| Type: | Package |
| Title: | An Empirical Model for Underdispersed Count Data |
| Version: | 0.1.2 |
| Description: | Count regression models for underdispersed small counts (lambda < 20) based on the three-parameter exponentially weighted Poisson distribution of Ridout & Besbeas (2004) <doi:10.1191/1471082X04st064oa>. |
| License: | MIT + file LICENSE |
| Encoding: | UTF-8 |
| LazyData: | true |
| RoxygenNote: | 7.3.2 |
| Depends: | R (≥ 2.10) |
| LinkingTo: | BH, Rcpp |
| Imports: | Rcpp, mvtnorm |
| Suggests: | covr, DHARMa, testthat (≥ 3.0.0) |
| Config/testthat/edition: | 3 |
| NeedsCompilation: | yes |
| Packaged: | 2025-04-22 11:05:05 UTC; philipp.boerschsupan |
| Author: | Philipp Boersch-Supan
 [aut, cre],
James Clarke
[aut] |
| Maintainer: | Philipp Boersch-Supan <pboesu@gmail.com> |
| Repository: | CRAN |
| Date/Publication: | 2025-04-22 11:20:02 UTC |
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.
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
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
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 = 500,
sum_limit = round(max(Y) * 3),
start_val = NULL
)
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. |
start_val |
list, defaults to fitting a Poisson regression; specify starting values |
Value
an ewp model
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
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
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
Estimate marginal means
Description
Estimate marginal means
Usage
mmean(object, cov, ci = TRUE, nsamples = 250, ...)
Arguments
object |
ewp model object |
cov |
character, covariate to find marginal mean for |
ci |
logical, defaults to TRUE, whether or not to include confidence intervals |
nsamples |
numeric, defaults to 250, number of samples for use in obtaining the confidence intervals |
... |
ignored |
Value
printout of the marginal means
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
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.
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
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
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.
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.
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