naginterfaces.library.correg.coeffs_​pearson_​subset

naginterfaces.library.correg.coeffs_pearson_subset(x, kvar)

coeffs_pearson_subset computes means and standard deviations, sums of squares and cross-products of deviations from means, and Pearson product-moment correlation coefficients for selected variables.

For full information please refer to the NAG Library document for g02bg

https://support.nag.com/numeric/nl/nagdoc_30.1/flhtml/g02/g02bgf.html

Parameters

xfloat, array-like, shape $(n,m)$

$x[\text{i}-1,\text{j}-1]$ must be set to $x_{\text{i}\text{j}}$, the value of the $\text{i}$th observation on the $\text{j}$th variable, for $\text{j}=1,2,\dots ,m$, for $\text{i}=1,2,\dots ,n$.

kvarint, array-like, shape $\left(\text{nvars}\right)$

$kvar\left[\text{j}\right]$ must be set to the column number in $x$ of the $\text{j}$th variable for which information is required, for $\text{j}=0,\dots ,p-1$.

Returns

xbarfloat, ndarray, shape $\left(\text{nvars}\right)$

The mean value, ${\overline{x}}_{\text{j}}$, of the variable specified in $kvar[\text{j}-1]$, for $\text{j}=1,2,\dots ,p$.

stdfloat, ndarray, shape $\left(\text{nvars}\right)$

The standard deviation, $s_{\text{j}}$, of the variable specified in $kvar[\text{j}-1]$, for $\text{j}=1,2,\dots ,p$.

sspfloat, ndarray, shape $(\text{nvars},\text{nvars})$

$ssp[\text{j}-1,\text{k}-1]$ is the cross-product of deviations, $S_{\text{j}\text{k}}$, for the variables specified in $kvar[\text{j}-1]$ and $kvar[\text{k}-1]$, for $\text{k}=1,2,\dots ,p$, for $\text{j}=1,2,\dots ,p$.

rfloat, ndarray, shape $(\text{nvars},\text{nvars})$

$r[\text{j}-1,\text{k}-1]$ is the product-moment correlation coefficient, $R_{\text{j}\text{k}}$, between the variables specified in $kvar[\text{j}-1]$ and $kvar[\text{k}-1]$, for $\text{k}=1,2,\dots ,p$, for $\text{j}=1,2,\dots ,p$.

Raises

NagValueError

(errno $1$)

On entry, $n=⟨value⟩$.

Constraint: $n\ge 2$.

(errno $2$)

On entry, $\text{nvars}=⟨value⟩$ and $m=⟨value⟩$.

Constraint: $\text{nvars}\ge 2$ and $\text{nvars}\le m$.

(errno $4$)

On entry, $\text{i}=⟨value⟩$, $kvar[\text{i}-1]=⟨value⟩$ and $m=⟨value⟩$.

Constraint: $1\le kvar[\text{i}-1]\le m$.

Notes

No equivalent traditional C interface for this routine exists in the NAG Library.

The input data consist of $n$ observations for each of $m$ variables, given as an array

$$\left[x_{ij}\right]\text{,}i=1,2,\dots ,n(n\ge 2),j=1,2,\dots ,m(m\ge 2)\text{,}$$

where $x_{ij}$ is the $i$th observation on the $j$th variable, together with the subset of these variables, $v_1,v_2,\dots ,v_p$, for which information is required.

The quantities calculated are:

1. Means:

   $${\overline{x}}_j=\frac{1}{n}\sum _{i=1}^n{x}_{ij}\text{,}j=v_1,v_2,\dots ,v_p\text{.}$$

2. Standard deviations:

   $$s_j=\sqrt{\frac{1}{n-1}\sum _{i=1}^n{(x_{ij}-{\overline{x}}_j)}^2}\text{,}j=v_1,v_2,\dots ,v_p\text{.}$$

3. Sums of squares and cross-products of deviations from zero:

   $$S_{jk}=\sum _{i=1}^n(x_{ij}-{\overline{x}}_j)(x_{ik}-{\overline{x}}_k)\text{,}j,k=v_1,v_2,\dots ,v_p\text{.}$$

4. Pearson product-moment correlation coefficients:

   $$R_{jk}=\frac{S_{jk}}{\sqrt{S_{jj}{S}_{kk}}}\text{,}j,k=v_1,v_2,\dots v_p\text{.}$$

   If $S_{jj}$ or $S_{kk}$ is zero, $R_{jk}$ is set to zero.