Displaying 20 results from an estimated 3000 matches similar to: "PCA on image data"
2008 Jan 04
1
PCA error: svd(x, nu=0) infinite or missing values
Hi,
I am trying to do a PCA on my data but I keep getting the error message
svd(x, nu=0) infinite or missing values
>From the messages posted on the subject, I understand that the NAs in my
data might be the problem, but I thought na.omit would take care of that.
Less than 5% of my cells are missing data. However, the NAs are not
regularly distributed across my matrix: certain cases and
2008 Sep 09
4
PCA and % variance explained
After doing a PCA using princomp, how do you view how much each component
contributes to variance in the dataset. I'm still quite new to the theory of
PCA - I have a little idea about eigenvectors and eigenvalues (these
determine the variance explained?). Are the eigenvalues related to loadings
in R?
Thanks,
Paul
--
View this message in context:
2005 Jul 08
2
extract prop. of. var in pca
Dear R-helpers,
Using the package Lattice, I performed a PCA.
For example
pca.summary <- summary(pc.cr <- princomp(USArrests, cor = TRUE))
The Output of "pca.summary" looks as follows:
Importance of components:
Comp.1 Comp.2 Comp.3 Comp.4
Standard deviation 1.5748783 0.9948694 0.5971291 0.41644938
Proportion of Variance 0.6200604
2010 Apr 02
2
Biplot for PCA using labdsv package
Hi everyone,
I am doing PCA with labdsv package. I was trying to create a biplot graphs
in order to observe arrows related to my variables. However when I run the
script for this graph, the console just keep saying:
*Error in nrow(y) : element 1 is empty;
the part of the args list of 'dim' being evaluated was:
(x)*
could please someone tell me what this means? what i am doing
2009 Feb 13
4
PCA functions
Hi All, would appreciate an answer on this if you have a moment;
Is there a function (before I try and write it !) that allows the input of a
covariance or correlation matrix to calculate PCA, rather than the actual
data as in princomp()
Regards
Glenn
[[alternative HTML version deleted]]
2010 Jun 30
3
Factor Loadings in Vegan's PCA
Hi all,
I am using the vegan package to run a prcincipal components analysis
on forest structural variables (tree density, basal area, average
height, regeneration density) in R.
However, I could not find out how to extract factor loadings
(correlations of each variable with each pca axis), as is straightforwar
in princomp.
Do anyone know how to do that?
Moreover, do anyone knows
2006 Jan 25
1
combining variables with PCA
hello R_team
having perfomed a PCA on my fitted model with the function:
data<- na.omit(dataset)
data.pca<-prcomp(data,scale =TRUE),
I´ve decided to aggregate two variables that are highly correlated.
My first question is:
How can I combine the two variables into one new predictor?
and secondly:
How can I predict with the newly created variable in a new dataset?
Guess I need the
2008 Jul 01
2
PCA : Error in eigen(cv,
Hi all,
I am doing bootstrap on a distance matrix, in which samples have been
drawn with replacement. After that I do PCA on a resulted matrix, and
these 2 steps are repeated 1000 times.
pca(x) is a vector where I wanted to store all 1000 PCAs; and x is from
1 to 1000
SampleD is a new matrix after resampling;
I am getting the following error message, which I don't understand:
....
2010 Nov 30
3
pca analysis: extract rotated scores?
Dear all
I'm unable to find an example of extracting the rotated scores of a
principal components analysis. I can do this easily for the un-rotated
version.
data(mtcars)
.PC <- princomp(~am+carb+cyl+disp+drat+gear+hp+mpg, cor=TRUE, data=mtcars)
unclass(loadings(.PC)) # component loadings
summary(.PC) # proportions of variance
mtcars$PC1 <- .PC$scores[,1] # extract un-rotated scores of
2009 Oct 28
2
Labelling individual points on 3D PCA scatterplot
Hi There,
I'm attempting to plot 10 values on a three-dimensional PCA with text labels
next to each point. While i have no trouble doing this on 2D plots using the
'text' or 'textxy' function, I cannot find a function to do this on a 3D
plot.
I am using princomp for my PCA:
>PCA<-princomp(eucdata, cor=TRUE)
>PCA$scores [,1:3] # the three principal components i
2008 Feb 14
1
Principal component analysis PCA
Hi,
I am trying to run PCA on a set of data with dimension 115*300,000. The
columns represnt the snps and the row represent the individuals. so this is
what i did.
#load the data
code<-read.table("code.txt", sep='\t', header=F, nrows=300000)
# do PCA #
pr<-prcomp(code, retx=T, center=T)
I am getting the following error message
"Error: cannot allocate vector of
2009 Jan 13
1
PCA loadings differ vastly!
hi, I have two questions:
#first (SPSS vs. R):
I just compared the output of different PCA routines in R (pca, prcomp,
princomp) with results from SPSS. the loadings of the variables differ
vastly! in SPSS the variables load constantly higher than in R.
I made sure that both progr. use the correlation matrix as basis. I
found the same problem with rotated values (varimax rotation and rtex=T
2004 Nov 24
2
LDA with previous PCA for dimensionality reduction
Dear all, not really a R question but:
If I want to check for the classification accuracy of a LDA with
previous PCA for dimensionality reduction by means of the LOOCV method:
Is it ok to do the PCA on the WHOLE dataset ONCE and then run the LDA
with the CV option set to TRUE (runs LOOCV)
-- OR--
do I need
- to compute for each 'test-bag' (the n-1 observations) a PCA
2004 Jul 14
2
PCA in R
Hello,
I'm attempting to run a PCA on an example data set. I ran it
just fine, but I don't know how to few the output? I listed what the
variable got stored in it, but I don't know how I can get anything else out
of it. Are there other ways to view the results?
Also, I'm confused about the last line "6 variables and 8 observations"
Aren't the rows the
2000 Apr 26
1
Factor Rotation
How does one rotate the loadings from a principal component analysis?
Help on function prcomp() from package mva mentions rotation:
Arguments
retx a logical value indicating whether the rotated
variables should be returned.
Values
rotation the matrix of variable loadings (i.e., a matrix
whose olumns contain the eigenvectors). The
function princomp returns this in the element
2010 Apr 16
1
PCA scores
Hi all,
I have a difficulty to calculate the PCA scores. The PCA scores I calculated
doesn't match with the scores generated by R,
mypca<-princomp(mymatrix, cor=T)
myscore<-as.matrix(mymatrix)%*%as.matrix(mypca$loadings)
Does anybody know how the mypca$scores were calculated? Is my formula not
correct?
Thanks a lot!
Phoebe
[[alternative HTML version deleted]]
2003 Apr 26
3
PCA
Hi, I have a dataset of dimensions 50 x 15000, and tried to use princomp or prcomp on this dataset with 15000 columns as variables, but it seems that the 2 functions can;t handle this large number of columns, anyone has nay suggestions to get around this? Thanks
---------------------------------
[[alternate HTML version deleted]]
2011 Jun 06
2
adding an ellipse to a PCA plot
Hi,
I created a principal component plot using the first two principal
components. I used the function princomp() to calculate the scores.
now, I would like to superimpose an ellipse representing the center
and the 95% confidence interval of a series of points in my plot (as
to illustrate the grouping of my samples).
I looked at the ellipse() function in the ellipse package but can't
get it
2007 Dec 18
1
PCA - "cov.wt(z) : 'x' must contain finite values only"
I am trying to run PCA on a matrix (the first column and row are
headers). There are several cells with NA's. When I run PCA with the
following code:
______________________________________
setwd("I:/PCA")
AsianProp<-read.csv("Matrix.csv", sep=",", header=T, row.names=1)
attach(AsianProp)
AsianProp
AsianProp.pca<-princomp(AsianProp, na.omit)
2008 Mar 27
4
Execute R with *.RData argument
Dear R developers,
i would like to start R with a *.RData argument under Linux.
Something like R -f /home/user/workspace.RData
Is this possible?
Thanks in advance for any answers.
--
View this message in context: http://www.nabble.com/Execute-R-with-*.RData-argument-tp16323374p16323374.html
Sent from the R help mailing list archive at Nabble.com.