search for: yarn

Hi list, I am looking at the data yarn in package, I don't understand what is dimension of this data set. I did the following: > library(pls) > data(yarn) > dim(yarn) [1] 28 3 > head(yarn) NIR.1 NIR.2 NIR.3 NIR.4 NIR.5 NIR.6 NIR.7 NIR.8 NIR.9 NIR.10 NIR.11 1 3.06630 3.08610 3.10790 3.09720 2....

All, I am working with NIR spectral data and it was great to find that the example in ?plsr also used spectral data. Unfortunately, I am having difficulty figuring out how the "yarn" dataset is structured to allow for the plsr model to read: library(pls) data(yard) yarn.oscorespls <- mvr(density ~ NIR, 6, data = yarn, validation = "CV", method = "oscorespls") dim(yarn) yarn Specifically, it is VERY convenient to be able to have the ~256 variab...

Hi~ helpers. I was surprised by R data type. I use to data type like table. I don't know R allow several data type such as list, number, matrix in the same matrix. I want to use plsr in pls package to anlaysis data. So, I read R help and example of plsr. that example show me that data as yarn has three colname as NIR, density and train. the NIR was 268(No. of col) X 71(No. of row) matrix, but density was just 71 list yarn[1] indicate NIR, and yarn[2] indicate density so I just tested like that TEST=c(matrix , list) But It was wrong. how can I make like matrix? Of cource, the pyth...

...thod to study the any correlation between the tests. My current data is in data.frame and I have formed horizontal(1-407) to be the exact data, and (408) to be my results data(Yes and No) I have also binarized these Yes and No to 1 and -1s. However, when I refer to PCR manual on R, the example of yarn.pcr <- pcr(density ~ NIR, 6, data = yarn, validation = "CV"), I am not sure how can I adapt the command based line to my sample dataset. It seems that they label each horizontal (columns) as NIR and followed by Density (which is my results data). My doubt is do I have to label these...

Hi. I was used to draw 2D plot to show distribution of my samples. It was very powerful in small samples. Recently , I handle a lot of type of samples as more 15. so, I want to use more dimention. I was used that I conduct plsda function and draw biplot, such as, p1=plsda(mw, sp) biplot(p1) How can I draw 3D graph with my data?? I don't know exactly numerical value each spot. please,

...then str(dd) gives: 'data.frame': 10 obs. of 2 variables: $ x: AsIs [1:10, 1:15] 0.700073.... -0.44371.... -0.46625.... 0.977337.... 0.509786.... ... $ y: num 0.4676 -1.4343 -0.3671 0.0637 -0.231 ... This looks and works OK. Now, there exists a CRAN package called pls. It has a yarn data set in it. > data(yarn) > str(yarn) 'data.frame': 28 obs. of 3 variables: $ NIR : num [1:28, 1:268] 3.07 3.07 3.08 3.08 3.1 ... ..- attr(*, "dimnames")=List of 2 .. ..$ : NULL .. ..$ : NULL $ density: num 100 80.2 79.5 60.8 60 ... $ train : logi TRUE...

..."oscorespls", "model.frame")) cl[[1]] <- as.name("mvr") res <- eval(cl, parent.frame()) ... Recently, Prof. Brian Ripley kindly pointed out that this doesn't work properly when the 'pls' package in not attached: > data(yarn, package='pls') > pls::plsr(density ~ NIR, 6, data = yarn, validation = "CV") <environment: R_GlobalEnv> Error in eval(expr, envir, enclos) : could not find function "mvr" I first believed that cl[[1]] <- as.name("pls::mvr") would fix the pro...

...olumns), the first component being a matrix and the second component a vector. Whatever I have tried so far, I end up with a data.frame containing all the columns from the matrix plus the vector which is not what I am after. I have seen this kind of data.frame among R example datasets (oliveoil and yarn). I would greatly appreciate some help with this problem! Kind regards, Lina Hultin Rosenberg Karolinska Biomics Center Karolinska Institute Sweden

...to column vectors automatically. How should I create a data frame of t$y & t$x such that >is.vector(t$y) returns TRUE >is.matrix(t$x) returns TRUE That is, how to make x column of the data frame a matrix? Just like the test data ¡§gasoline¡¨ in your PLS package: >library(pls) >data(yarn) >is.vector(density) returns TRUE >is.matrix(NIR) returns TURE (NIR is a matrix) Best regards, Shaowen Cheng ________________________________ The information in this e-mail may be confidential; it is intended for use solely by the individual or entity named as the recipient hereof. Disclosu...

Hi, I want to calculate PLS package in R. Now I want to calculate R, MSEP, RMSEP and R2 of PLSR and PCR using this. I also add this in library of R. How I can calculate R, MSEP, RMSEP and R2 of PLSR and PCR in R. I s any other method then please also suggest me. Simply I want to calculate these value. Thanking you. -- Nitish Kumar Mishra Junior Research Fellow BIC, IMTECH, Chandigarh, India

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...

...> b <- 1:5 > x <- list(a=a, b=b) > x $a [,1] [,2] [,3] [,4] [,5] [1,] 1 3 5 7 9 [2,] 2 4 6 8 10 $b [1] 1 2 3 4 5 > x$a [,1] [,2] [,3] [,4] [,5] [1,] 1 3 5 7 9 [2,] 2 4 6 8 10 > x$b [1] 1 2 3 4 5 oliveoil and yarn datasets have been mentioned. [[alternative HTML version deleted]]

Hopefully simple question: What is the best way to name, and treat factor columns for data that has lots of columns? This is my column list: id pID50 D.1 D.2 D.3 D.4 D.5 , etc. all the way to D.185 I was under the impression from several R examples in pls that if you name your columns like above, you should be able to simply call all the D factors with "D", instead of going in and

...39; now always means "number of components", and `comps' always means "component number". The argument `cumulative' has been removed. - A new data set 'gasoline' has been included. - The 'NIR' and 'sensory' data sets have been renamed to 'yarn' and 'oliveoil'. See the file CHANGES in the sources for all changes. -- Bj?rn-Helge Mevik _______________________________________________ R-packages mailing list R-packages at stat.math.ethz.ch https://stat.ethz.ch/mailman/listinfo/r-packages

...39; now always means "number of components", and `comps' always means "component number". The argument `cumulative' has been removed. - A new data set 'gasoline' has been included. - The 'NIR' and 'sensory' data sets have been renamed to 'yarn' and 'oliveoil'. See the file CHANGES in the sources for all changes. -- Bj?rn-Helge Mevik _______________________________________________ R-packages mailing list R-packages at stat.math.ethz.ch https://stat.ethz.ch/mailman/listinfo/r-packages

Way to go YubNub! It''ll be interesting to see: 1) What so many people have to say and 2) How your rails app holds up to the slashdotting. Great work!

...Girth, Height and Volume for Black Cherry Trees uspop Populations Recorded by the US Census volcano Topographic Information on Auckland's Maunga Whau Volcano warpbreaks The Number of Breaks in Yarn during Weaving women Average Heights and Weights for American Women Use 'data(package = .packages(all.available = TRUE))' to list the data sets in all *available* packages. Brett Stansfield Environmental Scientist - Water Quality Hawke's Bay Regional Council 102...

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...areas, especially in deep learning withinmachine learning. In addition, a lot of workload is being pushed to elastic environments utilizing various virtualization technologies on different levels like hypervisors (e.g. VMWare, Xen, KVM), kernel (Docker, Kubernetes) or on the resource managerlevel (YARN, Mesos). In this track we invite submissions addressing these problems. Suggested Themes and Topics:Technology - What technologies and best practices exist for GPU - hardware accelerator virtualization and usage of hardware accelerators in virtual environments on the hypervisor, kernel or resource...

...areas, especially in deep learning withinmachine learning. In addition, a lot of workload is being pushed to elastic environments utilizing various virtualization technologies on different levels like hypervisors (e.g. VMWare, Xen, KVM), kernel (Docker, Kubernetes) or on the resource managerlevel (YARN, Mesos). In this track we invite submissions addressing these problems. Suggested Themes and Topics:Technology - What technologies and best practices exist for GPU - hardware accelerator virtualization and usage of hardware accelerators in virtual environments on the hypervisor, kernel or resource...