> How can I modify my R codes to simulate the sample size, the presence of
Y-outliers and the presence of missing data ?
I don't know what it means for data to have 50% Y-outliers.
That's new to me...
As for the rest of your question.
Modify your code so that a single function, say sim.test() computes your
simulated statistics, for n sample size and m missing values, and returns
the results, say as a two-element list.
Then write a top level script (or function), something like:
+ ns = c (50, 100, 200, 300, 500)
+ ms = (1:5) * 0.1
+ n = rep (ns, each=5)
+ m = rep (ms, times=5)
+ GAM.stat = MARS.stat = numeric (25)
+ for (i in 1:25)
+ { results = sim.test (n [i], m [i], ...other.args...)
+ GAM.stat [i] = results$GAM.stat
+ MARS.stat [i] = results$MARS.stat
+ }
+ cbind (n, m, GAM.stat, MARS.stat)
Note that from past experience, what you are doing may produce misleading
results.
Because your results are dependent on your simulated data.
(Different simulated data will produce different results, and different end
conclusions).
I haven't checked how the functions, you've used to fit models, handle
missing values.
But assuming that missing values are NAs, this should be easy to do.
Do you want *each* x variable to have m% missing values, or *all* the x
variables (collectively), to have m% missing values?
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