bartlett.test {stats} | R Documentation |
bartlett.test(x, ...) ## Default S3 method: bartlett.test(x, g, ...) ## S3 method for class 'formula' bartlett.test(formula, data, subset, na.action, ...)
x | a numeric vector of data values, or a list of numeric data vectors representing the respective samples, or fitted linear model objects (inheriting from class "lm"). |
g | a vector or factor object giving the group for the corresponding elements of x. Ignored if x is a list. |
formula | a formula of the form lhs ~ rhs where lhs gives the data values and rhs the corresponding groups. |
data | an optional matrix or data frame (or similar: see model.frame) containing the variables in the formulaformula. By default the variables are taken from environment(formula). |
subset | an optional vector specifying a subset of observations to be used. |
na.action | a function which indicates what should happen when the data contain NAs. Defaults togetOption("na.action"). |
... | further arguments to be passed to or from methods. |
x is a list, its elements are taken as the samples or fitted linear models to be compared for homogeneity of variances. In this case, the elements must either all be numeric data vectors or fitted linear model objects, g is ignored, and one can simply use bartlett.test(x) to perform the test. If the samples are not yet contained in a list, usebartlett.test(list(x, ...)).x must be a numeric data vector, and g must be a vector or factor object of the same length as x giving the group for the corresponding elements of x."htest" containing the following components:statistic | Bartlett's K-squared test statistic. |
parameter | the degrees of freedom of the approximate chi-squared distribution of the test statistic. |
p.value | the p-value of the test. |
method | the character string "Bartlett test of homogeneity of variances". |
data.name | a character string giving the names of the data. |
var.test for the special case of comparing variances in two samples from normal distributions; fligner.test for a rank-based (nonparametric) k-sample test for homogeneity of variances; ansari.test and mood.test for two rank based two-sample tests for difference in scale.require(graphics) plot(count ~ spray, data = InsectSprays) bartlett.test(InsectSprays$count, InsectSprays$spray) bartlett.test(count ~ spray, data = InsectSprays)
Mas neste caso como aplicar o bartlett.test?bartlett.test(modelo2$residuals,fat1) # Homogeneidade de variâncias
bartlett.test(modelo2$residuals,fat2) # Homogeneidade de variânciasSó assim? Por fatores separadamente?
André Oliveira Souza.
Graduação em Matemática, mestrado em estatística aplicada.Instituto Federal de Educação, Ciência e Tecnologia do Espirito Santo. IFES
Em Quinta-feira, 1 de Janeiro de 2015 18:40, henrique jose de paula alves paula alves <jpahenrique@gmail.com> escreveu:
O teste de Bartley deve funcionar bem.Em 1 de janeiro de 2015 17:52, Andre Oliveira <andreolsouza@yahoo.com.br> escreveu:Pessoal boa tarde, dado meu modelo em fatorial.modelo=aov(respsota~factor1*factor2)Como avaliar homogeneidade de variâncias?O que fiz aqui foi ..par(mfrow=c(2,2))
boxplot(resp~fat1)
boxplot(resp~fat2)
boxplot(resp~fat1*fat2)
interaction.plot(fat1,fat2,resp,ylab="Médias",main="Interação Fat1 vs Fat2")Mas está muito subjetivo.
André Oliveira Souza.
Graduação em Matemática, mestrado em estatística aplicada.Instituto Federal de Educação, Ciência e Tecnologia do Espirito Santo. IFES
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