The t2way
function computes a twoway ANOVA for trimmed means with interactions effects. Corresponding post hoc tests are in mcp2atm
. pbad2way
performs a twoway ANOVA using Mestimators for location with mcp2a
for post hoc tests.
1 2 3 4 
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 
The pbad2way
function returns pvalues only. If it happens that the variancecovariance 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 
pvalue first main effect 
Qb 
second main effect 
B.p.value 
pvalue second main effect 
Qab 
interaction effect 
AB.p.value 
pvalue interaction effect 
call 
function call 
varnames 
variable names 
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
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20  ## 2way ANOVA on trimmed means
t2way(attractiveness ~ gender*alcohol, data = goggles)
## post hoc tests
mcp2atm(attractiveness ~ gender*alcohol, data = goggles)
## 2way ANOVA on MOM estimator
pbad2way(attractiveness ~ gender*alcohol, data = goggles)
## post hoc tests
mcp2a(attractiveness ~ gender*alcohol, data = goggles)
## 2way 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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