Hello,
I previously submitted the below query to r-sig-geo, but have had no
response. Before I start bothering individual maintainers, I wonder
if anyone on this list has any experience with the package and (or!)
can diagnose my problems?
Thanks,
Calum
Hello,
I am having a little trouble with
I would like to create an artificial neural network with R but I don't know
its
parameters jet (number of layers, number of neurons,...).
I downloaded the package ANN and I use the function ANNGA, but I'm
afraid I haven't really created a neural network. In fact, at the end of the
process I
Am 25.11.2013 13:06, schrieb Knut Krueger:
how can I read exel files where the decimal sign is comma instead dot.
I get the data as ascii and when converting 3,5 with as.numeric
the 3,5 will be converted to NA
I think here is a major bug because no warning is genereated.
It is impossible
Hi everyone,
I have a large dataset with missing values. I tried using glmnet, but it seems
that it cannot handle NA values in the design matrix. I also tried lars, but I
get an error too. Does anyone know of any package for computing the lasso
solution which handles NA values?
?na.omit
??
But it is certainly true that omitting missing values first and
analyzing the remaining data can:
1. Leave you with no data to analyze
2. Result in biased and misleading conclusions if the missingness
mechanism is related to the covariates.
In general, handling data with missing
On Nov 29, 2013, at 6:18 AM, Knut Krueger wrote:
Am 25.11.2013 13:06, schrieb Knut Krueger:
how can I read exel files where the decimal sign is comma instead dot.
I get the data as ascii and when converting 3,5 with as.numeric the 3,5
will be converted to NA
I think here is a major
Am 29.11.2013 18:31, schrieb David Winsemius:
On Nov 29, 2013, at 6:18 AM, Knut Krueger wrote:
Am 25.11.2013 13:06, schrieb Knut Krueger:
how can I read exel files where the decimal sign is comma instead dot.
I get the data as ascii and when converting 3,5 with as.numeric the 3,5 will
be
Please understand that this is a contributed package, and definitive assistance
can only be provided by the package author. Type
maintainer(XLConnect)
for contact information, and read the package info at
cran.r-project.org/web/packages/XLConnect/index.html. R-devel is unlikely to be
a more
Hi,
I used that because 10% of the values in the data were already NA.
You are right. Sorry, ?match() is unnecessary. I was trying another solution
with match() which didn't work out and forgot to check whether it was adequate
or not.
set.seed(49)
Hi,
Have you tried p.adjust=none
pairwise.wilcox.test(daily_long$value,daily_long$variable,
paired=T,p.adj=none)
pairwise.wilcox.test(Ozone, Month, p.adj = none)
A.K.
Dear list member,
I want to compare if
the rank order is significantly different for seven different measures. So we
Hi Arun,
Thanks a lot. It works perfectly.
Here is the complete code - for all those who are interested to see Rel
Cum Freq oscillating to reach the Expected Value
# Bernouilli Trial where:
v.fly=c(G,B) # Outcome is Green or Blue fly
n=100 # No of Events / Trials
v.smp = seq(1:n) # Event Id
Hi,
This is the input data frame:
###
df.1 = read.table(header=T,text=
id gender WMC_alcohol WMC_caffeine WMC_no.drug RT_alcohol RT_caffeine
RT_no.drug
1 1 female 3.7 3.7 3.9 488 236 371
2 2 female 6.4 7.3 7.9 607 376 349
3 3 female 4.6 7.4 7.3 643 226
On Nov 29, 2013, at 10:30 AM, Knut Krueger wrote:
Am 29.11.2013 18:31, schrieb David Winsemius:
On Nov 29, 2013, at 6:18 AM, Knut Krueger wrote:
Am 25.11.2013 13:06, schrieb Knut Krueger:
how can I read exel files where the decimal sign is comma instead dot.
I get the data as ascii and
Hi Burhan,
No problem. One suggestion in this code would be:
with(df.1, cumsum(E.Occur==TRUE)/(seq_len(nrow(df.1 ##==TRUE is not
needed
identical( with(df.1, cumsum(E.Occur)/(seq_len(nrow(df.1, with(df.1,
cumsum(E.Occur==TRUE)/(seq_len(nrow(df.1 )
is.logical(TRUE)
#[1]
Dear list member,
I want to compare if the rank order is significantly different for seven
different measures. So we have same sample but different measures which
reduces the problem to a paired one sample Wilcox test if I understood the
test correctly. In constructed toy examples for my sake
I see your desired output has rather fewer data than the input data frame.
Instead of making us pore over a bunch of numbers, can you explain exactly
what filtering you wish to do to get the specific subset
of {male/female} {alcohol/caffeine} you're trying to get?
BHM wrote
Hi,
This is
Pardon the pedantry, but doyou know what a Venn Diagram is? Because there
are two or three
packages at CRAN which will generate Venn diagrams for you given exactly
that sort of source data.
Yi.Zou wrote
Hi all,
I am thinking of making a graph with three dataset A,B,C with shared
common
On Nov 29, 2013, at 9:42 AM, Burhan ul haq wrote:
Hi,
This is the input data frame:
###
df.1 = read.table(header=T,text=
id gender WMC_alcohol WMC_caffeine WMC_no.drug RT_alcohol RT_caffeine
RT_no.drug
1 1 female 3.7 3.7 3.9 488 236 371
2 2
Hi all,
I am attempting to create a weights object and perform a Moran I test as
well. I have a very large spatial weights matrix (roughly 22,000x22,000)
that was created in Excel and read into R, and I'm now trying to implement:
library(spdep)
SW=mat2listw(matrix)
I am getting the following
An essentially identical approach that may be a tad clearer -- but
requires additional space -- first creates a logical vector for the
locations of the NA's in the unlisted data.frame. Further NA positions
are randomly added and then the augmented vector is used as a logical
matrix to index where
Hi all!
I am just starting my adventure with R, so excuse me naive questions.
My data look like that:
http://r.789695.n4.nabble.com/file/n4681391/data_descr_img.jpg
I have 3 independent variables (F.1, F.2 and F.3) and 334 other variables
(r.1, r.2, ... r.334) - each one of these will be
Hi,
First, a big thanks to all those who replied.
I am including all the replies in one email for easier reference later:
# Input from David
#
reshape(df.1, idvar=1:2, sep=_, direction=long,
varying=names(df.1)[3:8])
#
# Input from Dennis
#
dfr1 - reshape(df.1, idvar = c(id, gender),
Hi
Anyone have experience with very large datasets and the Bayesian Network
package, bnlearn? In my experience R doesn't react well to very large
datasets.
Is there a way to divide up the dataset into pieces and incrementally learn
the network with the pieces? This would also be helpful incase
Hi,
The link seems to be not working. From the description, it looks like:
set.seed(432)
dat1 - as.data.frame(matrix(sample(200,154*337,replace=TRUE),ncol=337))
colnames(dat1) - c(paste(F,1:3,sep=.),paste(r,1:334,sep=.))
lst1 - lapply(paste(r,1:334,sep=.),function(x)
Hi Arun,
Thanks again. Comment noted :)
Amazing use of regular expressions in your solutions. Any reference, or
book you would recommend.
Cheers !
On Fri, Nov 29, 2013 at 10:56 PM, arun smartpink...@yahoo.com wrote:
Hi Burhan,
No problem. One suggestion in this code would be:
On 30/11/2013 04:52, Jejo Koola wrote:
Hi
Anyone have experience with very large datasets and the Bayesian Network
package, bnlearn? In my experience R doesn't react well to very large
datasets.
Maybe, but a million is not 'very large': R handles billions of
observations without problems on
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