Description Usage Arguments Value Author(s) References See Also Examples
The function calculates F-bar for inequality constrained hypotheses (Type B). See Silvapulle & Sen (2011) for background on the F-bar statistic. The code of Vanbrabant (2017) is the basis to this Fbar function.
1 |
data |
A dataframe with two variables: (1) a dependent variable, and (2) a grouping variable. |
Amat |
A p by q matrix, where p is the number of means in the ANOVA model, and q is the number of constraints to be imposed on the model. Each row represents one constraint where the parameter with the lower value according to the constraint receives the value -1, and the parameter with the higher value according to the constraint receives the value 1. Other parameters within the same row obtain the value 0. |
The value for the F-bar statistic.
M. A. J. Zondervan-Zwijnenburg
Silvapulle, M. J., & Sen, P. K. (2011). Constrained statistical inference: Order, inequality, and shape constraints (Vol. 912). John Wiley & Sons. doi: 10.1002/9781118165614.ch1
Vanbrabant, L. (2017). restriktor: Restricted Statistical Estimation and Inference for Linear Models. R package version 0.1-55. https://CRAN.R-project.org/package=restriktor
See also runShiny
, Fbar.dif
, and Fbar.exact
.
1 2 3 4 5 6 7 | data <- data.frame(y=ChickWeight$weight,g=ChickWeight$Diet)
aggregate(data$y,by=list(data$g),mean)
#create matrices HR: g4>(g1,g2,g3)
HR <- create_matrices(varnames = c("g1","g2","g3","g4"),
hyp = "g4>(g1,g2,g3)")
Fbar.ineq(data,Amat=HR$Amat)
|
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