dose.distr: Inverse regression model for radiation biodosimetry
In radir: Inverse-Regression Estimation of Radioactive Doses

Description Usage Arguments Value Author(s) References See Also Examples

The function allows the user to estimate radiation doses distribution using the methodology described in Higueras et al. (2014).

1 2	dose.distr(f, pars, beta, cov, cells, dics, m.prior="gamma", d.prior="uniform", prior.param=c(0,"Inf"), stdf=6, nsim=1000)

`f`	dose-response function, as an `expression`. Must be differenciable in the domain of parameters.
`pars`	string vector containing the parameters in `f`.
`beta`	estimates of the parameters.
`cov`	variance-covariance matrix.
`cells`	patient information, number of cells examined.
`dics`	patient information, observed number of aberrations.
`m.prior`	string containing the prior distribution of the mean. It can be `gamma` (the default value) or `normal`.
`d.prior`	string containing the prior distribution of the dose. It can be `gamma` or `uniform` (the default value).
`prior.param`	vector of length 2 containing the parameters of the distribution of the dose prior. The parametrization for the `uniform` distribution is the usual, based on the support, and an improper uniform distribution is allowed, setting the second parameter to `Inf`. Its default value is the non-informative prior. The `gamma` distribution is parametrized in terms of the mean and standard deviation.
`stdf`	Approximated standard deviation factor. This input is useful to control the ends of the calibrative density; i.e. in case the tails of the calibrative dose density are very long this value could be reduced, or viceversa. Its default value is 6.
`nsim`	Number of simulations to base the results on. Its default value is 1000.

An object of class dose.radir containing the distribution of the estimated doses.

David Moriña (Barcelona Graduate School of Mathematics), Manuel Higueras (Basque Center for Applied Mathematics) and Pedro Puig (Universitat Autònoma de Barcelona)

Mantainer: David Moriña Soler <david.morina@uab.cat>

Higueras M, Puig P, Ainsbury EA, Rothkamm K. A new inverse regression model applied to radiation biodosimetry. Proc R Soc A 2015;471, http://dx.doi.org/10.1098/rspa.2014.0588

radir-package, ci.dose.radir, pr.dose.radir

### Example 3 (a)
f <- expression(b1*x+b2*x^2)
pars <- c("b1","b2")
beta <- c(3.126e-3, 2.537e-2)
cov  <- matrix(c(7.205e-06,-3.438e-06,-3.438e-06,2.718e-06),nrow=2)

### (a)
ex1.a <- dose.distr(f, pars, beta, cov, cells=1811, dics=102, 
m.prior="normal", d.prior="uniform", prior.param=c(0, Inf))