Another way to see your plots is the TkPredict function in the TeachingDemos
package. It will default the variables to their medians for numeric predictors
and baseline level for factors, but then you can set all of those to something
more meaningful one time using the controls, then cycle
On Apr 27, 2011, at 02:39 , Duncan Murdoch wrote:
On 26/04/2011 11:13 AM, Paul Johnson wrote:
Is anybody working on a way to standardize the creation of newdata
objects for predict methods?
[snip]
I think it is time the R Core Team would look at this tell us what
is the right way to do
On Wed, Apr 27, 2011 at 3:55 AM, peter dalgaard pda...@gmail.com wrote:
On Apr 27, 2011, at 02:39 , Duncan Murdoch wrote:
On 26/04/2011 11:13 AM, Paul Johnson wrote:
Is anybody working on a way to standardize the creation of newdata
objects for predict methods?
[snip]
I think it is time
Among many solutions, I generally use the following code, which avoids the
ideal average individual, by considering the mean across of the predicted
values:
averagingpredict - function(model, varname, varseq, type, subset=NULL)
{
if(is.null(subset))
mydata - model$data
else
Is anybody working on a way to standardize the creation of newdata
objects for predict methods?
When using predict, I find it difficult/tedious to create newdata data
frames when there are many variables. It is necessary to set all
variables at the mean/mode/median, and then for some variables of
On 26/04/2011 11:13 AM, Paul Johnson wrote:
Is anybody working on a way to standardize the creation of newdata
objects for predict methods?
They're generally just dataframes. Use the data.frame() function.
When using predict, I find it difficult/tedious to create newdata data
frames when
