Description Usage Arguments Value Note Author(s) See Also Examples
Simulates SNP data, where a specified proportion of cases and controls is explained by specified set of SNP interactions. Can also be used to simulate a data set with a multi-categorical response, i.e.\ a data set in which the cases are divided into several classes (e.g., different diseases or subtypes of a disease).
1 2 3 4 5 |
n.obs |
either an integer specifying the total number of
observations, or a vector of length 2 specifying the number
of cases and the number of controls. If |
n.snp |
integer specifying the number of SNPs. |
vec.ia |
a vector of integers specifying the orders of the interactions
that explain the cases. |
prop.explain |
either an integer or a vector of |
list.ia.val |
a list of |
vec.ia.num |
a vector of |
vec.cat |
a vector of the same length of |
maf |
either an integer, or a vector of length 2 or |
prob.val |
a vector consisting of the probabilities for drawing a 0, 1, or 2,
if |
list.equal |
list of same structure as |
prob.equal |
a numeric value specifying the probability that a 1 is drawn when generating
|
rm.redundancy |
should redundant SNPs be removed from the explaining interactions?
It is possible that one specify an explaining i-way interaction, but an interaction
between (i-1) of the variables contained in the i-way
interaction already explains all the cases (and controls) that the i-way interaction
should explain. In this case, the redundant SNP is removed if |
shuffle |
logical. By default, the first |
shuffle.obs |
should the observations be shuffled? |
rand |
integer. Sets the random number generator in a reproducible state. |
An object of class simulatedSNPs
composed of
data |
a matrix with |
cl |
a vector of length |
tab.explain |
a table naming the explanatory interactions and the numbers of cases and controls explained by them. |
ia |
character vector naming the interactions. |
maf |
vector of length |
Currently, the genotypes of all SNPs are simulated independently from each other (except for the SNPs that belong to the same explanatory interaction).
Holger Schwender holger.schwender@udo.edu
simulateSNPglm
, simulateSNPcatResponse
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 | ## Not run:
# Simulate a data set containing 2000 observations (1000 cases
# and 1000 controls) and 50 SNPs, where one three-way and two
# two-way interactions are chosen randomly to be explanatory
# for the case-control status.
sim1 <- simulateSNPs(2000, 50, c(3, 2, 2))
sim1
# Simulate data of 1200 cases and 800 controls for 50 SNPs,
# where 90% of the observations showing a randomly chosen
# three-way interaction are cases, and 95% of the observations
# showing a randomly chosen two-way interactions are cases.
sim2 <- simulateSNPs(c(1200, 800), 50, c(3, 2),
prop.explain = c(0.9, 0.95))
sim2
# Simulate a data set consisting of 1000 observations and 50 SNPs,
# where the minor allele frequency of each SNP is 0.25, and
# the interactions
# ((SNP1 == 2) & (SNP2 != 0) & (SNP3 == 1)) and
# ((SNP4 == 0) & (SNP5 != 2))
# are explanatory for 200 and 250 of the 500 cases, respectively,
# and for none of the 500 controls.
list1 <- list(c(2, 0, 1), c(0, 2))
list2 <- list(c(1, 0, 1), c(1, 0))
sim3 <- simulateSNPs(1000, 50, c(3, 2), list.ia.val = list1,
list.equal = list2, vec.ia.num = c(200, 250), maf = 0.25)
## End(Not run)
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