Calculate a table of statistics for (multiple) regression mdels with a linear predictor
termtable(object, summary = summary(object), testtype = NULL,
r2x = TRUE, rlv = TRUE, rlv.threshold = getOption("rlv.threshold"),
testlevel = getOption("testlevel"), ...)relevance.modelclasses
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
result of a model fitting function like lm
result of summary(object). If NULL, the
summary will be called.
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.
logical: should the collinearity measures “R2.x” (see below)
for the terms be calculated?
logical: Should relevances be calculated?
Relevance thresholds, vector containing the elements
rel:threshold for relative effects,
coef:for standardized coefficients,
drop:for drop effects,
pred:for prediction intervals.
1 - confidence level
further arguments, ignored
Werner A. Stahel
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
Werner A. Stahel (2020). Measuring Significance and Relevance instead of p-values. Submitted
getcoeftable;
for printing options, print.inference
data(swiss)
rr <- lm(Fertility ~ . , data = swiss)
rt <- termtable(rr)
rt
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