search for: principled

Dear All, In a package, I want to use PCA function. The structure I used follow this page: http://www.statmethods.net/advstats/factor.html. fit<-principle(mydata, nfactors=9, rotation=TRUE) or: result<-PCA(mydata) But I don't known why R language in my computer noticed: "not found principle", "not found PCA". I download and installed

Dear all: I try to analyse a dataset which contain one binary response variable and serveral predict variables, but multiple colinear problem exists in my dataset, some paper suggest that logistic regression for principle components is suit for these noise data, but i only find R can done principle component regression using "pls" package, is there any package that can do the task i

Dear all, Can anyone point me to a package that can perform Common principle components? Thank you. Jos?? P. Granadeiro

Setting: 200 input variables, 1 binary target variable. Run a principle component analysis on the data then use the output of the principle component analysis (the generated factors) as input into a neural network -but first having partitioned the pca data into training and testing sets so that a neural network model can be trained on the first partition and tested on the second. I was told that

hello, Im still working on ruby koans. Now I have to do some sandwhich code. The exercise looks like this : require File.expand_path(File.dirname(__FILE__) + ''/edgecase'') class AboutSandwichCode < EdgeCase::Koan def count_lines(file_name) file = open(file_name) count = 0 while line = file.gets count += 1 end count ensure file.close if

please reply to hdgbyi@public.guangzhou.gd.cn or bgpgong@hotmail.com, thank you. UNCERTAINTY PRINCIPLE IS UNTENABLE By reanalysing the experiment of Heisenberg Gamma-Ray Microscope and one of ideal experiment from which uncertainty principle is derived , it is found that actually uncertainty principle can not be obtained from these two ideal experiments . And it is found that

please reply to hdgbyi@public.guangzhou.gd.cn or bcpgong@hotmail.com, thank you. UNCERTAINTY PRINCIPLE IS UNTENABLE By reanalysing the experiment of Heisenberg Gamma-Ray Microscope and one of ideal experiment from which uncertainty principle is derived , it is found that actually uncertainty principle can not be obtained from these two ideal experiments . And it is found that

Dear Sirs: Please pardon me I am very new to R. I have been using MATLAB. I was wondering if R would allow me to do principal components analysis on a very large dataset. Specifically, our dataset has 68800 variables and around 6000 observations. Matlab gives "out of memory" errors. I have tried also doing princomp in pieces, but this does not seem to quite work for our approach.

Dear R Should I be concerned if the loadings to a Principle Component Analysis are as follows: Loadings: Comp.1 Comp.2 Comp.3 Comp.4 X100m -0.500 0.558 0.661 X200m -0.508 0.379 0.362 -0.683 X400m -0.505 -0.274 -0.794 -0.197 X800m -0.486 -0.686 0.486 0.239 Comp.1 Comp.2 Comp.3 Comp.4 SS loadings 1.00 1.00 1.00 1.00 Proportion Var 0.25 0.25 0.25

Hi everyone, I'm not clear about the principle of the vm snapshot based on qemu-dm, so the following below maybe a joke. Sorry for that... register_savevm() included in ioemu-qemu-xen/hw/*.c covers various kinds of hardware in order to save the state of current running vm. In this way, the file saved by the cmd of 'xm save domain snapshot.save' is of large size and the

Hello! I am doing Principle Componenets Analysis using "psych" package: mypc<-principal(mydata,5,scores=TRUE) However, I was asked to run a case-weighted PCA - using an individual weight for each case. I could use "corr" from "boot" package to calculate the case-weighed intercorrelation matrix. But if I use the intercorrelation matrix as input (instead of the

Hi all, I?d love to get your feedback on the conflicted package, which provides an alternative strategy for resolving ambiugous function names (i.e. when multiple packages provide identically named functions). conflicted 0.1.0 is already on CRAN, but I?m currently preparing a revision (<https://github.com/r-lib/conflicted>), and looking for feedback. As you are no doubt aware, R?s default

https://bugs.freedesktop.org/show_bug.cgi?id=96443 Bug ID: 96443 Summary: Missrendering of Reflections in Talos Principle Product: Mesa Version: git Hardware: Other OS: All Status: NEW Severity: normal Priority: medium Component: Drivers/DRI/nouveau Assignee: nouveau at

Hello everybody, I am afraid this might be a stupid question, but after scanning a little bit through the help archive and the internet, I didn't come up with an answer myself. I have done a principle component analysis on a data set, which yields the mean response of 14 olfactory neurons to 16 different odors. When plotting the result in a biplot, it nicely shows me the odors in

Thanks to Mr.Liviu Androvic and Mr.Richard Rowe helped me in PCA. Because I have just learn R language in a few day so I have many problem. 1) I don't know why PCA rotation function not run although I try many times. Would you please hepl me and explain how to read the PCA map (both of rotated and unrotated) in a concrete example. 2) Where I can find document relate: Plan S(A), S(A*B),

When pass :cache param to ActionDispatch::Session::MemCacheStore, it should be removed from options, otherwise it remains "published". This violates the OOP encapsulation principle. I created a LH ticket with a patch http://bit.ly/cGDaWs Luca -- lucaguidi.com twitter.com/jodosha -- You received this message because you are subscribed to the Google Groups "Ruby on Rails:

Interesting idea. Lazy and non-standard evaluation is going to happen; the language needs a way to contain it. I'll extend the proposal so that prefixing a formal argument with @ in function() marks the argument as auto-quoting, so it arrives as a language object without use of substitute(). Kind of like how '*' in C declares a pointer and dereferences one. subset <- function(x,

Hello, I have a problem with selecting the right type of sums of squares for an ANCOVA for my specific experimental data and hypotheses. I do have a basic understanding of the differences between Type-I, II, and III SSs, have read about the principle of marginality, and read Venable's "Exegeses on Linear Models" (http://www.stats.ox.ac.uk/pub/MASS3/Exegeses.pdf). I am pretty new to

Hi all! I have ''send_status'' table which looks something like these: id code title 1 sent Sent 2 error Sending error 3 success Success Next I would like to associate some processed records with their ''send_status''. Is it better to use: 1) record.status_id = SendStatus.find(:one, :condition => "code=''sent''").id to

Hi, If I have an R object UUU, where the second element is U2, based on "g" column of my.table my.table of UUU is: mmm ggg gindex map Info aaa123 U1 1 1 1 aaa124 U1 1 2 1 bbb1378 U2 2 1 1 bbb8888 U2 2 2 0 bbb1389 U2 2 3