[R] outer() or some other function for regression prediction with 2 IVs

[Michael Weylandt]

Your problem is the sum() call -- it's not vectorized (in the regular sense) so it breaks some of the internal assumptions of outer(). 

Easiest way is probably to do matrix multiplication (%*%) directly here

Michael

On Jul 9, 2012, at 10:19 PM, Joseph Clark <joeclark77 at hotmail.com> wrote:

> 
> Hi there, I'm trying to prep some data for a persp() surface plot
> 
> representing the predictions from a regression with two inddependent
> 
> variables. The regression model "m3" has an intercept, 2 linear terms,
> 
> and 2 squared terms.  The coefficients are given by coef(m3).
> 
> 
> 
> My approach to generating the predictions for a range of each of my IVs,
> 
> "s" and "d" was to use outer() like so:
> 
> 
> 
> predxn <- function(s,d) { sum( coef(m3) * c(1,s,s^2,d,d^2) ) }
> z <- outer(s_vector,d_vector,predxn)
> 
> 
> 
> I can't see what's wrong with this.  For each value of s_vector and d_vector,
> 
> it should multiply each coefficient by its term and give me a nice 
> 
> two-dimensional array "z" containing the predictions.
> 
> 
> 
> The problem is that the c() vector ends up with 1601 elements instead of 5.
> 
> There are 20 items each in s_vector and d_vector so it looks like each variable
> 
> in my function is using the whole "z" array of s's and d's rather than being 
> 
> calculated for each combination of values one at a time.
> 
> 
> 
> So, am I using outer() wrong?  Or did I write my function badly?
> 
> Or is there a better way to plot a 3d surface plot of my regression model?
> 
> 
> 
> 
> 
> 
> // joseph w. clark , phd candidate
> \\ usc marshall school of business                         
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