Displaying 20 results from an estimated 1000 matches similar to: "how to get actual value from predict in nnet?"
2009 Nov 02
1
modifying predict.nnet() to function with errorest()
Greetings,
I am having trouble calculating artificial neural network
misclassification errors using errorest() from the ipred package.
I have had no problems estimating the values with randomForest()
or svm(), but can't seem to get it to work with nnet(). I believe
this is due to the output of the predict.nnet() function within
cv.factor(). Below is a quick example of the problem I'm
2003 Jul 16
1
Help on NNET
Hi, Dear all,
I am just starting using R in my work and got some trouble to figure out some of the errors. Can anybody help me?
The following is the script:
read.csv('pupil.txt',header=TRUE,sep='\t')->pupil
samp<-c(1:50, 112:162, 171:220, 228:278)
pupil.nn2 <- nnet(Type ~ ., data = pupil, subset = samp, size = 2, rang = 0.1, decay = 5e-4, maxit = 200)
2006 Jun 23
1
Problems creating packages.
I'm creating my own package for personal and I'm having trouble
getting it to a point where R (v 2.3.1) will recognise it. I've
followed two different tutorials for how to create the package
structure and the DESCRIPTION file (
http://web.maths.unsw.edu.au/~wand/webcpdg/rpack.html ,
http://www.maths.bris.ac.uk/~maman/computerstuff/Rhelp/Rpackages.html#Lin-Lin
). I'm still getting
2009 Feb 18
1
Training nnet in two ways, trying to understand the performance difference - with (i hope!) commented, minimal, self-contained, reproducible code
Dear all,
Objective: I am trying to learn about neural networks. I want to see
if i can train an artificial neural network model to discriminate
between spam and nonspam emails.
Problem: I created my own model (example 1 below) and got an error of
about 7.7%. I created the same model using the Rattle package (example
2 below, based on rattles log script) and got a much better error of
about
2009 May 30
0
what is 'class.ind' here?
Hi. The there is an example in nnet help which is pasted in below.
I am not sure how they are generating 'targets'. What is the 'class.ind()
function doing?
In the help docs for it they say "Generates a class indicator function from
a given factor."
I tried putting a simple vector of the "classes" into test.cl (below) but I
get an error of
"(list) object
2013 May 20
0
Neural network: Amore adaptative vs batch why the results are so different?
I am using the iris example came with nnet package to test AMORE. I can see
the outcomes are similar to nnet with adaptative gradient descent. However,
when I changed the method in the newff to the batch gradient descent, even
by setting the epoch numbers very large, I still found all the iris
expected class=2 being classified as class=3. In addition, all those
records in the outcomes (y) are the
2004 Jan 09
3
ipred and lda
Dear all,
can anybody help me with the program below? The function predict.lda
seems to be defined but cannot be used by errortest.
The R version is 1.7.1
Thanks in advance,
Stefan
----------------
library("MASS");
library("ipred");
data(iris3);
tr <- sample(1:50, 25);
train <- rbind(iris3[tr,,1], iris3[tr,,2], iris3[tr,,3]);
test <- rbind(iris3[-tr,,1],
2002 Mar 17
3
apply problem
> data(iris)
# iris3 is first 3 rows of iris
> iris3 <- iris[1:3,]
# z compares row 1 to each row of iris3 and is correctly
computed
> z <- c(F,F,F)
> for(i in seq(z)) z[i] <- identical(iris3[1,],iris3[i,])
> z
[1] TRUE FALSE FALSE
# this should do the same but is incorrect
> apply(iris3,1,function(x)identical(x,iris3[1,]))
1 2 3
FALSE FALSE FALSE
2000 Mar 08
3
Reading data for discriminant analysis
Dear R users,
I want to do discriminant analysis on my data. I have
successfully followed the discriminant analysis in V & R on
the iris data:
> ir <- rbind (iris3[,,1],iris3[,,2],iris3[,,3])
> ir.species <- c(rep("s",50),rep("c",50),rep("v",50))
> a <- lda(log(ir),ir.species)
> a$svd^2/sum(a$svd^2)
[1] 0.996498601 0.003501399
> a.x <-
2004 Nov 02
2
lda
Hi !!
I am trying to analyze some of my data using linear discriminant analysis.
I worked out the following example code in Venables and Ripley
It does not seem to be happy with it.
============================
library(MASS)
library(stats)
data(iris3)
ir<-rbind(iris3[,,1],iris3[,,2],iris3[,,3])
ir.species<-factor(c(rep("s",50),rep("c",50),rep("v",50)))
2006 Sep 11
2
Translating R code + library into Fortran?
Hi all,
I'm running a monte carlo test of a neural network tool I've developed,
and it looks like it's going to take a very long time if I run it in R
so I'm interested in translating my code (included below) into something
faster like Fortran (which I'll have to learn from scratch). However, as
you'll see my code loads the nnet library and uses it quite a bit, and I
2009 Nov 17
1
Error running lda example: Session Info
>
> library(MASS)
> Iris <- data.frame(rbind(iris3[,,1], iris3[,,2], iris3[,,3]),
+ Sp = rep(c("s","c","v"), rep(50,3)))
> train <- sample(1:150, 75)
> table(Iris$Sp[train])
c s v
22 23 30
> z <- lda(Sp ~ ., Iris, prior = c(1,1,1)/3, subset = train)
Error in if (targetlist[i] == stringname) { : argument is of length
2011 Jun 14
1
heatmap with values
Hi!
I'm displaying a contingency table with heatmap():
> svm.predPix.tabla
svm.predPix CC DD LL NN NN2
CC 22 0 3 8 3
DD 0 27 0 1 0
LL 1 1 90 3 7
NN 2 0 1 11 4
NN2 0 0 5 1 20
> heatmap(svm.predPix.tabla[5:1,], Rowv=NA, Colv=NA,col =
rev(heat.colors(32)), scale="column", margins=c(5,10))
and I'm happy
2013 Jan 26
2
different legends in lattice panels
Hi listers,
I want to make lattice plots xyplots with the indication of legends
inside each panel with only the points and the lines actually ploted
inside each given panel according to the group(ing) factor.
The code below shows what I have achieved so far and I hope will make
clear what I want to have.
It seems to me that my solution is a very "dirty hack" and there
certainly is
2011 Mar 17
3
Beginner question: How to replace part of a filename in read.csv?
I would like to use samp as a part of a filename that I can change. My source
files are .csv files with date as the file name, and I would like to be able
to type in the date (later perhaps automate this using list.files) and then
read the csv and write the pdf automatically. I have tried different
combinations with "" and () around samp, but I keep getting the error
"object
2011 Sep 06
2
Generalizing call to function
Hello guys,
I would like to ask for help to understand what is going on in
"func2". My plan is to generalize "func1", so that are expected same
results in "func2" as in "func1". Executing "func1" returns...
0.25 with absolute error < 8.4e-05
But for "func2" I get...
Error in dpois(1, 0.1, 23.3065168689948, 0.000429064542600244,
2008 Jun 12
1
About Mcneil Hanley test for a portion of AUC!
Dear all
I am trying to compare the performances of several methods using the AUC0.1
and
not the whole AUC. (meaning I wanted to compare to AUC's whose x axis only
goes to
0.1 not 1)
I came to know about the Mcneil Hanley test from Bernardo Rangel Tura
and I referred to the original paper for the calculation of "r" which is an
argument of the function
cROC. I can only find the
2012 Sep 20
3
lattice dotplot reorder contiguous levels
my reproducible example
test<-structure(list(site = structure(c(1L, 1L, 1L, 1L, 1L, 1L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L,
3L, 3L, 3L, 3L, 3L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 5L), .Label = c("A",
"B", "C", "D", "E"), class = "factor"),
2009 Feb 24
2
lmer, estimation of p-values and mcmcsamp
(To the list moderator: I just subscribed to the list. Apologies for not
having done so longer before trying to post.)
Hi all,
I am currently using lmer to analyze data from an experiment with a
single fixed factor (treatment, 6 levels) and a single random factor
(block). I've been trying to follow the online guidance for estimating
p-values for parameter estimates on these and other
2000 Feb 24
2
(-1 as index) OR (envelope for QQ)
I'm new to R (and to S) and am wondering about code from pages 72 and
83 of MASS (Venables+Ripley, 3rd edition), to draw an envelope on a QQ
plot. Copying from the book, I've got:
#... code whose gist is "a.fit <- nls(..."
num.points <- length(resid(a.fit))
qqnorm(residuals(a.fit)) # illustrate data-model residuals
qqline(residuals(a.fit))
samp <-