![]() Maybe a skew-t distribution could give a better fit. But the fit is not too good: the skewness is captured, but the histogram has much more mass at the center. For sampling one could simply reject simulated values outside bounds. ![]() The probability mass outside the bounds is so low that that should not cause problems. text |z|}Īnd finally we show a histogram with the estimated skew-normal density overlaid: hist(dat,prob=TRUE,nclass="scott") # "scott" is from MASS I will disregard the bounds for now, and show some example code in R. Just some ideas, but yours seems to be a difficult case for distribution fitting. However, this does not feel entirely satisfactory: while a gamma distribution has a strict lower bound (at 0), the variable I am trying to model doesn't (there is no hard theoretical limit on how spiteful someone could be). One solution to this problem would be to transform my data by adding a constant so that all negative values become positive, and then fit the data to, e.g., a gamma distribution. However, most distributions that can be used to model positively-skewed data, like gamma or lognormal, cannot take negative values. However, there are also a few people with a negative altruism score - these "spiteful" people are willing to pay a cost to hurt others.įor simulation purposes, I am trying to fit this data to a parametric distribution. As you can see in the graph below, the data are positively skewed, with most people having an altruism score of 0. I have one value for each participant in the sample (N=479), describing how altruistic that person was. I am trying to model data about altruistic behavior in a simple lab experiment.
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