similar to: using which to identify a range of values

Hello, I am trying to do some fairly straightforward data summarization, i.e., the kind you would do with a pivot table in excel or by using SQL queires. I have a moderately sized data set of ~70,000 records and I am trying to compute some group averages and sum values within groups. the code example below shows how I am trying to go about doing this pti <-rnorm(70000,10) fid <-

Are there any packages in R that are suitable for doing time series forecasting using neural networks? I have looked in the nnet package and neural package and they both seem geared towards classification. thanks, Spencer [[alternative HTML version deleted]]

Hello, I am using glmer() from lmer(lme4) to run generalized linear mixed models. I can't figure out how to extract the z values for the fixed effects that are reported using the summary function . Any help would be appreciated. Thanks, Spencer [[alternative HTML version deleted]]

I am working with a dataset where there are 5 possible outcomes (coded 1:5), I would like to create 5 categorical variables (event1...event5). I am using a for loop an if statements, but I have a large dataset( approx 100,000 rows) it takes quite a bit of time, is there a way to speed this up? Here is some sample code of what I am currently doing. test2 <-rep(seq(1:5),2000) event1 <-

I have a set of values and their corresponding weights. I can use the function weighted.mean to calculate the weighted mean, I would like to be able to similarly calculate the weighted median and quantiles? Is there a function in R that can do this? thanks, Spencer [[alternative HTML version deleted]]

Hello, I would like to look at the compiled C code behind HoltWinters from the stats package. Is that possible? If so where do I find it? thanks, Spencer [[alternative HTML version deleted]]

I am using arima to develop a time series regression model, I am using arima b/c I have autocorrelated errors. Several of my independent variables are categorical and I have coded them as factors . When I run ARIMA I don't get any warning or error message, but I do not seem to get estimates for all the levels of the factor. Can/how does ARIMA handle factors in xreg? here is some example

I am fitting a regression model with a bs term and then making predictions based on the model. According to some info on the internet at http://www.stat.auckland.ac.nz/~yee/smartpred/DummiesGuide.txt there are some problems with using predict.lm when you have a model with terms such as bs,ns,or poly. However when I used one of the examples they said would illustrate the problems I get virtually

I am using psm to model some parametric survival data, the data is for length of stay in an emergency department. There are several ways a patient's stay in the emergency department can end (discharge, admit, etc..) so I am looking at modeling the effects of several covariates on the various outcomes. Initially I am trying to fit a survival model for each type of outcome using the psm

Hello, I have question regarding the lrm function and estimating the odds ratio between different levels of a factored variable. The following code example illustrates the problem I am having. I have a data set with an outcome variable (0,1) and an input variable (A,B,C). I would like to estimate the effect of C vs B, but when I perform the summary I only get A vs B and A vs C, even though I

I have been trying to use the ets function in the forecast package on a daily time series (ts2 is a ts object with frequency =7). However when I run the following code I get an error related to etsmodel. I have looked at ets and I can see that there is a call to the function etsmodel, but I cant seem to find info on the ets function anywhere. Does anyone know anything about the etsmodel function?

All, I am working with a large data set (~ 450,000 rows by 34 columns) I am trying to fit a regression model (I have tried to use several procedures psm (Design package) lm, glm). However whenever I try to fit the model I get the following error: Error: cannot allocate vector of size 1.1 Gb Here are the specs of the machine and version of R I am using Windows Server 2003 R2 Enterprise x64

hello, I have a question about making n step ahead forecasts in cases where test and validation sets are availiable. For instance, I would like to make one step ahead forecasts on the WWWusage data so I hold out the last 10 observations as the validation set and fit an ARIMA model on the first 90 observations. I then use a for loop to sequentially add 9 of the holdout observations to make 1

hello, I have been trying to use the predict function in the vars package to forecast from a seasonal VAR model. The following code sample illustrates what I am trying to do and the error that I get when trying to do it. I run the following code, that results in the following error: data(Canada) endoC <- Canada[1:72,1:3] exoC <- Canada[1:72,4] var.2c <- VAR(endoC, p = 2,

Dear R-users, I have found this not-so-recent post in the archives - http://tolstoy.newcastle.edu.au/R/devel/00a/0291.html - while I was looking for a particular way to reorder factor levels. The question addressed by the author was to know if the read.table function could be modified to order the levels of newly created factors "according to the order that they appear in the data

hello, I am trying to use predict.lm to make point forecasts based on a model with continuous and categorical independent variables I have no problems fitting the model using lm, but when I try to use predict to make point predictions. it reverts back to the original dataframe and gives me the point predictions for the fitted data rather than for the new data, I imagine that I am missing

On Thursday 13 November 2008 18:27, David Greene wrote: > On Wednesday 12 November 2008 19:13, Chris Lattner wrote: > > You shouldn't be refining the pointer, you should use: > > > > t1->refineAbstractType(t2) > > Ok, I tried this and I get further. But the type system seems to have > trouble when refining multiple types and those types resolve to the same

I have a set of timestamp data that I have in a text file that I would like to import into R for analysis. The timestamps are formated as follows: DT_1,DT_2 [2006/08/10 21:12:14 ],[2006/08/10 21:54:00 ] [2006/08/10 20:42:00 ],[2006/08/10 22:48:00 ] [2006/08/10 20:58:00 ],[2006/08/10 21:39:00 ] [2006/08/04 12:15:24 ],[2006/08/04 12:20:00 ] [2006/08/04 12:02:00 ],[2006/08/04 14:20:00 ] I can get

Many thanks Jim. What I,m trying to show with the fhist plot is the empirical distribution of the values of the left plot simulation. You say: However, I don't think that this plot illustrates quite what you think it does. Can you give me a clue to try to illustrate better if it is not showing what I believe it shows a better way to show it? Many thanks in advance. El 7 jun. 2017

Please can you send me some orientation? Many thanks in advance. Only if posible one book o similar example to understand why it is not what I try. El 8 jun. 2017 7:50 PM, "Pedro p?ramo" <percentil101 at gmail.com> escribi?: > Many thanks Jim. > > What I,m trying to show with the fhist plot is the empirical distribution > of the values of the left plot simulation.