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 glm
s 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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