Description Usage Arguments Details Value Author(s) References See Also Examples

Performs a trio logic regression analysis as proposed by Li et al. (2011), where trio logic regression is an adaptation of logic regression (Ruczinski et al., 2003) for case-parent trio data.

1 2 3 4 5 6 7 8 9 |

`x` |
either an object of class |

`y` |
a numeric vector specifying the case-pseudo-control status for the observations in |

`search` |
character string naming the search algorithm that should be used in the search for the best
trio logic regression model. By default, i.e. |

`nleaves` |
integer or vector of two integers specifying the maximum number of leaves, i.e.\ variables,
in the logic tree of the trio logic regression model (please note in trio logic regression the model
consists only of one logic tree).
Must be a single integer, if |

`penalty` |
a non-negative value for the |

`weights` |
a numeric vector containing one weight for each trio considered in |

`control` |
a list of control parameters for the search algorithms and the logic tree considered when fitting a
(trio) logic regression model. For these parameters, see |

`rand` |
integer. If specified, the random number generator will be set into a reproducible state. |

`formula` |
an object of class |

`data` |
a data frame containing the variables in the model. Each row of |

`recdom` |
a logical value or vector of length |

`...` |
for the |

Trio logic regression is an adaptation of logic regression to case-parent trio data. Virtually all
features for a standard logic regression analysis with the function `logreg`

available in the `R`

package `LogicReg`

are also available for a trio logic regression analysis,
either directly via `trioLR`

or via the function `trio.permTest`

for performing permutation tests.

For a detailed, comprehensive description on how to perform a logic regression analysis, and thus, a trio
logic regression analysis, see the `Details`

section of the help page for the function `logreg`

in the `R`

package `LogicReg`

. For a detailed explanation on how to specify the parameters for
simulated annealing, see the man page of the function `logreg.anneal.control`

in the `R`

package
`LogicReg`

.

Finally, an example for a trio logic regression analysis is given in the vignette `trio`

available
in the `R`

package `trio`

.

An object of class `trioLR`

composed of the same objects as an object of class `logreg`

. For details,
see the `Value`

section of the function `logreg`

from the `R`

package `LogicReg`

.

Holger Schwender, holger.schwender@udo.edu

Kooperberg, C. and Ruczinski, I. (2005). Identifying Interacting SNPs Using Monte Carlo Logic Regression.
*Genetic Epidemiology*, 28, 157-170.

Li, Q., Fallin, M.D., Louis, T.A., Lasseter, V.K., McGrath, J.A., Avramopoulos, D., Wolyniec, P.S., Valle, D.,
Liang, K.Y., Pulver, A.E., and Ruczinski, I. (2010). Detection of SNP-SNP Interactions in Trios of Parents
with Schizophrenic Children. *Genetic Epidemiology*, 34, 396-406.

Ruczinski, I., Kooperberg, C., and LeBlanc, M.L. (2003). Logic Regression. *Journal of Computational and
Graphical Statistics*, 12, 475-511.

`logreg`

, `trio.prepare`

, `trio.check`

, `trio.permTest`

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 | ```
# Load the simulated data.
data(trio.data)
# Prepare the data in trio.ped1 for a trio logic
# regression analysis by first calling
trio.tmp <- trio.check(dat = trio.ped1)
# and then applying
set.seed(123456)
trio.bin <- trio.prepare(trio.dat=trio.tmp, blocks=c(1,4,2,3))
# where we here assume the block structure to be
# c(1, 4, 2, 3), which means that the first LD "block"
# only consists of the first SNP, the second LD block
# consists of the following four SNPs in trio.bin,
# the third block of the following two SNPs,
# and the last block of the last three SNPs.
# set.seed() is specified to make the results reproducible.
# For the application of trio logic regression, some
# parameters of trio logic regression are changed
# to make the following example faster.
my.control <- lrControl(start=1, end=-3, iter=1000, output=-4)
# Please note typically you should consider much more
# than 1000 iterations (usually, at least a few hundred
# thousand).
# Trio regression can then be applied to the trio data in
# trio.ped1 by
lr.out <- trioLR(trio.bin, control=my.control, rand=9876543)
# where we specify rand just to make the results reproducible.
``` |

Embedding an R snippet on your website

Add the following code to your website.

For more information on customizing the embed code, read Embedding Snippets.