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* *
* Changes in segmented *
* *
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version 0.5-0.0
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* segmented.default() introduced. Now it is possible to estimate segmented relationships in arbitrary regression models (besides lm and glm) where specific methods do not exist (e.g. cox or quantile regression models).
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version 0.4-0.1 (not on CRAN)
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* segmented.lm() and segmented.glm() did not work if the starting model included additional "variables", such as 'threshold' in 'subset=age0.
* The breakpoint starting values when automatic selection is performed are now specified as equally spaced values (optionally as quantiles). see argument 'quant' in seg.control()
* added 'Authors@R' entry in the DESCRIPTION file
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version 0.2-9.1
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* Some bugs fixed: segmented.lm() and segmented.glm() did not finish correctly when no breakpoint was found; now segmented.lm() and segmented.glm() take care of flat relationships; plot.segmented() did not compute correctly the partial residuals for segmented glm fits.
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version 0.2-9.0
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* Bootstrap restarting implemented to deal with problems coming from flat segmented relationships. segmented now is less sensitive to starting values
supplied for 'psi'.
* At the convergence segmented now constrains the gap coefficients to be exactly zero. This is the default and it can be altered by the 'gap' argument
in seg.control().
* plot.segmented() has been re-written. It gains argument `res' for plotting partial residuals along with the fitted piecewise lines, and now it produces nicer (and typically smaller) plots.
* Some bugs fixed: davies.test() did not work correctly for deterministic data (thanks to Glenn Roberts for finding the error). davies.test() also
returns the `process', i.e. the different values of the evaluation points and corresponding test statistic.
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version 0.2-8.4
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* Some bugs fixed: segmented.glm() fitted a simple "lm" (and not "glm") (the error was introduced incidentally from 0.2-8.3, thanks to Véronique Storme for finding the error); broken.line() was not working for models without intercept and a null left slope; intercept() was not working correctly with multiple segmented variables.
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version 0.2-8.3
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* Some minor bugs fixed: segmented.lm() and segmented.glm() did not find the offset variable in the dataframe where the initial (g)lm was called for;
segmented.lm() and segmented.glm() sometimes returned an error when the automated algorithm was used (thanks to Paul Cohen for finding the error).
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version 0.2-8.2
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* Some minor bugs fixed (segmented.lm() and segmented.glm() *alway* included the left slope in the estimation process, although the number of
parameters was correct in the returned final fit. confint.segmented() did not order the estimated breakpoints for the variable having
rev.sgn=TRUE; intercept() missed the (currently meaningless) argument var.diff (thanks to Eric Fuchs for pointing out that). )
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version 0.2-8.1
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* Some minor bugs fixed (segmented.lm() and segmented.glm() were not working correctly with dataframe subset or when the starting
linear model included several intercepts (e.g., see the example about data("plant"); thanks to Nicola Ferrari for finding the error).
davies.test() did not work when the variable name of its argument `seg.Z' included reserved words, e.g. `seg.Z~dist'; thanks to Thom
White for finding the error).
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version 0.2-8
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* intercept() added. It computes the intercepts of the regression lines for each segment of the fitted segmented relationship.
* plot.segmented() now accepts a vector `col' argument to draw the fitted piecewise linear relationships with different colors.
* Some minor bugs fixed (summary.segmented were not working correctly).
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version 0.2-7.3
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* argument APC added to the slope() function to compute the `annual percent change'.
* Some minor bugs fixed (confint and slope were not working correctly when the estimated breakpoints were returned
in non-increasing order; offset was ignored in segmented.lm and segmented.glm; broken.line() was not working correctly
(and its argument gap was unimplemented), thanks to M. Rennie for pointing out that;
summary.segmented() was not working for models with no linear term, i.e. fitted via segmented(lm(y~0),..)).
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version 0.2-7.2
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* segmented.lm and segmented.glm now accept objects with formulas y~., Thanks to G. Ferrara for finding the error.
* Some bugs fixed (slope and confint were using the normal (rather than the t-distribution) to compute the CIs
in gaussian models).
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version 0.2-7.1
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* segmented.lm and segmented.glm now accept objects without 'explicit' formulas, namely returned by lm(my_fo,..) (and glm(my_fo,..)) where my_fo was defined earlier. Thanks to Y. Iwasaki for finding the error.
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version 0.2-7
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* A sort of automatic procedure for breakpoint estimation is implemented. See argument
stop.if.error in seg.control().
* davies.test() now accepts a one-sided formula (~x) rather than character ("x") to mean the segmented variable to be tested. davies.test also gains the arguments `beta0' and `dispersion'.
* Some bugs fixed.
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version 0.2-6
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* vcov.segmented() added.
* option var.diff for robust covariance matrix has been added in summary.segmented(), print.summary.segmented(), slope(), and confint().
* Some bugs fixed.