print.trioFS {trio} | R Documentation |
Prints or plots the most important interactions found in a trioFS analysis.
## S3 method for class 'trioFS' print(x, topX = 5, show.prop = TRUE, coded = FALSE, digits = 2, ...) ## S3 method for class 'trioFS' plot(x, topX = 15, show.prop = FALSE, coded = TRUE, cex = 0.9, pch = 16, col = 1, force.topX = FALSE, include0 = TRUE, add.v0 = TRUE, v0.col = "grey50", main = NULL, ...)
x |
an object of class |
topX |
integer specifying how many interactions should be shown.
If |
show.prop |
should the proportions of models containing the respective interactions be
added to the output (if |
coded |
should the coded variable names be displayed? Might be useful
if the actual variable names are pretty long. The coded variable name of
the j-th variable is |
digits |
number of digits shown in the |
cex |
a numeric value specifying the relative size of the text and symbols. |
pch |
specifies the used symbol. See the help of |
col |
the color of the text and the symbols. See the help of |
force.topX |
if |
include0 |
should the x-axis include zero regardless whether the importances of the shown interactions are much higher than 0? |
add.v0 |
should a vertical line be drawn at x = 0? Ignored if
|
v0.col |
the color of the vertical line at x = 0. See the help page of
|
main |
character string naming the title of the plot. If |
... |
Ignored. |
Holger Schwender, holger.schwender@udo.edu
# Load the simulated data. data(trio.data) # Prepare the data in trio.ped1 for a trioFS 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 trioFS, 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). # TrioFS can then be applied to the trio data in trio.ped1 by fs.out <- trioFS(trio.bin, control=my.control, rand=9876543) # where we specify rand just to make the results reproducible. # The output of trioFS can be printed by fs.out # By default, the five most important interactions are displayed. # If another number of interactions, e.g., 10, should be shown, # then this can be done by print(fs.out, topX = 10) # The importances can also be plotted by plot(fs.out)