jmv (version 2.5.6)

anovaOneW: One-Way ANOVA

Description

The Analysis of Variance (ANOVA) is used to explore the relationship between a continuous dependent variable, and one or more categorical explanatory variables. This 'One-Way ANOVA' is a simplified version of the 'normal' ANOVA, allowing only a single explanatory factor, however also providing a Welch's ANOVA. The Welch's ANOVA has the advantage that it need not assume that the variances of all groups are equal.

Usage

anovaOneW(data, deps, group, welchs = TRUE, fishers = FALSE,
  miss = "perAnalysis", desc = FALSE, descPlot = FALSE,
  norm = FALSE, qq = FALSE, eqv = FALSE, phMethod = "none",
  phMeanDif = TRUE, phSig = TRUE, phTest = FALSE, phFlag = FALSE,
  formula)

Value

A results object containing:

results$anova					a table of the test results
results$desc					a table containing the group descriptives
results$assump$norm					a table containing the normality tests
results$assump$eqv					a table of homogeneity of variances tests
results$plots					an array of groups of plots
results$postHoc					an array of post-hoc tables

Tables can be converted to data frames with asDF or as.data.frame. For example:

results$anova$asDF

as.data.frame(results$anova)

Arguments

data

the data as a data frame

deps

a string naming the dependent variables in data

group

a string naming the grouping or independent variable in data

welchs

TRUE (default) or FALSE, perform Welch's one-way ANOVA which does not assume equal variances

fishers

TRUE or FALSE (default), perform Fisher's one-way ANOVA which assumes equal variances

miss

'perAnalysis' or 'listwise', how to handle missing values; 'perAnalysis' excludes missing values for individual dependent variables, 'listwise' excludes a row from all analyses if one of its entries is missing.

desc

TRUE or FALSE (default), provide descriptive statistics

descPlot

TRUE or FALSE (default), provide descriptive plots

norm

TRUE or FALSE (default), perform Shapiro-Wilk test of normality

qq

TRUE or FALSE (default), provide a Q-Q plot of residuals

eqv

TRUE or FALSE (default), perform Levene's test for homogeneity of variances

phMethod

'none', 'gamesHowell' or 'tukey', which post-hoc tests to provide; 'none' shows no post-hoc tests, 'gamesHowell' shows Games-Howell post-hoc tests where no equivalence of variances is assumed, and 'tukey' shows Tukey post-hoc tests where equivalence of variances is assumed

phMeanDif

TRUE (default) or FALSE, provide mean differences for post-hoc tests

phSig

TRUE (default) or FALSE, provide significance levels for post-hoc tests

phTest

TRUE or FALSE (default), provide test results (t-value and degrees of freedom) for post-hoc tests

phFlag

TRUE or FALSE (default), flag significant post-hoc comparisons

formula

(optional) the formula to use, see the examples

Details

For convenience, this method allows specifying multiple dependent variables, resulting in multiple independent tests.

Note that the Welch's ANOVA is the same procedure as the Welch's independent samples t-test.

Examples

data('ToothGrowth')
dat <- ToothGrowth
dat$dose <- factor(dat$dose)

anovaOneW(formula = len ~ dose, data = dat)

#
#  ONE-WAY ANOVA
#
#  One-Way ANOVA (Welch's)
#  ----------------------------------------
#           F       df1    df2     p
#  ----------------------------------------
#    len    68.4      2    37.7    < .001
#  ----------------------------------------
#