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# SMM (stepwise mutation model) example set.seed ( 1 ) fit <- fwsim ( G = 100L, H0 = c ( 0L, 0L, 0L ), N0 = 10000L, mutmodel = c ( Loc1 = 0.001, Loc2 = 0.002, Loc3 = 0.003 )) summary ( fit ) fit # SMM (stepwise mutation model) example H0 <- matrix ( c ( 0L, 0L, 0L ), 1L, 3L, byrow = TRUE ) mutmodel <- init_mutmodel ( modeltype = 1L, mutpars = matrix ( c ( c ( 0.003, 0.001 ), rep ( 0.004, 2 ), rep ( 0.001, 2 )), ncol = 3, dimnames = list ( NULL, c ( "DYS19", "DYS389I", "DYS391" )))) mutmodel set.seed ( 1 ) fit <- fwsim ( G = 100L, H0 = H0, N0 = 10000L, mutmodel = mutmodel ) xtabs ( N ~ DYS19 + DYS389I, fit $ population ) plot ( 1L : fit $ pars $ G, fit $ pop_sizes, type = "l", ylim = range ( range ( fit $ pop_sizes ), range ( fit $ expected_pop_sizes ))) points ( 1L : fit $ pars $ G, fit $ expected_pop_sizes, type = "l", col = "red" ) set.seed ( 1 ) fit_fixed <- fwsim_fixed ( G = 100L, H0 = H0, N0 = 10000L, mutmodel = mutmodel ) # LMM (logistic mutation model) example mutpars.locus1 <- c ( 0.149, 2.08, 18.3, 0.149, 0.374, 27.4 ) # DYS19 mutpars.locus2 <- c ( 0.500, 1.18, 18.0, 0.500, 0.0183, 349 ) # DYS389I mutpars.locus3 <- c ( 0.0163, 17.7, 11.1, 0.0163, 0.592, 14.1 ) # DYS391 mutpars <- matrix ( c ( mutpars.locus1, mutpars.locus2, mutpars.locus3 ), ncol = 3 ) colnames ( mutpars ) <- c ( "DYS19", "DYS389I", "DYS391" ) mutmodel <- init_mutmodel ( modeltype = 2L, mutpars = mutpars ) mutmodel set.Advertisement Download Best FWsim Fireworks Simulator Alternative Mikkel Meyer Andersen and Poul Svante Eriksen Examples The expected population size for each generation
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Haplotypes in the end population after G generations Whether to print trace of the evolution (more verbose than progress).Įnsures that every generation has at least one child implemented by getting Poisson(α*M) + 1 children.Ī number specifying the plot (currently only 1: the actual population sizes vs the expected sizes).Ī list of haplotypes in the intermediate populations Whether to print progress of the evolution. Else, a vector of the generation numbers to save. NULL means that no intermediate population will be saved. To make alleles modulus 2 to immitate SNPs. If length 1, the value is reused in creating a vector of length G. Vector of length 1 or G of growth factors (1 correspond to expected constant population size). Alternatively, a numeric vector of length r of mutation probabilities (this will create a stepwise mutation model with r loci divide the mutation probabilities evenly between upwards and downwards mutation). sum(N0) is the size of initial population.Ī mutmodel object created with init_mutmodel. The i'th element is the count of the haplotype H0. The number of loci is the length or number of columns of H0.Ĭount of the H0 haplotypes. Must be a vector or matrix (if more than one initial haplotype). Number of generations to evolve (integer, remember postfix L). ) # S3 method for class 'fwsim' plot ( x, which = 1L. ) # S3 method for class 'fwsim' summary ( object. ) # S3 method for class 'fwsim' print ( x.
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) fwsim_fixed ( G, H0, N0, mutmodel, SNP = FALSE, save_generations = NULL, progress = TRUE, trace = FALSE. See init_mutmodel for details.įwsim ( G, H0, N0, mutmodel, alpha = 1.0, SNP = FALSE, save_generations = NULL, progress = TRUE, trace = FALSE, ensure_children = FALSE. It then follows that the sum of the number of haplotypes follows a Poisson(α*M) distribution (as just stated in the previous paragraph) and that n conditionally on N follows a Binomial(N, m/M) as expected. Of children n is Poisson(α*m) distributed independently of other haplotypes. Then we assume that N conditionally on M is Poisson(α*M) distributed for α > 0 ( α > 1 gives expected growth and 0 < α < 1 gives expected decrease).įor each haplotype x occuring m times in the i'th generation, the number Let M be the population size at generation i and N the population size at generation i + 1. Intermediate generations can be saved in order to study e.g. The population sizes are either fixed (traditional/original Fisher-Wright model) or random Poisson distributed with exponential growth supported. This package provides tools to simulate a population under the Fisher-Wright model with a stepwise neutral mutation process on r loci, where mutations on loci happen independently. In fwsim: Fisher-Wright Population Simulationĭescription Usage Arguments Value Author(s) Examples