| Version: | 1.1 |
| Date: | 2024-06-02 |
| Title: | Jackknife Euclidean / Empirical Likelihood Inference for Spearman's Rho |
| Description: | Functions for conducting jackknife Euclidean / empirical likelihood inference for Spearman's rho (de Carvalho and Marques (2012) <doi:10.1080/10920277.2012.10597644>). |
| Author: | Miguel de Carvalho [aut, cre] |
| Depends: | R (≥ 3.0.1) |
| Maintainer: | Miguel de Carvalho <miguel.decarvalho@ed.ac.uk> |
| License: | GPL (≥ 3) |
| Repository: | CRAN |
| Imports: | emplik, MASS |
| NeedsCompilation: | no |
| Packaged: | 2024-06-02 15:07:41 UTC; muad'dib |
| Date/Publication: | 2024-06-02 15:20:02 UTC |
Danish Fire Insurance Claims Database
Description
Danish Fire Insurance Claims Database includes 2167 industrial fire losses gathered from the Copenhagen Reinsurance Company over the period 1980–1990.
Usage
data(fire)Format
A dataframe with 2167 observations on five variables. The object
is of class data.frame.
Examples
data(fire)
attach(fire)
plot(building, contents, pch = 20, xlim = c(0,95), ylim = c(0,133),
xlab = "Loss of Building", ylab = "Loss of Contents",
main = "Danish Fire Insurance Claims")
Jackknife Euclidean / Empirical Likelihood Inference for Spearman's Correlation
Description
Computes jackknife Euclidean / empirical likelihood confidence intervals for Spearman's correlation.
Usage
spearmanCI(x, y, level = 0.95, method = "Euclidean", plot = FALSE)
Arguments
x |
vector with data. |
y |
vector with data. |
level |
the confidence level required. |
method |
this must be one of the strings |
plot |
logical; if |
Author(s)
Miguel de Carvalho
References
de Carvalho, M. and Marques, F. J. (2012). Jackknife Euclidean likelihood-based inference for Spearman's rho. North American Actuarial Journal, 16, 487–492.
Wang, R., and Peng, L. (2011). Jackknife empirical likelihood intervals for Spearman’s rho. North American Actuarial Journal, 15, 475–486.
Examples
## Real data example
data(fire)
attach(fire)
spearmanCI(building, contents)
## The intervals in de Carvalho and Marques (2012, Section 3.2)
## differ slightly as they are based on the estimate
## spearman <- function(x, y) {
## n <- length(x)
## F <- ecdf(x); G <- ecdf(y)
## return(12 / n * sum((F(x) - 1 / 2) * (G(y) - 1 / 2)))
## }
## Simulated data example
library(MASS)
pearson <- .7
Sigma <- matrix(c(1, pearson, pearson, 1), 2, 2)
xy <- mvrnorm(n = 1000, rep(0, 2), Sigma)
spearmanCI(xy[, 1], xy[, 2])
abline(v = 6 / pi * asin(pearson / 2), col = "grey", lty = 3)