Many Wald based tests for logistic and Poisson regressions with continuous predictors {MXM}

Many Wald based tests for logistic and Poisson regressions with continuous predictors

Description

Many Wald based tests for logistic and Poisson regressions with continuous predictors.

Usage

wald.logisticregs(y, x, tol = 1e-09, wei = NULL, check = FALSE, logged = FALSE, 
ncores = 1)  
wald.poissonregs(y, x, tol = 1e-09, wei = NULL, check = FALSE, logged = FALSE, 
ncores = 1)  

Arguments

y

A vector with either 0s and 1 (logistic regression) or discrete data, counts (Poisson regression).

x

A data.frame, the predictor variables. If you have no categorical variables, the fucntion will still work but it's better to use the score.glms because it is faster.

tol

The tolerance value to stop the Newton-Raphson iterations. It is set to 1e-09 by default.

wei

A vector of weights to be used for weighted regression. The default value is NULL. An example where weights are used is surveys when stratified sampling has occured.

check

A boolean variable indicating whether to chekc for variables with identical values. The defauls is FALSE.

logged

A boolean variable; it will return the logarithm of the pvalue if set to TRUE.

ncores

How many to cores to useq the default value is 1.

Details

Instead of using R built-in function glm we implemented the newton-Raphson in order to avoid unnecessary calculations. The functions are much faster.

Value

A matrix with three columns, the test statistic, its associated (logged) p-value and the BIC of each model.

Author(s)

Michail Tsagris

R implementation and documentation: Michail Tsagris [e-mail] and Manos Papadakis <[e-mail]>.

References

Draper, N.R. and Smith H. (1988). Applied regression analysis. New York, Wiley, 3rd edition.

McCullagh, Peter, and John A. Nelder. Generalized linear models. CRC press, USA, 2nd edition, 1989.

See Also

univregs, perm.univregs

Examples

## 20 variables, hence 20 univariate regressions are to be fitted
x <- matrix( rnorm(200 * 20), ncol = 20 )
y <- rpois(200, 4)
a <- wald.poissonregs(y, x)
b <- univregs(y, x, test = testIndPois)
cor(exp(a[, 2]), exp(b$pvalue) )