Computes confidence intervals for the changepoints (or jumpoints) in a fitted `stepmented' model.
# S3 method for 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, ...)A matrix including point estimate and confidence limits of the breakpoint(s) for the
stepmented variable possibly specified in parm.
a fitted stepmented object.
the stepmented variable of interest. If missing the first stepmented variable in object is considered.
the confidence level required, default to 0.95.
which confidence interval should be computed. One of "delta", "score", or "gradient". Can be abbreviated. Currently only "delta" allowed.
logical. Should the values (estimates and lower/upper limits) rounded to the smallest observed value?
logical. If TRUE, the confidence limits are computed using the Chebyshev inequality which yields conservative confidence intervals but it is 'robust' to the non-normality of the changepoint sampling distribution.
controls the number of digits to print when returning the output.
The regression parameter estimates. If unspecified (i.e. NULL), it is computed internally by coef(object).
The full covariance matrix of estimates. If unspecified (i.e. NULL), the covariance matrix is computed internally by vcov(object).
additional arguments passed to vcov.stepmented, namely k.
Vito M.R. Muggeo
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.
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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