comparisonsTable.cgPairedDifferenceFit: Create a table of comparisons between two groups with the...
In cg: Compare Groups, Analytically and Graphically

Description Usage Arguments Value Note Author(s) Examples

Create a table of comparisons based on the cgPairedDifferenceFit object. A cgPairedDifferenceComparisonsTable class object is created.

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## S4 method for signature 'cgPairedDifferenceFit'
comparisonsTable(fit, type="pairwisereflect",
 alpha=0.05, addpct=FALSE, display="print",  ...)

`fit`	An object of class `cgPairedDifferenceFit`.
`type`	Can be one of two values: `"pairwisereflect"` The default value, it calculates and lists both pairwise comparison orders. In other words, Groups A vs. B and B vs. A will be included. `"pairwise"` Calculates and lists only one of the two possible pairwise comparison orders. Ib other words, once A vs. B is specified, then the reflective B vs. A is not included. So the number of comparisons is half that produced by `"pairwisereflect"`. The ordering of group levels in the `fit` object is used to determine which ordering is included and which is not. If both orderings are of interest, such as for `settings$endptscale=="log"` in the fit objects, use the `"pairwisereflect"` value above.
`alpha`	Significance level, by default set to `0.05`.
`addpct`	Only relevant if `settings$endptscale=="original"` in the fit object. An column of percent differences is added for the comparisons, as a descriptive supplement to the original scale differences that are formally estimated.
`display`	One of three valid values: `"print"` The default value; It calls a `print` method for the created `cgPairedDifferenceComparisonsTable` object, which is a formatted text output of the table(s). `"none"` Supresses any printing. Useful, for example, when just assignment of the resulting object is desired. `"show"` Calls the default `showDefault` method, which will just print out the `cgPairedDifferenceComparisonsTable` components.
`...`	Additional arguments. Only one is currently valid: `model` For `cgPairedDifferenceFit` `fit` objects that have classical least squares `lm` or resistant & robust `rlm` fits, the following argument values are possible: `"both"` Comparison tables based on both the ordinary classical least squares and resistant & robust fits are created. This is the default when both fits are present in the `cgPairedDifferenceFit` object specified in the `fit` argument. If the resistant & robust fit is not available, this value is not relevant. `"olsonly"` Only a comparison table based on the ordinary classical least squares `olsfit` fit slot is performed. `"rronly"` Only a comparison table based on the resistant and robust `rrfit` fit slot is performed.

Creates an object of class cgPairedDifferenceComparisonsTable, with the following slots:

ols.comprs: The table of comparisons based on the olsfit component of the cgPairedDifferenceFit, unless model="rronly" is specified. In that case the slot value is NULL. See below for the data frame structure of the table.
rr.comprs: The table of comparisons based on the rrfit component of the cgPairedDifferenceFit object, if a valid resistant & robust fit object is present. If rrfit is a simple character value of "No fit was selected.", or model="olsonly" was specified, then the value is NULL. See below for the data frame structure of the table.
settings: A list of settings carried from the cgPairedDifferenceFit fit object, and the addition of some specified arguments in the method call above: alpha, type, and addpct. These are used for the print.cgPairedDifferenceComparisonsTable method, invoked for example when display="print".

The data frame structure of the comparisons table in a *.comprs slot consists of row.names that specify the comparison of the form A vs. B, and these columns:

estimate: The difference in group means in the comparison: A vs. B. If settings$endptscale=="log" in the fit object, this will be back-transformed to a percent difference scale.
se: The estimated standard error of the difference estimate. If settings$endptscale=="log" in the fit object, this estimate will be based on the Delta method, and will particularly begin to be a poor approximation when the standard error in the logscale exceeds 0.50.
lowerci: The lower 100 * (1-alpha) % confidence limit of the difference estimate. With the default alpha=0.05, this is 95%. If settings$endptscale=="log" in the fit object, the confidence limit is first computed in the logarithmic scale of analysis, and then back-transformed to a percent difference scale.
upperci: The upper 100 * (1-alpha) % confidence limit of the difference estimate. With the default alpha=0.05, this is 95%. If settings$endptscale=="log" in the fit object, the confidence limit is first computed in the logarithmic scale of analysis, and then back-transformed to a percent difference scale.
pval: The computed p-value from the test of the difference estimate.
meanA or geomeanA: The estimated mean for the left hand side "A" of the A vs. B comparison. If settings$endptscale=="log" in the fit object, this is a back-transform to the original scale, and therefore is a geometric mean, and will be labelled geomeanA. Otherwise it is the arithmetic mean and labelled meanA.
seA: The estimated standard error of the meanA estimate. If settings$endptscale=="log" in the fit object, this estimate will be based on the Delta method, and will particularly begin to be a poor approximation when the standard error in the logscale exceeds 0.50.
meanB or geomeanB: The estimated mean for the right hand side "B" of the A vs. B comparison. If settings$endptscale=="log" in the fit object, this is a back-transform to the original scale, and therefore is a geometric mean, and will be labelled geomeanB. Otherwise it is the arithmetic mean and labelled meanB.
seB: The estimated standard error of the meanB estimate. If settings$endptscale=="log" in the fit object, this estimate will be based on the Delta method, and will particularly begin to be a poor approximation when the standard error in the logscale exceeds 0.50.

An additional column addpct of percent differences is added if endptscale=="original" and addpct=TRUE, as a descriptive supplement to the original scale differences that are formally estimated. This is only possible for the model=="ols" case, since the original arithmetic means are not estimated in the Resistant & Robust model=="rr" case.

Contact cg@billpikounis.net for bug reports, questions, concerns, and comments.

Bill Pikounis [aut, cre, cph], John Oleynick [aut], Eva Ye [ctb]

data(anorexiaFT)

## log scale
anorexiaFT.data <- prepareCGPairedDifferenceData(anorexiaFT, format="groupcolumns",
                                                 analysisname="Anorexia FT",
                                                 endptname="Weight",
                                                 endptunits="lbs",
                                                 expunitname="Patient",
                                                 digits=1,
                                                 logscale=TRUE)
anorexiaFT.fit <- fit(anorexiaFT.data)


anorexiaFT.comps <- comparisonsTable(anorexiaFT.fit, display="none")
print(anorexiaFT.comps)

comparisonsTable(anorexiaFT.fit, model="olsonly") 
comparisonsTable(anorexiaFT.fit, model="rronly") 


## original scale evaluation
anorexiaFT.orig.data <- prepareCGPairedDifferenceData(anorexiaFT, format="groupcolumns",
                                                      analysisname="Anorexia FT",
                                                      endptname="Weight",
                                                      endptunits="lbs",
                                                      expunitname="Patient",
                                                      digits=1,
                                                      logscale=FALSE)
anorexiaFT.orig.fit <- fit(anorexiaFT.orig.data)

comparisonsTable(anorexiaFT.orig.fit)  

comparisonsTable(anorexiaFT.orig.fit, addpct=TRUE)