statsmodels.stats.oneway.test_scale_oneway

statsmodels.stats.oneway.test_scale_oneway(data, method='bf', center='median', transform='abs', trim_frac_mean=0.1, trim_frac_anova=0.0)[source]

Oneway Anova test for equal scale, variance or dispersion

This hypothesis test performs a oneway anova test on transformed data and includes Levene and Brown-Forsythe tests for equal variances as special cases.

Parameters:
datatuple of array_like or DataFrame or Series

Data for k independent samples, with k >= 2. The data can be provided as a tuple or list of arrays or in long format with outcome observations in data and group membership in groups.

method{“unequal”, “equal” or “bf”}

How to treat heteroscedasticity across samples. This is used as use_var option in anova_oneway and refers to the variance of the transformed data, i.e. assumption is on 4th moment if squares are used as transform. Three approaches are available:

“unequal”Variances are not assumed to be equal across samples.

Heteroscedasticity is taken into account with Welch Anova and Satterthwaite-Welch degrees of freedom. This is the default.

“equal”Variances are assumed to be equal across samples.

This is the standard Anova.

“bf”Variances are not assumed to be equal across samples.

The method is Browne-Forsythe (1971) for testing equality of means with the corrected degrees of freedom by Merothra. The original BF degrees of freedom are available as additional attributes in the results instance, df_denom2 and p_value2.

center“median”, “mean”, “trimmed” or float

Statistic used for centering observations. If a float, then this value is used to center. Default is median.

transform“abs”, “square” or callable

Transformation for the centered observations. If a callable, then this function is called on the centered data. Default is absolute value.

trim_frac_mean=0.1float in [0, 0.5)

Trim fraction for the trimmed mean when center is “trimmed”

trim_frac_anovafloat in [0, 0.5)

Optional trimming for Anova with trimmed mean and Winsorized variances. With the default trim_frac equal to zero, the oneway Anova statistics are computed without trimming. If trim_frac is larger than zero, then the largest and smallest observations in each sample are trimmed. see trim_frac option in anova_oneway

Returns:
resresults instance

The returned HolderTuple instance has the following main attributes and some additional information in other attributes.

statisticfloat

Test statistic for k-sample mean comparison which is approximately F-distributed.

pvaluefloat

If method="bf", then the p-value is based on corrected degrees of freedom following Mehrotra 1997.

pvalue2float

This is the p-value based on degrees of freedom as in Brown-Forsythe 1974 and is only available if method="bf".

df(df_denom, df_num)

Tuple containing degrees of freedom for the F-distribution depend on method. If method="bf", then df_denom is for Mehrotra p-values df_denom2 is available for Brown-Forsythe 1974 p-values. df_num is the same numerator degrees of freedom for both p-values.


Last update: Apr 18, 2024