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, predict.stepmented returns the fitted values including the offset.
Vito Muggeo
stepreg, 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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