Hello Tyler,
model.matrix(~(X1+X2+X3)^3-X1:X3)
T_i = X1:X2:X3. Let F_j = X3. (The numerical variables X1 and X2 are
not encoded at all. Then, again, T_{i(j)} = X1:X2, which in this
example is NOT dropped from the model. Hence the X3 in T_i must be
encoded by contrast, as indeed it is.
Arie
On
Hi Arie,
Given the heuristic, in all of my examples with a missing two-factor
interaction the three-factor interaction should be coded with dummy
variables. In reality, it is encoded by dummy variables only when the
numeric:numeric interaction is missing, and by contrasts for the other two.
The he
> Suharto Anggono Suharto Anggono
> on Sat, 4 Nov 2017 12:11:48 + writes:
> Removal of
> ans[nas] <- NA
> from the code of function 'ifelse' in R is not committed (yet). Why?
because I have been using it in my version of R-devel for this whole
year, but have forgotten
Hello Tyler,
You write that you understand what I am saying. However, I am now at
loss about what exactly is the problem with the behavior of R. Here
is a script which reproduces your experiments with three variables
(excluding the full model):
m=expand.grid(X1=c(1,-1),X2=c(1,-1),X3=c("A","B","C
On 05/11/2017 10:58 AM, peter dalgaard wrote:
On 5 Nov 2017, at 15:17 , Duncan Murdoch wrote:
On 04/11/2017 10:20 PM, Daniel Nordlund wrote:
Tirthankar,
"random number generators" do not produce random numbers. Any given
generator produces a fixed sequence of numbers that appear to meet
var
Le 05/11/2017 à 15:17, Duncan Murdoch a écrit :
On 04/11/2017 10:20 PM, Daniel Nordlund wrote:
Tirthankar,
"random number generators" do not produce random numbers. Any given
generator produces a fixed sequence of numbers that appear to meet
various tests of randomness. By picking a seed you
