Description Usage Arguments Author(s) References See Also Examples

Prints information on the trio logic regression model(s) fitted with `trioLR`

.

1 2 |

`x` |
an object of class |

`asDNF` |
should the disjunctive normal form of the logic expression represented by the logic tree be printed?
If |

`posBeta` |
should the disjunctive normal form be determined as if the sign of the coefficient in trio logic
regression model is positive? If |

`digits` |
number of digits used in the printing of the score and the parameter estimate of the fitted trio logic regression model(s). |

`...` |
ignored. |

Holger Schwender, holger.schwender@udo.edu, based on the `plot`

functions
implemented by Ingo Ruczinski and Charles Kooperberg in the `R`

package `LogicReg`

.

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.

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 34 35 36 37 38 39 40 41 | ```
# 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.
# The output of trioLR can then be displayed by
lr.out
# This output shows the detected logic expression. If this
# expression should be displayed in disjunctive normal form,
# then this can be done by
print(lr.out, asDNF = TRUE)
``` |

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