plot.mmc.multicomp

MMC (Mean--mean Multiple Comparisons) plot.

MMC (Mean--mean Multiple Comparisons) plot. The plot method documented here is no longer recommended for R; use mmcplot instead. This method is still necessary for S-Plus.

Usage

# S3 method for mmc.multicomp
plot(x,
     xlab="contrast value",
     ylab=none$ylabel,
     focus=none$focus,
     main= main.method.phrase,
     main2=main2.method.phrase,
     main.method.phrase=
       paste("multiple comparisons of means of", ylab),
     main2.method.phrase=paste("simultaneous ",
       100*(1-none$alpha),"% confidence limits, ",
       method, " method", sep="" ),
     ry.mmc=TRUE,
     key.x=par()$usr[1]+ diff(par()$usr[1:2])/20,
     key.y=par()$usr[3]+ diff(par()$usr[3:4])/3,
     method=if (is.null(mca)) lmat$method else mca$method,
     print.lmat=(!is.null(lmat)),
     print.mca=(!is.null(mca) && (!print.lmat)),
     iso.name=TRUE,
     x.offset=0,
     col.mca.signif="red",  col.mca.not.signif="black",
     lty.mca.signif=1,  lty.mca.not.signif=6,
     lwd.mca.signif=1,  lwd.mca.not.signif=1,
     col.lmat.signif="blue", col.lmat.not.signif="black",
     lty.lmat.signif=1, lty.lmat.not.signif=6,
     lwd.lmat.signif=1, lwd.lmat.not.signif=1,
     lty.iso=7, col.iso="darkgray", lwd.iso=1,
     lty.contr0=2, col.contr0="darkgray", lwd.contr0=1,
     decdigits.ybar=2,
     ...
     )

Note

plot.mmc.multicomp chooses sensible defaults for its many arguments. They will often need manual adjustment. The examples show several types of adjustments. We have changed the centering and scaling to avoid overprinting of label information. By default the significant contrasts are shown in a more intense color than the nonsignificant contrasts. We have an option to reduce the color intensity of the isomeans grid.

When there is overprinting of labels (a consequence of level means being close together), a tiebreaker plot may be needed. See ?MMC for an example.

References

Heiberger, Richard M. and Holland, Burt (2015). Statistical Analysis and Data Display: An Intermediate Course with Examples in R. Second Edition. Springer-Verlag, New York. https://www.springer.com/us/book/9781493921218

Heiberger, Richard M. and Holland, Burt (2006). "Mean--mean multiple comparison displays for families of linear contrasts." Journal of Computational and Graphical Statistics, 15:937--955.

Hsu, J. and Peruggia, M. (1994). "Graphical representations of Tukey's multiple comparison method." Journal of Computational and Graphical Statistics, 3:143--161.

See Also

mmc, plotMatchMMC, mmcplot.

Aliases

• plot.mmc.multicomp

Examples

# NOT RUN {
data(catalystm)
catalystm1.aov <- aov(concent ~ catalyst, data=catalystm)
summary(catalystm1.aov)

## See ?MMC to see why these contrasts are chosen
catalystm.lmat <- cbind("AB-D" =c( 1, 1, 0,-2),
                        "A-B"  =c( 1,-1, 0, 0),
                        "ABD-C"=c( 1, 1,-3, 1))
dimnames(catalystm.lmat)[[1]] <- levels(catalystm$catalyst)


catalystm.mmc <-
if.R(r={mmc(catalystm1.aov, linfct = mcp(catalyst = "Tukey"),
            focus.lmat=catalystm.lmat)}
    ,s={multicomp.mmc(catalystm1.aov, focus.lmat=catalystm.lmat,
                     plot=FALSE)}
)

# }
# NOT RUN {
## pairwise contrasts, default settings
plot(catalystm.mmc, print.lmat=FALSE)
# }
# NOT RUN {
## Centering, scaling, emphasize significant contrasts.
## Needed in R with 7in x 7in default plot window.
## Not needed in S-Plus with 4x3 aspect ratio of plot window.
plot(catalystm.mmc, x.offset=2.1, ry.mmc=c(50,58), print.lmat=FALSE)

## user-specified contrasts
plot(catalystm.mmc, x.offset=2.1, ry.mmc=c(50,58))

## reduce intensity of isomeans grid, number isomeans grid lines
plot(catalystm.mmc, x.offset=2.1, ry.mmc=c(50,58),
     lty.iso=2, col.iso='darkgray', iso.name=FALSE)

## both pairwise contrasts and user-specified contrasts
plot(catalystm.mmc, x.offset=2.1, ry.mmc=c(50,58), lty.iso=2,
     col.iso='darkgray', print.mca=TRUE)

# }
# NOT RUN {
## newer mmcplot
mmcplot(catalystm.mmc)
mmcplot(catalystm.mmc, type="lmat")
# }