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copied!<p>I think if you know the combination of parameters to be used for each model, then it is better to create a data frame of model names and parameters as shown below</p> <pre><code># create a data frame with model names and parameters # NOTE: i am assuming all models have equal number of parameters # if they are unequal, then store as list of models model = c('default', 'mymodel'); parm1 = c(0.5, 0.75); parm2 = c(1, 2); models.df = data.frame(model, parm1, parm2) </code></pre> <p>You can now simulate any of the models by passing it as an argument to your mySimulation function. I have used a dummy simulation example, which you can replace with your code.</p> <pre><code># function to run simulation based on model name mySimulation = function(model = 'default'){ # find row corresponding to model of interest mod.row = match(model, models.df$model) # extract parameters corresponding to model parms = models.df[mod.row, -1] # run dummy simulation of drawing normal random variables sim.df = rnorm(100, mean = parms[,1], sd = parms[,2]) return(sim.df) } </code></pre> <p>If you now want to run all your simulations in one step, you can use the excellent <code>plyr</code> package and invoke</p> <pre><code>library(plyr) sim.all = ldply(models.df$model, mySimulation) </code></pre> <p>If each of your simulations returns unequal number of values then you can use the function <code>llply</code> instead of <code>ldply</code>. </p> <p>If you provide more information about the return values of your simulation and details on what it does, this code can be easily tweaked to get what you want.</p> <p>Let me know if this works</p>

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