cg (version 1.0-0)

errorBarGraph.cgOneFactorFit: Create an Error Bar graph amongst groups in a cgOneFactorFit object

Description

Creates a graph to see comparisons amongst groups based on the cgOneFactorFit object. The method of Andrews, Sarner, and Snee (1980) is applied to visualizes significant differences via non-overlapping error bars.

Usage

## S3 method for class 'cgOneFactorFit':
errorBarGraph(fit, mcadjust = FALSE, alpha =0.05,
 cgtheme = TRUE, device = "single", ...)

Arguments

fit

A fit object of class cgOneFactorFit.

mcadjust

Do a multiple comparisons adjustment, based on the simultaneous inference capabilities of the multcomp package. See Details below. The default value is FALSE. If mcadjust=TRUE is specified, there will be a

alpha

Significance level, by default set to 0.05, which equates to a 95% confidence level.

cgtheme

When set to the default TRUE, ensures a trellis device is active with limited color scheme. Namely background, strip.shingle, and strip.background are each set to "white".

device

Can be one of three values: [object Object],[object Object],[object Object]

...

Additional arguments. Two are currently valid: [object Object],[object Object]

Value

• errorBarGraph.cgOneFactorFit returns an invisible NULL. The main purpose is the side effect of graphing to the current device.

concept

• error bars
• comparison

Details

When mcadjust=TRUE, a status message of "Some time may be needed as the critical point" "from the multcomp::summary.glht function call is calculated" is displayed at the console. This computed critical point is used for all interval calculations. The multcomp package provides a unified way to calculate critical points based on the comparisons of interest in a "family". Thus a user does not need to worry about choosing amongst the myriad names of multiple comparison procedures. The errorBarGraph.cgOneFactorFit method is only relevant for classical least squares and resistant & robust fits in the cgOneFactorFit object. There is an errorbargraph core function that could be used for approximations in other cases like accelerated failure time or unequal variance fits. The statistical method of Andrews, Sarner, and Snee (1980) is applied to visualize significant differences via non-overlapping error bars. The method is exact when there are equal sample sizes amongst the groups for the classical least squares case. When there are unequal group sample sizes or a resistant & robust fit is used to create the graph, the method is approximate, and this is noted in the main title section of the graph. For the unequal sample sizes, the harmonic mean is calculated to use for all the groups. The method's usefulness declines as the sample sizes become more disparate. When two groups are compared, nonoverlapping error bars indicate a statistically significant pairwise difference. Conversely, if the error bars overlap, there is no such significant difference. In cases of approximation, or borderline overlap that is seen, the cgOneFactorComparisonsTable object created with type="pairwisereflect" or type="pairwise" needs to be consulted to judge significance with a p-value. The minimum and maximum values across all the bar ends are added inside the plot region in blue, flush against the y-axis. The number of decimal places are determined by the digits value in the fit$settings slot. If group labels along the x-axis seem to overlap in the standard horizontal form, they will be rotated 45 degrees.

References

Andrews, H.P., Snee, R.D., Sarner, M.H. (1980). "Graphical Display of Means," The American Statistician, 34, 195-199. Hothorn, T., Bretz, F., Westfall, P., Heiberger, R.M., and Schuetzenmeister, A. (2010). The multcomp R package. Hothorn, T., Bretz, F., and Westfall, P. (2008). "Simultaneous Inference in General Parametric Models", Biometrical Journal, 50, 3, 346-363.

Examples

data(canine)
canine.data <- prepareCGOneFactorData(canine, format="groupcolumns",
                                      analysisname="Canine",
                                      endptname="Prostate Volume",
                                      endptunits=expression(plain(cm)^3),
                                      digits=1, logscale=TRUE, refgrp="CC")
canine.fit <- fit(canine.data)

errorBarGraph(canine.fit)

errorBarGraph(canine.fit, mcadjust=TRUE, model="olsonly")