Would a dominant genetic trait automatically increase in frequency with each generation? Would the rare variant get wiped out quickly?
In this simulation we study the effects that random mating has on the distribution of a genetic trait in the population. The simulation exposes the starting distribution and the size of the population under study. The number of generations / number of years of simulation can be adjusted using the settings button on the left.
Sample an outcome from the outcomes array based on the corresponding probabilities in the probabilities array.
Creates a radio button parameter with name and have each option in the options array selectable. Returns the selected option.
Create a textbox parameter with name and a required default value. Returns the value of the textbox.
scatter_graph(data, xlabel, ylabel)
Create a graph based on a data array and plot (data.xlabel, data.ylabel) on it. Can also pass xmin=null, xmax=null, ymin=null, ymax=null as added arguments.
Calling this function, stops the simulation.
Returns the average of the values in the array. It can be handy to store the values of metrics from each simulation run and average it across all runs to obtain an estimate of the metric.
Returns the estimated error (1.96 x standard error) of the average of the values at 95% confidence. In simple terms, you can assume that the estimate average(values) has error bounds +/- error_average(values). It can be handy to store the values of metrics from each simulation run and estimate both the average and the error of the average across all runs.
Returns the standard deviation of the values in the passed in array.