print.trioLR {trio} | R Documentation |
Prints information on the trio logic regression model(s) fitted with trioLR
.
## S3 method for class 'trioLR' print(x, asDNF=FALSE, posBeta=FALSE, digits = 3, ...)
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.
# 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)