A logical value specifying whether false
positive and negative errors should be applied.
edge.weights
A choice of whether the observed or estimated
edge transition probabilities should be used in the calculation
of probabilities. See oncotree.fit for explanation
of the difference. By default, estimated edge transition probabilies
if with.errors=TRUE and the observed ones if
with.errors=FALSE.
method
Simulation method, see Details for explanation of the options.
Details
There are three choices for the method of simulation; the best choice depends
on the size of the tree, required sample size, and whether errors are needed.
Method “S” generates the data based on the conditional probability definition
of the oncogenetic tree, and then ‘corrupts’ the resulting sample by
introducing random errors. This method is applicable in all circumstances, but can
be slower than other methods if N is large and with.errors=FALSE
is used.
Method “D1” calculates the joint distribution generated by the
tree exactly (using distribution.oncotree),
and the observations are generated by sampling this distribution. Thus if
with.errors=TRUE and the tree is large, this method might fail due
to the exponential growth in the number of potential outcomes. On the
other hand, for a moderately sized tree and a large desired sample size
N this is the most efficient method.
Method “D2” calculates the joint distribution generated by the tree without
false positives/negatives, samples from it, and then ‘corrupts’ the
resulting sample. If with.errors=FALSE is used then this method is
equivalent to method “D1”.
Value
A data set where each row is an independent observation.
R version 3.3.1 (2016-06-21) -- "Bug in Your Hair"
Copyright (C) 2016 The R Foundation for Statistical Computing
Platform: x86_64-pc-linux-gnu (64-bit)
R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.
R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.
Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.
> library(Oncotree)
Loading required package: boot
> png(filename="/home/ddbj/snapshot/RGM3/R_CC/result/Oncotree/generate.data.Rd_%03d_medium.png", width=480, height=480)
> ### Name: generate.data
> ### Title: Generate random data from an oncogenetic tree
> ### Aliases: generate.data
> ### Keywords: datagen models
>
> ### ** Examples
>
> data(ov.cgh)
> ov.tree <- oncotree.fit(ov.cgh)
>
> set.seed(7365)
> rd <- generate.data(200, ov.tree, with.errors=TRUE)
>
> #compare timing of methods
> system.time(generate.data(20, ov.tree, with.errors=TRUE, method="S"))
user system elapsed
0 0 0
> system.time(generate.data(20, ov.tree, with.errors=TRUE, method="D1"))
user system elapsed
0.752 0.036 0.785
> system.time(generate.data(20, ov.tree, with.errors=TRUE, method="D2"))
user system elapsed
0.040 0.000 0.043
>
>
>
>
>
>
> dev.off()
null device
1
>