similar to: Why isn't R recognising integers as numbers?

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 =

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 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 = >

Warranty on Accuracy, Precision, Legality, ... of R in Research (These questions may well have been raised.) What is the implied warranty of using R for research & publications, consulting, etc.? Alternately, how does one obtain such a warranty? Your answers will be much appreciated. Perhaps you can point me to some websites which discussed this subject in the past. Thanks & regards

I am trying something I haven't attempted before and the available documentation doesn't quite answer my questions (at least in a way I can understand). My usual course of action would be to extract my data from my DB, do whatever manipulation is necessary, either manually or using a C++ program, and then import the data into R. Now I need to try to do it all within R+RMySQL+MySQL. I

Can anyone explain such different output: > stableFit(s,alpha = 1.75, beta = 0, gamma = 1, delta = 0, + type = c("q", "mle"), doplot = TRUE, trace = FALSE, title = NULL, + description = NULL) Title: Stable Parameter Estimation Call: .qStableFit(x = x, doplot = doplot, title = title, description = description) Model: Student-t Distribution Estimated

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

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 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 =

Getting the basic stuff to work is trivially simple. I can connect, and, for example, get everything in any given table. What I have yet to find is how to deal with parameterized queries or how to do a simple insert (but not of a value known at the time the script is written - I ultimately want to put my script into a scheduled task, so the analysis can be repeated on updated data either daily

I thought I was finished, having gotten everything to work as intended. This is a model of risk, and the short term forecasts look very good, given the data collected after the estimates are produced (this model is intended to be executed daily, to give a continuing picture of our risk). But now there is a new requirement. I have weekly samples from a non-autonomous process (i.e. although well

I'm trying to use the survey package to calculate a risk difference with confidence interval for binge drinking between sexes. Variables are X_RFBING2 (Yes, No) and SEX. Both are factors. I can get the group prevalences easily enough with result <- svyby(~X_RFBING2, ~SEX, la04.svy, svymean, na.rm = TRUE) and then extract components from the svyby object with SE() and coef() to do the

Hi, I am running the following loop, but it takes hours to run as n is big. Is there any way "apply" can be used? Thanks. ### Start nclass <- dim(data)[[2]] - 1 z <- matrix(0, ncol = nclass, nrow = nclass) n <- dim(data)[[1]] x <- c(1:nclass) # loop starts for(loop in 1:n) { r <- data[loop, 1:nclass] classified <- x[r == max(r)]

Hi Louis, You could try this: # find the index of the maximum value in each row of _data_, # disregarding the last column classified <- apply(data[,-(nclass+1)],1,which.max) ## or, if the maximum may be repeated: classified <- apply(data[,-(nclass+1)], 1, FUN = function(x) which(x == max(x))) # the variable _truth_ is just the last column of _data_ ? truth <- data[,nclass + 1] ?table

Hello everybody, This is a bug involving functions in core R package: graphics::hist.default, grDevices::nclass.FD, and base::pretty.default. It is not yet on Bugzilla. I cannot submit it myself, as I do not have an account. Could somebody else add it for me, perhaps? That would be much appreciated. Kind regards, Sietse Sietse Brouwer Summary ------- Floating point errors can cause a data

Signed-off-by: Marcin Slusarz <marcin.slusarz at gmail.com> --- This patch relies on "drm/nouveau: remove >1 sclass support from nouveau_parent_create_". There are *many* *more* code paths without proper error handling - I counted at least 106 in 41 functions. If someone would like to do a bit of janitorial work I marked those code paths and uploaded "patch" here:

In the example in the documentation, I see: rs <- dbSendQuery(con, "select Agent, ip_addr, DATA from pseudo_data order by Agent") out <- dbApply(rs, INDEX = "Agent", FUN = function(x, grp) quantile(x$DATA, names=FALSE)) Maybe I am a bit thick, but it took me a while, and a kind hint from Phil, to figure much of this out. It is clear that the SQL

Hi I have recently been analyzing birthweight data from a clinic. The data has obvious defects in that there is digit preference on certain weights making them overrepresented. This shows as spikes in the histogram on certain well rounded weights like 2, 2.5, 3, etc. I would like to show this to government officials but can't figure out how I should present the finding in an easy to

It's unused (only one codepath passes sclass at all and it's always one), broken (overwrites the same field, leaking previous one) and confusing. Signed-off-by: Marcin Slusarz <marcin.slusarz at gmail.com> --- drivers/gpu/drm/nouveau/core/core/client.c | 2 +- drivers/gpu/drm/nouveau/core/core/parent.c | 3 +--

Is color specification like this available in R for setting postscript colors? > hue <- c(0, seq(from = 0, by = 1/(nclass), length = nclass)) > sat <- c(0, rep(1.0, nclass)) > bri <- c(0, rep(1.0, nclass)) > zcolors <- cbind(hue, sat, bri) > ps.options (setcolor=ps.setcolor.hsb,colors=zcolors)