface).

6 Error Indicators and Warnings

NE_ALLOC_FAIL
Dynamic memory allocation failed.
See Section 3.1.2 in the Introduction to the NAG Library CL Interface for further information.
NE_ARRAY_SIZE
On entry, pdx=value and m=value.
Constraint: if sordx=Nag_DataByObs, pdxm.
On entry, pdx=value and n=value.
Constraint: if sordx=Nag_DataByVar, pdxn.
NE_BAD_PARAM
On entry, argument value had an illegal value.
NE_INT
On entry, m=value.
Constraint: m1.
On entry, n=value.
Constraint: n1.
NE_INT_2
On entry, nt=value and n=value.
Constraint: 1ntn.
NE_INTERNAL_ERROR
An internal error has occurred in this function. Check the function call and any array sizes. If the call is correct then please contact NAG for assistance.
See Section 7.5 in the Introduction to the NAG Library CL Interface for further information.
NE_INVALID_STATE
On entry, state vector has been corrupted or not initialized.
NE_NO_LICENCE
Your licence key may have expired or may not have been installed correctly.
See Section 8 in the Introduction to the NAG Library CL Interface for further information.

7 Accuracy

Not applicable.

8 Parallelism and Performance

g05pwc is threaded by NAG for parallel execution in multithreaded implementations of the NAG Library.
g05pwc makes calls to BLAS and/or LAPACK routines, which may be threaded within the vendor library used by this implementation. Consult the documentation for the vendor library for further information.
Please consult the X06 Chapter Introduction for information on how to control and interrogate the OpenMP environment used within this function. Please also consult the Users' Note for your implementation for any additional implementation-specific information.

9 Further Comments

g05pwc will be computationality more efficient if each observation in x is contiguous, that is sordx=Nag_DataByObs.

10 Example

This example uses g05pwc to facilitate repeated random sub-sampling cross-validation.
A set of simulated data is randomly split into a training and validation datasets. g02gbc is used to fit a logistic regression model to each training dataset and then g02gpc is used to predict the response for the observations in the validation dataset. This process is repeated 10 times.
The counts of true and false positives and negatives along with the sensitivity and specificity is then reported.

10.1 Program Text

Program Text (g05pwce.c)

10.2 Program Data

Program Data (g05pwce.d)

10.3 Program Results

Program Results (g05pwce.r)