Inverse regression model for radiation biodosimetry

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Description

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

Usage

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

Arguments

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.

Value

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

Author(s)

David Moriña (Centre for Research in Environmental Epidemiology, CREAL), Manuel Higueras (Universitat Autònoma de Barcelona and Public Health England) and Pedro Puig (Universitat Autònoma de Barcelona)

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

References

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

See Also

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

Examples

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### 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))