t2way | R Documentation |
The t2way
function computes a two-way ANOVA for trimmed means with interactions effects. Corresponding post hoc tests are in mcp2atm
. pbad2way
performs a two-way ANOVA using M-estimators for location with mcp2a
for post hoc tests.
t2way(formula, data, tr = 0.2, ...) pbad2way(formula, data, est = "mom", nboot = 599, pro.dis = FALSE, ...) mcp2atm(formula, data, tr = 0.2, ...) mcp2a(formula, data, est = "mom", nboot = 599, ...)
formula |
an object of class formula. |
data |
an optional data frame for the input data. |
tr |
trim level for the mean. |
est |
Estimate to be used for the group comparisons: either |
nboot |
number of bootstrap samples. |
pro.dis |
If |
... |
currently ignored. |
t2way
does not report any degrees of freedom since an adjusted critical value is used.
pbad2way
returns p-values only; if it happens that the variance-covariance matrix in the Mahalanobis distance computation is singular, it is suggested to use the projection distances by setting pro.dis = TRUE
.
The functions t2way
and pbad2way
return an object of class t2way
containing:
Qa |
first main effect |
A.p.value |
p-value first main effect |
Qb |
second main effect |
B.p.value |
p-value second main effect |
Qab |
interaction effect |
AB.p.value |
p-value interaction effect |
call |
function call |
varnames |
variable names |
dim |
design dimensions |
The functions mcp2atm
and mcp2a
return an object of class mcp
containing:
effects |
list with post hoc comparisons for all effects |
contrasts |
design matrix |
Wilcox, R. (2012). Introduction to Robust Estimation and Hypothesis Testing (3rd ed.). Elsevier.
t1way
, med1way
, t2way
## 2-way ANOVA on trimmed means t2way(attractiveness ~ gender*alcohol, data = goggles) ## post hoc tests mcp2atm(attractiveness ~ gender*alcohol, data = goggles) ## 2-way ANOVA on MOM estimator pbad2way(attractiveness ~ gender*alcohol, data = goggles) ## post hoc tests mcp2a(attractiveness ~ gender*alcohol, data = goggles) ## 2-way ANOVA on medians pbad2way(attractiveness ~ gender*alcohol, data = goggles, est = "median") ## post hoc tests mcp2a(attractiveness ~ gender*alcohol, data = goggles, est = "median") ## extract design matrix model.matrix(mcp2a(attractiveness ~ gender*alcohol, data = goggles, est = "median"))
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