DeducerEZ: Implementation of some functions from package sQuote(ez) for...
In DeducerANOVA: Factorial ANOVA for Deducer

Description Usage Arguments Value Author(s) References See Also

Deducer plug-in enabling the analysis of factorial ANOVA designs, including within-subjects and mixed designs. Includes functions for reshaping within-subjects data from wide to long format.

DeducerEZ(data, dv, wid, between = NULL, observed = NULL, within = NULL, type = 3, detailed = FALSE, descriptives = FALSE, Tukey = FALSE, x = NULL, split = NULL, x_lab = NULL, y_lab = NULL, split_lab = NULL, test.var = NULL, at.var = NULL, var.equal = FALSE, p.adjust.method = "holm")

`data`	A data.frame containing the variables to be analyzed.
`dv`	A dot object such as .(dv). The dependent variable.
`wid`	A dot object such as .(wid). A unique identifier for each case or subject in the dataset. Will be converted to factor (with a warning).
`between`	Dot object such as .(payment,courtesy). A vector of between-subjects factors to be included in the model.
`observed`	Dot object such as .(status). A vector of observed (not manipulated) factors to be included in the model, distinguished only for calculations of generalized eta squared (see Bakeman, 2005).
`within`	Dot object such as .(time). A vector of within-subjects factors. If data are in wide format (one row per subject), the data will need to be converted to long format, in which a new variable is added that identifies the level of the within-subjects factor for a particular observation.
`type`	Type of Sum of Squares, using the SAS sQuote(Type II) or sQuote(Type III) terminology. Type 3 is the default, and tests the effects of each factor by comparing the fit of a model with all the terms to the fit of a model with all the terms except the one being tested. To provide results comparable to those produced by SPSS and SAS, DeducerEZ stipulates options(contrasts=c(contr.sum,contr.poly)).
`detailed`	Logical. Should detailed output, including Sum of Squares for each factor, be provided? Default is FALSE.
`descriptives`	Logical. Should descriptive statistics for the model be provided? Default is FALSE.
`Tukey`	Logical. If true, Tukey's Honestly Significant Difference for all possible pairs is computed on all between-subjects factors and interactions of between-subjects factors. This is accomplished by first constructing a linear model by crossing all between-subjects factors, calling aov on the lm, and then calling TukeyHSD on the aov.
`x`	Variable to be plotted along the x-axis. If blank, no plot is generated.
`split`	Variable to be used in the legend. Can be left blank to create a plot with just one factor.
`x_lab`	Character string used for the label of the x-axis.
`y_lab`	Character string used for the label of the y-axis.
`split_lab`	Character string used for the label of the legend.
`test.var`	Dot object such as .(payment) specifying the factor levels which will be compared (in pairs) using the sme (Simple Main Effects) function.
`at.var`	Dot object such as .(courtesy). Levels of test.var will be compared AT each level of at.var. Passed to the sme function.
`var.equal`	Logical value. Are the variances of the cells in the factorial design assumed to be equal? Default is FALSE. Passed to the sme function.
`p.adjust.method`	One of the character vector values of p.adjust.method. See help(p.adjust) for accepted values.

Returns a list of data.frames.

`Model Details`	A summary of the model details.
`ANOVA`	The second is the results of the ANOVA, including SS if they are requested in sQuote(detailed).
`Levene's test`	The third can be Levene's test for equality of variances if there are only between-subjects factors.
`Descriptives`	If they are requested, the last item is descriptive statistics: N, mean, SD.
`Tests of Simple Main Effects`	Output from the sme (Simple Main Effects) function. See help(sme) for details.