ABC_P2_norm: ABC Extimation of P2 for Normal Distribution In ABCp2: Approximate Bayesian Computational Model for Estimating P2

Description

This function fits offspring data to a special case of the normal distribution, in which zero and negative values of offspring are excluded, and estimates P2 based on that distribution and the specificed priors.

Usage

 1 ABC_P2_norm(n, ObsMean, M_Lo, M_Hi, SD_Lo, SD_Hi, delta, iter)

Arguments

 n number of observations. ObsMean the observed mean number of offspring sired by the second male. M_Lo minimum mean value for the distribution. M_Hi maximum mean value for the distribution. SD_Lo minimum standard deviation value for the distribution. SD_Hi maximum standard deviation value for the distribution. delta maximum allowed difference between the estimated mean and observed mean number of offspring produced by the second male. iter number of iterations used to build the posterior.

Value

 posterior Posterior distribution of P2 values. Avg Vector of values for the mean parameter. Std Vector of values for the standard deviation parameter.

Author(s)

M. Catherine Duryea, Andrew D. Kern, Robert M. Cox, and Ryan Calsbeek

Examples

 1 2 3 4 5 6 7 8 9 10 11 12 13 #Fit the Mean and Standard Deviation hyperpriors to a distribution of offspring. data(fungus) fit_dist_norm(fungus\$Total_Offspring) #Use hyperiors and priors calculated from the data to estimate P2. #Plot the saved distributions for the Mean and Standard Deviation parameters. #Adjust, if necessary. fungus_P2<-ABC_P2_norm(12, 9.9, 11.35, 17.31, 8.22, 12.44, 0.1, 100) hist(fungus_P2\$posterior) hist(fungus_P2\$Avg) hist(fungus_P2\$Std)

ABCp2 documentation built on May 1, 2019, 6:31 p.m.