View source: R/predict.stepmented.R
predict.stepmented | R Documentation |
Returns predictions and optionally associated quantities (standard errors or confidence intervals) from a fitted stepmented model object.
## S3 method for class 'stepmented'
predict(object, newdata, se.fit=FALSE, interval=c("none","confidence", "prediction"),
type = c("link", "response"), na.action=na.omit, level=0.95, .coef=NULL,
.vcov=NULL, apprx.fit=c("none","cdf"), apprx.se=c("cdf","none"), ...)
object |
a fitted stepmented model coming from |
newdata |
An optional data frame in which to look for variables with which to predict. If omitted, the fitted values are used. |
se.fit |
Logical. Should the standard errors be returned? |
interval |
Which interval? See |
type |
Predictions on the link or response scale? Only if |
na.action |
How to deal with missing data, if |
level |
The confidence level. |
.coef |
The regression parameter estimates. If unspecified (i.e. |
.vcov |
The estimate covariance matrix. If unspecified (i.e. |
apprx.fit |
The approximation of the |
apprx.se |
The same abovementioned approximation to compute the standard error. |
... |
further arguments, for instance |
Basically predict.stepmented
builds the right design matrix accounting for breakpoint and passes it
to predict.lm
or predict.glm
depending on the actual model fit object
.
predict.stepmented
produces a vector of predictions with possibly associated standard errors or confidence intervals.
See predict.lm
, predict.glm
, or predict.segmented
.
For stepmented glm fits with offset obtained starting from the model glm(.., offset=..)
, predict.stepmented
returns the fitted values without considering the offset.
Vito Muggeo
stepmented
, plot.stepmented
, predict.lm
, predict.glm
n=10
x=seq(-3,3,l=n)
set.seed(1515)
y <- (x<0)*x/2 + 1 + rnorm(x,sd=0.15)
segm <- segmented(lm(y ~ x), ~ x, psi=0.5)
predict(segm,se.fit = TRUE)$se.fit
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