abc (version 2.2.2)

gfit: Goodness of fit

Description

Perform a test for goodness-of-fit.

Usage

gfit(target, sumstat, nb.replicate, tol=.01, statistic=mean, subset=NULL, 
trace=FALSE)

Value

An object of class "gfit", which is a list with the following elements

dist.obs

the value of the goodness-of-fit statistic for the data.

dist.sim

a vector of size nb.replicate. It is a sample of the null distribution of the test statistic.

Arguments

target

a data frame or vector of the observed summary statistic.

sumstat

a vector, matrix or data frame of the simulated summary statistics.

nb.replicate

number of replicates used to estimate the null distribution of the goodness-of-fit statistic.

tol

a tolerance rate. Defaults to 0.01

statistic

define the goodness-of-fit statistic. Typical values are median (default), mean, or max. When using median fo instance, the g.o.f. statistic is the median of the distance between observed and accepted summary statistics.

subset

optional. A logical expression indicating elements or rows to keep. Missing values in sumstat are taken as FALSE.

trace

a boolean indicating if a trace should be displayed when calling the function. Default to FALSE.

Author

Louisiane Lemaire and Michael Blum.

Details

The null distribution is estimated using already performed simulations contained in sumstat as pseudo-observed datasets. For each pseudo-observed dataset, the rejection algorithm is performed to obtain a value of the goodness-of-fit statistic. A better estimate of the P-value is obtained for larger nb.replicate but the running time of the function is increased.

See Also

abc, plot.gfit, summary.gfit, gfitpca

Examples

## human demographic history
require(abc.data)
data(human)

##  Perform a test of goodness-of-fit.
##  The data are the European data and we test the fit of the bottleneck 
## model (good fit) and of the constant-size population model (poor fit) 
## Use larger values of \code{nb.replicate} (e.g. 1000)
## for real applications
res.gfit.bott=gfit(target=stat.voight["italian",], 
sumstat=stat.3pops.sim[models=="bott",], statistic=mean, nb.replicate=10)
res.gfit.const=gfit(target=stat.voight["italian",], 
sumstat=stat.3pops.sim[models=="const",], statistic=mean, nb.replicate=10)


## Plot the distribution of the null statistic and indicate where is the 
## observed value.
plot(res.gfit.bott, main="Histogram under H0")
## Call the function \code{summary}
## It computes the P-value, call \code{summary} on the vector 
## \code{dist.sim} and returns the value of the observed statistic
summary(res.gfit.bott)