# est_tlmcov: Estimate the covariance matrix of TL-moments estimations In TLMoments: Calculate TL-Moments and Convert Them to Distribution Parameters

## Description

Internal function. Use est_cov. Description not done yet.

## Usage

 ``` 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``` ```est_tlmcov( x, leftrim = 0L, rightrim = 0L, order = 1:3, distr = NULL, lambda.cov = TRUE, ratio.cov = TRUE, ... ) ## S3 method for class 'numeric' est_tlmcov( x, leftrim = 0L, rightrim = 0L, order = 1:3, distr = NULL, lambda.cov = TRUE, ratio.cov = TRUE, ... ) ## S3 method for class 'matrix' est_tlmcov( x, leftrim = 0L, rightrim = 0L, order = 1:3, distr = NULL, lambda.cov = TRUE, ratio.cov = TRUE, reg.weights = NULL, ... ) ## S3 method for class 'TLMoments' est_tlmcov( x, leftrim = attr(x, "leftrim"), rightrim = attr(x, "rightrim"), order = attr(x, "order"), distr = NULL, lambda.cov = TRUE, ratio.cov = TRUE, set.n = NA, ... ) ```

## Arguments

 `x` numeric vector or matrix containing data OR an object of TLMoments. `leftrim, rightrim` integer indicating lower and upper trimming parameters, have to be non-negative integers. `order` numeric vector giving the orders that are returned (default is first three L-moments). `distr` character of length 1 giving the distribution if parametric assumption should be used. `lambda.cov` boolean, if TRUE (default) TL-moment estimation covariance matrix is calculated. `ratio.cov` boolean, if TRUE (default) TL-moment-ratio estimation covariance matrix is calculated. `...` additional arguments. `reg.weights` numeric vector of weights for regionalized TLMoments. `set.n` hypothetical data length n if theoretical values are given.

## Value

a list of numeric matrices (if `lambda.cov` and `ratio.cov` are TRUE (default)), or a single matrix.

## Examples

 ``` 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 49 50 51 52 53 54``` ```### Numeric vectors x <- rgev(500, loc = 10, scale = 5, shape = .1) est_tlmcov(x) est_tlmcov(x, order = 2:3) est_tlmcov(x, rightrim = 1, order = 4:5) # cov(t(replicate(10000, # TLMoments(rgev(500, loc = 10, scale = 5, shape = .1))\$lambdas) # )) # cov(t(replicate(10000, # TLMoments(rgev(500, loc = 10, scale = 5, shape = .1))\$ratios) # )) est_tlmcov(x, ratio.cov = FALSE) est_tlmcov(x, lambda.cov = FALSE) est_tlmcov(x, distr = "gev") est_tlmcov(x, leftrim = 0, rightrim = 1) # cov(t(replicate(10000, # TLMoments(rgev(500, loc = 10, scale = 5, shape = .1), 0, 1, 3)\$lambdas # ))) # cov(t(replicate(10000, # TLMoments(rgev(500, loc = 10, scale = 5, shape = .1), 0, 1, 3)\$ratios # ))) ### Numeric matrices x <- matrix(rgev(600), nc = 3) est_tlmcov(x) est_tlmcov(x, order = 3:4) # cov(t(replicate(10000, # as.vector(TLMoments(matrix(rgev(600), nc = 3))\$lambdas[3:4, ]) # ))) # cov(t(replicate(10000, # as.vector(TLMoments(matrix(rgev(600), nc = 3))\$ratios[3:4, ]) # ))) est_tlmcov(x, ratio.cov = FALSE) est_tlmcov(x, lambda.cov = FALSE) TLMoments:::est_tlmcov(x, order = 2:3, distr = "gev") # cov(t(replicate(10000, # as.vector(TLMoments(matrix(rgev(600), nc = 3))\$lambdas[2:3, ]) # ))) # cov(t(replicate(10000, # as.vector(TLMoments(matrix(rgev(600), nc = 3))\$ratios[2:3, ]) # ))) ### TLMoments-object (theoretical calculation) tlm <- TLMoments(as.parameters(loc = 10, scale = 5, shape = .1, distr = "gev"), 0, 1) est_tlmcov(tlm, distr = "gev", set.n = 100) est_tlmcov(tlm, distr = "gev", set.n = 100, ratio.cov = FALSE) est_tlmcov(tlm, distr = "gev", set.n = 100, lambda.cov = FALSE) ```

TLMoments documentation built on Dec. 4, 2019, 5:06 p.m.