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_omega
cor
and lower
arguments to summary.brokenstick()
what
argument of get_omega()
by cor
whatknots
argument in plot()
, get_knots()
and get_omega()
summary()
and print()
functionalitysmocc_200
and fit_200
objectsroxygen 7.2.1
hgt_z
by a dynamic name (#8)what
argument in get_knots()
to whatknots
whatknots
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.R
brokenstick()
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.Rmd
predict.brokenstick()
examplehttps://github.com/growthcharts/brokenstick/
plot.brokenstick()
with the ability to plot imputed trajectoriesweightloss
datadegree > 1
what
to plot.brokenstick()
predict()
when the group variable is a factorboundary
parametermodel.matrix()
removes rows with NA
if degree = 0
hardhat
and recipes
brokenstick
object smaller since no blueprints are storedrecipe
interface to the brokenstick()
functionggplot2
to suggests
install.on.demand()
function from mice
recipes::recipe()
to inform R package installation processgrowthstandards
plot
examplesggplot2
out-of-range/missing messages through better filteringdegree = 0
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 svglite
pkg
argument in plot.brokenstick()
rbokeh
hbgd
(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 datasets
get_pev()
for proportion explained varianceget_knots()
gets a what
argumentplot()
plot()
ggplot2
ggplot2
plot defaultshow_references
flag to FALSEHere is the abstract of the lecture:
Broken stick model for individual growth curves
Stef van Buuren
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.