similar to: Upgrade process for libraries: can I use installed.packages on an old installation followed by install.packages in a new one

I found this a few months ago, but for the life of me I can't remember what the function or package was, and I have had no luck finding it this week. I have found, again, the functions for working with distributions like Cauchy, F, normal, &c., and ks.test, but I have not found the functions for estimating the distribution parameters given a vector of values. What I need to do is

I found it easy to use R when typing data manually into it. Now I need to read data from a file, and I get the following errors: > refdata = > read.table("K:\\MerchantData\\RiskModel\\refund_distribution.csv", header > = TRUE) Error in scan(file, what, nmax, sep, dec, quote, skip, nlines, na.strings, : line 1 did not have 42 elements > refdata = >

For a model I am working on, I have samples organized by year and week of the year. For this model, the data (year and week) comes from the basic sample data, but I require a value representing the amount of time since the sample was taken (actually, for the purpose of the model, it is sufficient to use the number of weeks from the middle of the sample week to the present). What I have found so

I have hundreds of megabytes of price data time series, and perl scripts that extract it to tab delimited files (I have C++ programs that must analyse this data too, so I get Perl to extract it rather than have multiple connections to the DB). I can read the data into an R object without any problems. thedata = read.csv("K:\\Work\\SignalTest\\BP.csv", sep = "\t", header =

I have a number of files containing anywhere from a few dozen to a few thousand integers, one per record. The statement "refdata18 = read.csv("K:\\MerchantData\\RiskModel\\Capture.Week.18.csv", header = TRUE,na.strings="")" works fine, and if I type refdata18, I get the integers displayed, one value per record (along with a record number). However, when I try "

>From the documentation I have found, it seems that one of the functions from package plyr, or a combination of functions like split and lapply would allow me to have a really short R script to analyze all my data (I have reduced it to a couple hundred thousand records with about half a dozen records. I get the same result from ddply and split/lapply: >

I am in a situation where I have to fit a distrution, such as cauchy or normal, to an empirical dataset. Well and good, that is easy. But I wanted to assess just how good the fit is, using ks.test. I am concerned about the following note in the docs (about the example provided): "Note that the distribution theory is not valid here as we have estimated the parameters of the normal

I suspect I'm looking in the wrong places, so guidance to the relevant documentation would be as welcome as a little code snippet. I have time series data stored in a MySQL database. There is the usual DATE field, along with a double precision number: there are daily values (including only normal working days: Monday through Friday). I actually have to do a couple things here. Because of

I have a simple query as follows: "SELECT m_id,sale_date,YEAR(sale_date),WEEK(sale_date),return_type,DATEDIFF(return_date,sale_date) AS elapsed_time FROM risk_input" I can get, and view, all the data that that query returns. The question is, sale_date is a timestamp, and I need to call split to group this data by m_id and the week in which the sale occurred. Obviously, I would

Here is my little scriptlet: optdata = read.csv("K:\\MerchantData\\RiskModel\\AutomatedRiskModel\\soptions.dat", header = FALSE, na.strings="") attach(optdata) library(MASS) setwd("K:\\MerchantData\\RiskModel\\AutomatedRiskModel") for (i in 1:length(V4) ) { x = read.csv(as.character(V4[[i]]), header = FALSE, na.strings=""); y = x[,1]; fp =

I have not found anything about this except the following from the DBI documentation : Bind variables: the interface is heavily biased towards queries, as opposed > to general > purpose database development. In particular we made no attempt to define > “bind > variables”; this is a mechanism by which the contents of R/S objects are > implicitly > moved to the database during SQL

Here is what I tried: optdata = read.csv("K:\\MerchantData\\RiskModel\\AutomatedRiskModel\\soptions.dat", header = FALSE, na.strings="") optdata attach(optdata) for (i in 1:length(V4) ) { x = read.csv(V4[[i]], header = FALSE, na.strings="");x } And here is the outcome (just a few of the 60 records successfully read): > optdata = >

I wasn't even aware I was using midnightStandard. You won't find it in my script. Here is the relevant loop: date1 = timeDate(charvec = Sys.Date(), format = "%Y-%m-%d") date1 dow = 3; for (i in 1:length(V4) ) { x = read.csv(as.character(V4[[i]]), header = FALSE, na.strings=""); y = x[,1]; year = V2[[i]]; week = V3[[i]]; dtstr =

R version 2.12.0, 64 bit on Windows. Here is a short script that illustrates the problem: library(tseries) library(xts) setwd('C:\\cygwin\\home\\Ted\\New.Task\\NKs-01-08-12\\NKs\\tests') x = read.table("quotes_h.2.dat", header = FALSE, sep="\t", skip=0) str(x) y <- data.frame(as.POSIXlt(paste(x$V2,substr(x$V4,4,8),sep=" "),format='%Y-%m-%d

Here is a relatively simple script (with comments as to the logic interspersed): # Some of these libraries are probably not needed here, but leaving them in place harms nothing: library(tseries) library(xts) library(quantmod) library(fGarch) library(fTrading) library(ggplot2) # Set the working directory, where the data file is located, and read the raw data

Here is the function that makes the data.frames in the list: funweek <- function(df) if (length(df$elapsed_time) > 5) { res = fitdist(df$elapsed_time,"exp") year = df$sale_year[1] sample = df$sale_week[1] mid = df$m_id[1] estimate = res$estimate sd = res$sd samplesize = res$n loglik = res$loglik aic = res$aic bic = res$bic chisq =

Hi, I am trying to do a bayesian prediction for soil pollution data above a certain threshold, using geoR. Everything is working fine until i am doing the krig.bayes. I tried to do the prediction on a grid 67 by 113 cells and my computer is freezing to death. At larger numbers of cells it tells me after a while that it reaches the max. memory of 511 Mb. My computer has only 512 Mb of RAM.

Hi Folks, I'm currently installing R-1.7.1 off CRAN. As it happens, I have a CD (kindly made for me by Linux Emporium) containing all the libraries which were on CRAN early this year when I installed R-1.6.1. This is highly convenient, since the alternative would be several hours on-line. While a recent library will on installation announce the fact should it need a newer version of R than

I started a brand new session in R 2.10.1 (on Windows). If it matters, I am running the community edition of MySQL 5.0.67, and it is all running fine. I am just beginning to examine the process of getting timer series data from one table in MySQL, computing moving averages and computing a selection of estimates based on relations among moving averages of different variates, and storing all the

Tested it in two different environments lately with exactly same results. Was trying to get better read performance from local mounts with hundreds of thousands maildir email files by using SSD, hoping that .gluster file stat read will improve which does migrate to hot tire. After seeing what you described for 24 hours and confirming all move around on the tires is done - killed it. Here are my