#' Data Driven Smooth Test for Extreme Value Distribution
#'
#' Performs data driven smooth test for composite hypothesis of extreme value distribution.
#' Null density is given by
#' \eqn{
#' f(z;\gamma)=1/\gamma_2 \exp((z-\gamma_1)/\gamma_2- \exp((z-\gamma_1)/\gamma_2))}, \eqn{z \in R}.
#'
#' We model alternatives similarly as in Kallenberg and Ledwina (1997) and Janic-Wroblewska (2004) using Legendre's polynomials or cosines.
#' For more details see: \url{http://www.biecek.pl/R/ddst/description.pdf}.
#'
#' @aliases ddst.extr.Nk
#'
#' @param x a (non-empty) numeric vector of data values
#' @param base a function which returns an orthonormal system, possible choice: \code{ddst.base.legendre} for the Legendre polynomials and \code{ddst.base.cos} for the cosine system
#' @param d.n an integer specifying the maximum dimension considered, only for advanced users
#' @param c a calibrating parameter in the penalty in the model selection rule
#' @param nr an integer specifying the number of runs for a p-value and a critical value computation if any
#' @param compute.p a logical value indicating whether to compute a p-value or not
#' @param alpha a significance level
#' @param compute.cv a logical value indicating whether to compute a critical value corresponding to the significance level alpha or not
#' @param ... further arguments
#'
#' @return
#' An object of class \code{htest}
#' \item{statistic }{the value of the test statistic.}
#' \item{parameter }{the number of choosen coordinates (k).}
#' \item{method }{a character string indicating the parameters of performed test. }
#' \item{data.name }{a character string giving the name(s) of the data. }
#' \item{p.value }{the p-value for the test, computed only if \code{compute.p=TRUE}.}
#'
#' @importFrom evd pgumbel
#' @importFrom orthopolynom slegendre.polynomials
#' @importFrom polynom polynomial
#' @references
#' Hosking, J.R.M., Wallis, J.R., Wood, E.F. (1985). Estimation of the generalized extreme-value distribution by the method of probability-weighted moments. \eqn{ Technometrics} 27, 251--261.
#'
#' Janic-Wroblewska, A. (2004). Data-driven smooth test for extreme value distribution. \eqn{ Statistics} 38, 413--426.
#'
#' Janic, A. and Ledwina, T. (2008). Data-driven tests for a location-scale family revisited. \eqn{ J. Statist. Theory. Pract. Special issue on Modern Goodness of Fit Methods. accepted.}.
#'
#' Kallenberg, W.C.M., Ledwina, T. (1997). Data driven smooth tests for composite hypotheses: Comparison of powers. \eqn{ J. Statist. Comput. Simul.} \bold{ 59}, 101--121.
#'
#' @export
#'
#' @examples
#' library(evd)
#' set.seed(7)
#'
#' # for given vector of 19 numbers
#' z <- c(13.41, 6.04, 1.26, 3.67, -4.54, 2.92, 0.44, 12.93, 6.77, 10.09,
#' 4.10, 4.04, -1.97, 2.17, -5.38, -7.30, 4.75, 5.63, 8.84)
#' \dontrun{
#' t <- ddst.evd.test(z, compute.p = TRUE, d.n = 10)
#' t
#' plot(t)
#'
#' # H0 is true
#' x <- -qgumbel(runif(100),-1,1)
#' t <- ddst.evd.test (x, compute.p = TRUE, d.n = 10)
#' t
#' plot(t)
#'
#' # H0 is false
#' x <- rexp(80,4)
#' t <- ddst.evd.test (x, compute.p = TRUE, d.n = 10)
#' t
#' plot(t)
#' }
#' @keywords htest
`ddst.evd.test` <-
function(x,
base = ddst.base.legendre,
d.n = 10,
c = 100,
nr = 100000,
compute.p = TRUE,
alpha = 0.05,
compute.cv = TRUE,
...) {
# method.name = as.character(substitute(base))
base = ddst.base.legendre
method.name = "ddst.base.legendre"
n = length(x)
if (n < 5)
stop("length(x) should be at least 5")
sx = sort(x)
er2 = sum(sx * (1:n - n:1)) / (n * (n - 1) * log(2))
er1 = mean(x) + 0.5772156649 * er2
maxN = max(min(d.n, length(x) - 2, 20), 1)
u = NULL
for (j in 1:maxN)
u[j] = ddst.phi(1 - pgumbel(-x, -er1, er2), j, base)
coord = NULL
gg1 = mean(1 - exp((x - er1) / er2))
gg2 = mean((1 - exp((x - er1) / er2)) * (x - er1) / er2 + 1)
for (k in 1:d.n) {
korekta = u[1:k] + t(MMextr12[[k]]) %*% sM22 %*% c(gg1, gg2)
coord[k] = t(korekta) %*% MMextr[[k]] %*% korekta * n
}
l = ddst.IIC(coord, n, c)
attr(l, "names") = "T*"
t = coord[l]
attr(t, "names") = "W*T*"
result = list(statistic = t,
parameter = l,
coordinates = coord - c(0, coord[-d.n]),
method = "Data Driven Smooth Test for Extreme Values")
result$data.name = paste(paste(as.character(substitute(x)), collapse = ""),
", base: ",
method.name,
", c: ",
c,
", d.n: ",
d.n,
sep = "")
class(result) = c("htest", "ddst.test")
if (compute.p | compute.cv) {
tmp = numeric(nr)
for (i in 1:nr) {
y = rnorm(n)
tmpC = ddst.extr.Nk(y, base, Dmax = d.n, n = length(y))
l = ddst.IIC(tmpC, n, c)
tmp[i] = tmpC[l]
}
p.val = mean(tmp > t)
if (compute.p) {
result$p.value = mean(tmp > t)
}
if (compute.cv) {
result$cv = quantile(tmp, alpha)
}
}
result
}
`MMextr12` <-
list(
structure(c(0.866025403784439,-0.234141092407759), .Dim = c(2L,
1L)),
structure(
c(
0.866025403784439,
-0.234141092407759,
0.372677996249965,
1.10810510880576
),
.Dim = c(2L, 2L)
),
structure(
c(
0.866025403784439,-0.234141092407759,
0.372677996249965,
1.10810510880576,
0.220479275922049,
0.215016389907248
),
.Dim = 2:3
),
structure(
c(
0.866025403784439,-0.234141092407759,
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1.10810510880576,
0.220479275922049,
0.215016389907248,
0.15,
0.489380181546808
),
.Dim = c(2L, 4L)
),
structure(
c(
0.866025403784439,
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0.372677996249965,
1.10810510880576,
0.220479275922049,
0.215016389907248,
0.15,
0.489380181546808,
0.110554159678513,
0.142331828329152
),
.Dim = c(2L,
5L)
),
structure(
c(
0.866025403784439,
-0.234141092407759,
0.372677996249965,
1.10810510880576,
0.220479275922049,
0.215016389907248,
0.15,
0.489380181546808,
0.110554159678513,
0.142331828329152,
0.0858464589396188,
0.294178041927853
),
.Dim = c(2L, 6L)
),
structure(
c(
0.866025403784439,
-0.234141092407759,
0.372677996249965,
1.10810510880576,
0.220479275922049,
0.215016389907248,
0.15,
0.489380181546808,
0.110554159678513,
0.142331828329152,
0.0858464589396188,
0.294178041927853,
0.069160416896561,
0.100333337378287
),
.Dim = c(2L, 7L)
),
structure(
c(
0.866025403784439,-0.234141092407759,
0.372677996249965,
1.10810510880576,
0.220479275922049,
0.215016389907248,
0.15,
0.489380181546808,
0.110554159678513,
0.142331828329152,
0.0858464589396188,
0.294178041927853,
0.069160416896561,
0.100333337378287,
0.0572653559113564,
0.202528656207688
),
.Dim = c(2L, 8L)
),
structure(
c(
0.866025403784439,
-0.234141092407759,
0.372677996249965,
1.10810510880576,
0.220479275922049,
0.215016389907248,
0.15,
0.489380181546808,
0.110554159678513,
0.142331828329152,
0.0858464589396188,
0.294178041927853,
0.069160416896561,
0.100333337378287,
0.0572653559113564,
0.202528656207688,
0.0484322104837853,
0.0754369796383129
),
.Dim = c(2L, 9L)
),
structure(
c(
0.866025403784439,
-0.234141092407759,
0.372677996249965,
1.10810510880576,
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0.215016389907248,
0.15,
0.489380181546808,
0.110554159678513,
0.142331828329152,
0.0858464589396188,
0.294178041927853,
0.069160416896561,
0.100333337378287,
0.0572653559113564,
0.202528656207688,
0.0484322104837853,
0.0754369796383129,
0.0416597790450531,
0.150701656278937
),
.Dim = c(2L, 10L)
),
structure(
c(
0.866025403784439,-0.234141092407759,
0.372677996249965,
1.10810510880576,
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0.215016389907248,
0.15,
0.489380181546808,
0.110554159678513,
0.142331828329152,
0.0858464589396188,
0.294178041927853,
0.069160416896561,
0.100333337378287,
0.0572653559113564,
0.202528656207688,
0.0484322104837853,
0.0754369796383129,
0.0416597790450531,
0.150701656278937,
0.0363320569947933,
0.0594232320521485
),
.Dim = c(2L, 11L)
),
structure(
c(
0.866025403784439,
-0.234141092407759,
0.372677996249965,
1.10810510880576,
0.220479275922049,
0.215016389907248,
0.15,
0.489380181546808,
0.110554159678513,
0.142331828329152,
0.0858464589396188,
0.294178041927853,
0.069160416896561,
0.100333337378287,
0.0572653559113564,
0.202528656207688,
0.0484322104837853,
0.0754369796383129,
0.0416597790450531,
0.150701656278937,
0.0363320569947933,
0.0594232320521485,
0.0320512820512820,
0.117963013416300
),
.Dim = c(2L, 12L)
),
structure(
c(
0.866025403784439,
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0.15,
0.489380181546808,
0.110554159678513,
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0.294178041927853,
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0.100333337378287,
0.0572653559113564,
0.202528656207688,
0.0484322104837853,
0.0754369796383129,
0.0416597790450531,
0.150701656278937,
0.0363320569947933,
0.0594232320521485,
0.0320512820512820,
0.117963013416300,
0.0285502880368496,
0.0484291463698838
),
.Dim = c(2L, 13L)
),
structure(
c(
0.866025403784439,-0.234141092407759,
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0.15,
0.489380181546808,
0.110554159678513,
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0.294178041927853,
0.069160416896561,
0.100333337378287,
0.0572653559113564,
0.202528656207688,
0.0484322104837853,
0.0754369796383129,
0.0416597790450531,
0.150701656278937,
0.0363320569947933,
0.0594232320521485,
0.0320512820512820,
0.117963013416300,
0.0285502880368496,
0.0484291463698838,
0.0256436419387357,
0.0956922480067001
),
.Dim = c(2L, 14L)
),
structure(
c(
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0.15,
0.489380181546808,
0.110554159678513,
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0.0858464589396188,
0.294178041927853,
0.069160416896561,
0.100333337378287,
0.0572653559113564,
0.202528656207688,
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0.150701656278937,
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0.0594232320521485,
0.0320512820512820,
0.117963013416300,
0.0285502880368496,
0.0484291463698838,
0.0256436419387357,
0.0956922480067001,
0.0231990181784584,
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),
.Dim = c(2L, 15L)
),
structure(
c(
0.866025403784439,
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0.15,
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0.0231990181784584,
0.0404954537147379,
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.Dim = c(2L, 16L)
),
structure(
c(
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),
structure(
c(
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.Dim = c(2L, 18L)
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structure(
c(
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structure(
c(
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.Dim = c(2L, 20L)
)
)
`MMextr` <-
list(
structure(32.3209404708014, .Dim = c(1L, 1L)),
structure(
c(
34.3970537843995,-2.66186048577395,
-2.66186048577395,
3.41286826654214
),
.Dim = c(2L,
2L)
),
structure(
c(
122.44648288656,
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19.0055823736559,
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4.10237936855581
),
.Dim = c(3L, 3L)
),
structure(
c(
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4.10932422878602,
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2.03465806649702
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.Dim = c(4L,
4L)
),
structure(
c(
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.Dim = c(5L, 5L)
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structure(
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)
)
`sM22` <-
structure(
c(
1.10866489886031,
-0.257022055546625,
-0.257022055546625,
0.607927101854027
),
.Dim = c(2L, 2L)
)
`ddst.extr.Nk` <-
function(x,
base = ddst.base.legendre,
Dmax = 5,
n = length(x)) {
sx = sort(x)
er2 = sum(sx * (1:n - n:1)) / (n * (n - 1) * log(2))
er1 = mean(x) + 0.5772156649 * er2
maxN = max(min(Dmax, length(x) - 2, 20), 1)
u = NULL
for (j in 1:maxN)
u[j] = ddst.phi(1 - pgumbel(-x, -er1, er2), j, base)
coord = NULL
gg1 = mean(1 - exp((x - er1) / er2))
gg2 = mean((1 - exp((x - er1) / er2)) * (x - er1) / er2 + 1)
for (k in 1:Dmax) {
korekta = u[1:k] + t(MMextr12[[k]]) %*% sM22 %*% c(gg1, gg2)
coord[k] = t(korekta) %*% MMextr[[k]] %*% korekta * n
}
coord
}
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