ds.poisson: Poisson Regression

In paularaissa/distStatsClient: Distributed Multiple Imputations

Description

Computes Poisson regression model. It can be used to fit univariate, multivariate and weighted Poisson models.

Usage

ds.poisson(formula, learningrate = 0.01, dif = 1e-09, checks = FALSE,
  datasources = NULL)

Arguments

formula	a character that can be coerced to an object of class formula. It is a symbolic description of the model to be fitted. The Newton Raphson Method is apllied to compute the Poisson coefficients.
learningrate	a numeric, controls how much we are adjusting the regression model.
dif	a numeric, controls the learning convergence.
checks	a boolean, if TRUE (default) checks that verify elements on the server side such checks lengthen the run-time so the default is FALSE and one can switch these checks on (set to TRUE) when faced with some error(s).
datasources	a list of opal object(s) obtained after login in to opal servers; these objects hold also the data assign to R, as data frame, from opal datasources.

Details

It is a wrapper for the client side function getDerivative, setting the model family as 'poisson'.

Value

Returns a list with the following components:

call	the model formula.
coefficients	a vector of poisson regression coefficients.
xtxw	a data matrix, the Hessian matrix.
xtyp	a data matrix, that integrates the computation of derivatives.

Dependencies

getDerivative

Author(s)

Paula Raissa Costa e Silva

See Also

Other regressions: ds.linear, ds.logistic, getDerivative

Examples

{
ds.poisson('D$y ~ D$x')
}

paularaissa/distStatsClient documentation built on June 19, 2019, 12:43 a.m.