View source: R/predict.segmented.r
predict.segmented | R Documentation |
Returns predictions and optionally associated quantities (standard errors or confidence intervals) from a fitted segmented model object.
## S3 method for class 'segmented'
predict(object, newdata, se.fit=FALSE, interval=c("none","confidence", "prediction"),
type = c("link", "response"), na.action=na.omit, level=0.95, .coef=NULL, ...)
object |
a fitted segmented 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. |
... |
further arguments. |
Basically predict.segmented
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.segmented
produces a vector of predictions with possibly associated standard errors or confidence intervals.
See predict.lm
or predict.glm
.
For segmented glm fits with offset, predict.segmented
returns the fitted values including the offset.
Vito Muggeo
segreg
, segmented
, plot.segmented
, broken.line
, 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)$se.fit
#wrong (smaller) st.errors (assuming known the breakpoint)
olm<-lm(y~x+pmax(x-segm$psi[,2],0))
predict(olm,se.fit = TRUE)$se.fit
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