NAG CL Interface
g05pwc (subsamp_xyw)
1
Purpose
g05pwc generates a dataset suitable for use with repeated random sub-sampling validation.
2
Specification
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 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.
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
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:
– Integer
Input
-
On entry: , the number of observations in the training dataset.
Constraint:
.
-
2:
– Integer
Input
-
On entry: , the number of observations.
Constraint:
.
-
3:
– Integer
Input
-
On entry: , the number of variables.
Constraint:
.
-
4:
– Nag_DataByObsOrVar
Input
-
On entry: determines how variables are stored in
x.
Constraint:
or .
-
5:
– double
Input/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
g05pwc.
On exit: values of for the training and validation datasets, with held in observations to and in observations to .
-
6:
– Integer
Input
-
On entry: the stride separating row elements in the two-dimensional data stored in the array
x.
Constraints:
- if , ;
- otherwise .
-
7:
– double
Input/Output
-
Note: the dimension,
dim, of the array
y
must be at least
- , when ;
- otherwise is not referenced and may be NULL.
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
g05pwc.
On exit: values of for the training and validation datasets, with held in elements to and in elements to .
-
8:
– double
Input/Output
-
Note: the dimension,
dim, of the array
w
must be at least
- , when ;
- otherwise is not referenced and may be NULL.
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
g05pwc.
On exit: values of for the training and validation datasets, with held in elements to and in elements to .
-
9:
– Integer
Communication 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 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, 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 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.
g05pwc will be computationality more efficient if each observation in
x is contiguous, that is
.
9
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
times.
The counts of true and false positives and negatives along with the sensitivity and specificity is then reported.
9.1
Program Text
9.2
Program Data
9.3
Program Results