Displaying 20 results from an estimated 8000 matches similar to: "howto optimize operations between pairs of rows in a single matrix like cor and pairs"
2010 Apr 16
2
managing data and removing lines
Hi,
I am very new to R and I've been trying to work through the R book to gain a
better idea of the code (which is also completely new to me).
Initially I imputed my data from a text file and that seemed to work ok, but
I'm trying to examine linear relationships between gdist and gair, gdist and
gsub, m6dist and m6air, etc.
This didn't work and I think it might have something to do
2004 Jul 30
2
pairwise difference operator
There was a BioConductor thread today where the poster wanted to find
pairwise difference between columns of a matrix. I suggested the slow
solution below, hoping that someone might suggest a faster and/or more
elegant solution, but no other response.
I tried unsuccessfully with the apply() family. Searching the mailing
list was not very fruitful either. The closest I got to was a cryptic
chunk
2013 Mar 29
1
pairs(X,Y) analog of cor(X,Y)?
With a data frame containing some X & Y variables I can get the between
set correlations
with cor(X,Y):
> cor(NLSY[,1:2], NLSY[3:6])
antisoc hyperact income educ
math 0.043381307 -0.07581733 0.25487753 0.2876875
read -0.003735785 -0.07555683 0.09114299 0.1884101
Is there somewhere an analog of pairs(X,Y) that will produce the pairwise
plots of each X against each
2005 Jan 26
3
Still avoiding loops
Dear all,
I have a matrix X with 47 lines and say 500 columns - values are in {0,1}.
I'd like to compare lines.
For that, I first did:
for (i in 1:(dim(X)[1]-1))
for (j in (i+1):dim(X)[1]) {
Y <- X[i,]+Y[j,]
etc.
but, since it takes a long time, I would prefer avoding loops;
for that, my first idea was to add this matrix:
X1=X[,rep(1:46,46:1)]
to this one:
res=NULL
for (i in
2008 Jan 02
2
strange behavior of cor() with pairwise.complete.obs
Hi all,
I'm not quite sure if this is a feature or a bug or if I just fail to understand
the documentation:
If I use cor() with pairwise.complete.obs and method=pearson, the result is a
scalar:
->cor(c(1,2,3),c(3,4,6),use="pairwise.complete.obs",method="pearson")
[1] 0.9819805
The documentation says that
" '"pairwise.complete.obs"' only
2004 Oct 22
1
cor, cov, method "pairwise.complete.obs"
Hi UseRs,
I don't want to die beeing idiot...
I dont understand the different results between:
cor() and cov2cov(cov()).
See this little example:
> x=matrix(c(0.5,0.2,0.3,0.1,0.4,NA,0.7,0.2,0.6,0.1,0.4,0.9),ncol=3)
> cov2cor(cov(x,use="pairwise.complete.obs"))
[,1] [,2] [,3]
[1,] 1.0000000 0.4653400 -0.1159542
[2,] 0.4653400 1.0000000
2011 Oct 12
2
Nonlinear regression aborting due to error
Colleagues,
I am fitting an Emax model using nls. The code is:
START <- list(EMAX=INITEMAX, EFFECT=INITEFFECT, C50=INITC50)
CONTROL <- list(maxiter=1000, warnOnly=T)
#FORMULA <- as.formula(YVAR ~ EMAX - EFFECT * XVAR^GAMMA / (XVAR^GAMMA + C50^GAMMA)) ## alternate version of formula
FORMULA <- as.formula(YVAR ~ EMAX - EFFECT / (1 + (C50/XVAR)^GAMMA))
FIT <-
2010 Jun 09
1
bug? in stats::cor for use=complete.obs with NAs
Arrrrr,
I think I've found a bug in the behavior of the stats::cor function when
NAs are present, but in case I'm missing something, could you look over
this example and let me know what you think:
> a = c(1,3,NA,1,2)
> b = c(1,2,1,1,4)
> cor(a,b,method="spearman", use="complete.obs")
[1] 0.8164966
> cor(a,b,method="spearman",
2008 Dec 03
1
how to handle irregularly spaced data as timeseries
I have a set of modeled climate data recorded at irregular intervals.
The format of the data is such that there are monthly measurements for
the years 2000, 2020, 2050, 2080, etc. Therefore I have 12 regular
records, a skip of some number of years, then 12 more monthly records,
another skip, and so on.... I created a dataframe from .txt with the
read.table() command. For starters I need
2007 Jul 20
1
how to determine/assign a numeric vector to "Y" in the cor.test function for spearman's correlations?
Hello to all of you, R-expeRts!
I am trying to compute the cor.test for a matrix that i labelled mydata
according to mydata=read.csv...
then I converted my csv file into a matrix with the
mydata=as.matrix(mydata)
NOW, I need to get the p-values from the correlations...
I can successfully get the spearman's correlation matrix with:
cor(mydata, method="s",
2008 Dec 21
2
data format issue
Dear all-
I have a dataset (see a sample below - but the whole dataset is June
2005 - June 2008). The "LST" format is "YYMMDDHHmm" and I would like to
get the hourly average of the "mph" for the summer months (spanning all
years). I have been trying to use "aggregate" but am not having much
success at all! any thoughts would be greatly appreciated.
2005 Mar 02
5
Differences between package and library terminology
Just out of curiosity, what is the difference between the terms for
package and library ? Why are we loading a package with the library()
command ?
If this is a case of RTFM, I would be happy to do so if pointed in the
right direction. I have searched the FAQ and mail archives and only came
up with http://tolstoy.newcastle.edu.au/R/help/05/02/12162.html but this
still does not explain what is
2010 Sep 14
1
NA confusion (length question)
Hi folks,
I am running a very simple regression using
mylm <- lm(mass ~ tarsus, na.action=na.exclude)
I would like the use the residuals from this analysis for more
regression but I'm running into a snag when I try
cbind(mylm$residuals, mydata) # where my data is the original data set
The error tells me that it cannot use cbind because the length of
mylm$residuals is
2011 Oct 09
1
sapply(pred,cor,y=resp)
Hello. I am wondering why I am getting NA for all in cors=sapply(pred,cor,y=resp). I suppose that each column in pred has NAs in them. Is there some way to fix this? Thanks
> str(pred)
'data.frame': 200 obs. of 13 variables:
$ mnO2: num 9.8 8 11.4 4.8 9 13.1 10.3 10.6 3.4 9.9 ...
$ Cl : num 60.8 57.8 40 77.4 55.4 ...
$ NO3 : num 6.24 1.29 5.33 2.3 10.42 ...
$ NH4 : num 578
2009 Dec 16
1
number of observations used in cor when use="pairwise.obs"
Dear R gurus,
to compute the correlation matrix of "n" variables with "n_obs" observations
each,
possibly including NA, I use cor(M, use="pairwise.obs")
where m is a "n" x "nobs" matrix.
Now I want to know the number of observations actually used in this
computation,
namely for each pair of columns in M, say pair (i,j), I want to compute
sum(
2008 Apr 04
2
pairwise.t.test for paired data
Dear R-help, I have a question about pairwise.t.test and adjustment for
multiple comparisons for paired data points.
I have the following data:
n=c("x", "x", "x", "x", "x", "x", "x", "x", "x", "x", "y", "y",
"y", "y", "y", "y",
2005 May 25
2
cor vs cor.test
Using Windows System, R 2.1.0
d is a data frame, 48 rows, 10 columns
cor(d) works properly providing all pairwise Pearson correlation
coefficients among columns
cor.test(d) gives error message "Error in cor.test.default(d) : argument
"y" is missing, with no default"
Why?
Thanks,
MCG
2005 Mar 29
1
improved pairs.formula?
Dear all,
I would like to suggest changing the pairs.formula command such that a
command like
pairs(GNP ~ . - Year - GNP.deflator, longley)
would behave in a similar fashion as
lm(GNP ~ . - Year - GNP.deflator, longley)
i.e., make a pairwise scatterplot of GNP and all other variables in
the (longley) dataframe except for Year and GNP.deflator. The above
command, with the
2005 Jul 25
5
passing formula arguments cv.glm
I am trying to write a wrapper for the last example in help(cv.glm) that
deals with leave-one-out-cross-validation (LOOCV) for a logistic model.
This wrapper will be used as part of a bigger program.
Here is my wrapper funtion :
logistic.LOOCV.err <- function( formu=NULL, data=NULL ){
cost.fn <- function(cl, pred) mean( abs(cl-pred) > 0.5 )
glmfit <- glm(
2003 Mar 17
4
X11 connection error in web cgi mode only
Dear all,
I am trying to create a web interface using Perl-CGI to call R plots and
to display them.
The following codes works perfectly fine when I copy and paste into the
console directly or if I save it into script.file and then R --no-save <
script.file producing the graphs.
jpeg("graph.jpeg", width=400, height=400)
plot(rnorm(100))
dev.off()
Now, I put the line system("R