Package 'RcmdrPlugin.aRnova'

Generalized Linear Model

Description

This is a minor modification of generalizedLinearModel where size effects are computed and displayed for logistic regression

Usage

generalizedLinearModel_()

See Also

generalizedLinearModel

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Status, Authoritarianism, and Conformity

Description

The Moore data frame has 45 rows and 4 columns. The data are for subjects in a social-psychological experiment, who were faced with manipulated disagreement from a partner of either of low or high status. The subjects could either conform to the partner's judgment or stick with their own judgment.

Usage

Moore

Format

This data frame contains the following columns:

partner.status

Partner's status. A factor with levels: high, low.

conformity

Number of conforming responses in 40 critical trials.

fcategory

F-Scale Categorized. A factor with levels (note levels out of order): high, low, medium.

fscore

Authoritarianism: F-Scale score.

Source

Moore, J. C., Jr. and Krupat, E. (1971) Relationship between source status, authoritarianism and conformity in a social setting. Sociometry 34, 122–134.

Personal communication from J. Moore, Department of Sociology, York University.

References

Fox, J. (2008) Applied Regression Analysis and Generalized Linear Models, Second Edition. Sage.

Fox, J. and Weisberg, S. (2011) An R Companion to Applied Regression, Second Edition, Sage.

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Multiway ANOVA

Description

This is a modification of Rcmdr::multiWayAnova() where supplementary options have been added.

Usage

multiWayAnova_()

Details

Options:

• 'SS type': type of sum of squared, default: type = 2. See Details in Anova

• 'Effect size': compute and prints effect size (partial eta squares)

• 'Summary statistics for groups': prints summary statistics for groups formed by all combinatuions of factors

• 'Pairwise comparisons of means': performs post-hoc Tukey's HSD test on significant (p < .05) or close to significant (p < 0.1) effects.

On OK, the following operations are carried out:

• Computes ANOVA using Anova

• Computes effect sizes (partial eta squared)

• Prints a summary of marginal statistics (count, min, max, mean, ds)

• runs post-hoc analysis on significant or close to significant effects

• Generates an 'extended' dataset (extension .ext) containing additionak columns '<factorA.factorB:...>' that allows differentiate measures from groups or subjects with same factors levels. This 'extended' dataset is useful for ploting means and post-hoc analysis

Value

None

See Also

Anova for the computation of ANOVA

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O'Brien and Kaiser's Repeated-Measures Data

Description

These contrived repeated-measures data are taken from O'Brien and Kaiser (1985). The data are from an imaginary study in which 16 female and male subjects, who are divided into three treatments, are measured at a pretest, postest, and a follow-up session; during each session, they are measured at five occasions at intervals of one hour. The design, therefore, has two between-subject and two within-subject factors.

The contrasts for the treatment factor are set to $-2, 1, 1$ and $0, -1, 1$. The contrasts for the gender factor are set to contr.sum.

Usage

OBrienKaiser

Format

A data frame with 16 observations on the following 17 variables.

treatment

a factor with levels control A B

gender

a factor with levels F M

pre.1

pretest, hour 1

pre.2

pretest, hour 2

pre.3

pretest, hour 3

pre.4

pretest, hour 4

pre.5

pretest, hour 5

post.1

posttest, hour 1

post.2

posttest, hour 2

post.3

posttest, hour 3

post.4

posttest, hour 4

post.5

posttest, hour 5

fup.1

follow-up, hour 1

fup.2

follow-up, hour 2

fup.3

follow-up, hour 3

fup.4

follow-up, hour 4

fup.5

follow-up, hour 5

Source

O'Brien, R. G., and Kaiser, M. K. (1985) MANOVA method for analyzing repeated measures designs: An extensive primer. Psychological Bulletin 97, 316–333, Table 7.

Examples

OBrienKaiser
contrasts(OBrienKaiser$treatment)
contrasts(OBrienKaiser$gender)

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One way ANOVA

Description

This is a modification of Rcmdr::oneWayAnova() where supplementary options have been added.

Usage

oneWayAnova_()

Details

Options:

• 'Effect size': compute and prints effect size (partial eta squared)

• 'Summary statistics for groups': prints summary statistics for groups formed by the beween subject factor

• 'Pairwise comparisons of means': performs post-hoc Tukey's HSD test.

On OK, the following operations are carried out:

• Computes ANOVA using aov

• Computes effect sizes (partial eta squared)

• Prints a summary of marginal statistics (count, min, max, mean, ds)

• runs post-hoc analysis

Value

None

See Also

aov for the computation of ANOVA

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Chemical Composition of Pottery

Description

The data give the chemical composition of ancient pottery found at four sites in Great Britain. They appear in Hand, et al. (1994), and are used to illustrate MANOVA in the SAS Manual. (Suggested by Michael Friendly.)

Usage

Pottery

Format

A data frame with 26 observations on the following 6 variables.

Site

a factor with levels AshleyRails Caldicot IsleThorns Llanedyrn

Al

Aluminum

Fe

Iron

Mg

Magnesium

Ca

Calcium

Na

Sodium

Source

Hand, D. J., Daly, F., Lunn, A. D., McConway, K. J., and E., O. (1994) A Handbook of Small Data Sets. Chapman and Hall.

Examples

Pottery

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Repeated measures ANOVA

Description

Dialog box to (i) select the within-subject variables corresponding to the factors defined in repMeasAnovaSetup, (ii) select the between-suject factors, (iii) set options and (iv) launch the repeated measures anova.

Usage

repMeasAnova(.withinfactors, .withinlevels)

Arguments

.withinfactors	list of within-subject factors
.withinlevels	list of within-subject variables

Details

Options:

• 'SS type': type of sum of squares, default: type = 2. See Details in Anova

• 'Effect size': compute and prints effect size (partial eta squared)

• 'Summary statistics for groups': prints summary statistics for groups formed by all combinations of factors

• 'Pairwise comparisons of means': performs post-hoc Tukey's HSD test on significant (p < .05) or close to significant (p < 0.1) effects.

On OK, the following operations are carried out:

• Generates a dataset containing complete cases and converted from 'wide' to 'long' format (extension .cplt.lg), with the following columns added:

  • 'id' (factor): identifies the subjects.

  • 'DV' (numeric): the measure or dependent variable.

  • 'trial' (int): variable that differentiates multiple measures ('DV') from the same subject ('id').

  • '<factorA>' (factor): levels of the within-suject factor A (one column per within subject factor)

  • '<factorA.factorB:...>' (factor): factor that differentiates multiple measures from groups or subjects with same factors levels

  This 'long' dataset is useful for ploting means and post-hoc analysis

• Computes repeated measure ANOVA using Anova

• Computes effect sizes (partial eta squared)

• Prints a summary of marginal statistics (count, min, max, mean, ds)

• runs post-hoc analysis on significant or close to significant effects

Value

None

Author(s)

Jessica Mange [email protected]

Arnaud Travert [email protected]

See Also

repMeasAnovaSetup for the definition of within factors, Anova for the computation of ANOVA

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Repeated measure ANOVA setup

Description

Dialog box to enter the names and levels of within-factors.

Usage

repMeasAnovaSetup()

Details

Up to three factors can be entered. A valid within-factor entry must consist in a syntactically valid name (see make.names) and 2 levels or more.

On OK:

• The first valid entries are kept and stored in .withinfactors and .withinlevels for factor names and levels, respectively.

• The next dialog box (repMeasAnova(.withinfactors, .withinfactors) is launched.

Author(s)

Jessica Mange [email protected]

Arnaud Travert [email protected]

See Also

repMeasAnova.

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