search for: spectroscopic

Dear R-users, I would like to process some spectroscopic data with R, and I was hoping some people might have some example code on how to do this. I would like to be able to do the following things: * Detect outlier-spectra -> This can be done by using scoreplot from the pls package * Determine the range of the spectrum to be used -> For this, I...

Hi, I am currently analysis Raman spectroscopic data with the hyperSpec package. I consulted the documentation on this package and I found an example work-flow dedicated to Raman spectroscopy (see the address : http://hyperspec.r-forge.r-project.org/chondro.pdf) I am currently trying to remove outliers thanks to PCA just as they did in the docu...

As of R 1.7.0, princomp no longer accept matrices with more coloumns than rows. I'm curious: Why was this decision made? I work a lot with data where more coloumns than rows is more of a rule than an exception (for instance spectroscopic data). To me, princomp have two advantages above prcomp: 1) It has a predict method, and 2) it has a biplot method. A biplot method shouldn't be too difficult to implement (I believe I've seen one on R-help). A predict method seems to be more difficult, because the prcomp object doesn...

I am having a similar problem understanding the data structure of the "yarn" dataset described in the "[R] data structure for plsr" posts. I have spectroscopic data I'd like to run through a PLSR and have read the tutorial series, but still do not understand the data format required for the code to process my data. My current data structure consists of a .csv file read into R containing 15 columns (a charcoal dilution series going from 100% to 0%) and...

I have a decent sized matrix (36 x 11,000) that I have preformed a PCA on with prcomp(), but due to the large number of variables I can't plot the result with biplot(). How else can I plot the PCA output? I tried posting this before, but got no responses so I'm trying again. Surely this is a common problem, but I can't find a solution with google? The University of Dundee is a

So I have 2 sets of data - a training data set and a test data set. I've been doing the analysis on the training data set and then using predict and feeding the test data through that. There are 114 rows in the training data and 117 in the test data and 1024 columns in both. It's actually the same set of data split into two. The rows are made of 5 different numbers. They do represent

...vae (SNe). The surveys'' main goal is to try to understand the mysterious dark energy which seems to make up ~70% of the energy density of the Universe. The number of these SNe that will be detected is expected to be moderately large (~10^5). In the past astronomers have studied these using spectroscopic data which allow you to accurately classify supernovae but that will not be possible in the future. Instead one will have to rely on measurements of flux in broad bands to classify supernovae. This challenge then is to try to classify SNe using photometry only and they have provided training &...

...olab.2017.07.004>. The OHPL method exploits the homogeneity structure in high-dimensional data and enjoys the grouping effect to select groups of important variables automatically. This feature makes it particularly useful for high-dimensional datasets with strongly correlated variables, such as spectroscopic data. For more information, please see https://OHPL.io. Cheers, -Nan -- https://nanx.me _______________________________________________ R-packages mailing list R-packages at r-project.org https://stat.ethz.ch/mailman/listinfo/r-packages

Dear all, I found pls.pcr package will give different results if the data are centered and scaled using scale(). I am not sure about when I should scale my data, and whether the dependent variable should be scaled. If the dependent variable is scaled, how I give a prediction to the real data? I appreciate for any suggestions and comments. Best regards, Jinsong ===== (Mr.) Jinsong Zhao Ph.D.

...pernovae (SNe). The surveys' main goal is to try to understand the mysterious dark energy which seems to make up ~70% of the energy density of the Universe. The number of these SNe that will be detected is expected to be moderately large (~10^5). In the past astronomers have studied these using spectroscopic data which allow you to accurately classify supernovae but that will not be possible in the future. Instead one will have to rely on measurements of flux in broad bands to classify supernovae. This challenge then is to try to classify SNe using photometry only and they have provided training &...

Dear all, I'm looking for users of the hyperSpec package for handling (hyper)spectral or spectroscopic data in R which I maintain. First of all, I made a few announcements concerning the further development which can be found in the hyperSpec-help mailing list an on which I hope to get user feedback: see http://lists.r-forge.r-project.org/pipermail/hyperspec-help/2012-July/thread.html. My second i...

...olab.2017.07.004>. The OHPL method exploits the homogeneity structure in high-dimensional data and enjoys the grouping effect to select groups of important variables automatically. This feature makes it particularly useful for high-dimensional datasets with strongly correlated variables, such as spectroscopic data. For more information, please see https://OHPL.io. Cheers, -Nan -- https://nanx.me _______________________________________________ R-packages mailing list R-packages at r-project.org https://stat.ethz.ch/mailman/listinfo/r-packages

Hi, I have the following question about creating data frames. I want to create a data frame with 2 components: a vector and a matrix. Let me use a simple example: y <- rnorm(10) x <- matrix(rnorm(150), nrow=10) Now if I do dd <- data.frame(x=x, y=y) I get a data frame with 16 colums, but if, according to the documentation, I do dd <- data.frame(x=I(x), y=y) then str(dd)

Hello, I have a large data matrix (68x13112), each row corresponding to one observation (patients) and each column corresponding to the variables (points within an NMR spectrum). I would like to carry out some kind of clustering on these data to see how many clusters are there. I have tried the function clara() from the package cluster. If I use the matrix as is, I can perform the clara

> On Aug 14, 2017, at 12:22 PM, fs <mail at friedrich-schuster.de> wrote: > > Hi, and sorry for asking such an unspecific question. > > Does anybody know of statistical / data mining methods that are available in R > that are not in SAS ? With SAS I mean the SAS System Version 9.4 and SAS > Enterprise Miner. I don't expect a complete list, just two or three

Hi, and sorry for asking such an unspecific question. Does anybody know of statistical / data mining methods that are available in R that are not in SAS ? With SAS I mean the SAS System Version 9.4 and SAS Enterprise Miner. I don't expect a complete list, just two or three examples or hints where and what to look for. I found some older comparisons, and the R methods mentioned there

Hello all, Before getting to my question, I would like to apologize for asking this question here. My question is not directly an R question, however, I still find the topic relevant to R community of users - especially due to only * partial* (current) support for interactive data visualization (see here: http://cran.r-project.org/web/views/Graphics.html were with iplots we are waiting for