bruceR: MANOVA – R documentation

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MANOVA

Multi-factor ANOVA.

Description

Easily perform multi-factor ANOVA (between-subjects, within-subjects, and mixed designs), with or without covariates (ANCOVA).

This function is based on and extends the afex::aov_ez() function. You only need to specify the data, dependent variable(s), and factors (between-subjects and/or within-subjects). Almost all results you need will be displayed in an elegant manner, including effect sizes (partial η^2) and their confidence intervals (CIs). 90% CIs for partial η^2 are reported, following the suggestion by Steiger (2004).

Usage

MANOVA(
  data,
  subID = NULL,
  dv = NULL,
  dvs = NULL,
  dvs.pattern = "",
  between = NULL,
  within = NULL,
  covariate = NULL,
  sph.correction = "none",
  which.observed = NULL,
  nsmall = 2
)

Arguments

`data`	Data frame. Both long-format and wide-format can be used. If using long-format data, please also set subID. If using wide-format data (i.e., one subject occupies one row, and repeated measures occupy multiple columns), the function can *automatically* transform the data into long-format.
`subID`	Subject ID. If using long-format data, you should set the subject ID. If using wide-format data, no need to set this parameter.
`dv`	Variable name of dependent variable. If using long-format data, then `dv` is the outcome variable. If using wide-format data, then `dv` can only be used for complete between-subjects design. For designs with repeated measures, please use `dvs` and `dvs.pattern`.
`dvs`	[only for "wide-format" data and designs with repeated measures] Variable names of repeated measures. You can use `":"` to specify a range of variables: e.g., `"A1B1:A2B3"` (similar to the SPSS syntax "TO"; the variables should be put in order) You can also use a character vector to specify variable names: e.g., `c("Cond1", "Cond2", "Cond3")`
`dvs.pattern`	[only for "wide-format" data and designs with repeated measures] If you set `dvs`, you must also set the pattern of variable names by using regular expressions. Examples: `"Cond(.)"` can extract levels from `"Cond1", "Cond2", "Cond3", ...` You can rename the factor name by using `within`: e.g., `within="Condition"` `"X(..)Y(..)"` can extract levels from `"X01Y01", "X02Y02", "XaaYbc", ...` `"X(.+)Y(.+)"` can extract levels from `"X1Y1", "XaYb", "XaY002", ...` Tips on regular expression: `"(.)"` extracts any single character (can be number, letter, or other symbols) `"(.+)"` extracts >= 1 character(s) `"(.*)"` extracts >= 0 character(s) `"([0-9])"` extracts any single number `"([a-z])"` extracts any single letter each pair of `"()"` extracts levels for each factor
`between`	Between-subjects factors. Character string (e.g., `"A"`) or vector (e.g., `c("A", "B")`). Default is `NULL`.
`within`	Within-subjects factors. Character string (e.g., `"A"`) or vector (e.g., `c("A", "B")`). Default is `NULL`.
`covariate`	Covariates (if necessary). Character string (e.g., `"age"`) or vector (e.g., `c("gender", "age", "edu")`). Default is `NULL`.
`sph.correction`	[only effective for repeated measures with >= 3 levels] Sphericity correction method to adjust the degrees of freedom (df) when the sphericity assumption is violated. Default is `"none"`. If Mauchly's test of sphericity is significant, you may set it to `"GG"` (Greenhouse-Geisser) or `"HF"` (Huynh-Feldt).
`which.observed`	*[only effective for computing generalized η^2]* Factors that are observed or measured (e.g., gender, age group, measured covariates) but not experimentally manipulated. Default is `NULL`. The generalized η^2 requires correct specification of the observed (vs. manipulated) variables. (If all the variables in `between` and `within` are set to `observed`, then generalized η^2 will be equal to η^2.)
`nsmall`	Number of decimal places of output. Default is 2.

Value

A result object returned by afex::aov_ez().

References

Olejnik, S., & Algina, J. (2003). Generalized eta and omega squared statistics: Measures of effect size for some common research designs. Psychological Methods, 8(4), 434-447. doi: 10.1037/1082-989X.8.4.434

Steiger, J. H. (2004). Beyond the F test: Effect size confidence intervals and tests of close fit in the analysis of variance and contrast analysis. Psychological Methods, 9(2), 164-182. doi: 10.1037/1082-989X.9.2.164

Examples

#### Between-Subjects Design ####

between.1
MANOVA(data=between.1, dv="SCORE", between="A")

between.2
MANOVA(data=between.2, dv="SCORE", between=c("A", "B"))

between.3
MANOVA(data=between.3, dv="SCORE", between=c("A", "B", "C"))


#### Within-Subjects Design ####

within.1
MANOVA(data=within.1, dvs="A1:A4", dvs.pattern="A(.)",
       within="A")
## the same:
MANOVA(data=within.1, dvs=c("A1", "A2", "A3", "A4"), dvs.pattern="A(.)",
       within="MyFactor")  # renamed the within-subjects factor

within.2
MANOVA(data=within.2, dvs="A1B1:A2B3", dvs.pattern="A(.)B(.)",
       within=c("A", "B"))

within.3
MANOVA(data=within.3, dvs="A1B1C1:A2B2C2", dvs.pattern="A(.)B(.)C(.)",
       within=c("A", "B", "C"))


#### Mixed Design ####

mixed.2_1b1w
MANOVA(data=mixed.2_1b1w, dvs="B1:B3", dvs.pattern="B(.)",
       between="A", within="B")
MANOVA(data=mixed.2_1b1w, dvs="B1:B3", dvs.pattern="B(.)",
       between="A", within="B", sph.correction="GG")

mixed.3_1b2w
MANOVA(data=mixed.3_1b2w, dvs="B1C1:B2C2", dvs.pattern="B(.)C(.)",
       between="A", within=c("B", "C"))

mixed.3_2b1w
MANOVA(data=mixed.3_2b1w, dvs="B1:B2", dvs.pattern="B(.)",
       between=c("A", "C"), within="B")


#### Other Examples ####
data.new=mixed.3_1b2w
names(data.new)=c("Group", "Cond_01", "Cond_02", "Cond_03", "Cond_04")
MANOVA(data=data.new, dvs="Cond_01:Cond_04", dvs.pattern="Cond_(..)",
       between="Group", within="Condition")  # renamed the within-subjects factor

?afex::obk.long
MANOVA(data=afex::obk.long, subID="id", dv="value",
       between=c("treatment", "gender"), within=c("phase", "hour"), cov="age",
       sph.correction="GG")

bruceR

Broadly Useful Convenient and Efficient R Functions

v0.6.2

GPL-3

Authors

Han-Wu-Shuang Bao [aut, cre]

Initial release

2021-04-08