search for: michaelltaylor

I've been using R for a while, but now find myself needing to understand time-series objects for a short course I am teaching. I am putting together some daily financial datasets for illustration, but having some trouble in aggregating the data to months or weeks. I am getting a "cannot change frequency from 1 to 12" message. I am not sure that aggregation is where I want to go

...anew)<1e-5) {print (anew) break}, I assign aaa[i]<- anew (where i is the number of simulation). But I found although some anew is not printed, which I assume NA comes, the resulting matrix does not have NAs, they all be numbers. Why? Regards, Zhen >From: michaell taylor <mt at michaelltaylor.com> >To: R-help at stat.math.ethz.ch >CC: nusbj at hotmail.com >Subject: Re: [R] run R under unix >Date: 09 Oct 2003 08:00:15 -0400 > >One can run R 'txt', script files thusly: > >1. create the txt file (foo.txt) script. >2. at a command prompt type : > R -...

I am trying to figure out how to pause the execution of a script and release the R command prompt to users and then continue execution of the script from the point it was stopped. This operation is analogous to the "pause" command in stata. If I remember correctly, insertion of a pause command in a stata script stopped script execution and allows users to manipulate objects that

Listers, I am still attempting to apply regression results from one dataframe to another. A significant problem in this process is that the new dataframe is quite large and has a complex missing data structure which differs from the 'in-sample' data.frame. Moreover, the equation represents a time series simulation structure demanding that the equation be applied iterative year by year.

I am having trouble understanding the way naresid is used when using predict/residual in a new data frame. >From the example below, the NAs are displayed when predicting inside the original frame, but are dropped when applied to a new frame. ultimately I need to cbind the residuals and predictions to a dataframe. Any help would be appreciated. Thanks in advance. >

I am attempting to optimize a regression model's parameters to meet a specific target for the sum of positive errors over sum of the dependent variable (minErr below). I see two courses of action , 1) estimate a linear model then iteratively reduce the regressors to achieve the desired positive error threshold (naturally the regressors and predicted values are biased - but this is