similar to: linear discriminant analysis in MASS

Hello, I am using WinXP, R version 2.3.1, and SAS for PC version 8.1. I have mostly used SAS over the last 4 years and would like to compare the output of PROC DISCRIM to that of lda( ) with respect to a very specific aspect. My data have k=3 populations and there are 3 variates in the feature space. When using using the code PROC DISCRIM DATA = FOO OUT = FOO_OUT OUTSTAT = FOOSTAT

I use Widows, R version 2.4.1 I have 4 questions on lda (MASS) (code is pasted below): 1st. How can I obtain the statistics and p-value associated with discriminant analysis? Am I supposed to calculate that manually by squaring the svd value and looking the p value up in a table? I am writing the following code: training.mx<-read.table('C:\\Documents and Settings\\silvia\\My

Hi, I'm a newbie in R and don't much aobut all the modules and their capabilities, but I'm interested in solving a problem about a discriminant analysis done with SPSS tool. The thing is that I would like to make a discrimant analysis similar to the one done with SPSS, but I can't find the way to solve it. I've been playing with R and I can handle more or less my data,

Hello, Does logistic regression really provide better results than lda or qda ? (my purpose is not classification but highlighting of discriminant variables). If this is true, where could I get an R script performing stepwise logistic regression ? Thanks -- Daniel AMORESE Lab. M2C "Morphodynamique Continentale et C?ti?re" UMR CNRS 6143 Caen/Rouen Centre de G?omorphologie UCBN

Hi all, I obtained a strage result with LDA (MASS) function in R with NIR data. I tried both CV (leave one out cross validation) and splitting my data in odd (training) and even (prediction) sets. In all the cases the minimum error was near to 0. Due to the strange result, I tried with SPSS IBM software and it give me around 11% of minimum error with and without leave one out cross validation.

Hello all, I wish to compute site scores using PCA (prcomp) on a matrix with missing values, for example: Drain Slope OrgL a 4 1 NA b 2.5 39 6 c 6 8 45 d 3 9 12 e 3 16 4 ... Where a,b... are sites. The command > pca<-prcomp(~ Drain + Slope + OrgL, data = t, center = TRUE, scale = TRUE, na.action=na.exclude) works great, and from

I am trying to write the following code in R. The code works in S+ and i am trying to do the program in R. x=discrim(admit~gpa+gmat,prior=c("uniform"),data=data.mm) i wrote the following in R: x=lda(admit~gpa+gmat,data=data.mm) i could not figure out how to write prior=c("uniform") in R. I would get an error every time. I think that it has something to do with

Hello: Please help me through my confusion. I am having trouble reconciling the difference between what I believe is the conventional definition of an odds ratio for a 2-by-2 table and the output produced by fisher.test() in R. Consider the following example: > Discrim <- matrix(c(1,10,24,17), + nr = 2, + dimnames = list(AGE = c('young', 'old'), +

madame, monsieur, help me please, Je debute avec R. J'ai r??alis?? une analyse factorielle discriminante avec R. J'obtient les resultat graphiques et les tables assici??es ?? l'analyse. Cependant je souhaiterais savoir si les distances separant les groupes (decades) sont statistiquement significatives. pour cela il me faut utiliser la distance de mahalanobis et la statistique de

Pass to --commands-from-file the name of a file containing customization commands in each line, as if they were specified as command line arguments. This eases the reuse of commands among different builder/customize/sysprep invocations. --- builder/cmdline.ml | 3 +- customize/customize_run.ml | 5 +++ generator/customize.ml | 98 ++++++++++++++++++++++++++++++++++++++++++++-- 3

Hi, this is the v2 of a series introducing basic support for registering/attaching/unregistering RHEL guests using subscription-manager, so it is possible to do for example: $ virt-customize -a rhel-guest.qcow2 \ --sm-credentials user:file:/path/to/password-file --sm-register \ --sm-attach file:/path/to/pool-file \ --install pkg1 --install pkg2 .. \ --sm-remove --sm-unregister

Hi! Will you please ask a newbie's question? Need linear discriminant analysis, need to capture discrim. function coefficiemts for further processing. Using R ver 1.4.0 package MASS function lda() Help says resulting object should have "components" (among others): means, scaling, svd, but program returns NULL for all of them Thank you!

The second patch is obviously not complete yet - for discussion only. Rich.

v2 -> v3: - Changes to the Curl API suggested by Pino.

v1 -> v2: - Fixed the bug with precedence of if / @. - Add some imperative list operators inspired by Perl, and use those for constructing the Curl arguments, and more. Rich.

v2 -> v3: - Renamed List.assoc_ -> List.assoc_lbl. - Rebased on top of current master branch. Rich.

Sorry, I forgot to mention that I used the package "klaR". > > Dear all, I am trying to do a n-fold cross-validation for a > regularized discrimant function analysis using rda from the package > klaR. However, I have problems to predict the groups from the > test/validation sample. The exmaples of the R documantation and > some online webpage also do not work.

R-help, I'm trying to evaluate the misclasification rate of an estimated discrimated function (hyDA) hyDA <- with(hyB, lda(Maturity ~ Length + Weight)) , so here is what I do (code from S-plus 6.1 Guide to Statistics Vol. 2 p 101) test <- predict(hyDA) tbl <- table(hyB$Maturity, test$class) tbl <- tbl[-2,-2] tbl1 <- cbind(tbl, error = (apply(tbl, 1, sum) - diag(tbl)) /

Thanks for reducing that for me! The outliner pulls out the following: OUTLINED_FUNCTION_0: // @OUTLINED_FUNCTION_0 .cfi_sections .debug_frame .cfi_startproc // %bb.0: adrp x29, g1 add x29, x29, :lo12:g1 adrp x30, g2 // This adrp shouldn’t have been outlined. ret It shouldn’t be pulling out that adrp. There’s a special case for arps in

In Std_utils we already extend Char and String. These commits take it a little further by extending List and adding a new Option submodule. All basically simple refactoring. Rich.