confint.stepmented | R Documentation |
Computes confidence intervals for the changepoints (or jumpoints) in a fitted ‘stepmented’ model.
## S3 method for class 'stepmented'
confint(object, parm, level=0.95, method=c("delta", "score", "gradient"),
round=TRUE, cheb=FALSE, digits=max(4, getOption("digits") - 1),
.coef=NULL, .vcov=NULL, ...)
object |
a fitted |
parm |
the stepmented variable of interest. If missing the first stepmented variable in |
level |
the confidence level required, default to 0.95. |
method |
which confidence interval should be computed. One of |
round |
logical. Should the values (estimates and lower/upper limits) rounded to the smallest observed value? |
cheb |
logical. If |
digits |
controls the number of digits to print when returning the output. |
.coef |
The regression parameter estimates. If unspecified (i.e. |
.vcov |
The full covariance matrix of estimates. If unspecified (i.e. |
... |
additional arguments passed to |
confint.stepmented
computes confidence limits for the changepoints. Currently the only option is 'delta'
, i.e. to compute the approximate covariance matrix via a smoothing approximation (see vcov.stepmented
) and to build the limits using the standard Normal quantiles. Note that, the limits are rounded to the lowest observed value, thus the resulting confidence interval might not be symmetric if the stepmented covariate has not equispaced values.
A matrix including point estimate and confidence limits of the breakpoint(s) for the
stepmented variable possibly specified in parm
.
Currently only method='delta' is allowed.
Vito M.R. Muggeo
stepmented
and lines.segmented
to plot the estimated breakpoints with corresponding
confidence intervals.
set.seed(10)
x<-1:100
z<-runif(100)
y<-2+2.5*(x>45)-1.5*(x>70)+z+rnorm(100)
o<-stepmented(y, npsi=2)
confint(o) #round=TRUE is default
confint(o, round=FALSE)
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