output binary matrix with number of columns equal to the
number of outcomes per observation.
maxOrder
maximum order of interactions to be considered in outcomes.
output
with values 0 or 1, indicating whether the fitting
process is muted or not.
direction
the mode of stepwise search and default is backward.
tune
tuning approach, available methods including AIC, BIC,
GACV, BGACV.
start
starting object of type mvbfit.
Details
The stepfit utilize the class structure of the underlying C++
code and stepwisd fitted the model with Newton-Raphson algorithm.
Value
An object of class mvbfit, for which some methods are
available.
See Also
mvblps, unifit, stepfit, mvb.simu
Examples
# fit a simple MVB log-linear model
n <- 1000
p <- 5
kk <- 2
tt <- NULL
alter <- 1
for (i in 1:kk) {
vec <- rep(0, p)
vec[i] <- alter
alter <- alter * (-1)
tt <- cbind(tt, vec)
}
tt <- 1.5 * tt
tt <- cbind(tt, c(rep(0, p - 1), 1))
x <- matrix(rnorm(n * p, 0, 4), n, p)
res <- mvb.simu(tt, x, K = kk, rep(.5, 2))
fitMVB <- mvbfit(x, res$response, output = 1)
Results
R version 3.3.1 (2016-06-21) -- "Bug in Your Hair"
Copyright (C) 2016 The R Foundation for Statistical Computing
Platform: x86_64-pc-linux-gnu (64-bit)
R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.
R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.
Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.
> library(MVB)
Loading required package: Rcpp
Loading required package: RcppArmadillo
> png(filename="/home/ddbj/snapshot/RGM3/R_CC/result/MVB/stepfit.Rd_%03d_medium.png", width=480, height=480)
> ### Name: stepfit
> ### Title: step-wisd multivariate model fitting
> ### Aliases: stepfit
>
> ### ** Examples
>
> # fit a simple MVB log-linear model
> n <- 1000
> p <- 5
> kk <- 2
> tt <- NULL
> alter <- 1
> for (i in 1:kk) {
+ vec <- rep(0, p)
+ vec[i] <- alter
+ alter <- alter * (-1)
+ tt <- cbind(tt, vec)
+ }
> tt <- 1.5 * tt
> tt <- cbind(tt, c(rep(0, p - 1), 1))
>
> x <- matrix(rnorm(n * p, 0, 4), n, p)
> res <- mvb.simu(tt, x, K = kk, rep(.5, 2))
> fitMVB <- mvbfit(x, res$response, output = 1)
fit started
iteration 0 gpnorm = 2.44293
iteration 8 gpnorm = 4.57629e-08
*** Converged ***
>
>
>
>
>
>
> dev.off()
null device
1
>