Levene.Test function is a Bayesian form of Levene's test
(Levene, 1960) of equality of variances.
This required argument must be an object of class
The method defaults to
This argument defaults to
This argument is required when the DV is multivariate,
This function is a Bayesian form of Levene's test. Levene's test is used to assess the probability of the equality of residual variances in different groups. When residual variance does not differ by group, it is often called homoscedastic (or homoskedastic) residual variance. Homoskedastic residual variance is a common assumption. An advantage of Levene's test to other tests of homoskedastic residual variance is that Levene's test does not require normality of the residuals.
Levene.Test function estimates the test statistic,
W, as per Levene's test. This Bayesian form, however,
estimates W from the observed residuals as
W.obs, and W from residuals that are
replicated from a homoskedastic process as W.rep.
Further, W.obs and W.rep are
estimated for each posterior sample. Finally, the probability that
the distribution of W.obs is greater than the
distribution of W.rep is reported (see below).
Levene.Test function returns a plot (or for multivariate Y,
a series of plots), and a vector with a length equal to the number of
Levene's tests conducted.
One plot is produced per univariate application of Levene's test. Each
plot shows the test statistic W, both from the observed process
(W.obs as a black density) and the replicated process (W.rep as a red
line). The mean of W.obs is reported, along with its 95% quantile-based
probability interval (see
p.interval), the probability
p(W.obs > W.rep), and the indicated
results, either homoskedastic or heteroskedastic.
Each element of the returned vector is the probability p(W.obs > W.rep). When the probability is p < 0.025 or p > 0.975, heteroskedastic variance is indicated. Otherwise, the variances of the groups are assumed not to differ effectively.
Statisticat, LLC. [email protected]
Levene, H. (1960). "Robust Tests for Equality of Variances". In I. Olkins, S. G. Ghurye, W. Hoeffding, W. G. Madow, & H. B. Mann (Eds.), Contributions to Probability and Statistics, p. 278–292. Stanford University Press: Stanford, CA.
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#First, update the model with IterativeQuadrature, LaplaceApproximation, # LaplacesDemon, PMC, or VariationalBayes. #Then, use the predict function, creating, say, object Pred. #Finally: #Levene.Test(Pred)
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