Displaying 20 results from an estimated 1000 matches similar to: "probabilities in svm output in e1071 package"
2010 Jun 24
1
help in SVM
HI, GUYS,
I used the following codes to run SVM and get prediction on new data set hh.
dim(all_h)
[1] 2034 24
dim(hh) # it contains all the variables besides the variables in all_h
data set.
[1] 640 415
require(e1071)
svm.tune<-tune(svm, as.factor(out) ~ ., data=all_h,
ranges=list(gamma=2^(-5:5), cost=2^(-5:5)))# find the best parameters.
bestg<-svm.tune$best.parameters[[1]]
2010 Apr 29
2
can not print probabilities in svm of e1071
> x <- train[,c( 2:18, 20:21, 24, 27:31)]
> y <- train$out
>
> svm.pr <- svm(x, y, probability = TRUE, method="C-classification",
kernel="radial", cost=bestc, gamma=bestg, cross=10)
>
> pred <- predict(svm.pr, valid[,c( 2:18, 20:21, 24, 27:31)],
decision.values = TRUE, probability = TRUE)
> attr(pred, "decision.values")[1:4,]
2010 Jun 17
1
help with nnet
> nnet.fit<-nnet(as.factor(out) ~ ., data=all_h, size=5, rang=0.3,
decay=5e-4, maxit=500) # model fitting
> summary(nnet.fit)
a 23-5-1 network with 126 weights
options were - entropy fitting decay=5e-04
HI, Guys,
I can not find the manual to describe how the model is built, is there a
more detailed description how nnet package works?
--
Sincerely,
Changbin
--
[[alternative
2010 Apr 06
2
help output figures in R
somfunc<- function (file) {
aa_som<-scale(file)
final.som<-som(data=aa_som, rlen=10000, grid=somgrid(5,4, "hexagonal"))
pdf(file="/home/cdu/changbin/file.pdf") #output graphic file.
plot(final.som, main="Unsupervised SOM")
dev.off()
}
I have many different files, if I want output pdf file with the same name
as for each dataset I feed to the function
2011 Mar 04
1
Probabilities outside [0, 1] using Support Vector Machines (SVM) in e1071
Hi All,
I'm attempting to use eps-regression or nu-regression SVM to compute
probabilities but the predict function applied to an svm model object
returns values outside [0, 1]:
Variable Data looks like:
Present X02 X03 X05 X06 X07 X13 X14 X15 X18
1 0 1634 48 2245.469 -1122.0750 3367.544 11105.013 2017.306 40 23227
2 0 1402 40 2611.519 -811.2500 3422.769 10499.425 1800.475 40 13822
3 0 1379
2010 May 18
2
get the row sums
> head(en.id.pr)
valid.gene_id b.pred rf.pred svm.pred
1521 2500151211 0 0 0
366 639679745 0 0 0
1965 2502081603 1 1 1
1420 644148030 1 1 1
1565 2500626489 1 1 1
1816 2501711016 1 1 1
> p.pred <- data.frame(en.id.pr, sum=apply(en.id.pr[,2:4], 1, sum)) #
2010 Jun 15
1
output from the gbm package
HI, Dear Greg and R community,
I have one question about the output of gbm package. the output of Boosting
should be f(x), from it , how to calculate the probability for each
observations in data set?
SInce it is stochastic, how can guarantee that each observation in training
data are selected at least once? IF SOME obs are not selected, how to
calculate the training error?
Thanks?
--
2010 Dec 16
1
my function does not work for large data set
Dear R community,
I have one function, it works for small data set, but does not work on large
data set, can anyone help me with this?
> #creat new variable by dividing each aa dimer by total_length.
> imper<-function(x, file) {
+ round(x/file$length, 5)
+ }
> dim(test)
[1] 999 2402
> test[varname[2:2401]]<-
2003 Dec 10
3
e1071:svm - default epsilon = 0.1 (NOT 0.5) (PR#5671)
In e1071 package/svm default epsilon value is set to 0.1 and not 0.5
as documentation says.
R
2010 Apr 15
2
r-loop
HI, Dear community,
I am building the following loop,
ww<-function(file) {
lossw<-vector()
for (x in seq(0.1, 0.9, by=0.1)) {
cat('xweight ', x, '\n')
lossw[i] <- cross.validation(file, x)$avg
}
return(lossw) }
MY question is how to index the lossw[i]?
for (i in 1:9)
for (x in seq(0.1, 0.9, by=0.1))
Thanks so much!
2010 May 05
3
sort the data set by one variable
> #sort the data by predicted probability
> b.order<-bo.id.pred[(order(-predict)),]
> b.order[1:20,]
gene_id predict
43 637882902 0.07823997
53 638101634 0.66256490
61 639084581 0.08587504
41 637832824 0.02461066
25 637261662 0.11613879
22 637240022 0.06350477
62 639084582 0.02238538
63 639097718 0.06792841
44 637943079 0.04532625
80 640158389 0.06582658
3 637006517 0.57648451
2010 May 25
4
R eat my data
HI, Dear R community,
My original file has 1932 lines, but when I read into R, it changed to 1068
lines, how comes?
cdu@nuuk:~/operon$ wc -l id_name_gh5.txt
1932 id_name_gh5.txt
> gene_name<-read.table("/home/cdu/operon/id_name_gh5.txt", sep="\t",
skip=0, header=F, fill=T)
> dim(gene_name)
[1] 1068 3
--
Sincerely,
Changbin
--
Changbin Du
DOE Joint Genome
2010 Oct 12
1
need help with nnet
HI, Dear R community,
My data set has 2409 variables, the last one is response variable. I have
used the nnet after feature selection and works. But this time, I am using
nnet to fit a model without feature selection. I got the following error
information:
> dim(train)
[1] 1827 2409
nnet.fit<-nnet(as.factor(out) ~ ., data=train, size=3, rang=0.3,
decay=5e-4, maxit=500) # model
2010 Apr 26
3
R.GBM package
HI, Dear Greg,
I AM A NEW to GBM package. Can boosting decision tree be implemented in
'gbm' package? Or 'gbm' can only be used for regression?
IF can, DO I need to combine the rpart and gbm command?
Thanks so much!
--
Sincerely,
Changbin
--
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2011 Jan 20
2
auc function
Hi, there.
Suppose I already have sensitivities and specificities. What is the quick R-function to calculate AUC for the ROC plot? There seem to be many R functions to calculate AUC.
Thanks.
Yulei
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2010 May 26
1
how to Store loop output from a function
HI, Dear R community,
I am writing the following function to create one data set(*tree.pred*) and
one vector(*valid.out*) from loops. Later, I want to use the data set from
this loop to plot curves. I have tried return, list, but I can not use the
*tree.pred* data and *valid.out* vector.
auc.tree<- function(msplit,mbucket) {
* tree.pred<-data.frame()
2006 Dec 07
1
svm plot question
I run the following code, all other is ok,
but plot(m.svm,p5.new,As~Cur) is not ok
Anyone know why?
install.packages("e1071")
library(e1071)
library(MASS)
p5 <- read.csv("http://www.public.iastate.edu/~aiminy/data/p_5_2.csv")
p5.new<-subset(p5,select=-Ms)
p5.new$Y<-factor(p5.new$Y)
levels(p5.new$Y) <- list(Out=c(1), In=c(0))
attach(p5.new)
2010 Feb 23
1
e1071: Cannot predict probabilities
Dear list.
I using the SVM-methods from the e1071, but I can't get the
probabilities when predicting.
Code:
x <- matrix(rbinom(100, 10, 0.3), ncol=2)
y <- apply(x, 1, sum)
fit <- svm(y ~ x, method = "C-classification", kernel = "radial",
probability = TRUE)
predict(fit, x, probability=TRUE)
Here predict doesn't containing any probabilities (not as attributes
2011 Feb 07
1
can I use the output of a neural network as the fitness function of genetic algorithm?
Hi Everyone,
I need to use genetic algorithm to find the minimum. The problem is, I
cannot define the fitness function, but I can build a neural network from
the input data and use
the output as a fitness function. Can this be done?
The other problem is, I know there are a few package in R related to GA.
So far I know all of them take a specific function as fitness function, is
2005 Apr 26
3
Error using e1071 svm: NA/NaN/Inf in foreign function call
Hello,
As far I saw in archive mailing list, I am not the first person with this problem. Anyway I was not able to pass this error once the information I got from the archive it is not very conclusive for this case. I have used linear, radial and sigmoid kernels for the same data in the same conditions and everything is ok. This problem just happens with the polynomial kernel. I send the