, January, 18 11:16 AM
To: Guelman, Leo
Cc: r-help@r-project.org; Charles C. Berry
Subject: Re: [R] Order of formula terms in model.matrix
On 18 Jan 2016, at 16:49 , Guelman, Leo wrote:
> Is it really the same model?
No, and I didn't say that they would be. I did say that they would b
Is it really the same model?
Following the example provided by Lars:
set.seed(1)
x1 <- rnorm(100)
f1 <- factor(sample(letters[1:3], 100, replace = TRUE))
trt <- sample(c(-1,1), 100, replace = TRUE)
y <- factor(sample(c(0,1), 100, T))
df <- data.frame(y=y, x1=x1, f1=f1, trt=trt)
fit1 <- glm(y ~ x
Dear list,
What is a better way relative to the one below to keep the order of factor
levels created from cut()? Notice, I'm simply pasting letters to levels before
converting to character so to keep the desired order of levels. This is not
very elegant... I'm converting to character so I can c
Dear List,
I'm trying to create a formula method, allowing for a "special"
character in the formula (i.e., similar to for example the gam package
with the character "s" in y ~ s(x)). I've checked and it seems this is
done through attr(,"specials"). However, the section of code below (as
an example
Or alternatively (though very similar to Peter's idea) you can do
ci <- contrasts
formals(ci)$contrasts <- FALSE
dd <- data.frame(a = gl(3,4), b = gl(4,1,12))
mm <- model.matrix(~ a + b, dd, contrasts = list(a=ci(dd$a),
b=ci(dd$b)))
Best,
Leo.
-Original Message-
From: r-help-boun...@r-p
I'm guessing this has something to do with numerical precision on the two
platforms.
Leo.
- Original Message -
From: Joshua Wiley
To: Axel Urbiz
Cc: R-help@r-project.org ; Ridgeway, Greg
Sent: Fri Feb 25 22:16:02 2011
Subject: Re: [R] Reproducibility issue in gbm (32 vs 64 bit)
Hi Axe
Dear R users,
I'm pleased to announce that the Greater Toronto Area (GTA) user's group
is now active on meetup.com, and taking suggestions for the first
meeting. If you are on the region, you can sign-up now at the link below
http://www.meetup.com/Greater-Toronto-Area-GTA-R-Users-Group
This gro
Dear users,
I'm working on binary classification problem using Support Vector
Machines (SVM). My objective is to train a series of SVM models on a
grid of hyperparameters and then select those that maximize the AUC
based on an independent validation sample.
My attempted code is shown below. It r
I'm using R 2.11.1 on Win XP (32-bit) with 3 GB of RAM. My data has
(only) 16.0 MB.
I want to create a VCorpus object using the Corpus function in the tm
package but I'm running into Memory allocation issues: "Error: cannot
allocate vector of size 372 Kb".
My data is stored in a csv file which
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