summary.segmented | R Documentation |
summary method for class segmented
.
## S3 method for class 'segmented'
summary(object, short = FALSE, var.diff = FALSE, p.df="p", .vcov=NULL, ...)
## S3 method for class 'summary.segmented'
print(x, short=x$short, var.diff=x$var.diff,
digits = max(3, getOption("digits") - 3),
signif.stars = getOption("show.signif.stars"),...)
object |
Object of class "segmented". |
short |
logical indicating if the ‘short’ summary should be printed. |
var.diff |
logical indicating if different error variances should be computed
in each interval of the segmented variable, see Details. If |
p.df |
A character as a function of |
.vcov |
Optional. The full covariance matrix for the parameter estimates. If provided, standard errors are computed (and displayed) according to this matrix. |
x |
a |
digits |
controls number of digits printed in output. |
signif.stars |
logical, should stars be printed on summary tables of coefficients? |
... |
further arguments. |
If short=TRUE
only coefficients of the segmented relationships are printed.
If var.diff=TRUE
and there is only one segmented variable, different error variances are
computed in the intervals defined by the estimated breakpoints of the segmented variable.
For the jth interval with n_j
observations, the error variance is estimated via RSS_j/(n_j-p)
,
where RSS_j
is the residual sum of squares in interval j, and p
is the number of model parameters. This number to be subtracted from n_j
can be changed via argument p.df
. For instance p.df="0"
uses RSS_j/(n_j)
, and p.df="p/K"
leads to RSS_j/(n_j-p/K)
, where K
is the number of groups (segments), and p/K
can be interpreted as the average number of model parameter in that group.
Note var.diff=TRUE
only affects the estimates covariance matrix. It does not affect the parameter estimates, neither the log likelihood and relevant measures, such as AIC or BIC. In other words, var.diff=TRUE
just provides 'alternative' standard errors, probably appropriate when the error variances are different before/after the estimated breakpoints. Also p-values
are computed using the t-distribution with 'naive' degrees of freedom (as reported in object$df.residual
).
If var.diff=TRUE
the variance-covariance matrix of the estimates is computed via the
sandwich formula,
(X^TX)^{-1}X^TVX(X^TX)^{-1}
where V is the diagonal matrix including the different group-specific error variance estimates. Standard errors are the square root of the main diagonal of this matrix.
A list (similar to one returned by segmented.lm
or segmented.glm
) with additional components:
psi |
estimated break-points and relevant (approximate) standard errors |
Ttable |
estimates and standard errors of the model parameters. This is similar
to the matrix |
gap |
estimated coefficients, standard errors and t-values for the ‘gap’ variables |
cov.var.diff |
if |
sigma.new |
if |
df.new |
if |
Vito M.R. Muggeo
print.segmented
, davies.test
##continues example from segmented()
# summary(segmented.model,short=TRUE)
## an heteroscedastic example..
# set.seed(123)
# n<-100
# x<-1:n/n
# y<- -x+1.5*pmax(x-.5,0)+rnorm(n,0,1)*ifelse(x<=.5,.4,.1)
# o<-lm(y~x)
# oseg<-segmented(o,seg.Z=~x,psi=.6)
# summary(oseg,var.diff=TRUE)$sigma.new
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