flan.test: Fluctuation Analysis parametric testing
In rcqls/flan: Fluctuation Analysis of Mutant Cell Counts

Description Usage Arguments Details Value See Also Examples

Performs one-sample and two-sample Fluctuation Analysis tests on mutant counts. Returns confidence intervals and p-values, based on asymptotic normality, from the values returned by mutestim.

  flan.test(mc,fn=NULL,mfn=NULL,cvfn=NULL,                   # user's data
               fitness=NULL,death=0,                         # user's parameters
               mutations0=1,mutprob0=NULL,fitness0=1,        # null hypotheses
               conf.level=0.95,                              # confidence level
               alternative=c("two.sided","less","greater"),  # alternative
               method=c("ML","GF","P0"),winsor=512,          # estimation method
               model=c("LD","H"))                            # clone growth model

`mc`	a numeric vector of mutant counts or a list of two numeric vectors of mutant counts.
`fn`	an optional numeric vector of final numbers or a list of two numeric vectors of final numbers.
`mfn`	mean final number of cells. Ignored if `fn` is non-missing.
`cvfn`	coefficient of variation of final number of cells. Ignored if `fn` is non-missing.
`fitness`	fitness parameter: ratio of growth rates of normal and mutant cells. If `fitness` is `NULL` (default) then the fitness will be estimated. Otherwise, the given value will be used to estimate the mean mutation number `mutations`
`death`	death probability. Must be smaller than 0.5.
`mutations0`	null hypothesis value for parameter mutations. See details.
`mutprob0`	null hypothesis value for parameter mutprob. See details.
`fitness0`	null hypothesis value for parameter fitness. See details.
`alternative`	a character string specifying the alternative hypothesis, must be one of `"two.sided"` (default), `"greater"` or `"less"`.
`conf.level`	confidence level of the interval.
`method`	estimation method as a character string: one of `ML` (default), `P0`, or `GF`. See `mutestim`.
`winsor`	winsorization parameter: positive integer. Only used when `method` is `ML` or when `method` is `P0` and `fitness` is NULL. See `mutestim`.
`model`	statistical lifetime model as a character string: one of `LD` (default) for Luria-Delbrück model with exponential lifetimes, or `H` for Haldane model with constant lifetimes.

flan.test tests the values of parameters mutations, mutprob, or fitness, with mutations0 mutprob0, fitness0 as null hypotheses. If fn or mfn are given, mutprob is tested, otherwise, mutations is tested. If fitness is given, it is not tested.

alternative may be a two dimensional vector specifying the alternatives for the two parameters to be tested.

For the two-sample tests, the values in mutations0, mutprob0 and fitness0 apply to the difference between the two samples.

Returns a list with class "flantest". The structure of a "flantest" object, is similar to that of a "htest" object (see also t.test). The class "flantest" contains the following components :

`Tstat`	the value of the computed statistic(s).
`parameter`	the values of `fitness` (if not tested) and `death`.
`p.value`	the p-value(s) of the test.
`conf.int`	confidence interval(s) for the parameter(s) relative to the specified alternative.
`estimates`	the estimate(s).
`null.value`	the specified hypothesized value(s).
`alternative`	a (vector of) character string(s) describing the alternative hypothesis.
`model`	the statistical lifetime model.
`method`	method used to compute the estimate(s).
`data.name`	a character string giving the name of the complete data.

mutestim.

# one sample test, without final numbers
b <- unlist(boeal)
# is the mean mutation number greater than 0.7, and the fitness greater than 0.8? 
flan.test(b,alternative="greater",mutations0=0.7,fitness0=0.8)
# is the mean mutation number less than 0.8, given the fitness?
flan.test(b,alternative="less",mutations0=0.8,fitness=0.84)

# one sample test, with final numbers
d <- david[[11]]
flan.test(d$mc,d$fn,alternative="less",mutprob0=2e-10,fitness0=2)

# two-sample test: test equality of parameters
b1 <- unlist(boeal[1:10])
b2 <- unlist(boeal[11:20])
flan.test(list(b1,b2))

# realistic random sample of size 100: mutation probability 1e-9,
# mean final number 1e9, coefficient of variation on final numbers 0.3,
# fitness 0.9, lognormal lifetimes, 5% mutant deaths
x <- rflan(100,mutprob=1e-9,mfn=1e9,cvfn=0.3,fitness=0.9,death=0.05)

# test on mutations and fitness, without final numbers
flan.test(x$mc,mutations0=1,fitness0=0.9)

# test on mutprob and fitness, with final numbers
flan.test(x$mc,x$fn,mutprob0=1e-9,fitness0=0.9)

# given fitness
flan.test(x$mc,x$fn,fitness=0.9,mutprob0=1e-9)

# take deaths into account
flan.test(x$mc,x$fn,mutprob0=1e-9,fitness0=0.9,death=0.05)

# change method
flan.test(x$mc,x$fn,mutprob0=1e-9,fitness0=0.9,death=0.05,method="GF")
flan.test(x$mc,x$fn,mutprob0=1e-9,fitness0=0.9,death=0.05,method="P0")

# change model
flan.test(x$mc,x$fn,mutprob0=1e-9,fitness0=0.9,model="H")