inference | R Documentation |
Calculates confidence intervals and relevance and significance values given estimates, standard errors and, for relevance, additional quantities.
inference(object = NULL, estimate = NULL, teststatistic = NULL,
se = NA, n = NULL, df = NULL,
stcoef = TRUE, rlv = TRUE, rlv.threshold = getOption("rlv.threshold"),
testlevel = getOption("testlevel"), ...)
object |
A data.frame containing, as its variables,
the arguments
... or a model fit object |
estimate |
estimate(s) of the parameter(s) |
teststatistic |
test statistic(s) |
se |
standard error(s) of the estimate(s) |
n |
number(s) of observations |
df |
degrees of freedom of the residuals |
stcoef |
standardized coefficients.
If |
rlv |
logical: Should relevances be calculated? |
rlv.threshold |
Relevance threshold(s). May be a simple number for simple inference, or a vector containing the elements
|
testlevel |
1 - confidence level |
... |
furter arguments, passed to
|
The estimates divided by standard errors are assumed to be
t-distributed with df
degrees of freedom.
For df==Inf
, this is the standard normal distribution.
A data.frame of class "inference"
, with the variables
effect, se |
estimated effect(s), often coefficients, and their standard errors |
ciLow, ciUp |
lower and upper limit of the confidence interval |
teststatistic |
t-test statistic |
p.value |
p value |
Sig0 |
significance value, i.e., test statistic divided by
critical value, which in turn is the |
ciLow, ciUp |
confidence interval for |
If rlv
is TRUE
,
stcoef |
standardized coefficient |
st.Low, st.Up |
confidence interval for |
Rle |
estimated relevance of |
Rls |
secured relevance, lower end of confidence interval
for the relevance of |
Rlp |
potential relevance, upper end of confidence interval ... |
Rls.symbol |
symbols for the secured relevance |
Rlvclass |
relevance class |
Werner A. Stahel
Werner A. Stahel (2020). New relevance and significance measures to replace p-values. PLOS ONE 16, e0252991, doi: 10.1371/journal.pone.0252991
link{twosamples}
,
link{termtable}, link{termeffects}
data(d.blast)
rr <-
lm(log10(tremor)~location+log10(distance)+log10(charge),
data=d.blast)
inference(rr)
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