Description Usage Arguments Details Value See Also Examples

stepwise fit multivariate log-linear Bernoulli model using Newton-Raphson algorithm.

1 2 3 4 5 |

`x` |
input design matrix. |

`y` |
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. |

The `stepfit`

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

An object of class `mvbfit`

, for which some methods are
available.

`mvblps`

, `unifit`

, `stepfit`

, `mvb.simu`

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 | ```
# 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)
``` |

MVB documentation built on May 2, 2019, 3:06 a.m.

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