NAG Library Function Document

nag_corr_cov (g02bxc)

1  Purpose

2  Specification

3  Description

4  References

5  Arguments

1:    $\mathbf{n}$ – IntegerInput

On entry: the number of observations in the dataset, $n$.

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

2:    $\mathbf{m}$ – IntegerInput

On entry: the total number of variables, $m$.

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

3:    $\mathbf{x}\left[\mathbf{n}\times \mathbf{tdx}\right]$ – const doubleInput

On entry: the data $\mathbf{x}\left[\left(\mathit{i}-1\right)\times \mathbf{tdx}+\mathit{j}-1\right]$ must contain the $\mathit{i}$th observation on the $\mathit{j}$th variable, $x_{\mathit{i}\mathit{j}}$, for $\mathit{i}=1,2,\dots ,n$ and $\mathit{j}=1,2,\dots ,m$.

4:    $\mathbf{tdx}$ – IntegerInput

On entry: the stride separating matrix column elements in the array x.

Constraint: $\mathbf{tdx}\ge \mathbf{m}$.

5:    $\mathbf{sx}\left[\mathbf{m}\right]$ – const IntegerInput

On entry: indicates which $p$ variables to include in the analysis.

$\mathbf{sx}\left[j-1\right]>0$

The $j$th variable is to be included.

$\mathbf{sx}\left[j-1\right]=0$

The $j$th variable is not to be included.

sx is set to NULL

All variables are included in the analysis, i.e., $p=m$.

Constraint: $\mathbf{sx}\left[\mathit{i}\right]\ge 0$, for $\mathit{i}=1,2,\dots ,m$.

6:    $\mathbf{wt}\left[\mathbf{n}\right]$ – const doubleInput

On entry: $w$, the optional frequency weighting for each observation, with $\mathbf{wt}\left[i-1\right]=w_i$. Usually $w_i$ will be an integral value corresponding to the number of observations associated with the $i$th data value, or zero if the $i$th data value is to be ignored. If wt is NULL then $w_i$ is set to $1$ for all $i$.

Constraint: if wt is not NULL, ${\displaystyle \sum _{\mathit{i}=1}^{\mathbf{n}}}\mathbf{wt}\left[\mathit{i}-1\right]>1.0$, $\mathbf{wt}\left[\mathit{i}-1\right]\ge 0.0$, for $\mathit{i}=1,2,\dots ,\mathbf{n}$.

7:    $\mathbf{sw}$ – double *Output

On exit: the sum of weights if wt is not NULL, otherwise sw contains the number of observations, $n$.

8:    $\mathbf{wmean}\left[\mathbf{m}\right]$ – doubleOutput

On exit: the sample means. $\mathbf{wmean}\left[j-1\right]$ contains the mean for the $j$th variable.

9:    $\mathbf{std}\left[\mathbf{m}\right]$ – doubleOutput

On exit: the standard deviations. $\mathbf{std}\left[j-1\right]$ contains the standard deviation for the $j$th variable.

10:  $\mathbf{r}\left[\mathbf{m}\times \mathbf{tdr}\right]$ – doubleOutput

On exit: the matrix of Pearson product-moment correlation coefficients. $\mathbf{r}\left[\left(j-1\right)\times \mathbf{tdr}+k-1\right]$ contains the correlation between variables $j$ and $k$, for $j,k=1,\dots ,p$.

11:  $\mathbf{tdr}$ – IntegerInput

On entry: the stride separating matrix column elements in the array r.

Constraint: $\mathbf{tdr}\ge \mathbf{m}$.

12:  $\mathbf{v}\left[\mathbf{m}\times \mathbf{tdv}\right]$ – doubleOutput

On exit: the variance-covariance matrix. $\mathbf{v}\left[\left(j-1\right)\times \mathbf{tdv}+k-1\right]$ contains the covariance between variables $j$ and $k$, for $j,k=1,\dots ,p$.

13:  $\mathbf{tdv}$ – IntegerInput

On entry: the stride separating matrix column elements in the array v.

Constraint: $\mathbf{tdv}\ge \mathbf{m}$.

14:  $\mathbf{fail}$ – NagError *Input/Output

The NAG error argument (see Section 3.6 in the Essential Introduction).

6  Error Indicators and Warnings

NE_2_INT_ARG_LT

On entry, $\mathbf{tdr}=〈\mathit{\text{value}}〉$ while $\mathbf{m}=〈\mathit{\text{value}}〉$.
The arguments must satisfy $\mathbf{tdr}\ge \mathbf{m}$.

On entry, $\mathbf{tdv}=〈\mathit{\text{value}}〉$ while $\mathbf{m}=〈\mathit{\text{value}}〉$. These arguments must satisfy $\mathbf{tdv}\ge \mathbf{m}$.

On entry, $\mathbf{tdx}=〈\mathit{\text{value}}〉$ while $\mathbf{m}=〈\mathit{\text{value}}〉$. These arguments must satisfy $\mathbf{tdx}\ge \mathbf{m}$.

NE_ALLOC_FAIL

Dynamic memory allocation failed.

NE_INT_ARG_LE

On entry, n must be greater than 1: $\mathbf{n}=〈\mathit{\text{value}}〉$.

NE_INT_ARG_LT

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

NE_NEG_SX

On entry, at least one element of sx is negative.

NE_NEG_WEIGHT

On entry, at least one of the weights is negative.

NE_POS_SX

On entry, no element of sx is positive.

NE_SW_LT_ONE

On entry, the sum of weights is less than 1.0.

NE_VAR_EQ_ZERO

A variable has zero variance.
At least one variable has zero variance. In this case v and std are as calculated, but r will contain zero for any correlation involving a variable with zero variance.

7  Accuracy

8  Parallelism and Performance

9  Further Comments

10  Example

10.1  Program Text

Program Text (g02bxce.c)

10.2  Program Data

Program Data (g02bxce.d)

10.3  Program Results

Program Results (g02bxce.r)