Create a table of comparisons between two groups with the cgPairedDifferenceFit object

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Description

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

Usage

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

Arguments

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.

Value

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.

Note

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

Author(s)

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

Examples

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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)

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