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Compute Levene's test for equal variances.
Levene's test is used to test the null hypothesis that the variances of k
groups are equal against the alternative that at least two of them are different.
npm install @stdlib/stats-levene-test
var leveneTest = require( '@stdlib/stats-levene-test' );
Calculates Levene's test for input arrays x
, y
, ..., z
holding numeric observations.
// Data from Hollander & Wolfe (1973), p. 116:
var x = [ 2.9, 3.0, 2.5, 2.6, 3.2 ];
var y = [ 3.8, 2.7, 4.0, 2.4 ];
var z = [ 2.8, 3.4, 3.7, 2.2, 2.0 ];
var out = leveneTest( x, y, z );
/* returns
{
'rejected': false,
'alpha': 0.05,
'df': [ 2, 11 ],
'pValue': ~0.1733,
'statistic': ~2.0638,
...
}
*/
The function accepts the following options
:
-
alpha:
number
on the interval[0,1]
giving the significance level of the hypothesis test. Default:0.05
. -
groups: an
array
of group indicators. Only applicable when providing a single numeric array holding all observations.
By default, the test is carried out at a significance level of 0.05
. To test at a different significance level, set the alpha
option.
var x = [ 2.9, 3.0, 2.5, 2.6, 3.2 ];
var y = [ 3.8, 2.7, 4.0, 2.4 ];
var z = [ 2.8, 3.4, 3.7, 2.2, 2.0 ];
var out = leveneTest( x, y, z, {
'alpha': 0.01
});
/* returns
{
'rejected': false,
'alpha': 0.01,
'df': [ 2, 11 ],
'pValue': ~0.1733,
'statistic': ~2.0638,
...
}
*/
In addition to providing multiple arrays, the function supports providing a single numeric array holding all observations along with an array of group indicators.
var arr = [
2.9, 3.0, 2.5, 2.6, 3.2,
3.8, 2.7, 4.0, 2.4,
2.8, 3.4, 3.7, 2.2, 2.0
];
var groups = [
'a', 'a', 'a', 'a', 'a',
'b', 'b', 'b', 'b',
'c', 'c', 'c', 'c', 'c'
];
var out = leveneTest( arr, {
'groups': groups
});
The returned object comes with a .print()
method which, when invoked, prints a formatted output of test results. The method accepts the following options:
-
digits: number of decimal digits displayed for the outputs. Default:
4
. -
decision:
boolean
indicating whether to print the test decision. Default:true
.
var x = [ 2.9, 3.0, 2.5, 2.6, 3.2 ];
var y = [ 3.8, 2.7, 4.0, 2.4 ];
var z = [ 2.8, 3.4, 3.7, 2.2, 2.0 ];
var out = leveneTest( x, y, z );
console.log( out.print() );
/* =>
Levene's test for Homogeneity of Variance
Null hypothesis: The variances in all groups are the same.
df 1: 2
df 2: 11
F score: 2.0638
P Value: 0.1733
Test Decision: Fail to reject null in favor of alternative at 5% significance level
*/
var leveneTest = require( '@stdlib/stats-levene-test' );
// Data from Hollander & Wolfe (1973), p. 116:
var x = [ 2.9, 3.0, 2.5, 2.6, 3.2 ];
var y = [ 3.8, 2.7, 4.0, 2.4 ];
var z = [ 2.8, 3.4, 3.7, 2.2, 2.0 ];
var out = leveneTest( x, y, z );
/* returns
{
'rejected': false,
'alpha': 0.05,
'df': [ 2, 11 ],
'pValue': ~0.1733,
'statistic': ~2.0638,
...
}
*/
var table = out.print();
/* returns
Levene's test for Homogeneity of Variance
Null hypothesis: The variances in all groups are the same.
df 1: 2
df 2: 11
F score: 2.0638
P Value: 0.1733
Test Decision: Fail to reject null in favor of alternative at 5% significance level
*/
-
@stdlib/stats-vartest
: two-sample F-test for equal variances -
@stdlib/stats-bartlett-test
: Bartlett’s test for equal variances.
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