Package 'ewp'

Extract coefficients

Description

Extract coefficients

Usage

## S3 method for class 'ewp'
coef(object, ...)

Arguments

object	an object of class ewp
...	ignored

Value

a vector of coefficient values. Beware that the lambda parameters are on the log-link scale, whereas the betas are estimated using an identity link.

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Probability mass function of the three-parameter EWP

Description

Probability mass function of the three-parameter EWP

Usage

dewp3(x, lambda, beta1, beta2, sum_limit = max(x) * 3)

Arguments

x	vector of (positive integer) quantiles.
lambda	centrality parameter
beta1	lower-tail dispersion parameter
beta2	upper tail dispersion parameter
sum_limit	summation limit for the normalizing factor

Value

a vector of probabilities

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Probability mass function of the three-parameter EWP

Description

Probability mass function of the three-parameter EWP

Usage

dewp3_cpp(x, lambda, beta1, beta2, sum_limit)

Arguments

x	vector of (positive integer) quantiles.
lambda	centrality parameter
beta1	lower-tail dispersion parameter
beta2	upper tail dispersion parameter
sum_limit	summation limit for the normalizing factor

Value

a probability mass

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Exponentially weighted Poisson regression model

Description

Exponentially weighted Poisson regression model

Usage

ewp_reg(
  formula,
  family = "ewp3",
  data,
  verbose = TRUE,
  method = "Nelder-Mead",
  hessian = TRUE,
  autoscale = TRUE,
  maxiter = 5000,
  sum_limit = round(max(Y) * 3)
)

Arguments

formula	an object of class "formula" (or one that can be coerced to that class): a symbolic description of the model to be fitted.
family	choice of "ewp2" or "ewp3"
data	a data frame containing the variables in the model.
verbose	logical, defaults to TRUE; print model fitting progress
method	string, passed to optim, defaults to 'BFGS'
hessian	logical, defaults to TRUE; calculate Hessian?
autoscale	logical, defaults to TRUE; automatically scale model parameters inside the optimisation routine based on initial estimates from a Poisson regression.
maxiter	numeric, maximum number of iterations for optim
sum_limit	numeric, defaults to 3*maximum count; upper limit for the sum used for the normalizing factor.

Value

an ewp model

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Extract fitted values

Description

Extract fitted values

Usage

## S3 method for class 'ewp'
fitted(object, ...)

Arguments

object	an object of class ewp
...	ignored

Value

a vector of fitted values on the response scale

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Linnet clutch sizes

Description

A dataset containing the clutch sizes for linnet, recreated from Ridout & Besbeas 2004

Usage

linnet

Format

A data frame with 5414 rows and 3 variables:

eggs

clutch size

cov1

a synthetic random noise covariate

cov2

a synthetic covariate that is positively correlated with the outcome

Source

Ridout & Besbeas 2004, P. Boersch-Supan

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Extract log likelihood

Description

Extract log likelihood

Usage

## S3 method for class 'ewp'
logLik(object, ...)

Arguments

object	an object of class ewp
...	ignored

Value

a numeric

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Predict from fitted model

Description

Predict from fitted model

Usage

## S3 method for class 'ewp'
predict(object, newdata, type = c("response"), na.action = na.pass, ...)

Arguments

object	ewp model object
newdata	optional data.frame
type	character; default="response", no other type implemented
na.action	defaults to na.pass()
...	ignored

Value

a vector of predictions

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Print ewp model object

Description

Print ewp model object

Usage

## S3 method for class 'ewp'
print(x, digits = max(3, getOption("digits") - 3), ...)

Arguments

x	ewp model object
digits	digits to print
...	ignored

Value

a summary printout of the ewp model call and fitted coefficients.

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Print ewp model summary

Description

Print ewp model summary

Usage

## S3 method for class 'summary.ewp'
print(x, digits = max(3, getOption("digits") - 3), ...)

Arguments

x	ewp model summary
digits	number of digits to print
...	additional arguments to printCoefmat()

Value

printout of the summary object

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Random samples from the three-parameter EWP

Description

Random samples from the three-parameter EWP

Usage

rewp3(n, lambda, beta1, beta2, sum_limit = 30)

Arguments

n	number of observations
lambda	centrality parameter
beta1	lower-tail dispersion parameter
beta2	upper tail dispersion parameter
sum_limit	summation limit for the normalizing factor

Value

random deviates from the EWP_3 distribution

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simulate from fitted model

Description

simulate from fitted model

Usage

## S3 method for class 'ewp'
simulate(object, nsim = 1, ...)

Arguments

object	ewp model object
nsim	number of response vectors to simulate. Defaults to 1.
...	ignored

Value

a data frame with 'nsim' columns.

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Model summary

Description

Model summary

Usage

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

Arguments

object	ewp model fit
...	ignored

Value

The function 'summary.ewp' computes and returns a list of summary statistics of the fitted ewp model.

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Extract estimated variance-covariance matrix

Description

Extract estimated variance-covariance matrix

Usage

## S3 method for class 'ewp'
vcov(object, ...)

Arguments

object	an object of class ewp
...	ignored

Value

a matrix

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