Probability weighted moments

Description

Calculates empirical probability weighted moments of specific order(s).

Usage

PWM(x, order = 0, na.rm = FALSE)

Arguments

Value

numeric vector, empirical PWM of orders order of x.

See Also

PWMs

Examples

PWM(rnorm(25))
PWM(rnorm(25), order = 2)
PWM(rnorm(25), order = c(0, 2, 4))

Probability weighted moments

Description

Calculates probability weighted moments up to a specific order. Note that PWMs start with order 0. Acceptable input types are numeric vectors, matrices, lists, and data.frames.

Usage

PWMs(x, ...)

## S3 method for class 'numeric'
PWMs(x, max.order = 4L, na.rm = FALSE, ...)

## S3 method for class 'matrix'
PWMs(x, max.order = 4L, na.rm = FALSE, ...)

## S3 method for class 'list'
PWMs(x, max.order = 4L, na.rm = FALSE, ...)

## S3 method for class 'data.frame'
PWMs(x, formula, max.order = 4L, na.rm = FALSE, ...)

## S3 method for class 'TLMoments'
PWMs(x, ...)

Arguments

Value

numeric vector, matrix, list, or data.frame consisting of the PWMs and with class PWMs. The object contains the following attributes:

• order: a integer vector with corresponding PWM orders

• source: a list with background information (used function, data, n, formula; mainly for internal purposes)

The attributes are hidden in the print-function for a clearer presentation.

References

Greenwood, J. A., Landwehr, J. M., Matalas, N. C., & Wallis, J. R. (1979). Probability weighted moments: definition and relation to parameters of several distributions expressable in inverse form. Water Resources Research, 15(5), 1049-1054.

Examples

# Generating data sets:
xmat <- matrix(rnorm(100), nc = 4)
xvec <- xmat[, 3]
xlist <- lapply(1L:ncol(xmat), function(i) xmat[, i])
xdat <- data.frame(
 station = rep(letters[1:2], each = 50),
 season = rep(c("S", "W"), 50),
 hq = as.vector(xmat)
)

# Calculating PWMs from data:
PWMs(xvec)
PWMs(xmat)
PWMs(xlist)
PWMs(xdat, formula = hq ~ station)
PWMs(xdat, formula = hq ~ season)
PWMs(xdat, formula = hq ~ .)
PWMs(xdat, formula = . ~ station + season)

# Calculating PWMs from L-moments:
PWMs(TLMoments(xvec))
PWMs(TLMoments(xmat))
PWMs(TLMoments(xlist))
PWMs(TLMoments(xdat, hq ~ station))
PWMs(TLMoments(xdat, hq ~ season))
PWMs(TLMoments(xdat, hq ~ .))
PWMs(TLMoments(xdat, . ~ station + season))

# In data.frame-mode invalid names are preceded by "."
xdat <- data.frame(
 beta0 = rep(letters[1:2], each = 50),
 beta1 = as.vector(xmat)
)
PWMs(xdat, formula = beta1 ~ beta0)

Trimmed L Moments

Description

Calculates empirical Trimmed L-moments of specific order(s) and trimming.

Usage

TLMoment(
  x,
  order = 1L,
  leftrim = 0L,
  rightrim = 0L,
  na.rm = FALSE,
  computation.method = "auto"
)

Arguments

Value

numeric vector, empirical TL(leftrim,rightrim)-moments of orders order of x

References

Elamir, E. A., & Seheult, A. H. (2003). Trimmed L-moments. Computational Statistics & Data Analysis, 43(3), 299-314.

Hosking, J. R. (1990). L-moments: analysis and estimation of distributions using linear combinations of order statistics. Journal of the Royal Statistical Society. Series B (Methodological), 105-124.

Hosking, J. R. M. (2007). Some theory and practical uses of trimmed L-moments. Journal of Statistical Planning and Inference, 137(9), 3024-3039.

Hosking, J. R. M., & Balakrishnan, N. (2015). A uniqueness result for L-estimators, with applications to L-moments. Statistical Methodology, 24, 69-80.

See Also

TLMoments

Examples

x <- rnorm(100)
TLMoment(x, order = 1)
TLMoment(x, order = 2, leftrim = 0, rightrim = 1)
TLMoment(x, order = c(1, 2, 3), leftrim = 2, rightrim = 2)
TLMoment(x, order = c(1, 3, 2), leftrim = 2, rightrim = 2)

Trimmed L-moments

Description

Calculates empirical or theoretical Trimmed L-moments and -ratios up to a specific order. If empirical moments should be calculated, acceptable input types are numeric vectors, matrices, lists, data.frames. TLMoments is type-preservative, so the input type is also the output type. If theoretical moments should be calculated, the input type has to be of class parameters or PWMs, so an object returned by parameters, as.parameters or PWMs, as.PWMs.

Usage

TLMoments(x, ...)

## S3 method for class 'numeric'
TLMoments(
  x,
  leftrim = 0L,
  rightrim = 0L,
  max.order = 4L,
  na.rm = FALSE,
  computation.method = "auto",
  ...
)

## S3 method for class 'matrix'
TLMoments(
  x,
  leftrim = 0L,
  rightrim = 0L,
  max.order = 4L,
  na.rm = FALSE,
  computation.method = "auto",
  ...
)

## S3 method for class 'list'
TLMoments(
  x,
  leftrim = 0L,
  rightrim = 0L,
  max.order = 4L,
  na.rm = FALSE,
  computation.method = "auto",
  ...
)

## S3 method for class 'data.frame'
TLMoments(
  x,
  formula,
  leftrim = 0L,
  rightrim = 0L,
  max.order = 4L,
  na.rm = FALSE,
  computation.method = "auto",
  ...
)

## S3 method for class 'PWMs'
TLMoments(x, leftrim = 0L, rightrim = 0L, ...)

## S3 method for class 'parameters'
TLMoments(
  x,
  leftrim = attr(x, "source")$trimmings[1],
  rightrim = attr(x, "source")$trimmings[2],
  max.order = 4L,
  ...
)

Arguments

Value

list of two dimensions: lambdas/ratios are a numeric vector, matrix, list, or data.frame consisting of the TL-moments/TL-moment-ratios. The list has the class TLMoments. The object contains the following attributes:

• leftrim: a numeric giving the used leftrim-argument

• rightrim: a numeric giving the used rightrim-argument

• order: a integer vector with corresponding TL-moment orders

• source: a list with background information (used function, data, n, formula, computation method; mainly for internal purposes)

The attributes are hidden in the print-function for a clearer presentation.

Methods (by class)

• numeric: TLMoments for numeric vector of data

• matrix: TLMoments for numeric matrix of data

• list: TLMoments for numeric list of data

• data.frame: TLMoments for numeric data.frame of data

• PWMs: TLMoments for PWMs-object

• parameters: TLMoments for parameters-object

References

Elamir, E. A., & Seheult, A. H. (2003). Trimmed L-moments. Computational Statistics & Data Analysis, 43(3), 299-314.

Hosking, J. R. M. (1990). L-moments: analysis and estimation of distributions using linear combinations of order statistics. Journal of the Royal Statistical Society. Series B (Methodological), 105-124.

Hosking, J. R. M. (2007). Some theory and practical uses of trimmed L-moments. Journal of Statistical Planning and Inference, 137(9), 3024-3039.

Hosking, J. R. M., & Balakrishnan, N. (2015). A uniqueness result for L-estimators, with applications to L-moments. Statistical Methodology, 24, 69-80.

See Also

PWMs, parameters, quantiles, summary.TLMoments, as.TLMoments

Examples

# Generating data sets:
xmat <- matrix(rnorm(100), nc = 4)
xvec <- xmat[, 3]
xlist <- lapply(1L:ncol(xmat), function(i) xmat[, i])
xdat <- data.frame(
 station = rep(letters[1:2], each = 50),
 season = rep(c("S", "W"), 50),
 hq = as.vector(xmat)
)

# Calculating TL-moments from data:
TLMoments(xvec, leftrim = 0, rightrim = 1)
TLMoments(xmat, leftrim = 1, rightrim = 1)
TLMoments(xlist, max.order = 7)
TLMoments(xdat, hq ~ station, leftrim = 0, rightrim = 2)
TLMoments(xdat, hq ~ season, leftrim = 0, rightrim = 2)
TLMoments(xdat, hq ~ ., leftrim = 0, rightrim = 2)

# Calculating TL-moments from PWMs:
TLMoments(PWMs(xvec))
TLMoments(PWMs(xmat), rightrim = 1)
TLMoments(PWMs(xlist), leftrim = 1, rightrim = 1)
TLMoments(PWMs(xdat, hq ~ station), leftrim = 0, rightrim = 2)
TLMoments(PWMs(xdat, hq ~ station + season), leftrim = 0, rightrim = 2)
TLMoments(as.PWMs(cbind(c(0.12, .41, .38, .33), c(.05, 0.28, .25, .22))), 0, 1)

# Calculating TL-moments from parameters:
(tlm <- TLMoments(xmat, leftrim = 0, rightrim = 1))
TLMoments(parameters(tlm, "gev"))

(tlm <- TLMoments(xdat, hq ~ station, leftrim = 0, rightrim = 2))
TLMoments(parameters(tlm, "gev"))

p <- as.parameters(loc = 3, scale = 2, shape = .4, distr = "gev")
TLMoments(p, rightrim = 1)

p <- as.parameters(cbind(loc = 10, scale = 4, shape = seq(0, .4, .1)), distr = "gev")
TLMoments(p, max.order = 6)

p <- as.parameters(list(
 list(loc = 3, scale = 2, shape = .4),
 list(loc = 3, scale = 2, shape = .2)
), distr = "gev")
TLMoments(p)

p <- as.parameters(data.frame(
 station = letters[1:2],
 loc = c(2, 3),
 scale = c(2, 2),
 shape = c(.4, .2)
), .~station, distr = "gev")
TLMoments(p)

Convert to PWMs-object

Description

Convert vector, matrix, list, or data.frame to PWMs-objects.

Usage

as.PWMs(x, ..., order = NULL)

## S3 method for class 'numeric'
as.PWMs(x, order = seq_along(x) - 1, ...)

## S3 method for class 'matrix'
as.PWMs(x, order = row(x)[, 1] - 1, ...)

## S3 method for class 'list'
as.PWMs(x, order = seq_along(x[[1]]) - 1, ...)

## S3 method for class 'data.frame'
as.PWMs(x, formula, order = NULL, ...)

Arguments

Value

object of class PWMs, see PWMs help page.

Methods (by class)

• numeric: as.PWMs for numeric data vectors

• matrix: as.PWMs for numeric data matrices

• list: as.PWMs for numeric data lists

• data.frame: as.PWMs for numeric data.frames

See Also

PWMs

Examples

xmat <- cbind(c(0.12, .41, .38, .33), c(.05, 0.28, .25, .22))
xvec <- xmat[, 1]
xlist <- lapply(1:ncol(xmat), function(i) xmat[, i])
xdat <- data.frame(
 station = letters[1:3],
 season = c("S", "W", "S"),
 b0 = c(.12, .15, .05),
 b1 = c(.41, .33, .28),
 b2 = c(.38, .18, .25)
)

as.PWMs(xvec)
as.PWMs(xvec[-2], order = c(0, 2, 3))

as.PWMs(xmat)
as.PWMs(xmat[-2, ], order = c(0, 2, 3))

as.PWMs(xlist)

as.PWMs(xdat, cbind(b0, b1, b2) ~ station)
as.PWMs(xdat, . ~ station + season)
as.PWMs(xdat, cbind(b0, b2) ~ station, order = c(0, 2))

p <- as.PWMs(xdat, cbind(b0, b1, b2) ~ station)
TLMoments(p)

(p <- as.PWMs(xdat, cbind(b0, b1) ~ station))
#parameters(TLMoments(p), "gev") # => error
#parameters(TLMoments(p), "gpd") # => error
parameters(TLMoments(p), "gpd", u = 10)

(p <- as.PWMs(xdat, cbind(b0, b2) ~ station, order = c(0, 2)))
#TLMoments(p) # => error

Convert to TLMoments-object

Description

Convert vector, matrix, list, or data.frame of TL-moments or TL-moment ratios or a PWMs-object to a TLMoments-object in order to be used with TLMoments-functions. The first position of a vector or the first row of a matrix is always used as the L1-moment. The ratios argument determines if the following positions or rows are used as TL-moments oder TL-moments ratios. The trimming has to be given using the leftrim and rightrim arguments.

Usage

as.TLMoments(x, ..., leftrim, rightrim, ratios)

## S3 method for class 'numeric'
as.TLMoments(x, leftrim = 0L, rightrim = 0L, ratios = FALSE, ...)

## S3 method for class 'matrix'
as.TLMoments(x, leftrim = 0L, rightrim = 0L, ratios = FALSE, ...)

## S3 method for class 'list'
as.TLMoments(x, leftrim = 0L, rightrim = 0L, ratios = FALSE, ...)

## S3 method for class 'data.frame'
as.TLMoments(x, formula, leftrim = 0L, rightrim = 0L, ratios = FALSE, ...)

Arguments

Value

object of class TLMoments, see PWMs help page.

Methods (by class)

• numeric: as.TLMoments for numeric data vectors

• matrix: as.TLMoments for numeric data matrices

• list: as.TLMoments for numeric data lists

• data.frame: as.TLMoments for numeric data.frames

See Also

TLMoments

Examples

### Vector or matrix as input
xmat <- cbind(c(1, .2, .05), c(1, .2, NA), c(1.3, NA, .1))
xvec <- xmat[, 1]
xlist <- lapply(1:ncol(xmat), function(i) xmat[, i])
xdat <- data.frame(
 station = rep(letters[1:3], each = 1),
 season = c("S", "W", "S"),
 L1 = c(1, 1, 1.3),
 L2 = c(.2, .2, .3),
 L3 = c(.05, .04, .1)
)

as.TLMoments(xvec, rightrim = 1)
as.TLMoments(xmat, rightrim = 1)
as.TLMoments(xlist, rightrim = 1)
as.TLMoments(xdat, cbind(L1, L2, L3) ~ station)
as.TLMoments(xdat, .~station+season)
as.TLMoments(xdat, cbind(L1, L2, L3) ~ .)

parameters(as.TLMoments(xvec, rightrim = 0), "gev")
#lmomco::lmom2par(lmomco::vec2lmom(c(1, .2, .25)), "gev")$para

xmat <- cbind(c(NA, .2, -.05), c(NA, .2, .2))
xvec <- xmat[, 1]

as.TLMoments(xvec, ratios = TRUE)
as.TLMoments(xmat, ratios = TRUE)
parameters(as.TLMoments(xvec, ratios = TRUE), "gev")
#lmomco::lmom2par(lmomco::vec2lmom(c(1, .2, -.05)), "gev")$para

xmat <- cbind(c(10, .2, -.05), c(10, .2, .2))
xvec <- xmat[, 1]

as.TLMoments(xvec, ratios = TRUE)
as.TLMoments(xmat, ratios = TRUE)
parameters(as.TLMoments(xvec, ratios = TRUE), "gev")
#lmomco::lmom2par(lmomco::vec2lmom(c(10, .2, -.05)), "gev")$para

Converting to parameters-objects

Description

Convert vector, matrix, list, or data.frame to parameters-objects.

Usage

as.parameters(..., distr = NULL)

## S3 method for class 'numeric'
as.parameters(..., distr)

## S3 method for class 'matrix'
as.parameters(x, distr, ...)

## S3 method for class 'list'
as.parameters(x, distr, ...)

## S3 method for class 'data.frame'
as.parameters(x, formula, distr, ...)

Arguments

Value

object of class parameters, see parameters help page.

Methods (by class)

• numeric: as.parameters for numeric data vectors

• matrix: as.parameters for numeric data matrices

• list: as.parameters for numeric data lists

• data.frame: as.parameters for numeric data.frames

See Also

parameters

Examples

# Vector input:
as.parameters(loc = 3, scale = 2, shape = .4, distr = "gev")
as.parameters(c(loc = 3, scale = 2, shape = .4), distr = "gev")

# Names can be shortened if unambiguous:
as.parameters(l = 3, sc = 2, sh = .4, distr = "gev")
as.parameters(m = 3, s = 1, distr = "norm")

# Wrong or ambiguous names lead to errors!
## Not run: 
as.parameters(l = 3, s = 2, s = .4, distr = "gev")
as.parameters(loc2 = 3, scale = 2, shape = .4, distr = "gev")

## End(Not run)

# If no names are given, a warning is given and they are guessed for gev, gpd, gum, and ln3.
as.parameters(3, 2, .4, distr = "gev")
as.parameters(c(3, 2, .4), distr = "gev")
## Not run: 
as.parameters(3, 2, .2, .4, distr = "gev") #=> doesn't work

## End(Not run)

# Matrix input:
# Parameters in matrices must have either matching rownames or colnames!
as.parameters(cbind(loc = 10, scale = 4, shape = seq(0, .4, .1)), distr = "gev")
as.parameters(rbind(loc = 10, scale = 4, shape = seq(0, .4, .1)), distr = "ln3")

# If no names are given, a guess is made based on number of rows
# or cols according to distribution (and a warning is given).
as.parameters(matrix(1:9, nr = 3), distr = "gev")
as.parameters(matrix(1:8, nc = 2), distr = "gum")


# The same principles apply for list input and data.frames:

# List input:
as.parameters(list(list(mean = 2, sd = 1), list(mean = 0, sd = 1)), distr = "norm")
as.parameters(list(c(m = 2, s = 1), c(m = 0, s = 1)), distr = "norm")
as.parameters(list(c(loc = 2, scale = 1), c(0, 1)), distr = "gum")
## Not run: 
as.parameters(list(c(loc = 2, scale = 1), c(0, 1, 2)), distr = "gum")

## End(Not run)

# Dataframe input:
xdat <- data.frame(station = c(1, 2), mean = c(2, 0), sd = c(1, 1))
as.parameters(xdat, cbind(mean, sd) ~ station, distr = "norm")
as.parameters(xdat, . ~ station, distr = "norm")
as.parameters(xdat, cbind(mean, sd) ~ ., distr = "norm")

xdat <- data.frame(station = c(1, 2), m = c(2, 0), s = c(1, 1))
as.parameters(xdat, cbind(m, s) ~ station, distr = "norm")
## Not run: 
as.parameters(xdat, cbind(m, s) ~ station, distr = "gev")

## End(Not run)

###

# Results of as.parameters can be used in the normal TLMoments-scheme:
# they can be transfered to quantiles or to TLMoments.

xdat <- data.frame(station = c(1, 2), mean = c(2, 0), sd = c(1, 1))
quantiles(as.parameters(xdat, cbind(mean, sd) ~ ., distr = "norm"), c(.99))

# quantile estimation
p <- as.parameters(loc = 3, scale = 2, shape = .4, distr = "gev")
quantiles(p, c(.9, .95))
p <- as.parameters(cbind(loc = 10, scale = 4, shape = seq(0, .4, .1)), distr = "gev")
quantiles(p, c(.9, .95))
p <- as.parameters(list(list(mean = 2, sd = 1), list(mean = 0, sd = 1)), distr = "norm")
quantiles(p, c(.95, .975))

# With magrittr
library(magrittr)
as.parameters(loc = 3, scale = 2, shape = .4, distr = "gev") %>% quantiles(c(.9, .99))

Covariance matrix of PWMs, TLMoments, parameters, or quantiles

Description

Calculation of the empirical or theoretical covariance matrix of objects of the classes PWMs, TLMoments, parameters, or quantiles.

Usage

est_cov(x, ...)

## S3 method for class 'PWMs'
est_cov(x, select = attr(x, "order"), ...)

## S3 method for class 'TLMoments'
est_cov(x, select = attr(x, "order"), ...)

## S3 method for class 'parameters'
est_cov(x, select = c("loc", "scale", "shape"), ...)

## S3 method for class 'quantiles'
est_cov(x, select = attr(x, "p"), ...)

Arguments

Details

Covariance matrices of PWMs and TLMoments are calculated without parametric assumption by default. Covariance matrices of parameters and quantiles use parametric assumption based on their stored distribution attribute (only GEV at the moment). Parametric (GEV) calculation can be enforced by specifying distr=\"gev\", non-parametric calculation by using np.cov=TRUE.

Value

numeric matrix (if x is of class PWMs, parameters, or quantiles) or a list of two matrices (lambdas and ratios, if x is of class TLMoments).

See Also

PWMs, TLMoments, parameters, quantiles

Examples

### 1: PWMs:

xvec <- rgev(100, shape = .1)
xmat <- cbind(rgev(100, shape = .1), rgev(100, shape = .3))

# Covariance estimation of PWMs normally without parametric assumption:
est_cov(PWMs(xvec))
est_cov(PWMs(xvec), select = 0:1)
est_cov(PWMs(xmat))
est_cov(PWMs(xmat), select = 3)
est_cov(PWMs(xmat[, 1, drop = FALSE]), select = 2:3)

# Parametric assumptions (only GEV by now) can be used:
est_cov(PWMs(xvec), distr = "gev")
est_cov(PWMs(xvec), distr = "gev", select = c(1, 3))

## Not run: 
cov(t(replicate(100000,
  as.vector(PWMs(cbind(rgev(100, shape = .1), rgev(100, shape = .3)), max.order = 1)))
))

## End(Not run)


### 2. TLMoments:

xvec <- rgev(100, shape = .1)
xmat <- cbind(rgev(100, shape = .1), rgev(100, shape = .3))

# Covariance estimation of TLMoments normally without parametric assumption:
est_cov(TLMoments(xvec))
est_cov(TLMoments(xvec, rightrim = 1))
est_cov(TLMoments(xvec), select = 3:4)

# Parametric assumptions (only GEV by now) can be used:
est_cov(TLMoments(xvec), distr = "gev")

# Matrix inputs
est_cov(TLMoments(xmat))
est_cov(TLMoments(xmat), select = 3:4)
est_cov(TLMoments(xmat[, 1, drop = FALSE]), select = 3:4)

# Covariance of theoretical TLMoments only with parametric assumption:
est_cov(as.TLMoments(c(14, 4, 1)), distr = "gev", set.n = 100)
est_cov(as.TLMoments(c(14, 4, 1), rightrim = 1), distr = "gev", set.n = 100)

# Regionalized TLMoments
est_cov(regionalize(TLMoments(xmat), c(.75, .25)))
est_cov(regionalize(TLMoments(xmat), c(.75, .25)), distr = "gev", select = 3:4)


### 3. Parameters:

xvec <- rgev(100, shape = .1)
xmat <- cbind(rgev(100, shape = .1), rgev(100, shape = .3))

# Covariance estimation of parameters normally with parametric assumption:
est_cov(parameters(TLMoments(xvec), "gev"))
est_cov(parameters(TLMoments(xvec, rightrim = 1), "gev"))
est_cov(parameters(TLMoments(xvec, rightrim = 1), "gev"), select = c("scale", "shape"))

# A nonparametric estimation can be enforced with np.cov:
est_cov(parameters(TLMoments(xvec), "gev"), np.cov = TRUE)
est_cov(parameters(TLMoments(xvec, rightrim = 1), "gev"), np.cov = TRUE)

# Matrix inputs
est_cov(parameters(TLMoments(xmat), "gev"))
est_cov(parameters(TLMoments(xmat), "gev"), select = "shape")
est_cov(parameters(TLMoments(xmat[, 1]), "gev"), select = "shape")

# Theoretical values (leftrim and/or rightrim have to be specified)
para <- as.parameters(loc = 10, scale = 5, shape = .2, distr = "gev")
est_cov(para, set.n = 100)
est_cov(para, rightrim = 1, set.n = 100)

## Not run: 
var(t(replicate(10000, parameters(TLMoments(rgev(100, 10, 5, .2)), "gev"))))

## End(Not run)
## Not run: 
var(t(replicate(10000, parameters(TLMoments(rgev(100, 10, 5, .2), rightrim = 1), "gev"))))

## End(Not run)

# Parameter estimates from regionalized TLMoments:
est_cov(parameters(regionalize(TLMoments(xmat), c(.75, .25)), "gev"))


### 4. Quantiles:

xvec <- rgev(100, shape = .2)
xmat <- cbind(rgev(100, shape = .1), rgev(100, shape = .3))

# Covariance estimation of parameters normally with parametric assumption:
q <- quantiles(parameters(TLMoments(xvec), "gev"), c(.9, .95, .99))
est_cov(q)
est_cov(q, select = c("0.9", "0.99"))
est_cov(q, select = .95)

# A nonparametric estimation can be enforced with np.cov:
est_cov(q, np.cov = TRUE)

# Matrix inputs
param <- parameters(TLMoments(xmat, 0, 1), "gev")
q <- quantiles(param, c(.9, .95, .99))
est_cov(q)
est_cov(q, select = .99)
param <- parameters(TLMoments(xmat[, 1, drop = FALSE], 0, 1), "gev")
q <- quantiles(param, c(.9, .95, .99))
est_cov(q, select = .99)

# Theoretical values
q <- quantiles(as.parameters(loc = 10, scale = 5, shape = .3, distr = "gev"), c(.9, .99))
est_cov(q)
est_cov(q, leftrim = 0, rightrim = 1)
est_cov(q, leftrim = 0, rightrim = 1, set.n = 100)

# Quantile estimates from regionalized TLMoments:
param <- parameters(regionalize(TLMoments(xmat), c(.75, .25)), "gev")
est_cov(quantiles(param, c(.9, .99)))

Estimate the covariance matrix of parameter estimations

Description

Internal function. Use est_cov. Description not done yet.

Usage

est_paramcov(x, distr = "", leftrim = 0L, rightrim = 0L, ...)

## S3 method for class 'numeric'
est_paramcov(x, distr, leftrim = 0L, rightrim = 0L, np.cov = FALSE, ...)

## S3 method for class 'matrix'
est_paramcov(
  x,
  distr,
  leftrim = 0L,
  rightrim = 0L,
  np.cov = FALSE,
  reg.weights = NULL,
  ...
)

## S3 method for class 'parameters'
est_paramcov(
  x,
  distr = attr(x, "distribution"),
  leftrim = attr(x, "source")$trimmings[1],
  rightrim = attr(x, "source")$trimmings[2],
  set.n = NA,
  ...
)

Arguments

Value

numeric matrix

Examples

### Numeric vectors
x <- rgev(500, shape = .2)

parameters(TLMoments(x), "gev")
est_paramcov(x, "gev", 0, 0)
#cov(t(replicate(10000, parameters(TLMoments(rgev(500, shape = .2)), "gev"))))

parameters(TLMoments(x, rightrim = 1), "gev")
est_paramcov(x, "gev", 0, 1)
#cov(t(replicate(10000,
#   parameters(TLMoments(rgev(500, shape = .2), rightrim = 1), "gev")
#)))

parameters(TLMoments(x, rightrim = 2), "gev")
est_paramcov(x, "gev", 0, 2)
#cov(t(replicate(10000,
#  parameters(TLMoments(rgev(500, shape = .2), rightrim = 2), "gev")
#)))

### Numeric matrices
x <- matrix(rgev(600, shape = .2), nc = 3)

parameters(TLMoments(x), "gev")
est_paramcov(x, "gev", 0, 0)
#cov(t(replicate(5000,
#  as.vector(parameters(TLMoments(matrix(rgev(600, shape = .2), nc = 3)), "gev")))
#))

### parameters-object
x <- as.parameters(loc = 3, scale = 2, shape = .4, distr = "gev")
est_paramcov(x)
est_paramcov(x, leftrim = 0, rightrim = 0)
est_paramcov(x, leftrim = 0, rightrim = 0, set.n = 100)
# distr-argument can be neglected

Estimate the covariance matrix of PWM estimations

Description

Internal function. Use est_cov. Description not done yet.

Usage

est_pwmcov(x, order = 0:3, distr = NULL, distr.trim = c(0, 0))

## S3 method for class 'numeric'
est_pwmcov(x, order = 0:3, distr = NULL, distr.trim = c(0, 0))

## S3 method for class 'matrix'
est_pwmcov(x, order = 0:3, distr = NULL, distr.trim = c(0, 0))

Arguments

Value

numeric matrix

Examples

### Numeric vectors
x <- rgev(500, shape = .2)
est_pwmcov(x)
est_pwmcov(x, distr = "gev")

### Numeric matrices
x <- matrix(rgev(600, shape = .2), nc = 3)
est_pwmcov(x, order = 0:2)
est_pwmcov(x, order = 0:2, distr = "gev")

Estimate the covariance matrix of quantile estimations

Description

Internal function. Use est_cov. Description not done yet.

Usage

est_quancov(x, distr = "", p = NULL, leftrim = 0L, rightrim = 0L, ...)

## S3 method for class 'numeric'
est_quancov(x, distr, p, leftrim = 0L, rightrim = 0L, np.cov = FALSE, ...)

## S3 method for class 'matrix'
est_quancov(
  x,
  distr,
  p,
  leftrim = 0L,
  rightrim = 0L,
  np.cov = FALSE,
  reg.weights = NULL,
  ...
)

## S3 method for class 'quantiles'
est_quancov(
  x,
  distr = attr(x, "distribution"),
  p = attr(x, "p"),
  leftrim = attr(x, "source")$trimmings[1],
  rightrim = attr(x, "source")$trimmings[2],
  set.n = NA,
  ...
)

Arguments

Value

numeric matrix

Examples

### Numeric vectors
x <- rgev(500, shape = .2)

quantiles(parameters(TLMoments(x), "gev"), c(.9, .95, .99))
est_quancov(x, "gev", c(.9, .95, .99), 0, 0)
#cov(t(replicate(5000,
#  quantiles(parameters(TLMoments(rgev(500, shape = .2)), "gev"), c(.9, .95, .99))
#)))

quantiles(parameters(TLMoments(x, rightrim = 1), "gev"), c(.9, .95, .99))
est_quancov(x, "gev", c(.9, .95, .99), 0, 1)
#cov(t(replicate(5000,
#  quantiles(
#    parameters(TLMoments(rgev(500, shape = .2), rightrim = 1), "gev"),
#    c(.9, .95, .99)
#  )
#)))

### Numeric matrices
x <- matrix(rgev(600, shape = .2), nc = 3)

quantiles(parameters(TLMoments(x), "gev"), c(.9, .95, .99))
est_quancov(x, "gev", c(.9, .95, .99), 0, 0)

est_quancov(x, "gev", .9, 0, 0)
#cov(t(replicate(5000,
# quantiles(
#   parameters(TLMoments(matrix(rgev(600, shape = .2), nc = 3)),
#  "gev"), .9)
#  )
#))

### quantiles object
q <- quantiles(as.parameters(loc = 3, scale = 2, shape = .4, distr = "gev"), c(.9, .99))
est_quancov(q)
est_quancov(q, leftrim = 0, rightrim = 0)
est_quancov(q, leftrim = 0, rightrim = 0, set.n = 10)

Estimate the covariance matrix of TL-moments estimations

Description

Internal function. Use est_cov. Description not done yet.

Usage

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

Value

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

Examples

### 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)

Converting TL-moments to distribution parameters

Description

Converts TL-moments (or PWMs) to distribution parameters. By now, conversions for gev, gumbel, gpd, and ln3 are available. Important trimming options are calculated through known formulas (see references for some of them), other options are calculated through a numerical optimization.

Usage

parameters(x, distr, ...)

## S3 method for class 'PWMs'
parameters(x, distr, ...)

## S3 method for class 'TLMoments'
parameters(x, distr, ...)

Arguments

Value

numeric vector, matrix, list, or data.frame of parameter estimates with class parameters. The object contains the following attributes:

• distribution: a character indicating the used distribution

• source: a list with background information (used function, data, n, formula, trimmings; mainly for internal purposes)

The attributes are hidden in the print-function for a clearer presentation.

Methods (by class)

• PWMs: parameters for PWMs-object

• TLMoments: parameters for TLMoments-object

References

Elamir, E. A. H. (2010). Optimal choices for trimming in trimmed L-moment method. Applied Mathematical Sciences, 4(58), 2881-2890.

Fischer, S., Fried, R., & Schumann, A. (2015). Examination for robustness of parametric estimators for flood statistics in the context of extraordinary extreme events. Hydrology and Earth System Sciences Discussions, 12, 8553-8576.

Hosking, J. R. (1990). L-moments: analysis and estimation of distributions using linear combinations of order statistics. Journal of the Royal Statistical Society. Series B (Methodological), 105-124.

Hosking, J. R. M. (2007). Some theory and practical uses of trimmed L-moments. Journal of Statistical Planning and Inference, 137(9), 3024-3039.

See Also

PWMs, TLMoments, quantiles, summary.parameters, as.parameters. Built-in distributions: pgev, pgum, pgpd, pln3.

Examples

xmat <- matrix(rgev(100, shape = .2), nc = 4)
xvec <- xmat[, 3]
xlist <- lapply(1L:ncol(xmat), function(i) xmat[, i])
xdat <- data.frame(
 station = rep(letters[1:2], each = 50),
 season = rep(c("S", "W"), 50),
 hq = as.vector(xmat)
)

# TLMoments-objects or PWMs-objects can be used. However, in case of PWMs
# simply the TLMoments(., leftrim = 0, rightrim = 0)-variant is used.

parameters(PWMs(xvec), "gev")
tlm <- TLMoments(xvec, leftrim = 0, rightrim = 0)
parameters(tlm, "gev")

tlm <- TLMoments(xmat, leftrim = 1, rightrim = 1)
parameters(tlm, "gum")

tlm <- TLMoments(xlist)
parameters(tlm, "gpd")

tlm <- TLMoments(xdat, hq ~ station, leftrim = 0, rightrim = 2)
parameters(tlm, "gev")

tlm <- TLMoments(xdat, hq ~ station + season, leftrim = 0, rightrim = 2)
parameters(tlm, "gev")


# If no explicit formula is implemented, it is tried to calculate
# parameters numerically. The attribute source$param.computation.method
# indicates if this is the case.

tlm <- TLMoments(rgum(200, loc = 5, scale = 2), leftrim = 1, rightrim = 4)
parameters(tlm, "gum")

tlm <- TLMoments(rgev(200, loc = 10, scale = 5, shape = .4), leftrim = 2, rightrim = 2)
parameters(tlm, "gev")

tlm <- TLMoments(rln3(200, loc = 3, scale = 1.5, shape = 2), leftrim = 0, rightrim = 1)
parameters(tlm, "ln3")

# Numerical calculation is A LOT slower:
## Not run: 
system.time(replicate(500,
  parameters(TLMoments(rgum(100, loc = 5, scale = 2), 1, 1), "gum")
))[3]
system.time(replicate(500,
  parameters(TLMoments(rgum(100, loc = 5, scale = 2), 1, 2), "gum")
))[3]

## End(Not run)

# Using magrittr
library(magrittr)

TLMoments(rgpd(500, loc = 10, scale = 3, shape = .3), rightrim = 0) %>%
 parameters("gpd")

TLMoments(rgpd(500, loc = 10, scale = 3, shape = .3), rightrim = 0) %>%
 parameters("gpd", u = 10)

TLMoments(rgpd(500, loc = 10, scale = 3, shape = .3), rightrim = 1) %>%
 parameters("gpd")

TLMoments(rgpd(500, loc = 10, scale = 3, shape = .3), rightrim = 2) %>%
 parameters("gpd")

Generalized Extreme Value distribution

Description

Cumulative distribution function, density function, quantile function and generation of random variates of the generalized extreme value distribution.

Usage

pgev(q, loc = 0, scale = 1, shape = 0)

dgev(x, loc = 0, scale = 1, shape = 0)

qgev(p, loc = 0, scale = 1, shape = 0)

rgev(n, loc = 0, scale = 1, shape = 0)

Arguments

See Also

pgum, pgpd, pln3

Generalized Pareto distribution

Description

Cumulative distribution function, density function, quantile function and generation of random variates of the generalized Pareto distribution.

Usage

pgpd(q, loc = 0, scale = 1, shape = 0)

dgpd(x, loc = 0, scale = 1, shape = 0)

qgpd(p, loc = 0, scale = 1, shape = 0)

rgpd(n, loc = 0, scale = 1, shape = 0)

Arguments

See Also

pgev, pgum, pln3

Gumbel distribution

Description

Cumulative distribution function, density function, quantile function and generation of random variates of the Gumbel distribution.

Usage

pgum(q, loc = 0, scale = 1)

dgum(x, loc = 0, scale = 1)

qgum(p, loc = 0, scale = 1)

rgum(n, loc = 0, scale = 1)

Arguments

See Also

pgev, pgpd, pln3

Three-parameter lognormal distribution

Description

Cumulative distribution function, density function, quantile function and generation of random variates of the three-parameter lognormal distribution.

Usage

pln3(q, loc = 0, scale = 1, shape = 1)

dln3(x, loc = 0, scale = 1, shape = 1)

qln3(p, loc = 0, scale = 1, shape = 1)

rln3(n, loc = 0, scale = 1, shape = 1)

Arguments

See Also

pgev, pgum, pgpd

L-Moment-ratio diagram

Description

Generates a ggplot2 object containing a scatterplot of TL skewness and TL kurtosis as well as the theoretical curves and points of several distributions (for now: GEV, GPD, LN3, GUM, EXP, NORM).

Usage

## S3 method for class 'TLMoments'
plot(x, ...)

## S3 method for class 'numeric'
plot.TLMoments(x, distr = "all", add_center = FALSE, use_internal = TRUE, ...)

## S3 method for class 'matrix'
plot.TLMoments(x, distr = "all", add_center = TRUE, use_internal = TRUE, ...)

## S3 method for class 'list'
plot.TLMoments(x, distr = "all", add_center = TRUE, use_internal = TRUE, ...)

## S3 method for class 'data.frame'
plot.TLMoments(x, distr = "all", add_center = TRUE, use_internal = TRUE, ...)

Arguments

Details

distr: this can either be a vector containing the abbreviations of the theoretical distributions (gev, gpd, ln3, pe3, glo, gum, exp, or norm) or one of the shortcuts \"all\" (default), \"only-lines\", or \"only-points\" that indicate all distributions, all distributions displayed as lines (i.e. gev, gpd, ln3, pe3, glo), or all distributions displayed as points (ie. gum, exp, norm), respectively.

Values of theoretical distributions are pre-calculated for several trimmings. If other trimmings are selected this results in a (small) delay for calculation.

Value

A ggplot object.

Methods (by class)

• numeric: plot.TLMoments for numeric vector

• matrix: plot.TLMoments for numeric matrix

• list: plot.TLMoments for numeric list

• data.frame: plot.TLMoments for numeric data.frame

Examples

## Not run: 
xmat <- matrix(rgev(1000, shape = .1), nc = 10)
xvec <- xmat[, 3]
xlist <- lapply(1L:ncol(xmat), function(i) xmat[, i])
xdat <- data.frame(
 station = rep(letters[1:10], each = 100),
 season = rep(c("S", "W"), 50),
 hq = as.vector(xmat)
)

library(ggplot2)
plot(TLMoments(xvec))
plot(TLMoments(xlist), distr = c("gev", "gum"), add_center = FALSE)
plot(TLMoments(xmat), distr = "only-points")
plot(TLMoments(xmat), distr = "only-lines") + scale_colour_viridis_d()
plot(TLMoments(xmat, 0, 1))
plot(TLMoments(xmat, 0, 1)) + coord_cartesian(xlim = c(-.05, .4), ylim = c(.05, .2))
plot(TLMoments(xdat, hq ~ station, 1, 0))

plot(TLMoments(xmat), add_center = FALSE)
plot(TLMoments(xmat), use_internal = FALSE)
plot(TLMoments(xmat, 2, 3))

## End(Not run)

Calculating quantiles from distribution parameters

Description

Calculates quantiles from distribution parameters received by parameters or from a named vector.

Usage

quantiles(x, p, distr = attr(x, "distribution"))

Arguments

Value

numeric vector, matrix, list, or data.frame of the quantiles and with class quantiles. The object contains the following attributes:

• distribution: a character indicating the used distribution

• p: a vector with the calculated quantiles

• source: a list with background information (used function, data, n, formula, trimmings; mainly for internal purposes)

The attributes are hidden in the print-function for a clearer presentation.

See Also

PWMs, TLMoments, parameters, summary.quantiles

Examples

# Generating data sets:
xmat <- matrix(rnorm(100), nc = 4)
xvec <- xmat[, 3]
xlist <- lapply(1L:ncol(xmat), function(i) xmat[, i])
xdat <- data.frame(
 station = rep(letters[1:2], each = 50),
 season = rep(c("S", "W"), 50),
 hq = as.vector(xmat)
)

# Calculating quantiles from parameters-object
tlm <- TLMoments(xvec, leftrim = 0, rightrim = 1)
quantiles(parameters(tlm, "gev"), c(.9, .99))
tlm <- TLMoments(xmat, leftrim = 1, rightrim = 1)
quantiles(parameters(tlm, "gum"), c(.9, .95, .999))
tlm <- TLMoments(xlist)
quantiles(parameters(tlm, "gpd"), .999)
tlm <- TLMoments(xdat, hq ~ station, leftrim = 2, rightrim = 3)
quantiles(parameters(tlm, "gev"), seq(.1, .9, .1))
tlm <- TLMoments(xdat, hq ~ station + season, leftrim = 0, rightrim = 2)
quantiles(parameters(tlm, "gum"), seq(.1, .9, .1))

# Distribution can be overwritten (but parameters have to fit)
tlm <- TLMoments(xvec, leftrim = 0, rightrim = 1)
params <- parameters(tlm, "gev")
quantiles(params, c(.9, .99))
quantiles(params[1:2], c(.9, .99), distr = "gum")
evd::qgumbel(c(.9, .99), loc = params[1], scale = params[2])


# Using magrittr
library(magrittr)
rgev(50, shape = .3) %>%
  TLMoments(leftrim = 0, rightrim = 1) %>%
  parameters("gev") %>%
  quantiles(c(.99, .999))

# Calculating quantiles to given parameters for arbitrary functions
quantiles(c(mean = 10, sd = 3), c(.95, .99), "norm")
qnorm(c(.95, .99), mean = 10, sd = 3)

# These give errors:
#quantiles(c(loc = 10, scale = 5, shape = .3), c(.95, .99), "notexistingdistribution")
#quantiles(c(loc = 10, scale = 5, shpe = .3), c(.95, .99), "gev") # wrong arguments

Calculation of regionalized TL-moments

Description

regionalize takes the result of TLMoments and calculates a weighted mean of TL-moments and TL-moment ratios.

Usage

regionalize(x, ...)

## S3 method for class 'numeric'
regionalize(x, ...)

## S3 method for class 'matrix'
regionalize(x, w = attr(x, "source")$n, reg.lambdas = TRUE, ...)

## S3 method for class 'data.frame'
regionalize(x, w = attr(x, "source")$n, reg.lambdas = TRUE, ...)

## S3 method for class 'list'
regionalize(x, w = attr(x, "source")$n, reg.lambdas = TRUE, ...)

Arguments

Value

list of two dimensions: lambdas/ratios are numeric vectors consisting of the regionalized TL-moments/TL-moment-ratios. The list has the class TLMoments. The object contains the following attributes:

• leftrim: a numeric giving the used leftrim-argument

• rightrim: a numeric giving the used rightrim-argument

• order: a integer vector with corresponding TL-moment orders

• source: a list with background information (used function, data, n, formula, computation.method; mainly for internal purposes)

Examples

xmat <- matrix(rgev(100), nc = 4)
xvec <- xmat[, 3]
xlist <- lapply(1L:ncol(xmat), function(i) xmat[, i])
xdat <- data.frame(
 station = rep(letters[1:2], each = 50),
 season = rep(c("S", "W"), 50),
 hq = as.vector(xmat)
)

regionalize(TLMoments(xmat))
regionalize(TLMoments(xlist))
regionalize(TLMoments(xdat, hq ~ station))
# For numeric vector TLMoments, nothing happens:
regionalize(TLMoments(xvec))

tlm <- TLMoments(xmat)
regionalize(tlm)
regionalize(tlm, reg.lambdas = FALSE)

parameters(regionalize(tlm), "gev")
parameters(regionalize(tlm, reg.lambdas = FALSE), "gev")

quantiles(parameters(regionalize(tlm), "gev"), c(.99, .999))
quantiles(parameters(regionalize(tlm, reg.lambdas = FALSE), "gev"), c(.99, .999))


# With magrittr
library(magrittr)
matrix(rgev(200, shape = .3), nc = 5) %>%
 TLMoments(rightrim = 1) %>%
 regionalize %>%
 parameters("gev") %>%
 quantiles(c(.99, .999))

returnPWMs

Description

Sets attributes to PWMs objects and returns them. This function is for internal use.

Usage

returnPWMs(out, order, ...)

Arguments

Value

An object of class PWMs.

returnParameters

Description

Sets attributions to parameters objects and returns them. This function is for internal use.

Usage

returnParameters(out, distribution, ...)

Arguments

Value

An object of class parameters.

returnQuantiles

Description

Sets attributions to quantiles objects and returns them. This function is for internal use.

Usage

returnQuantiles(out, distribution, p, ...)

Arguments

Value

An object of class quantiles.

returnTLMoments

Description

Sets attributions to TLMoments objects and returns them. This function is for internal use.

Usage

returnTLMoments(out, leftrim, rightrim, order, ...)

Arguments

Value

An object of class TLMoments.

Summary PWMs

Description

Calculating and printing of summary statistics to a given PWMs-object.

Usage

## S3 method for class 'PWMs'
summary(object, ci.level = 0.9, ...)

Arguments

Value

A summary.PWMs-object, a list with dimensions

• pwm

• ci.level

• ci

• cov

It is printed with print.summary.PWMs.

See Also

PWMs, est_cov

Examples

x <- cbind(rgev(100, shape = .2), rgev(100, shape = .2))

summary(PWMs(x[, 1]))
summary(PWMs(x[, 1]), distr = "gev")
summary(PWMs(x[, 1]), distr = "gev", select = 1:2)

summary(PWMs(x))
summary(PWMs(x), select = 1:2)

## Not run: 
summary(as.PWMs(c(15, 4, .5)))

## End(Not run)

Summary TLMoments

Description

Calculating and printing of summary statistics to a given TLMoments-object.

Usage

## S3 method for class 'TLMoments'
summary(object, ci.level = 0.9, ...)

Arguments

Value

A summary.TLMoments-object, a list with dimensions

• tlm

• ci.level

• lambda.ci

• lambda.cov

• ratio.ci

• ratio.cov

It is printed with print.summary.TLMoments.

See Also

TLMoments, est_cov

Examples

tlm <- TLMoments(rgev(100, shape = .2))
summary(tlm)

tlm <- TLMoments(rgev(100, shape = .2), rightrim = 1)
summary(tlm, select = 3:4)

tlm <- TLMoments(rgev(100, shape = .2), max.order = 2, rightrim = 1)
summary(tlm)

tlm <- TLMoments(matrix(rgev(100, shape = .2), nc = 2))
summary(tlm, select = 3:4)

tlm <- TLMoments(matrix(rgev(100, shape = .2), nc = 2), max.order = 3)
summary(tlm, ci = .95, distr = "gev")

tlm <- as.TLMoments(c(15, 5, 1.3))
summary(tlm, distr = "gev", set.n = 100)

Summary parameters

Description

Calculating and printing of summary statistics to a given parameters-object.

Usage

## S3 method for class 'parameters'
summary(object, ci.level = 0.9, ...)

Arguments

Value

A summary.parameters-object, a list with dimensions

• param

• ci.level

• ci

• cov

It is printed with print.summary.parameters.

See Also

parameters, est_cov

Examples

x <- cbind(rgev(100, shape = .2), rgev(100, shape = .2))

p <- parameters(TLMoments(x[, 1]), "gev")
summary(p)
summary(p, select = c("scale", "shape"))

p <- parameters(TLMoments(x[, 1], rightrim = 1), "gev")
summary(p)

p <- parameters(TLMoments(x), "gev")
summary(p)
summary(p, select = "shape")

p <- as.parameters(loc = 10, scale = 5, shape = .3, distr = "gev")
summary(p)
summary(p, rightrim = 1, set.n = 250)

Summary quantiles

Description

Calculating and printing of summary statistics to a given quantiles-object.

Usage

## S3 method for class 'quantiles'
summary(object, ci.level = 0.9, ...)

Arguments

Value

A summary.quantiles-object, a list with dimensions

• q

• ci.level

• ci

• cov

It is printed with print.summary.quantiles.

See Also

quantiles, est_cov

Examples

x <- cbind(rgev(100, shape = .2), rgev(100, shape = .2))

q <- quantiles(parameters(TLMoments(x[, 1]), "gev"), c(.9, .95, .99))
summary(q)
summary(q, select = c(.9, .99))

q <- quantiles(parameters(TLMoments(x[, 1], rightrim = 1), "gev"), .95)
summary(q)

q <- quantiles(parameters(TLMoments(x), "gev"), c(.9, .95, .99))
summary(q)
summary(q, select = .95)

q <- quantiles(as.parameters(loc = 10, scale = 5, shape = .3, distr = "gev"), c(.9, .99))
summary(q)
summary(q, rightrim = 1, set.n = 250)