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.
printIter
Number of iterations to be printed if output is true.
Details
The mvbfit 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.
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$response, output = 1)
Results
R version 3.3.1 (2016-06-21) -- "Bug in Your Hair"
Copyright (C) 2016 The R Foundation for Statistical Computing
Platform: x86_64-pc-linux-gnu (64-bit)
R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.
R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.
Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.
> library(MVB)
Loading required package: Rcpp
Loading required package: RcppArmadillo
> png(filename="/home/ddbj/snapshot/RGM3/R_CC/result/MVB/mvbfit.Rd_%03d_medium.png", width=480, height=480)
> ### Name: mvbfit
> ### Title: multivariate Bernoulli logistic model fitting
> ### Aliases: mvbfit
>
> ### ** Examples
>
> # fit a simple MVB log-linear model
> n <- 1000
> p <- 5
> kk <- 2
> tt <- NULL
> alter <- 1
> for (i in 1:kk) {
+ vec <- rep(0, p)
+ vec[i] <- alter
+ alter <- alter * (-1)
+ tt <- cbind(tt, vec)
+ }
> tt <- 1.5 * tt
> tt <- cbind(tt, c(rep(0, p - 1), 1))
>
> x <- matrix(rnorm(n * p, 0, 4), n, p)
> res <- mvb.simu(tt, x, K = kk, rep(.5, 2))
> fitMVB <- mvbfit(x, res$response, output = 1)
fit started
iteration 0 gpnorm = 2.48357
iteration 8 gpnorm = 6.14042e-09
*** Converged ***
>
>
>
>
>
>
> dev.off()
null device
1
>