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Last data update: 2014.03.03

R: univariate model fitting

unifit

R Documentation

univariate model fitting

Description

fit univariate log-linear model using Newton-Raphson algorithm.

Usage

unifit(formula, data = list(),
       family = c("gaussian", "binomial"),
       output = 0)

Arguments

`formula`	a symbolic description of the model to be fit.
`data`	an optional data frame containing the variables in the model. By default the variables are taken from the environment from which `unifit` is called.
`family`	a GLM family, currently support gaussian and binomial.
`output`	with values 0 or 1, indicating whether the fitting process is muted or not.

Details

The unifit utilize the class structure of the underlying C++ code and fitted the model with Newton-Raphson algorithm.

Value

An object of class mvbfit, for which some methods are available.

Examples

n <- 100
p <- 4
x <- matrix(rnorm(n * p, 0, 4), n, p)
eta <- x 
pr <- exp(eta) / (1+ exp(eta))
res <- rbinom(n, 1, pr)
fit <- unifit(res ~ x - 1, family = 'binomial')

Results


R version 3.3.1 (2016-06-21) -- "Bug in Your Hair"
Copyright (C) 2016 The R Foundation for Statistical Computing
Platform: x86_64-pc-linux-gnu (64-bit)

R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.

R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.

Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.

> library(MVB)
Loading required package: Rcpp
Loading required package: RcppArmadillo
> png(filename="/home/ddbj/snapshot/RGM3/R_CC/result/MVB/unifit.Rd_%03d_medium.png", width=480, height=480)
> ### Name: unifit
> ### Title: univariate model fitting
> ### Aliases: unifit
> 
> ### ** Examples
> 
> n <- 100
> p <- 4
> x <- matrix(rnorm(n * p, 0, 4), n, p)
> eta <- x 
> pr <- exp(eta) / (1+ exp(eta))
> res <- rbinom(n, 1, pr)
> fit <- unifit(res ~ x - 1, family = 'binomial')
> 
> 
> 
> 
> 
> dev.off()
null device 
          1 
>