termtable: Statistics for Linear Models, Including Relevance Statistics

In relevance: Calculate Relevance and Significance Measures

Statistics for Linear Models, Including Relevance Statistics

Description

Calculate a table of statistics for (multiple) regression mdels with a linear predictor

Usage

termtable(object, summary = summary(object), testtype = NULL,
  r2x = TRUE, rlv = TRUE, rlv.threshold = getOption("rlv.threshold"), 
  testlevel = getOption("testlevel"), ...)

relevance.modelclasses

Arguments

object	result of a model fitting function like lm
summary	result of summary(object). If NULL, the summary will be called.
testtype	type of test to be applied for dropping each term in turn. If NULL, it is selected according to the class of the object, see Details.
r2x	logical: should the collinearity measures “R2.x” (see below) for the terms be calculated?
rlv	logical: Should relevances be calculated?
rlv.threshold	Relevance thresholds, vector containing the elements rel: threshold for relative effects, coef: for standardized coefficients, drop: for drop effects, pred: for prediction intervals.
testlevel	1 - confidence level
...	further arguments, ignored

Details

relevance.modelclasses collects the names of classes of model fitting results that can be handled by termtable.

If testtype is not specified, it is determined by the class of object and its attribute family as follows:

"F":

or t for objects of class lm, lmrob and glm with families quasibinomial and quasipoisson,

"Chi-squared":

for other glms and survreg

Value

data.frame with columns

coef:

coefficients for terms with a single degree of freedom

df:

degrees of freedom

se:

standard error of coef

statistic:

value of the test statistic

p.value, p.symbol:

p value and symbol for it

Sig0:

significance value for the test of coef==0

ciLow, ciUp:

confidence interval for coef

stcoef:

standardized coefficient (standardized using the standard deviation of the 'error' term, sigma, instead of the response's standard deviation)

st.Low, st.Up:

confidence interval for stcoef

R2.x:

collinearity measure (= 1 - 1 / vif, where vif is the variance inflation factor)

coefRle:

estimated relevance of coef

coefRls:

secured relevance, lower end of confidence interval for the relevance of coef

coefRlp:

potential relevance, the upper end of the confidence interval.

dropRle, dropRls, dropRlp:

analogous values for drop effect

predRle, predRls, predRlp:

analogous values for prediction effect

In addition, it has attributes

testtype:

as determined by the argument testtype or the class and attributes of object.

fitclass:

class and attributes of object.

family, dist:

more specifications if applicable

Author(s)

Werner A. Stahel

References

Werner A. Stahel (2020). Measuring Significance and Relevance instead of p-values. Submitted

See Also

getcoeftable; for printing options, print.inference

Examples

  data(swiss)
  rr <- lm(Fertility ~ . , data = swiss)
  rt <- termtable(rr)
  rt

relevance documentation built on Oct. 19, 2024, 3 a.m.