R help - Jul 2012 - outer() or some other function for regression prediction with 2 IVs

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

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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