MANOVA: Multi-factor ANOVA.
In bruceR: Broadly Useful Convenient and Efficient R Functions
View source: R/bruceR-stats_3_manova.R
MANOVA R Documentation
Multi-factor ANOVA.
Description
Multi-factor ANOVA (between-subjects, within-subjects, and mixed designs),
with and without covariates (ANCOVA).
This function is based on and extends afex::aov_ez().
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 together,
including effect sizes (partial η^2) and their confidence intervals (CIs).
90% CIs for partial η^2 (two-sided) are reported, following Steiger (2004).
In addition to partial η^2, it also reports generalized η^2, following Olejnik & Algina (2003).
How to prepare your data and specify the arguments of MANOVA?
-
Wide-format data (one person in one row, and repeated measures in multiple columns):
- Betweem-subjects design
MANOVA(data=, dv=, between=, ...)
- Within-subjects design
MANOVA(data=, dvs=, dvs.pattern=, within=, ...)
- Mixed design
MANOVA(data=, dvs=, dvs.pattern=, between=, within=, ...)
-
Long-format data (one person in multiple rows, and repeated measures in one column):
- Betweem-subjects design
(not applicable)
- Within-subjects design
MANOVA(data=, subID=, dv=, within=, ...)
- Mixed design
MANOVA(data=, subID=, dv=, between=, within=, ...)
Usage
MANOVA(
data,
subID = NULL,
dv = NULL,
dvs = NULL,
dvs.pattern = NULL,
between = NULL,
within = NULL,
covariate = NULL,
ss.type = "III",
sph.correction = "none",
aov.include = FALSE,
digits = 3,
nsmall = digits,
file = NULL
)
Arguments
data
Data frame. Both wide-format and long-format are supported.
subID
Subject ID (the column name). Only necessary for long-format data.
dv
Dependent variable.
For wide-format data, dv only can be used for between-subjects designs.
For within-subjects and mixed designs, please use dvs and dvs.pattern.
For long-format data, dv is the outcome variable.
dvs
Repeated measures. Only for wide-format data (within-subjects or mixed designs).
Can be:
-
"start:stop" to specify the range of variables
(sensitive to the order of variables):
e.g., "A1B1:A2B3" is matched to all variables in the data
between "A1B1" and "A2B3"
a character vector to directly specify variables
(insensitive to the order of variables):
e.g., c("Cond1", "Cond2", "Cond3") or cc("Cond1, Cond2, Cond3")
See cc for its usage.
dvs.pattern
If you use dvs, you should also specify the pattern of variable names
using regular expression.
Examples:
-
"Cond(.)" extracts levels from "Cond1", "Cond2", "Cond3", ...
You may rename the factor using the within argument (e.g., within="Condition")
-
"X(..)Y(..)" extracts levels from "X01Y01", "X02Y02", "XaaYbc", ...
-
"X(.+)Y(.+)" extracts levels from "X1Y1", "XaYb", "XaY002", ...
Tips on regular expression:
-
"(.)" extracts any single character (number, letter, and other symbols)
-
"(.+)" extracts >= 1 character(s)
-
"(.*)" extracts >= 0 character(s)
-
"([0-9])" extracts any single number
-
"([a-z])" extracts any single letter
More information: Link 1 (in English) and
Link 2 (in Chinese)
between
Between-subjects factor(s). Multiple variables should be included in a character vector c().
within
Within-subjects factor(s). Multiple variables should be included in a character vector c().
covariate
Covariates. Multiple variables should be included in a character vector c().
ss.type
Type of sums of squares (SS) for ANOVA. Default is "III".
Possible values are "II", "III", 2, or 3.
sph.correction
[Only for repeated measures with >= 3 levels]
Sphericity correction method for adjusting 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).
aov.include
Include the aov object in the returned object?
Default is FALSE, as suggested by afex::aov_ez()
(please see the include_aov argument in this help page, which provides a detailed explanation).
If TRUE, you should also specify model.type="univariate" in EMMEANS.
digits, nsmall
Number of decimal places of output. Default is 3.
file
File name of MS Word (.doc).
Details
If observations are not uniquely identified in user-defined long-format data,
the function takes averages across those multiple observations for each case.
In technical details, it specifies fun_aggregate=mean in afex::aov_ez()
and values_fn=mean in tidyr::pivot_wider().
Value
A result object (list) returned by
afex::aov_ez(),
along with several other elements:
between, within,
data.wide, data.long.
Interaction Plot
You can save the returned object and use the emmeans::emmip() function
to create an interaction plot (based on the fitted model and a formula specification).
For usage, please see the help page of emmeans::emmip().
It returns an object of class ggplot, which can be easily modified and saved using ggplot2 syntax.
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.
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.
See Also
TTEST, EMMEANS, bruceR-demodata
Examples
#### Between-Subjects Design ####
between.1
MANOVA(between.1, dv="SCORE", between="A")
between.2
MANOVA(between.2, dv="SCORE", between=c("A", "B"))
between.3
MANOVA(between.3, dv="SCORE", between=c("A", "B", "C"))
## How to create an interaction plot using `emmeans::emmip()`?
## See help page for its usage: ?emmeans::emmip()
m = MANOVA(between.2, dv="SCORE", between=c("A", "B"))
emmip(m, ~ A | B, CIs=TRUE)
emmip(m, ~ B | A, CIs=TRUE)
emmip(m, B ~ A, CIs=TRUE)
emmip(m, A ~ B, CIs=TRUE)
#### Within-Subjects Design ####
within.1
MANOVA(within.1, dvs="A1:A4", dvs.pattern="A(.)",
within="A")
## the same:
MANOVA(within.1, dvs=c("A1", "A2", "A3", "A4"), dvs.pattern="A(.)",
within="MyFactor") # renamed the within-subjects factor
within.2
MANOVA(within.2, dvs="A1B1:A2B3", dvs.pattern="A(.)B(.)",
within=c("A", "B"))
within.3
MANOVA(within.3, dvs="A1B1C1:A2B2C2", dvs.pattern="A(.)B(.)C(.)",
within=c("A", "B", "C"))
#### Mixed Design ####
mixed.2_1b1w
MANOVA(mixed.2_1b1w, dvs="B1:B3", dvs.pattern="B(.)",
between="A", within="B")
MANOVA(mixed.2_1b1w, dvs="B1:B3", dvs.pattern="B(.)",
between="A", within="B", sph.correction="GG")
mixed.3_1b2w
MANOVA(mixed.3_1b2w, dvs="B1C1:B2C2", dvs.pattern="B(.)C(.)",
between="A", within=c("B", "C"))
mixed.3_2b1w
MANOVA(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.new,
dvs="Cond_01:Cond_04",
dvs.pattern="Cond_(..)",
between="Group",
within="Condition") # rename the factor
# ?afex::obk.long
MANOVA(afex::obk.long,
subID="id",
dv="value",
between=c("treatment", "gender"),
within=c("phase", "hour"),
cov="age",
sph.correction="GG")
bruceR documentation built on March 7, 2023, 5:41 p.m.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
View source: R/bruceR-stats_3_manova.R
| MANOVA | R Documentation |
Multi-factor ANOVA.
Description
Multi-factor ANOVA (between-subjects, within-subjects, and mixed designs), with and without covariates (ANCOVA).
This function is based on and extends afex::aov_ez().
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 together,
including effect sizes (partial η^2) and their confidence intervals (CIs).
90% CIs for partial η^2 (two-sided) are reported, following Steiger (2004).
In addition to partial η^2, it also reports generalized η^2, following Olejnik & Algina (2003).
How to prepare your data and specify the arguments of MANOVA?
-
Wide-format data (one person in one row, and repeated measures in multiple columns):
- Betweem-subjects design
MANOVA(data=, dv=, between=, ...)- Within-subjects design
MANOVA(data=, dvs=, dvs.pattern=, within=, ...)- Mixed design
MANOVA(data=, dvs=, dvs.pattern=, between=, within=, ...)
-
Long-format data (one person in multiple rows, and repeated measures in one column):
- Betweem-subjects design
(not applicable)
- Within-subjects design
MANOVA(data=, subID=, dv=, within=, ...)- Mixed design
MANOVA(data=, subID=, dv=, between=, within=, ...)
Usage
MANOVA( data, subID = NULL, dv = NULL, dvs = NULL, dvs.pattern = NULL, between = NULL, within = NULL, covariate = NULL, ss.type = "III", sph.correction = "none", aov.include = FALSE, digits = 3, nsmall = digits, file = NULL )
Arguments
data |
Data frame. Both wide-format and long-format are supported. |
subID |
Subject ID (the column name). Only necessary for long-format data. |
dv |
Dependent variable.
|
dvs |
Repeated measures. Only for wide-format data (within-subjects or mixed designs). Can be:
|
dvs.pattern |
If you use Examples:
Tips on regular expression:
|
between |
Between-subjects factor(s). Multiple variables should be included in a character vector |
within |
Within-subjects factor(s). Multiple variables should be included in a character vector |
covariate |
Covariates. Multiple variables should be included in a character vector |
ss.type |
Type of sums of squares (SS) for ANOVA. Default is |
sph.correction |
[Only for repeated measures with >= 3 levels] Sphericity correction method for adjusting the degrees of freedom (df) when the sphericity assumption is violated. Default is |
aov.include |
Include the |
digits, nsmall |
Number of decimal places of output. Default is |
file |
File name of MS Word ( |
Details
If observations are not uniquely identified in user-defined long-format data,
the function takes averages across those multiple observations for each case.
In technical details, it specifies fun_aggregate=mean in afex::aov_ez()
and values_fn=mean in tidyr::pivot_wider().
Value
A result object (list) returned by
afex::aov_ez(),
along with several other elements:
between, within,
data.wide, data.long.
Interaction Plot
You can save the returned object and use the emmeans::emmip() function
to create an interaction plot (based on the fitted model and a formula specification).
For usage, please see the help page of emmeans::emmip().
It returns an object of class ggplot, which can be easily modified and saved using ggplot2 syntax.
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.
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.
See Also
TTEST, EMMEANS, bruceR-demodata
Examples
#### Between-Subjects Design ####
between.1
MANOVA(between.1, dv="SCORE", between="A")
between.2
MANOVA(between.2, dv="SCORE", between=c("A", "B"))
between.3
MANOVA(between.3, dv="SCORE", between=c("A", "B", "C"))
## How to create an interaction plot using `emmeans::emmip()`?
## See help page for its usage: ?emmeans::emmip()
m = MANOVA(between.2, dv="SCORE", between=c("A", "B"))
emmip(m, ~ A | B, CIs=TRUE)
emmip(m, ~ B | A, CIs=TRUE)
emmip(m, B ~ A, CIs=TRUE)
emmip(m, A ~ B, CIs=TRUE)
#### Within-Subjects Design ####
within.1
MANOVA(within.1, dvs="A1:A4", dvs.pattern="A(.)",
within="A")
## the same:
MANOVA(within.1, dvs=c("A1", "A2", "A3", "A4"), dvs.pattern="A(.)",
within="MyFactor") # renamed the within-subjects factor
within.2
MANOVA(within.2, dvs="A1B1:A2B3", dvs.pattern="A(.)B(.)",
within=c("A", "B"))
within.3
MANOVA(within.3, dvs="A1B1C1:A2B2C2", dvs.pattern="A(.)B(.)C(.)",
within=c("A", "B", "C"))
#### Mixed Design ####
mixed.2_1b1w
MANOVA(mixed.2_1b1w, dvs="B1:B3", dvs.pattern="B(.)",
between="A", within="B")
MANOVA(mixed.2_1b1w, dvs="B1:B3", dvs.pattern="B(.)",
between="A", within="B", sph.correction="GG")
mixed.3_1b2w
MANOVA(mixed.3_1b2w, dvs="B1C1:B2C2", dvs.pattern="B(.)C(.)",
between="A", within=c("B", "C"))
mixed.3_2b1w
MANOVA(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.new,
dvs="Cond_01:Cond_04",
dvs.pattern="Cond_(..)",
between="Group",
within="Condition") # rename the factor
# ?afex::obk.long
MANOVA(afex::obk.long,
subID="id",
dv="value",
between=c("treatment", "gender"),
within=c("phase", "hour"),
cov="age",
sph.correction="GG")
bruceR documentation built on March 7, 2023, 5:41 p.m.
R Package Documentation
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Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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