ds.oneWayTest: Distributed test for Equal Means in a One-Way Layout
In paularaissa/distStatsClient: Distributed Multiple Imputations

Description Usage Arguments Details Value Dependencies Author(s) Examples

Test whether two or more samples from normal distributions have the same means. The variances are not necessarily assumed to be equal.

1 2	ds.oneWayTest(formula, subset = NULL, na.action, var.equal = TRUE, datasources = NULL)

`formula`	a text, transformed to formula object of the form y ~ x where 'y' gives the sample values and 'x' the corresponding groups.
`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 to getOption("na.action").
`var.equal`	a logical variable indicating whether to treat the variances in the samples as equal. If TRUE, then a simple F test for the equality of means in a one-way analysis of variance is performed. If FALSE, an approximate method of Welch (1951) is used, which generalizes the commonly known 2-sample Welch test to the case of arbitrarily many samples.
`datasources`	a list of opal object(s) obtained after login in to opal servers; these objects hold also the data assign to R, as `data frame`, from opal datasources.

If the right-hand side of the formula contains more than one term, their interaction is taken to form the grouping.

A list containing the following components:

`statistic`	the value of the test statistic.
`parameter`	the degrees of freedom of the exact or approximate F distribution of the test statistic.
`p.value`	the p-value of the test.

getLength, getVariance

Paula Raissa Costa e Silva

{
#For equal variances
ds.oneWayTest(folate ~ ventilation, data = redCellFolate, var.equal=TRUE)
#Not assuming equal variances
ds.oneWayTest(folate ~ ventilation, data = redCellFolate)
#The result must be the same as
ds.anova(ds.linear(folate ~ ventilation, data = redCellFolate))
}