Functionality for multivairate Bernoulli distribution including
log-linear models, lasso variable selection and mixed effects models.
Details
Package:
MVB
Type:
Package
Version:
1.0
Date:
2012-03-21
License:
GPL (>=2)
Author(s)
Bin Dai
<daibin at stat dot wisc dot edu>
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$response, output = 1)
Results
R version 3.3.1 (2016-06-21) -- "Bug in Your Hair"
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> library(MVB)
Loading required package: Rcpp
Loading required package: RcppArmadillo
> png(filename="/home/ddbj/snapshot/RGM3/R_CC/result/MVB/MVB-package.Rd_%03d_medium.png", width=480, height=480)
> ### Name: MVB-package
> ### Title: MVB as Multivariate Bernoulli
> ### Aliases: MVB-package MVB
> ### Keywords: Multivariate Bernoulli, lasso
>
> ### ** Examples
>
> # fit a simple MVB log-linear model
> n <- 1000
> p <- 5
> kk <- 2
> tt <- NULL
> alter <- 1
> for (i in 1:kk) {
+ vec <- rep(0, p)
+ vec[i] <- alter
+ alter <- alter * (-1)
+ tt <- cbind(tt, vec)
+ }
> tt <- 1.5 * tt
> tt <- cbind(tt, c(rep(0, p - 1), 1))
>
> x <- matrix(rnorm(n * p, 0, 4), n, p)
> res <- mvb.simu(tt, x, K = kk, rep(.5, 2))
> fitMVB <- mvbfit(x, res$response, output = 1)
fit started
iteration 0 gpnorm = 2.47037
iteration 8 gpnorm = 4.97457e-07
*** Converged ***
>
>
>
>
>
> dev.off()
null device
1
>