relevance-package {relevance} | R Documentation |
Calculates Relevance and Significance values for simple models and for many types of regression models
The DESCRIPTION file:
Package: | relevance |
Type: | Package |
Title: | Calculate Relevance |
Version: | 1.0 |
Date: | 2020-08-26 |
Author: | Werner A. Stahel |
Maintainer: | Werner A. Stahel <stahel@stat.math.ethz.ch> |
Depends: | stats |
Suggests: | survival, knitr |
VignetteBuilder: | knitr |
Description: | Calculates Relevance and Significance values for simple models and for many types of regression models |
License: | GPL-2 |
Repository: | R-Forge |
Repository/R-Forge/Project: | regdevelop |
Repository/R-Forge/Revision: | 183 |
Repository/R-Forge/DateTimeStamp: | 2020-09-23 20:40:36 |
Date/Publication: | 2020-09-23 20:40:36 |
Index of help topics:
confintF Confidence Interval for the Non-Central F and Chisquare Distribution dropNA Drop NA Values from Vector getcoeftable Extract Componente of a Fit getopt Get Option that Influence the Output of Relevance Functions relevance-package Calculate Relevance rlstats Calculate Relevance and Significance Statistics sumNA Count NA Values termeffects all coefficients of a model fit termtable Statistics for Linear Models, Including Relevance Statistics
This package is being built.
Relevance is a measure that expresses the relevance of an effect. The simplest case is a single sample supposedly normally distributed observations, and we are interested in the expectation, estimated by the mean of the observations. There is a threshold for the expectation, below which an effect is judged too small to be of interest. The estimated relevance Rle is then simply the estimated effect divided by the threshold. If it is larger than 1, the effect is thus judged relevant. The two other values that characterize the relevance are the limits of the confidence interval for the relevance, called the secured relevance Rls and the potential relevance Rlp. If Rle $>1$, then one might say that the effect is "significantly relevant".
Another useful measure, meant to replace the p-value, is the "significance" Sg0. In the simple case, it divides the estimated effect by the critical value of the (t-) test statistic. Thus, the statistical test of the null hypothesis of zero expectation is significant if Sg0 is $>1$.
These measures are also calculated for the comparison of two groups, for proportions, and most importantly for regression models. For models with linear predictors, relevances are obtained for standardized coefficients as well as for the effect of dropping terms.
The most important functions are
rlstats
For models with a linear predictor, the results are given by tables.
A coeftable is produced by rlstats
.
termtable
contains the relevance of the terms as characterized by
the effect of dropping the term from the model formula.
It also includes the relevance of the coefficients as produced by
rlstats
for terms with a single degree of freedom.
termeffects
calculates the relevances for the coefficients
related to each term and is thus only of interest for terms with
more than one degree of freedom.
Werner A. Stahel
Maintainer: Werner A. Stahel <stahel@stat.math.ethz.ch>
Stahel, Werner A. (2020). Measuring Significance and Relevance instead of p-values. In preparation.
data(swiss) rr <- lm(Fertility ~ . , data = swiss) rt <- termtable(rr) rt names(rt) data.frame(rt)