hide
entry in the brokenstick object. This replaces the whatknots argument.hide field to brokenstick objecthide arguments to coef.brokenstick(), summary.brokenstick(), plot(),
get_knots() and get_omegacor and lower arguments to summary.brokenstick()what argument of get_omega() by corwhatknots argument in plot(), get_knots() and get_omega()summary() and print() functionalitysmocc_200 and fit_200 objectsroxygen 7.2.1hgt_z by a dynamic name (#8)what argument in get_knots() to whatknotswhatknots in get_omega()plot_trajectory() with shape and linetype optionsplot.brokenstick() on how to create a decent black and white figure of trajectoriesknots = 0:3 by knots = 0:2 in examplesbrokenstick() to 5. The former default produced a solution without internal knots. The new default produces a generally more informative starting model when the user does not specify knots (using knots = c(..., ...)) or the number of knots (using k = ...).strip_data argument in predict() by the a more intuitive include_data argument. By default, observed data are now included into the predictions, similar to predict.lm().Argument 'newdata' is required for a light brokenstick object. of brokenstick() into a warning and returns NULL.predict(fit_200_light, x = "knots") now produces warning message instead of crashingfit_200 and fit_200_light to use automatic boundary (2.68y) instead of 3 yrspredict()brokenstick-article.Rmd to manual/manual.Rmd, include high-res version on the site and take out of the package to save spacevignettes/bibliography.bib to title casemodel.frame.brokenstick() function that adheres to conventionsfitted() and residuals() to vectorsprint.brokenstick() helperlibrary() to character argumentlibrary(lme4) from code to evade changing the search pathformula list elementsigma2j vector from the light brokenstick classFunction brokenstick() in version 2.0.0 sets the Kasim-Raudenbush sampler as the default method. The former method lme4::lmer() remains available by setting method = "lmer" argument.
Version 2.0.0 adopts the variable names of the coda package (e.g., start, end, thin, niter, and so on) and stores the results of the Kasim-Raudenbush sampler as objects of class mcmc.
For method = "kr" one may now inspect the solution of the sampler by standard functions from the coda package. For method = "lmer" we can apply functions from the lme4 package for merMod objects.
Version 2.0.0 redefines the brokenstick class. New entries include call, formula, internal, sample, light, data, imp and mod. Removed entries are knots (renamed to internal) and draws (renamed to imp). We may omit the newdata argument for the training data. Setting light = TRUE creates a small version of the brokenstick object. Objects of class brokenstick are not backwards compatible, so one should regenerate objects of class brokenstick in order use newer features in 2.0.0.
Version 2.0.0 conforms to classic model fitting interface in R. Renames the new_data argument to newdata to conform to predict.lm(). Methods plot() and predict() no longer require a newdata argument. All special cases of predict() updated and explained in documentation and examples.
Version 2.0.0 adds methods coef(), fitted(), model.frame(), model.matrix(), print() and summary for the brokenstick object.
Simplifies algorithmic control. Renames control_brokenstick() to set_control() and removes a layer in the control list.
rgamma() calls in KR-algorithm for edge cases.predict_brokenstick() can now work with the both (internal) training and (external) test data. type argument from predict.brokenstick()get_omega() to extract the variance-covariance matrix of the broken stick estimates"dropfirst" to get_knots()test-brokenstick_edge.Rbrokenstick()warn_splines in make_basis() to suppress uninteresting warns from splines::bs()knotnames argument in make_basis()x in make_basis() is now a vector instead of a column vectorxname argument in make_basis() to set the xnamepredict()This version adds a couple of minor alterations.
description field\dontrun{} directivesparse_formula() to remove ::: from examples: and _ charactersoldfriends.Rmdpredict.brokenstick() examplehttps://github.com/growthcharts/brokenstick/plot.brokenstick() with the ability to plot imputed trajectoriesweightloss datadegree > 1what to plot.brokenstick()predict() when the group variable is a factorboundary parametermodel.matrix() removes rows with NA if degree = 0hardhat and recipesbrokenstick object smaller since no blueprints are storedrecipe interface to the brokenstick() functionggplot2 to suggestsinstall.on.demand() function from micerecipes::recipe() to inform R package installation process growthstandardsplot examples ggplot2 out-of-range/missing messages through better filteringdegree = 0This version jump illustrates big and breaking changes:
brokenstick adopted the tidymodels philosophy, and now includes
a dependency on hardhat. It is now possible to fit a model using
five different interfaces. There is no need anymore the hardcode variable
names in the source data.
This version introduces a new estimation method, the Kasim-Raudenbush
sampler. The new method is more flexible and faster than lme4::lmer()
when the number of knots is large.
This version introduces two simple correlation models that may be used to smooth out the variance-covariance matrix of the random effects.
The definition of the brokenstick class has changed. Objects of
class brokenstick do no longer store the training data.
The brokenstick_export class is retired.
The predict() function is fully rewritten as has now a new interface.
Since the brokenstick class does not store the training data anymore,
the predict() function now obtains a new_data argument. Syntax that
worked for brokenstick package before 0.70.0 does not work anymore
and should be updated. The shape argument replaces the output
argument.
The plot() function is rewritten, and now requires a new_data
specification.
Retired functions: brokenstick() replaces fit_brokenstick(),
predict.brokenstick() replaces predict.brokenstick_export(),
get_r2() replaces get_pev()
Removed functions: get_data(), get_X(), export()
ggplot objects sharper in vignettes by svglitepkg argument in plot.brokenstick()rbokehhbgd (which is no longer developed) by growthstandards packagesmocc_50/fit_50 by smocc_200/fit_200 NEWS.md file to track changes to the packagesmocc_50 and fit_50 demo datasmocc.hgtwgt, smocc_hgtwgt and fit_206 datasetsget_pev() for proportion explained varianceget_knots() gets a what argumentplot()plot()ggplot2ggplot2 plot defaultshow_references flag to FALSEHere is the abstract of the lecture:
Broken stick model for individual growth curves
Stef van Buuren
1) Netherlands Organization for Applied Scientific Research TNO 2) Utrecht University
The broken stick model describes a set of individual curves by a linear mixed model using second-order linear B-splines. The model can be used
The user specifies a set of break ages at which the straight lines connect. Each individual obtains an estimate at each break age, so the set of estimates of the individual form a smoothed version of the observed trajectory.
The main assumptions of the broken stick model are that the development between the break ages follows a straight line, and that the broken stick estimates follow a common multivariate normal distribution. In order to conform to the assumption of multivariate normality, the user may fit the broken stick model on suitably transformed data that yield the standard normal (Z-score) scale.
This lecture outlines the model and introduces the brokenstick R package.
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