Robust ANCOVA
This function computes robust ANCOVA for 2 independent groups and one covariate. It compares trimmed means. No parametric assumption (e.g. homogeneity) is made about the form of the regression lines. A running interval smoother is used. A bootstrap version which computes confidence intervals using a percentile t-bootstrap is provided as well.
ancova(formula, data, tr = 0.2, fr1 = 1, fr2 = 1, pts = NA, ...) ancboot(formula, data, tr = 0.2, nboot = 599, fr1 = 1, fr2 = 1, pts = NA, ...)
formula: an object of class formula.data: an optional data frame for the input data.tr: trim level for the mean.fr1: values of the span for the first group (1 means unspecified)fr2: values of the span for the second group (1 means unspecified)pts: can be used to specify the design points where the regression lines are to be compared; if NA design points are chosen.nboot: number of bootstrap samples...: currently ignored.Returns an object of class ancova containing:
evalpts: covariate values (including points close to these values) where the test statistic is evaluated
n1: number of subjects at evaluation point (first group)
n2: number of subjects at evaluation point (first group)
trDiff: trimmed mean differences
se: standard errors for trimmed mean differences
ci.low: lower confidence limit for trimmed mean differences
ci.hi: upper confidence limit for trimmed mean differences
test: values of the test statistic
crit.vals: critical values
p.vals: p-values
fitted.values: fitted values from interval smoothing
call: function call
Wilcox, R. (2012). Introduction to Robust Estimation and Hypothesis Testing (3rd ed.). Elsevier.
t2way
head(invisibility) ancova(mischief2 ~ cloak + mischief1, data = invisibility) ## specifying covariate evaluation points ancova(mischief2 ~ cloak + mischief1, data = invisibility, pts = c(3, 4, 8, 1)) ## bootstrap version ancboot(mischief2 ~ cloak + mischief1, data = invisibility)