llm | R Documentation |
Fit an L-shape linear model with the cut point estimated by the profile likelihood method.
## S3 method for class 'formula' llm(formula, data=list(...), epsilon = 0.025, ...)
formula |
an object of class " |
data |
an optional data frame, list or enviroment containing the variables in the model. |
epsilon |
Step width for the profile likelihood method, default is 0.025 |
... |
additional arguments to be passed to the low level regression fitting functions (see below). |
Define a L shape linear funcation that change slope at c0:
when x <c0, y = b1 + b2*x
when x>=c0, y = b1 + b2*x + b3*(x-x0) = (b1-b3*c0) + (b2+b3)*x
llm returns an object of class inheriting from "llm" which inherits from the class glm. See later in this section.
An object of class "llm" is a list containing at least the following components:
coefficients |
a named vector of coefficients from 'llm' |
residuals |
the residuals, that is response minus fitted values. |
fitted.values |
the fitted mean values. |
rank |
the numeric rank of the fitted linear model. |
df.residual |
the residual degrees of freedom. |
call |
the matched call. |
terms |
the 'terms' object used. |
c.max |
the maximum likelihood estimate for the threshold parameter(s). |
loglik |
the log-likelihood with the final values of the coefficients. |
Bingshu E. Chen (bingshu.chen@queensu.ca)
Liu, S. S. and Chen, B. E. (2020). Continuous threshold models with two-way interactions in survival analysis. Canadian Journal of Statistics. Vol. 48, page 751-772.
brm
,
lm
,
glm
#### simulate the data and fit a L-shape model. n = 50 ; p <- 2 X = matrix(rnorm(n * p), n, p) # no intercept! w = X[, 1]; age = X[, 2] wc = w - 0.2; sigma = 0.25 y = rnorm(n, -0.1+0.7*w-1.2*ifelse(wc>0, wc, 0), sigma) fit=llm(y~w+age) print(fit) print(summary(fit)) #### to plot the L-shape function # plot(fit)
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