この節の作者: Ravi Selker, Jonathon Love, Damian Dropmann
One-Way ANOVA (anovaOneW
)¶
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
)
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,
shows 'gamesHowell' shows Games-Howell post-hoc tests where no equivalence of variances is assumed, and
'tukey' 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.
Output¶
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$anova)
Examples¶
data('ToothGrowth')
dat <- ToothGrowth
dat$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
# ----------------------------------------
#