I have a model which predicts contamination levels of nurses' hands after touching surfaces. It depends on 4 variables: surface contamination (V), hand contact area (A), transfer efficiency of germs ($\lambda$) and hand hygiene efficacy (h): \begin{equation}Y \sim f(\lambda, V, A,h)\end{equation}
Where: \begin{equation} h=LN~(1.5,0.1),\end{equation} \begin{equation} \lambda=\Gamma(15,3),\end{equation} \begin{equation} A=LN~(7,1.9),\end{equation} \begin{equation} V=LN~(2.5,1.9),\end{equation}
I ran a Monte Carlo simulation to produce 10000 values of Y. But I'd like to work out which of my variables has the most effect on Y. In other words I want to work out whether hand hygiene efficacy is more important than surface cleanliness.
Can Soboljansen in R deal with data input instead of the actual model? If so could you show me how please as I've not got it to so far.
soboljansen
function, so I can't help you with specifics. However, from thesoboljansen
help file, I assume that you specify how to handle the categorical variable through what you pass to themodel
argument. So, if you want type of care to be represented using dummy codes and if you want to model interaction terms between care and your other variables, you would either write a function that modeled care in that way or fit a model that included those terms. You would then pass that function / model object tosoboljansen
during your function call. $\endgroup$