Sorry this may well be defined as Off Topic. I apologize in advance.
I am interested in performing what I think would be a probabilistic sensitivity
simulation. I've done some crude ones before in excel but I'm wondering if R
can help me do it more effectively?
I have a set of theoretical variables for simplicity lets use (what I think) is
an easier example: I have a peg and a hole which I want to predict how often
they would not fit together securely.
The peg should be 1.8mm in diameter.
But there are some variables which might affect if it is actually 1.8mm but I'd
expect it to be normally distributed around 1.8mm with a known standard
deviation. There are also variables such as temperature which would influence
if the peg was the correct size (there would be a known relationship to
temperature, and a mean temperature with a standard deviation would be known)
The hole should be 1.8mm diameter as well. Again there are variables that
affect if it is, drill size, drilling method, substrate being drilled,
temperature at time of drilling, temperature at time of fitting peg (would be
same as above). I'd be looking to model if the peg would fit in the hole, AND
if it fitted how well it fitted.
I'd then want to run a simulation of say 500 scenarios, randomly picking
temperature, hole characteristics etc for each simulation. From there I'd
get the number of times in 500 samples the peg wouldn't fit. I could then try
adjusting a variable - say using a different drilling method and see if that
'easier' but less reliable drilling method actually would affect the number of
times the peg didn't fit the hole properly.
So from what I understand this is a MonteCarlo simulation of probability. And
this is where I go off topic! Am I right? I know its off topic - so what I'd
like to know is can someone point me to where I can find out?
Then if it is a monte-carlo can someone point me to a good description of how
to get R to model this?
Ta
Calum
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