Description Usage Arguments Details Value Note Examples
Significance level of a postulate value for the changepoint's x-coordinate or (x,y)-coordinates.
1 2 3 |
theta0 |
postulate value for theta, the changepoint's x-coordinate |
alpha0 |
postulate value for alpha, the changepoint's y-coordinate |
method |
"clr", "mc" or "af" which stand for conditional likelihood-ratio, conditional likelihood-ratio by Monte Carlo, or approximate-F, details below |
accuracy |
maximum absolute error in numerical integration by the "clr" method, or in Monte Carlo evaluation by the "mc" method, has no effect for the "af" method |
output |
TRUE or FALSE |
The default evaluation method "clr" evaluates the conditional likelihood-ratio significance level using a geometric-expectation formula derived by Siegmund-Zhang, which slightly over-estimates.
Method "mc" evaluates the conditonal likelihood-ratio significance level by direct Monte Carlo simulation, which avoids the over-estimate of the "clr" method.
Method "af" evaluates significance level by estimating the distribution of the likelihood-ratio statistic from the related F-distribution (or chi-squared distribution if variance is known) that would be exact if the model were linear. This method is not exact. It is common in nonlinear regression.
Returns the numerical significance level if 'output' is set explicitly to TRUE or FALSE, otherwise only prints the result.
The 'accuracy' argument does not include the slight over-estimate that is inherent in the "clr" method, nor the approximation inherent in the Approximate-F method.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 | ## Data for Patient B from Smith and Cook (1980):
y = c(37.3, 47.1, 51.5, 67.6, 75.9, 73.3, 69.4, 61.5, 31.8, 19.4)
x = 1:10
sc <- blmr(y,x)
sc$sl(6.1)
sc$sl(6.1,"af")
sc$sl(6.1,"mc",0.00001)
tmp <- sc$sl(6.1,"clr",0.001,FALSE)
tmp
sc$sl(6.1,88.2)
sc$sl(6.1,88.2,"clr",0.00001)
sc$sl(6.1,88.2,"af")
sc$sl(6.1,88.2,"mc",0.00001)
tmp2 <- sc$sl(6.1,88.2,"af",.001,TRUE)
tmp2
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