similar to: Forming Portfolios for Fama / French Regression

Hi All, I am running quantile regression in a "for loop" starting with 1 variable and adding a variable at a time reaching a maximum of 20 variables. I get the following warning messages after my "for" loop runs. Should I be concerned about these messages? I am building predictive models and am not interested in inference. Warning messages: 1: In

I need to use the Bhattacharyya distance metric to determine population separation. Has anyone written a Bhattacharyya distance metric function in R? -- View this message in context: http://old.nabble.com/Bhattacharyya-distance-metric-tp26221259p26221259.html Sent from the R help mailing list archive at Nabble.com.

Hi, I am trying to read a SAS version 9.1.3 SAS dataset into R (to preserve the SAS labels), but am unable to do so (I have read in a CSV version). I first created a transport file using the SAS code: libname ces2 'D:\CES Analysis\Data'; filename transp 'D:\CES Analysis\Data\fadata.xpt'; /* create a transport file - R cannot read file created by proc cport */ proc

Hi, I use nnet for my classification problem and have a problem concerning the calculation of the final value for my validation data.(nnet only calculates the final value for the training data). I made my own final value formula (for the training data I get the same value as nnet): # prob-matrix pmatrix <- cat*fittedValues tmp <- rowSums(pmatrix) # -log likelihood

Hi, I have a problem. I have a data frame looking like: ID val A? .3 B? 1.2 C? 3.4 D? 2.2 E? 2.0 I need to CREATE the following TABLE: CASE?? DIFF A-A??? 0 A-B??? -0.9 A-C??? -3.1 A-D??? -1.9 A-E??? -1.7 B-A??? ... B-B??? ... B-C B-D B-E C-A C-B C-C C-D C-E D-A D-B D-C D-D D-E E-A E-B E-C E-D E-E WHERE CASE IS THE COUPLE OF ELEMENTS CONSIDEREDM AND DIFF IS THE computed DIFFERENCE between

Hi: For those of you who are adept at both SAS and R, I have the following questions: a) What are some reasons / tasks for which you would use R over SAS and vice versa? b) What are some things for which R is a must have that SAS cannot fulfill the requirements? I am on the ramp up on both of them. The general feeling that I am getting by following this group is that R updates to the product are

I want to create a neural network, and then everytime it receives new data, instead of creating a new nnet, i want to use a backpropagation algorithm to adjust the weights in the already created nn. I'm using nnet package, I know that nn$wts gives the weights, but I cant find out which weights belong to which conections so I could implement the backpropagation algorithm myself. But if anyone

Dear All, I am having some problem in apply function. I have some data like below. I want to get a range vector (which is max-min value for each row , ignoring NA values.) > Species.all[1:10,] V2 V3 V4 V5 V6 V7 V8 V9 1 57543 55938 47175 54922 36032 5785 29497 7286 2 42364 40472 29887 40107 19723 2691 14445 3258 3 19461 19646 18538 22392 6744 794

Hi All, I am trying to learn Neural Networks. I found that R has packages which can help build Neural Nets - the popular one being AMORE package. Is there any book / resource available which guides us in this subject using the AMORE package? Any help will be much appreciated. Thanks, Indrajit

Dear R-helpers, I had a conversation with a guy working in a "business intelligence" department at a major Spanish bank. They rely on recursive partitioning methods to rank customers according to certain criteria. They use both SAS EM and Salford Systems' CART. I have used package R part in the past, but I could not provide any kind of feature comparison or the like as I have no

Hi all, I was wondering whether there is a standard method to carry out fama-macbeth regressions in R. I have spent the last few hours looking around the help pages but nothing seems to be written about this. Thanks a lot! [[alternative HTML version deleted]]

Hi all, I am also looking for interesting statistical experiments about testing and estimating CAPM, APT, Fama models, etc. using R using financial series data... please give me some pointers... I have been searching the R archives for the past a few hours and I vaguely got to know that there are programs do these interesting statistical things, but I just could not find where are they... I have

I'm trying to find 50 portfolios on the efficient frontier using the Black-Litterman model but have not found a suitable method for doing so. I tried using the "portfoliosFrontier" function given in the package fPortfolio using the "optimalPortfolios.fPort" function on package "BLCOP" but does not provide satisfactory results -- View this message in context:

I am exploring neural networks (adding non-linearities) to see if I can get more predictive power than a linear regression model I built. I am using the function nnet and following the example of Venables and Ripley, in Modern Applied Statistics with S, on pages 246 to 249. I have standardized variables (z-scores) such as assets, age and tenure. I have other variables that are binary (0 or 1). In

As a follow-up to my email below: The input data frame to nnet() has dimensions: > dim(coreaff.trn.nn) [1] 5088 8 And the predictions from the neural network (35 records are dropped - see email below for more details) has dimensions: > pred <- predict(coreaff.nn1) > dim(pred) [1] 5053 1 So, the following line of R code does not work as the dimensions are

Sorry for these multiple postings. I solved the problem using na.omit() to drop records with missing values for the time being. I will worry about imputation, etc. later. I calculated the sum of squared errors for 3 models, linear regression, neural networks, and support vector machines. This is the first run. Without doing any parameter tuning on the SVM or playing around with the number of

Hi, I am using function mars() in package mda to find knots in a whole bunch of predictor variables. I hope to be able to replicate all or some of the basis functions that the MARS software from Salford Systems creates. When I ran mars() on a small dataset, I was able to get the knots. However, when I tried running mars() on a larger dataset (145 predictor variables), for a different

Burns Statistics has just released its POP Portfolio Optimizer, which is available for a license fee. This has an interface designed to run under either S-PLUS or R. In addition to portfolio selection and asset allocation, there is functionality to generate random portfolios, and to estimate statistical factor models. The website includes a new working paper on the best approach to using

R-Devel-ers: I have an S4 method that simply formats an object: setGeneric("formatMe", function(x) standardGeneric("formatMe")) setMethod("formatMe", "ANY", function(x) format(x)) If I issue the above in an R session, then define an S4 class with its own format method, I get the desired result: > setClass("A",contains="numeric")

We would like to announce the availability of the 'portfolio' package in R for analysing equity portfolios. Version 0.2-0 is now available on CRAN. To take a look, you can: > install.packages("portfolio") ... > vignette("portfolio") and play around. Those who would just like to check out an introduction can simply look at: