CRE: A Model with Correlated Random Effects in Poisson and Probit...

Description Usage Arguments Value References See Also Examples

View source: R/CRE.R

Description

Estimate a model in panel counting data, in which the selection equation is a Probit model with random effects on individuals, and the outcome equation is a Poisson model with random effects on the same individuals. The random effects on the same individual are correlated across two equations.

Usage

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CRE(sel_form, out_form, id, data = NULL, par = NULL, par_files = NULL,
  delta = 1, max_delta = 3, sigma = 1, max_sigma = 3, rho = 0,
  lower = c(rho = -1), upper = c(rho = 1), method = "L-BFGS-B",
  H = c(10, 10), psnH = 20, prbH = 20, accu = 10000, reltol = 1e-08,
  verbose = 0, tol_gtHg = Inf)

Arguments

sel_form

Formula for selection equation, a probit model with random effects

out_form

Formula for outcome equation, a Poisson model with random effects

id

A vector that represents the identity of individuals, numeric or character

data

Input data, a data frame

par

Starting values for estimates

par_files

Loading initial values from saved ProbitRE and PoissonRE estimates

delta

Variance of random effects in Probit model

max_delta

Largest allowed initial delta

sigma

Variance of random effects in Poisson model

max_sigma

Largest allowed initial sigma

rho

Correlation between random effects in Probit and Poisson models

lower

Lower bound for estiamtes

upper

Upper bound for estimates

method

Searching algorithm, don't change default unless you know what you are doing

H

A vector of length 2, specifying the number of points for inner and outer Quadratures

psnH

Number of Quadrature points for Poisson RE model

prbH

Number of Quddrature points for Probit RE model

accu

L-BFGS-B only, 1e12 for low accuracy; 1e7 for moderate accuracy; 10.0 for extremely high accuracy. See optim

reltol

Relative convergence tolerance. default typically 1e-8

verbose

Level of output during estimation. Lowest is 0.

tol_gtHg

tolerance on gtHg, not informative for L-BFGS-B

Value

A list containing the results of the estimated model

References

1. Jing Peng and Christophe Van den Bulte. Participation vs. Effectiveness of Paid Endorsers in Social Advertising Campaigns: A Field Experiment. Working Paper.

2. Jing Peng and Christophe Van den Bulte. How to Better Target and Incent Paid Endorsers in Social Advertising Campaigns: A Field Experiment. In Proceedings of the 2015 International Conference on Information Systems.

See Also

Other PanelCount: CRE_SS; PLN_RE; PoissonRE; ProbitRE

Examples

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data(rt)
# Note: estimation may take 2~3 minutes
est = CRE(isRetweet~fans+tweets+as.factor(tweet.id),
                   num.words~fans+tweets+as.factor(tweet.id),
                   id=rt$user.id, data=rt)

PanelCount documentation built on May 2, 2019, 3:21 p.m.