Homogeneity of Variance Plot
Oneway analysis of variance makes the assumption that the variances of
the groups are equal.
Brown and Forsyth, 1974 present the recommended test of this assumption.
The Brown and Forsyth test statistic is the $F$ statistic resulting
from an ordinary one-way analysis of variance on the
absolute deviations from the median. The
graphs the components of the Brown and Forsyth test statistic.
plot.hov(x, data = sys.parent(), method = "bf", ## x is a formula transpose.in = TRUE, ...) ## users will normally use the formula above and will not call the ## method directly. plot.hov.bf(x, group, ## x is the response variable y.name = deparse(substitute(x)), group.name = deparse(substitute(group)), transpose.in = TRUE, ...) ## users will normally use the formula above and will not call the ## panel function directly. panel.hov(..., transpose = TRUE)
- Formula appropriate for oneway anova in
plot.hov. Response variable in
- Character string defining method. At this time the only recognized method is "bf" for the Brown-Forsyth method.
- transpose.in, transpose
TRUEin R. Normally
TRUEin S-Plus to force vertical boxplots.
- name of response variable, defaults to variable name in formula.
- name of factor, defaults to variable name in formula.
- additional arguments.
"trellis"object with three panels containing boxplots for each group: The observed data
"y", the data with the median subtracted
"y-med(y)", and the absolute deviations from the median
"abs(y-med(y))"The Brown and Forsyth test statistic is the $F$ statistic resulting from an ordinary one-way analysis of variance on the data points in the third panel.
Heiberger, Richard~M. and Holland, Burt (2004b). Statistical Analysis and Data Display: An Intermediate Course with Examples in S-Plus, R, and SAS. Springer Texts in Statistics. Springer. ISBN 0-387-40270-5. Brown, M.~B. and Forsyth, A.~B. (1974). Robust tests for equality of variances. Journal of the American Statistical Association, 69:364--367.
turkey <- read.table(hh("datasets/turkey.dat"), header=FALSE) names(turkey) <- c("diet","wt.gain") turkey$diet <- factor(turkey$diet, labels=c("control","A1","A2","B1","B2")) hov(wt.gain ~ diet, data=turkey) plot.hov(wt.gain ~ diet, data=turkey)