prediction from model 1 (Setosa) for entire dataset
>
> P2 <- ... # prediction from model 2 for entire dataset
>
> I <- Species=="setosa" #
>
> Predictions <- P1 * I + P2 * ( 1 - I )
>
> On Monday, December 10, 2012, Brian Feeny wrote:
>
> I ha
I have a dataset and I wish to use two different models to predict. Both
models are SVM. The reason for two different models is based
on the sex of the observation. I wish to be able to make predictions and have
the results be in the same order as my original dataset. To
illustrate I will us
5, 2012, at 11:49 PM, arun wrote:
>
>
> Hi,
>
> Would it be okay to use:
> y<-na.omit(y[myindex]<-x)
> y
> # [1] -1.36025132 -0.57529211 1.18132359 0.41038489 1.83108252 -0.03563686
> #[7] 1.25267314 1.08311857 1.56973422 -0.30752939
>
> A.K.
&g
I would like to take the values of observations and map them to a new index. I
am not sure how to accomplish this. The result would look like so:
x[1,2,3,4,5,6,7,8,9,10]
becomes
y[2,4,6,8,10,12,14,16,18,20]
The "newindex" would not necessarily be this sequence, but a sequence I have
stored i
tibco.com
>
>
>> -----Original Message-
>> From: r-help-boun...@r-project.org [mailto:r-help-boun...@r-project.org] On
>> Behalf
>> Of Brian Feeny
>> Sent: Saturday, December 01, 2012 8:04 PM
>> To: r-help@r-project.org
>> Subject: [R] How t
I am able to split my df into two like so:
dataset <- trainset
index <- 1:nrow(dataset)
testindex <- sample(index, trunc(length(index)*30/100))
trainset <- dataset[-testindex,]
testset <- dataset[testindex,-1]
So I have the index information, how could I re-combine the data using that
back into
r levels to avoid leading numbers and try again.
>
> Max
>
>
>
>
> On Thu, Nov 29, 2012 at 10:18 PM, Brian Feeny wrote:
>
>
> Yes I am still getting this error, here is my sessionInfo:
>
> > sessionInfo()
> R version 2.15.2 (2012-10-26)
>
t; Upgrade to the version just released on cran and see if you still have the
> issue.
>
> Max
>
>
> On Thu, Nov 29, 2012 at 6:55 PM, Brian Feeny wrote:
> I have never been able to get class probabilities to work and I am relatively
> new to using these tools, and I am l
I have never been able to get class probabilities to work and I am relatively
new to using these tools, and I am looking for some insight as to what may be
wrong.
I am using caret with kernlab/ksvm. I will simplify my problem to a basic data
set which produces the same problem. I have read th
0
5 4 7 9
6 5 3 10
does this make sense? I am hoping there is a way to accomplish this.
Brian
On Nov 23, 2012, at 11:42 PM, Brian Feeny wrote:
>
> I am trying to make it so two columns with similar data use the same internal
> numbers for same fa
I am trying to make it so two columns with similar data use the same internal
numbers for same factors, here is the example:
> read.csv("test.csv",header =FALSE,sep=",")
V1V2 V3
1 sun moonstars
2 stars moon sun
3 cat dog catdog
4 dog moon sun
5 bird pla
Fold01: k= 7
>
> + Fold01: k= 9
>
> - Fold01: k= 9
>
> + Fold01: k=11
>
> - Fold01: k=11
>
>
>
> + Fold10: k=17
>
> - Fold10: k=17
>
> + Fold10: k=19
>
> - Fold10: k=19
>
> + Fold10: k=21
>
> - Fold10: k=21
>
I am used to packages like e1071 where you have a tune step and then pass your
tunings to train.
It seems with caret, tuning and training are both handled by train.
I am using train and trainControl to find my hyper parameters like so:
MyTrainControl=trainControl(
method = "cv",
number=5,
Thank you! I searched in the manual, but I did not see where this is
mentioned, I looked under operators
and in some of the formula documentation.
Brian
On Nov 23, 2012, at 3:15 AM, Michael Weylandt
wrote:
>
>
> On Nov 23, 2012, at 4:26 AM, Brian Feeny wrote:
>
>>
I know if I have a dataframe with columns y, x1, x2 and I wish to have y as my
y value and x1 and x2 as x values I can do:
y ~ x1 + x2
or
y ~.
but can someone explain what . actually is or what its transposed into?
I searched for this with no success, reading the "formula" manual pages.
Bria
Has anyone used doMC to speed up an SVM grid search? I am considering doing
like so:
library(doMC)
registerDoMC()
foreach (i=0:3) %dopar% {
tuned_part1 <- tune.svm(label~., data = trainset, gamma = 10^(-10:-6),
cost = 10^(-1:1))
tuned_part2 <- tune.svm(label~., data = trainset,
Has anyone used doMC to speed up an SVM grid search? I am considering doing
like so:
library(doMC)
registerDoMC()
foreach (i=0:3) %dopar% {
tuned_part1 <- tune.svm(label~., data = trainset, gamma = 10^(-10:-6),
cost = 10^(-1:1))
tuned_part2 <- tune.svm(label~., data = trainset,
http://cran.r-project.org/web/views/Cluster.html
might be a good start
Brian
On Nov 21, 2012, at 1:36 PM, KitKat wrote:
> Thank you for replying!
> I made a new post asking if there are any websites or files on how to
> download package mclust (or other Bayesian cluster analysis packages) an
I have a dataframe in which I have values 0-255, I wish to transpose them such
that:
if value > 127.5 value = 1
if value < 127.5 value = -1
I did something similar using the "binarize" function of the biclust package,
this transforms my dataframe to 0 and 1 values, but I wish
to use -1 and 1
responding to my own question, I see in ?svm man it states fitted() and
predict() can do the same thing:
# test with train data
pred <- predict(model, x)
# (same as:)
pred <- fitted(model)
On Nov 21, 2012, at 1:08 AM, signal wrote:
> Did you ever receive a response to this? I did not see one
I have a dataset that has many columns which are NA or constant, and so I
remove them like so:
same <- sapply(dataset, function(.col){
all(is.na(.col)) || all(.col[1L] == .col)
})
dataset <- dataset[!same]
This works GREAT (thanks to the r-users list archive I found this)
however, then
0, 2012, at 2:30 PM, Brian Feeny wrote:
> I am new to R, so I am sure I am making a simple mistake. I am including
> complete information in hopes
> someone can help me.
>
> Basically my data in R looks good, I write it to a file, and every value is
> off by 1.
>
> Here
I am new to R, so I am sure I am making a simple mistake. I am including
complete information in hopes
someone can help me.
Basically my data in R looks good, I write it to a file, and every value is off
by 1.
Here is my flow:
> str(prediction)
Factor w/ 10 levels "0","1","2","3",..: 3 1 10
Just curious, once you have a model that works well, does it make sense to then
tune it against 100% of the dataset (with known outcomes)
so you can apply it to data you wish to predict for or is that a bad approach?
I have done like is explained in this thread many times, taken a sample,
learn
I have a rather basic set of data. It is simply a variable that can be 0, 1 or
2 and its value over a series of time t0 - t9 like so:
y: 1 1 2 0 1 2 2 1 2
1
x: t0 t1 t2 t3 t4 t5 t6 t7 t8
I am new to R as well, it sounds like you would want to look at clustering,
perhaps k-means clustering.
Brian
On Nov 18, 2012, at 12:19 AM, avadhoot velankar
wrote:
> I am working on morphometry of hairs and want to see if selected variables
> are giving significantly distinct groups.
>
> I
Thank you Michael and David. I am onto agrep and adist and they look very
useful for what I am wanting to do. My initial results are promising!
Brian
On Nov 17, 2012, at 6:20 PM, R. Michael Weylandt wrote:
> On Sat, Nov 17, 2012 at 11:00 PM, Brian Feeny wrote:
>> I am looking for
I am looking for a library/function in R that can compare two phrases and give
me a score, or somehow classify them as correct as possible.
The "phrases" are obfuscated/messy. I am not concerned about which is
"correct" (for example spell checking), I am only concerned in grouping them
so that
= paste("V", seq_len(ncol), sep =
""))
Thank you for your help
Brian
On Nov 17, 2012, at 4:34 PM, Brian Feeny wrote:
>
> On Nov 17, 2012, at 4:27 PM, Duncan Murdoch wrote:
>>>
>>
>> I would suggest reading the help file: read.delim onl
On Nov 17, 2012, at 4:27 PM, Duncan Murdoch wrote:
>>
>
> I would suggest reading the help file: read.delim only looks at the first 5
> lines to determine the number of columns if you don't specify the colClasses.
>
> Duncan Murdoch
>
Duncan,
I have tried to pass colClasses but R complains
I am trying to read in a pipe delimited file that has rows with varying number
of columns, here is my sample data:
A|B|C|D
A|B|C|D|E|F
A|B|C|D|E
A|B|C|D|E|F|G|H|I
A|B|C|D
A|B|C|D|E|F|G|H|I|J
You can see line 6 has 10 columns. Yet, I can't explain why R does like so:
> test <- read.delim("mypat
17, 2012, at 11:25 AM, David Winsemius wrote:
>
> On Nov 16, 2012, at 9:39 PM, Brian Feeny wrote:
>
>> I have a dataframe that has a header like so:
>>
>> classvalue1 value2 value3
>>
>> class is a factor
>>
>> the actual values in t
I have a dataframe that has a header like so:
class value1 value2 value3
class is a factor
the actual values in the columns value1, value2 and value3 are 0-255, I wish to
binarize these using biclust.
I can do this like so:
binarize(dataframe[,-1])
this will return a dataframe, but then I
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