Given a segmented model (typically returned by a segmented method), broken.line
  computes the fitted values (and relevant standard errors) for the specified `segmented' relationship.
broken.line(ogg, term = NULL, link = TRUE, interc=TRUE, se.fit=TRUE, isV=FALSE, 
  .vcov=NULL, .coef=NULL, ...)A list having one component if (if se.fit=FALSE), and two components (if se.fit=TRUE) list representing predictions and standard errors for the segmented covariate values.
A fitted object of class segmented (returned by any segmented method).
Three options. i) A named list (whose name should be one of the segmented covariates in the model ogg) 
    including the covariate values for which segmented predictions should be computed; ii) a character meaning 
    the name of any segmented covariate in the model (and predictions corresponding to the observed covariate values are returned); 
    iii) It can be NULL if the model includes a single segmented covariate (and predictions corresponding to the observed covariate values are returned).
Should the predictions be computed on the scale of the link function if ogg is a segmented glm fit? Default to TRUE.
Should the model intercept be added? (provided it exists).
If TRUE also standard errors for predictions are returned.
A couple of logicals indicating if the segmented terms \((x-\psi)_+\) and \(I(x>\psi)\) in the model matrix should be replaced by their smoothed counterparts when computing the standard errors. If a single logical is provided, it is applied to both terms.
Optional. The full covariance matrix of estimates. If NULL (and se.fit=TRUE), the matrix is computed internally via vcov.segmented().
The regression parameter estimates. If unspecified (i.e. NULL), it is computed internally by coef(ogg).
Additional arguments to be passed on to vcov.segmented() when computing the standard errors for the predictions, namely
    is, var.diff, p.df. See summary.segmented and vcov.segmented.
Vito M. R. Muggeo
If term=NULL or term is a valid segmented covariate name, 
  predictions for that segmented variable are the relevant fitted values from the model. If term 
  is a (correctly named) list with numerical values, predictions corresponding to such specified values 
  are computed. If link=FALSE and ogg inherits from the class "glm", predictions and possible standard 
  errors are returned on the response scale. The standard errors come from the Delta method.
  Argument link is ignored whether ogg does not inherit from the class "glm".
segmented, predict.segmented, plot.segmented, vcov.segmented
set.seed(1234)
z<-runif(100)
y<-rpois(100,exp(2+1.8*pmax(z-.6,0)))
o<-glm(y~z,family=poisson)
o.seg<-segmented(o,seg.Z=~z)
if (FALSE) plot(z,y)
if (FALSE) points(z,broken.line(o.seg,link=FALSE)$fit,col=2) #ok, but use plot.segmented()!
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