Description Usage Arguments Details Value Note References See Also Examples
Exact significance tests for a changepoint in linear or multiple linear regression. Confidence intervals and confidence regions with exact coverage probabilities for the changepoint.
1 2 
formula 
a formula expression as for regression models, of
the form 
type 
"LL", "LT" or "TL" which stand for lineline, linethreshold or thresholdline, defined below. 
data 
an optional dataframe that assigns values in

subset 
expression saying which subset of the data to use. 
weights 
vector or matrix. 
inverse 
if TRUE then 'weights' specifies the inverse of the weights vector or matrix, as for a covariance matrix. 
var.known 
is the variance known? 
na.action 
a function to filter missing data. 
contrasts 
an optional list; see 'contrasts.arg' in

offset 
a constant vector to be subtracted from the responses vector. 
... 
other arguments to 
A brokenline model consists of two straight lines joined at a changepoint. Three versions are
1 2 3 4 5 
where e ~ Normal( 0, var * inv(weights) ). The LT and TL versions omit 'alpha' if the formula is without intercept, such as 'y~x+0'. Parameters 'theta', 'alpha', 'B', 'Bp', 'var' are unknown, but 'weights' is known.
The same models apply for a multipleregression formula such as 'y ~ x1 + x2 + ... + xn' where 'alpha' becomes the coefficient of the "1"vector and 'theta' the changepoint for the coefficient of the first predictor term, 'x1'.
The test for the presence of a changepoint is by a postulate value outside the range of 'x'values. Thus, in the LL model 'sl( min(x1)  1 )' would give the exact significance level of the null hypothesis "single line" versus the alternate hypothesis "broken line."
Exact inferences about the changepoint 'theta' or '(theta,alpha)' are based on the distribution of its likelihoodratio statistic, conditional on sufficient statistics for the other parameters. This method is called conditional likelihoodratio (CLR) for short.
'lm.br' returns a list that includes a C++ object with accessor
functions. Functions sl
, ci
and cr
get significance levels, confidence intervals,
and confidence regions for the changepoint's xcoordinate or
(x,y)coordinates. Other functions are mle
to get maximum likelihood estimates and sety
to set new yvalues.
The returned object also lists 'coefficients', 'fitted.values' and 'residuals', the same as for an 'lm' output list.
Data can include more than one 'y' value for a repeat 'x' value. If variance is known, then 'var' = 1 and 'weights' is the inverse of the variances vector or variancecovariance matrix.
Knowles, M., Siegmund, D. and Zhang, H.P. (1991) Confidence regions in semilinear regression, _Biometrika_, *78*, 1531.
Siegmund, D. and Zhang, H.P. (1994), Confidence regions in broken line regression, in "Changepoint Problems", _IMS Lecture Notes – Monograph Series_, *23*, eds. E. Carlstein, H. Muller and D. Siegmund, Hayward, CA: Institute of Mathematical Statistics, 292316.
vignette( "lm.br" )
demo( testscript )
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48  # Smith & Cook (1980), "Straight Lines with a Changepoint: A Bayesian
# Analysis of some Renal Transplant Data", Appl Stat, *29*, 180189,
# reciprocal of blood creatinine L/micromol vs day after transplant.
creatinine < c(37.3, 47.1, 51.5, 67.6, 75.9, 73.3, 69.4, 61.5, 31.8, 19.4)
day < 1:10
sc < lm.br( creatinine ~ day )
sc $ mle()
sc $ ci()
sc $ sl( day[1]  1.5 ) # test for the presence of a changepoint
plot( sc$residuals )
# A 'TL' example, data from figure 1 in Chiu et al. (2006), "Bentcable
# regression theory and applications", J Am Stat Assoc, *101*, 542553,
# log(salmon abundance) vs year.
salmon < c( 2.50, 2.93, 2.94, 2.83, 2.43, 2.84, 3.06, 2.97, 2.94, 2.65,
2.92, 2.71, 2.93, 2.60, 2.12, 2.08, 1.81, 2.45, 1.71, 0.55, 1.30 )
year < 1980 : 2000
chiu < lm.br( salmon ~ year, 'tl' )
chiu $ ci()
# A multiple regression example, using an R dataset,
# automobile milespergallon versus weight and horsepower.
lm.br( mpg ~ wt + hp, data = mtcars )
# An example with variance known, for the Normal approximations of binomial
# random variables using formula 2.28 of Cox and Snell (1989).
# Ex. 3.4 of Freeman (2010) "Inference for binomial changepoint data" in
# _Advances in Data Analysis_, ed. C Skiadas, Boston: Birkhauser, 345352.
trials < c( 15, 82, 82, 77, 38, 81, 12, 97, 33, 75,
85, 37, 44, 96, 76, 26, 91, 47, 41, 35 )
successes < c( 8, 44, 47, 39, 24, 38, 3, 51, 16, 43,
47, 27, 33, 64, 41, 18, 61, 32, 33, 24 )
log_odds < log( (successes  0.5)/(trials  successes  0.5) )
variances < (trials1)/( successes*(trialssuccesses) )
group < 1 : 20
lm.br( log_odds ~ group, 'TL', w= variances, inv= TRUE, var.known= TRUE )
# An example that shows different confidence regions from inference by
# conditional likelihoodratio (CLR) versus approximateF (AF).
y < c( 1.6, 3.2, 6.3, 4.8, 4.3, 4.0, 3.5, 1.8 )
x < 1:8
eg < lm.br( y ~ x )
eg$cr( output='t' )
eg$cr( method = 'aF', output='t' )

Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.