L_1way_RM_ANOVA: Likelihood Supports for One-way Repeated Measures ANOVA

In likelihoodR: Likelihood Analyses for Common Statistical Tests

Likelihood Supports for One-way Repeated Measures ANOVA

Description

This function calculates support for the treatment means versus the null model, Type of correction for the parameters in the null versus the means model can be selected. support for the first contrast versus the group means model and the support for the first versus the second contrast. Also gives the F, p and partial eta-squared values for the overall analysis, and F and p for the first contrast. Assumes sphericity.

Usage

L_1way_RM_ANOVA(dat, group, ID, correct=1, contrast1=NULL, contrast2=NULL, verb=TRUE)

Arguments

dat	a (non-empty) numeric vector of data values.
group	a vector the same length as data, coding for k groups.
ID	is an identifier for each case.
correct	specifies the correction: 0 = none, 1 = Occam's bonus (default), 2 = AIC
contrast1	first contrast, default = NULL.
contrast2	second contrast, default = NULL.
verb	show output, default = TRUE.

Value

$S.12 - support for groups means hypothesis versus null, uncorrected

$S.12c - corrected

$capplied - type of correction

$S.1m = support for first contrast versus means model.

$S.cont.12 - support for first versus second contrast.

$contrast1 - first contrast.

$contrast2 - second contrast.

$gp.means - group means.

$df - degrees of freedom for ANOVA.

$F.val - F value for overall analysis.

$P.val - p value for the overall analysis.

Fval.c1 - F value for the first contrast.

$df.1 - degrees of freedom for the contrast.

$P.val1 - p value for the contrast.

References

Cahusac, P.M.B. (2020) Evidence-Based Statistics, Wiley, ISBN : 978-1119549802

Dixon P. The effective number of parameters in post hoc models. Behavior Research Methods. 2013; 45(3):604.

Dixon P. The p-value fallacy and how to avoid it. Canadian Journal of Experimental Psychology/Revue canadienne de psychologie expérimentale. 2003; 57(3):189.

Glover S, Dixon P. Likelihood ratios: a simple and flexible statistic for empirical psychologists. Psychonomic Bulletin and Review. 2004; 11(5):791.

Examples

# sleep data example, p 97
dat <- c(0.7,	-1.6,	-0.2,	-1.2,	-0.1,	3.4,	3.7,	0.8,	0,	2,
1.9,	0.8,	1.1,	0.1,	-0.1,	4.4,	5.5,	1.6,	4.6,	3.4,
1.5,	1.4,	0.0,	-0.7,	0.5,	5.1,	5.7,	1.5,	4.7,	3.5)
treat <- gl(3,10,30)
patients <- gl(10,1,30)
contrast1 <- c(-1, 0, 1)  # linear
contrast2 <- c(-2, 1, 1)  # 1st vs treatments 2 & 3

m=L_1way_RM_ANOVA(dat, treat, patients, correct=1, contrast1, contrast2)
m

likelihoodR documentation built on Oct. 22, 2024, 5:06 p.m.