LWest-methods {LambertW} | R Documentation |
Useful information for estimate output (class LWest
): parameter estimate widehat{theta}, standard errors, theoretical support, etc.; nice formating (print
), and plot
s (see Details).
## S3 method for class 'LWest': summary(object, ...) ## S3 method for class 'summary.LWest': print(x, ...) ## S3 method for class 'LWest': print(x, ...) ## S3 method for class 'LWest': plot(x, QQ=FALSE, ...)
object |
object of class LWest |
x |
object of class LWest |
QQ |
should a LambertW QQ plot be displayed? default FALSE |
... |
further arguments passed to or from other methods. |
print.LWest
prints only very basic information about widehat{theta} (to prevent an overload of data/information in the console when executing an estimator).
print.summary.LWest
tries to be smart about formatting the coefficients, standard errors, etc. and additionally gives "significance stars".
plot.LWest
plots a (1) histogram, (2) empirical density of the data y
. These are compared to the theoretical Gaussian (or t) and Lambert W times Gaussian (or t) densities (3) implied by widehat{theta}.
summary
gives an object of class summary.LWest
. A list containing
call |
function call |
coefmat |
matrix with 4 columns: widehat{theta}, its standard errors, t-statistic, and corresponding (two-sided) p-values |
distname |
input distribution; default: "normal" , alternative "t" |
n |
number of observations |
data |
original data set (y ) |
input |
back-transformed data |
support |
support of Y |
data.range |
empirical data range |
method |
estimation method |
hessian |
Hessian at the optimum. Numerically obtained for method = "MLE" ; for method = "IGMM" only an approximation, as it is a diagonal matrix derived from the asymptotic covariance matrix obtained by simulations. |
p_1 |
Probability that one observation was caused by input lying on the non-principal branch; see p_1 . |
p_1n |
Probability that one or more of the n observation was caused by input lying on the non-principal branch; see p_1 . |
Georg M. Goerg
Goerg, G.M. (2009). “Lambert W Random Variables - A new class of skewed distribution functions”. Unpublished
data(AA) attach(AA) X=AA[AA$sex=="f",] y=X$bmi fit.ml=MLE_LambertW(y) A=summary(fit.ml) print(A) plot(fit.ml)