| steel {rankFD} | R Documentation | 
Steel-type multiple contrast tests
Description
The function implements purely nonparametric Steel-type multiple contrast tests for either making many-to-one (Dunnett-type) or all pairwise (Tukey-type) comparisons. Null hypotheses are formulated in terms of the distribution functions.
Usage
steel(
  formula,
  data,
  control = NULL,
  alternative = c("two.sided", "less", "greater"),
  info = TRUE,
  correlation = TRUE
)
Arguments
formula | 
 A model   | 
data | 
 A data.frame, list or environment containing the variables in 
  | 
control | 
 Specification of the control group for making many-to-one-comparisons. If NULL, all-pairwise comparisons are performed.  | 
alternative | 
 Specification of the direction of the alternative. Default is two-sided.  | 
info | 
 Logical. If TRUE, additional output information and explanation is printed to the console.  | 
correlation | 
 Logical. If TRUE, the correlation matrix is printed.  | 
Details
The steel() function calculates the Steel-type tests as explained by Munzel, U., Hothorn, L. A. (2001). A unified approach to simultaneous rank test procedures in the unbalanced one-way layout. Biometrical Journal: Journal of Mathematical Methods in Biosciences, 43(5), 553-569.
Value
A list containing the following components:
Data.Info | 
 Groups and sample sizes of the data  | 
Analysis | 
 Data frame containing the test results (comparison, relative effect estimator, standard error, test statistic and p-value.)  | 
Correlation | 
 Estimated correlation matrix  | 
References
Brunner, E., Bathke, A.C., Konietschke, F. Rank and Pseudo-Rank Procedures for Independent Observations in Factorial Designs. Springer International Publishing, 2018.
Munzel, U., Hothorn, L. A. (2001). A unified approach to simultaneous rank test procedures in the unbalanced one-way layout. Biometrical Journal: Journal of Mathematical Methods in Biosciences, 43(5), 553-569.
Konietschke, F., Hothorn, L. A., Brunner, E. (2012). Rank-based multiple test procedures and simultaneous confidence intervals. Electronic Journal of Statistics, 6, 738-759.
Examples
data(Muco)
model.oneway <- steel(HalfTime ~ Disease, data = Muco,info=TRUE,correlation=TRUE)