Hi all,
I would like to ask a question related to Kendall package. I ran Kendall
(x,y) and
saw the results. But I am not sure which tau values R reported. I have
ties in
my data set, so I want tau-b. Can anybody tell how Kendall package is
calculating tau values? I have looked at the package
Yes, if the Special_Dates are not sorted then f4 needs to sort them.
Perhaps closestValue should just sort its vec argument.
I didn't realize that the output should not have any duplicate
entries. I thought it should have the same number of rows
as the input A.
Bill Dunlap
Spotfire, TIBCO Softwa
On Aug 19, 2012, at 5:56 PM, Sapana Lohani wrote:
Hi,
My data.frame "A" has FID like this
FID
a
a
b
b
b
c
c
d
d
d
d
Now my second data.frame "B" has age value for a, b, c, d like
FID Age
a 5
b 7
c 9
d 3
How can search for the Age column in "B" and replace the value
Hi,
My data.frame "A" has FID like this
FID
a
a
b
b
b
c
c
d
d
d
d
Now my second data.frame "B" has age value for a, b, c, d like
FID Age
a 5
b 7
c 9
d 3
How can search for the Age column in "B" and replace the values in "A" so that
my new "A" looks like this
FID Age
On Aug 19, 2012, at 5:14 PM, Dikán Szabolcs András wrote:
Dear R Users! I'm new in R! I've got a data.frame. For
example:a<-data.frame(cbind( 0:1, 1:10)) X1 X2
1 0 1
2 1 2
3 0 3
4 1 4
5 0 5
6 1 6
7 0 7
8 1 8
9 0 9
10 1 10 Firstly i want to create combinations
Dear R Users! I'm new in R! I've got a data.frame. For
example:a<-data.frame(cbind( 0:1, 1:10)) X1 X2
1 0 1
2 1 2
3 0 3
4 1 4
5 0 5
6 1 6
7 0 7
8 1 8
9 0 9
10 1 10 Firstly i want to create combinations from X2, for
example:combn(a$X2,2) It's ok, butsecondly I wo
On Aug 19, 2012, at 4:34 PM, White, William Patrick wrote:
Also it occurred to me that my initial explanation was not
explicitly clear as to what the desired output is. What I am trying
to get is a moving absolute deviation between the two sets of numbers.
The phrase "a moving absolute dev
Also it occurred to me that my initial explanation was not explicitly clear as
to what the desired output is. What I am trying to get is a moving absolute
deviation between the two sets of numbers. This is not to be confused with the
mean absolute deviation, or the median absolute deviation whic
The first method described produces a value other than the desired output every
time the Firstset value is positive and the Secondset is negative, such as the
second instance when seed is set to 123. The second method described produces a
value other than the desired output every time the Firsts
Hello,
You're right, your solution is much faster, but it doesn't remove
duplicates.
When I ran f4() with larger datasets it poduced an error,
Error in findInterval(x, vec) : 'vec' must be sorted non-decreasingly
So here they all are.
f1 <- function(A, B){
m <- merge(A, B)
result <-
And the following, f4, uses the same algorithm as f2 but codes
it somewhat more efficiently. It uses the same closestValue()
function.
f4 <- function (A, B) {
A$TYPE <- as.factor(A$TYPE)
uA <- levels(A$TYPE)
As <- split(A$DATE, A$TYPE)
Bs <- split(B$Special_Date, factor(B$TYPE, l
Hi Uwe,
I can attach the data file to an email or send you a link so you can
download it. Which one do you prefer?
Thanks for your help ...
Best, Reza
On Sun, Aug 19, 2012 at 4:10 PM, Uwe Ligges wrote:
>
>
> On 19.08.2012 11:06, Reza Salimi-Khorshidi wrote:
>
>> Dear list,
>> I am using the ksv
The following, f2(A,B), should do well with lots of rows in A and B
as long as the number of types is not huge.
f2 <- function(A, B) {
types <- as.character(unique(A$TYPE))
result <- numeric(nrow(A))
Bs <- split(B$Special_Date, B$TYPE)
for(type in types) {
w <- A$TYPE == ty
Hi: I can't go into all the details ( Lutz Hamel has a very nice intro book
for SVM's and I wouldn't
do the details justice anyway ) but the objective function in an SVM is
maximizing the margin ( think of the margin as the amount of seperation
between the 2 classes in a 2 class problem ). The obje
On Aug 19, 2012, at 12:58 PM, David Winsemius wrote:
On Aug 19, 2012, at 12:04 PM, White, William Patrick wrote:
On the surface this seems pretty simple, but I flummoxed. I have
two sets of numbers they bounce around zero, positive one and
negative one. They have a relationship between th
On Aug 19, 2012, at 12:04 PM, White, William Patrick wrote:
On the surface this seems pretty simple, but I flummoxed. I have two
sets of numbers they bounce around zero, positive one and negative
one. They have a relationship between them, where one diverges away
from the other. I want cre
On the surface this seems pretty simple, but I flummoxed. I have two sets of
numbers they bounce around zero, positive one and negative one. They have a
relationship between them, where one diverges away from the other. I want
create a second set of numbers that tracks that divergence.
#Lets m
Hi everybody,
I am new in e1071 and with SVMs. I am trying to understand the performance
of SVMs but I face with a situation that I thought as not meaningful.
I added the R code for you to see what I have done.
/set.seed(1234)
data <- data.frame( rbind(matrix(rnorm(1500, mean = 10, sd = 5),ncol
Hello,
Em 19-08-2012 17:33, William Dunlap escreveu:
Did you omit
m <- merge(A, B)
from your code?
Yes, completely forgot! It should be before the split/lapply.
Rui Barradas
Bill Dunlap
Spotfire, TIBCO Software
wdunlap tibco.com
-Original Message-
From: r-help-boun...@r-projec
How many different types are there? Just a handful or many thousands?
For this sort of problem it is often handy to write a function which generates
datasets of the sort you are thinking of but parameterized by the
number of rows, levels, etc., so you can see how the execution time
varies with the
On 18.08.2012 21:32, Daniel Blankenheim wrote:
Hey
my name is daniel, i am writing my bachelor thesis and wondering if you can
help me.
i am trying to generate a regression tree via rpart. to
reduce the error of the model i use cross validation, but instead
of reducing the cross validation er
On 19.08.2012 11:06, Reza Salimi-Khorshidi wrote:
Dear list,
I am using the ksvm function from kernlab as follows:
(1) learning
svm.pol4 <- ksvm(class.labs ~ ., data = train.data, prob.model = T, scale
= T, kernel = "polydot")
(2) prediction
svm.pol.prd4 <- predict(svm.pol4, train.data, ty
Dear R users,
apologies if this has been debated before, but I was unable to find it
anywhere (with respect to shrinkage approach).
I am trying to evaluate explained deviance of each model term in a GAM.
I am using a the mgcv library for fitting a GAM to binary data.
Thin plate regression spline
Hello
I created a radar chart using the function of radarchart from the
fmsb package in R software.
The matrix I am using is as follows:
x<-c(c(rep(4.5,7),c(rep(0,7)), 3.34, 3.28, 1.37, 1.12, 3.52, 4.07, 3.66));
a<-matrix(x,nrow=3, ncol=7,byrow=T)
I would like to show the range of c(0,5)
Thank you very much Rui
On 19 August 2012 13:49, Rui Barradas wrote:
> Hello,
>
> Yes you can, if you have memory problems, say so and we'll see it then.
> In the mean time, there's something you should change, to allow for several
> minima but to only return one per combination of TYPE and DATE
Hello,
Yes you can, if you have memory problems, say so and we'll see it then.
In the mean time, there's something you should change, to allow for
several minima but to only return one per combination of TYPE and DATE.
Replace this
x[which(min(a) == a), ]
by this
x[which.min(a), ]
Rui Bar
Dear Riu, Many thanks for your suggestion
However these are just simplified examples... in reality the dataset A
contains millions of observations and B several thousands of rows...
Could I still use a modified form of your suggestion?
Thanks
On 19 August 2012 12:51, Rui Barradas wrote:
> Hello
Hello,
Try the following.
A <- read.table(text="
TYPE DATE
A2
A5
A20
B10
B2
", header = TRUE)
B <- read.table(text="
TYPE Special_Date
A 2
A 6
A 20
A 22
B 5
B
Dear R-help
Î would like to know if there is a short solution in R for this
merging problem...
Let say I have a dataset A as:
TYPE DATE
A2
A5
A20
B10
B2
(there can be duplicates for the same type and date)
and I have another dataset
Dear list,
I am using the ksvm function from kernlab as follows:
(1) learning
> svm.pol4 <- ksvm(class.labs ~ ., data = train.data, prob.model = T, scale
= T, kernel = "polydot")
(2) prediction
> svm.pol.prd4 <- predict(svm.pol4, train.data, type = "probabilities")[,2]
But unfortunately, when ca
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