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* *
* Changes in segmented *
* *
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version 0.5-2.1
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* pscore.test() now works also for "glm" fits
* plot.segmented() now plots the partial residuals as "component + working residuals" (rather than Pearson residuals, relevant only for glm fits).
* segmented.default() now works for fits obtained by MASS::rlm().
===============
version 0.5-2.0
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* pscore.test() introduced. The function tests for a breakpoint using a (pseudo) score statistic which is more powerful than davies.test(), especially when the breakpoint is expected to be in the middle of the covariate range and the signal-to-noise ratio is high.
* argument 'digits' added in seg.control() to fix the number of digits of the breakpoint estimate during the iterative estimation algorithm.
* bug fixed: conf.level>0 in plot.segmented() did not work for objects returned by segmented.default().
===============
version 0.5-1.5 (not on CRAN)
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* arguments 'gap' and 'show.gap' removed in intercept() and in plot.segmented(). (they are meaningless, as segmented() always returns joined piecewise lines, i.e. with no gaps).
* slope() and broken.line() (and then plot.segmented() which uses them) did not work for objects returned by segmented.default() (Thanks to Marcos Krull for reporting).
===============
version 0.5-1.4
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* segmented.Arima() should be slightly faster, as starting values are passed in arima() (via 'init') throughout the iterative process.
* plot.segmented() is expected to work for objects returned by segmented.Arima.
* print.summary.segmented() does not print anymore the t-values for the gap coefficients (this information is meaningless as the gap coeffs are always set to zero in the returned model).
* Bug fixed: intercept() ignored argument 'rev.sgn'; points.segmented() missed argument 'transf'.
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version 0.5-1.3 (not on CRAN)
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* plot.segmented() gains argument 'transf' to plot 'transf(values)' rather 'values' on the current plot.
* print.summary.segmented() now uses round() rather than signif() when displaying the breakpoint estimate.
* Bug fixed: psi=NA was not working in the segmented.* methods; this bug was incidentally introduced in the last version (thanks to Bertrand Sudre for first reporting that).
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version 0.5-1.2
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* For 1 breakpoint models, 'psi' argument can be missing (default) when calling the segmented methods.
* Bug fixed: lines.segmented() did not plot the dots when the fit object included multiple breakpoints and the argument 'shift' was set to FALSE (thanks to Jan Bull for reporting). There were some troubles with variable names including dots (thanks to Melanie Zoelck which first reported this bug).
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version 0.5-1.1
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* segmented.default now accepts 'gee' fits (Thanks to John Boulanger for his input)
* Minor change: argument 'col.dens' changed to 'dens.col' in plot.segmented() ('col.dens' made ineffective 'col')
* Minor change: error/warning messages introduced in davies.test() if k<10; print.segmented slightly changed in displaying the estimated breakpoints.
* Bug fixed: segmented did not terminate appropriately the algorithm with automatic selection of breakpoints concerning more than one variable (thanks to Ali Hashemi for reporting).
===============
version 0.5-1.0
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* segmented.Arima() introduced. Now it is possible to estimate segmented relationships in "Arima" fits (although the summarizing and plotting methods do not work..)
* plot.segmented() gains arguments 'dens.rug' and 'col.dens' to display in the plot (on the x axis) also the smoothed density of the segmented covariate.
* Bug fixed: segmented.lm did not work if it.max=0 (but segmented.glm did), thanks to Eric Nussbaumer for reporting. segmented.lm and segmented.glm did work if the starting linear model included weights (this bug was introduced incidentally since version 0.4-0.1; thanks to Michael Rutter for reporting). segmented.lm and segmented.glm did not check appropriately inadmissible breakpoints (thanks to Erica Tennenhouse for reporting).
segmented.lm and segmented.glm did not handle correctly variable names equal to function names. davies.test() did not work with 'segmented' objects (to test for and additional breakpoint). points.segmented() missed the argument 'rev.sgn'.
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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).
===============
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
===============
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.
===============
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.
===============
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).
===============
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). )
===============
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).
===============
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).
===============
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),..)).
===============
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).
===============
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.
===============
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.
===============
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.