On 9/5/07, D. R. Evans [EMAIL PROTECTED] wrote:
D. R. Evans said the following at 09/04/2007 04:14 PM :
I am 100% certain that there is an easy way to do this, but after
I have reconsidered this and now believe it to be essentially impossible
(or at the very least remarkably difficult)
On 9/6/07, Gustaf Rydevik [EMAIL PROTECTED] wrote:
On 9/5/07, D. R. Evans [EMAIL PROTECTED] wrote:
D. R. Evans said the following at 09/04/2007 04:14 PM :
I am 100% certain that there is an easy way to do this, but after
I have reconsidered this and now believe it to be essentially
D. R. Evans said the following at 09/04/2007 04:14 PM :
I am 100% certain that there is an easy way to do this, but after
I have reconsidered this and now believe it to be essentially impossible
(or at the very least remarkably difficult) although I don't understand why
it is so :-(
At least, I
If your main goal is to do a loess fit, then make predictions from that,
then using the 'get' function may do what you want:
tmp.var - get(ORDINATE)
lo - loess(percent ~ ncms * tmp.var, d, ...
grid - expand.grid(tmp.var=MINVAL:MAXVAL, ncms=MINCMS:MAXCMS)
predict(lo, grid)
Here you stick with
For the column names of the result of expand.grid(), I would just assign
them the values I wanted, like this:
x - expand.grid(tmp=1:3,y=1:2)
x
tmp y
1 1 1
2 2 1
3 3 1
4 1 2
5 2 2
6 3 2
colnames(x)[1] - whatever
x
whatever y
11 1
22 1
33 1
4
I am 100% certain that there is an easy way to do this, but after
experimenting off and on for a couple of days, and searching everywhere I
could think of, I haven't been able to find the trick.
I have this piece of code:
...
attach(d)
if (ORDINATE == 'ds')
{ lo - loess(percent ~ ncms *
D. R. Evans wrote:
I am 100% certain that there is an easy way to do this, but after
experimenting off and on for a couple of days, and searching everywhere I
could think of, I haven't been able to find the trick.
I have this piece of code:
...
attach(d)
if (ORDINATE == 'ds')
{ lo
You can use substitute() for this. The drawback with this approach is
that the formula in the call in the printed value of loess() is ugly.
x - data.frame(y=rnorm(20), x1=rnorm(20), x2=rnorm(20))
loess(y~x2, data=x)
Call:
loess(formula = y ~ x2, data = x)
Number of Observations: 20
