NAG Library Function Document
nag_rand_subsamp_xyw (g05pwc)
1
Purpose
nag_rand_subsamp_xyw (g05pwc) generates a dataset suitable for use with repeated random sub-sampling validation.
2
Specification
#include <nag.h> |
#include <nagg05.h> |
void |
nag_rand_subsamp_xyw (Integer nt,
Integer n,
Integer m,
Nag_DataByObsOrVar sordx,
double x[],
Integer pdx,
double y[],
double w[],
Integer state[],
NagError *fail) |
|
3
Description
Let denote a matrix of observations on variables and and each denote a vector of length . For example, might represent a matrix of independent variables, the dependent variable and the associated weights in a weighted regression.
nag_rand_subsamp_xyw (g05pwc) generates a series of training datasets, denoted by the matrix, vector, vector triplet of observations, and validation datasets, denoted with 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
nag_rand_init_repeatable (g05kfc) (for a repeatable sequence if computed sequentially) or
nag_rand_init_nonrepeatable (g05kgc) (for a non-repeatable sequence) must be called prior to the first call to
nag_rand_subsamp_xyw (g05pwc).
4
References
None.
5
Arguments
- 1:
– IntegerInput
-
On entry: , the number of observations in the training dataset.
Constraint:
.
- 2:
– IntegerInput
-
On entry: , the number of observations.
Constraint:
.
- 3:
– IntegerInput
-
On entry: , the number of variables.
Constraint:
.
- 4:
– Nag_DataByObsOrVarInput
-
On entry: determines how variables are stored in
x.
Constraint:
or .
- 5:
– doubleInput/Output
-
Note: the dimension,
dim, of the array
x
must be at least
- when
;
- when
.
The way the data is stored in
x is defined by
sordx.
If , contains the th observation for the th variable, for and .
If , contains the th observation for the th variable, for and .
On entry:
x must hold
, the values of
for the original dataset. This may be the same
x as returned by a previous call to
nag_rand_subsamp_xyw (g05pwc).
On exit: values of for the training and validation datasets, with held in observations to and in observations to .
- 6:
– IntegerInput
-
On entry: the stride separating row elements in the two-dimensional data stored in the array
x.
Constraints:
- if , ;
- otherwise .
- 7:
– doubleInput/Output
-
If the original dataset does not include
then
y must be set to
NULL.
On entry:
y must hold
, the values of
for the original dataset. This may be the same
y as returned by a previous call to
nag_rand_subsamp_xyw (g05pwc).
On exit: values of for the training and validation datasets, with held in elements to and in elements to .
- 8:
– doubleInput/Output
-
If the original dataset does not include
then
w must be set to
NULL.
On entry:
w must hold
, the values of
for the original dataset. This may be the same
w as returned by a previous call to
nag_rand_subsamp_xyw (g05pwc).
On exit: values of for the training and validation datasets, with held in elements to and in elements to .
- 9:
– IntegerCommunication Array
-
Note: the dimension,
, 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:
– NagError *Input/Output
-
The NAG error argument (see
Section 3.7 in How to Use the NAG Library and its Documentation).
6
Error Indicators and Warnings
- NE_ALLOC_FAIL
-
Dynamic memory allocation failed.
See
Section 2.3.1.2 in How to Use the NAG Library and its Documentation for further information.
- NE_ARRAY_SIZE
-
On entry, and .
Constraint: if , .
On entry, and .
Constraint: if , .
- NE_BAD_PARAM
-
On entry, argument had an illegal value.
- NE_INT
-
On entry, .
Constraint: .
On entry, .
Constraint: .
- NE_INT_2
-
On entry, and .
Constraint: .
- 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 2.7.6 in How to Use the NAG Library and its Documentation 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 2.7.5 in How to Use the NAG Library and its Documentation for further information.
7
Accuracy
Not applicable.
nag_rand_subsamp_xyw (g05pwc) will be computationality more efficient if each observation in
x is contiguous, that is
.
9
Example
This example uses nag_rand_subsamp_xyw (g05pwc) to facilitate repeated random sub-sampling cross-validation.
A set of simulated data is randomly split into a training and validation datasets.
nag_glm_binomial (g02gbc) is used to fit a logistic regression model to each training dataset and then
nag_glm_predict (g02gpc) is used to predict the response for the observations in the validation dataset. This process is repeated
times.
The counts of true and false positives and negatives along with the sensitivity and specificity is then reported.
9.1
Program Text
Program Text (g05pwce.c)
9.2
Program Data
Program Data (g05pwce.d)
9.3
Program Results
Program Results (g05pwce.r)