View source: R/04_simulation.R
sim_dummy | R Documentation |
Wrapper of freqpcr()
suitable for the performance test using a randomly-generated data object.
sim_dummy( CqList, EPCR, zeroAmount, P = NULL, K = NULL, targetScale = NULL, sdMeasure = NULL, beta, diploid, maxtime, print.level, aux = NULL, verbose = TRUE, ... )
CqList |
Object belonging to the CqList class, typically the output from |
EPCR |
(η) Amplification efficiency per PCR cycle, given as a positive numeric. When |
zeroAmount |
A numeric between 0 and 1, usually near 0, giving the residue rate of restriction enzyme digestion in RED-ΔΔCq method. |
P, K, targetScale, sdMeasure |
If NULL (default), the parameter is considered unknown and estimated via |
beta, diploid, maxtime, print.level |
Configuration parameters which are passed directly to |
aux |
Additional information to be displayed on the console. The default is |
verbose |
Prints more information e.g. system time. Default is |
... |
Additional arguments passed to |
Object of the S4 class CqFreq, which is same as freqpcr()
.
Other estimation procedures:
freqpcr()
,
knownqpcr_unpaired()
,
knownqpcr()
# Prepare the parameter values. K <- 2 # You already know the size of K in this case. EPCR <- 0.97 # The sizes of EPCR and zeroAmount must always be supplied. zeroAmount <- 1.6e-03 is.diploid <- FALSE # First, make a dummy Cq dataset with six bulk DNA samples, # each of which comprises of eight haploid individuals. dmy_cq <- make_dummy( rand.seed=1, P=0.75, K=K, ntrap=6, npertrap=8, scaleDNA=1e-07, targetScale=1.5, baseChange=0.3, EPCR=EPCR, zeroAmount=zeroAmount, sdMeasure=0.3, diploid=is.diploid ) # Estimate the population allele frequency on the dummy dataset, presupposing K = 2. sim_dummy( CqList=dmy_cq, EPCR=EPCR, zeroAmount=zeroAmount, K=K, beta=TRUE, diploid=is.diploid, maxtime=60, print.level=2, aux="test" ) # If the maximum calculation time was too short to converge, nlm() returns error. # Then sim_dummy() returns a matrix filled with zeros. sim_dummy( CqList=dmy_cq, EPCR=EPCR, zeroAmount=zeroAmount, beta=TRUE, diploid=is.diploid, maxtime=0.01, print.level=2 )
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