LambertW_fit-methods {LambertW}R Documentation

Methods for Lambert W\times F estimates

Description

S3 methods (print, plot, summary, etc.) for LambertW_fit class returned by the MLE_LambertW or IGMM estimators.

plot.LambertW_fit plots a (1) histogram, (2) empirical density of the data y. These are compared (3) to the theoretical F_X(x \mid \widehat{\boldsymbol β}) and (4) Lambert W \times F_X(y \mid \widehat{\boldsymbol β}) densities.

print.LambertW_fit prints only very basic information about \widehat{θ} (to prevent an overload of data/information in the console when executing an estimator).

print.summary.LambertW_fit tries to be smart about formatting the coefficients, standard errors, etc. and also displays "significance stars" (like in the output of summary.lm).

summary.LambertW_fit computes some auxiliary results from the estimate such as standard errors, theoretical support (only for type="s"), skewness tests (only for type="hh"), etc. See print.summary.LambertW_fit for print out in the console.

Usage

## S3 method for class 'LambertW_fit'
plot(x, xlim = NULL, show.qqplot = FALSE, ...)

## S3 method for class 'LambertW_fit'
print(x, ...)

## S3 method for class 'summary.LambertW_fit'
print(x, ...)

## S3 method for class 'LambertW_fit'
summary(object, ...)

Arguments

x, object

object of class LambertW_fit

xlim

lower and upper limit of x-axis for cdf and pdf plots.

show.qqplot

should a Lambert W \times F QQ plot be displayed? Default: FALSE.

...

further arguments passed to or from other methods.

Value

summary returns a list of class summary.LambertW_fit containing

call

function call

coefmat

matrix with 4 columns: \widehat{θ}, its standard errors, t-statistic, and two-sided p-values

distname

see Arguments

n

number of observations

data

original data (y)

input

back-transformed input data

support

support of output random variable Y

data.range

empirical data range

method

estimation method

hessian

Hessian at the optimum. Numerically obtained for method = "MLE"; for method = "IGMM" a diagonal-matrix approximation from covariance matrix obtained by simulations for n = 1000 samples in Goerg (2011).

p_m1, p_m1n

Probability that one (or n) observation were caused by input from the non-principal branch (see p_m1); only for type = "s".

symmetry.p.value

p-value from Wald test of identical left and right tail parameters (see test_symmetry); only for type = "hh".

Examples


# See ?LambertW-package
[Package LambertW version 0.6.7 Index]