add.term: Add many single terms to a model

In Rfast2: A Collection of Efficient and Extremely Fast R Functions II

Add many single terms to a model

Description

Add many single terms to a model.

Usage

add.term(y, xinc, xout, devi_0, type = "logistic", logged = FALSE,
tol = 1e-07, maxiters = 100, parallel = FALSE)    

Arguments

y	The response variable. It must be a numerical vector.
xinc	The already included indendent variable(s).
xout	The independent variables whose conditional association with the response is to be calculated.
devi_0	The deviance for Poisson, logistic, qpoisson, qlogistic and normlog regression or the log-likelihood for the Weibull, spml and multinomial regressions. See the example to understand better.
type	The type of regression, "poisson", "logistic", "qpoisson" (quasi Poisson), "qlogistic" (quasi logistic) "normlog" (Gaussian regression with log-link) "weibull", "spml" and "multinom".
logged	Should the logarithm of the p-value be returned? TRUE or FALSE.
tol	The tolerance value to terminate the Newton-Raphson algorithm when fitting the regression models.
maxiters	The maximum number of iterations the Newton-Raphson algorithm will perform.
parallel	Should the computations take place in parallel? TRUE or FALSE.

Details

The function is similar to the built-in function add1. You have already fitted a regression model with some independent variables (xinc). You then add each of the xout variables and test their significance.

Value

A matrix with two columns. The test statistic and its associated (logged) p-value.

Author(s)

Stefanos Fafalios.

R implementation and documentation: Stefanos Fafalios <[e-mail]>.

References

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

Presnell Brett, Morrison Scott P. and Littell Ramon C. (1998). Projected multivariate linear models for directional data. Journal of the American Statistical Association, 93(443): 1068-1077.

See Also

bic.regs, logiquant.regs, sp.logiregs

Examples

x <- matrix( rnorm(200 * 10), ncol = 10)
y <- rpois(200, 10)
devi_0 <- deviance( glm(y ~ x[, 1:2], poisson) )
a <- add.term(y, xinc = x[,1:2], xout = x[, 3:10], devi_0 = devi_0, type= "poisson")

y <- rbinom(200, 1, 0.5)
devi_0 <- deviance( glm(y ~ x[, 1:2], binomial) )
a <- add.term(y, xinc = x[,1:2], xout = x[, 3:10], devi_0 = devi_0, type= "logistic")


y <- rbinom(200, 2, 0.5)
devi_0 <- Rfast::multinom.reg(y, x[, 1:2])$loglik
a <- add.term(y, xinc = x[,1:2], xout = x[, 3:10], devi_0 = devi_0, type= "multinom")

y <- rgamma(200, 3, 1)
devi_0 <- Rfast::weib.reg(y, x[, 1:2])$loglik
a <- add.term(y, xinc = x[,1:2], xout = x[, 3:10], devi_0 = devi_0, type= "weibull")

Rfast2 documentation built on Aug. 8, 2023, 1:11 a.m.