similar to: For Loop - loading 10 sets of data and calculating

I have to load 10 different data frames and then manipulate those 10 data frames but would like to do this in a more simplified code than what i am doing. I have tried a couple of approaches but cannot get it to work correctly. So the initial (bulky) code is: #Bin 1 #------- #Loads bin data frame from csv files with acres and TAZ data Bin1_main <-

Dear R help; I'll preface this by saying that the example I've provided below is pretty long, turgid, and otherwise a deep dive into a series of functions I wrote for a simulation study. It is, however, reproducible and self-contained. I'm trying to do my first simulation study that's quite big, and so I'll say that the output of this simulation as I'd like it to be is

Dear R-Experts, I was wondering how to fit a cumulative gaussian to a set of empirical data using R. On the R website as well as in the mail archives, I found a lot of help on how to fit a normal density function to empirical data, but unfortunately no advice on how to obtain reasonable estimates of m and sd for a gaussian ogive function. Specifically, I have data from a psychometric function

Hello, I would like to elicit a discussion about the merits of a statically linked restricted chrooting shell like scponly which incorporates the functionality of scp and sftp. The benefits is that a chrooted user directory does not have to contain the binaries or libraries for scp and sftp and an administrator does not have to play games with the home path to chroot a user. The disadvantage, of

I've been attempting to perform some analysis on a model that was interfaced with R (R calls a library that takes SEXPs and converts the data frames into the internal structures of data), and I notice that for small data.frames the vectors don't get corrupt (n<200-ish). When I pass in larger data.frames, the vectors will become corrupt. I've been PROTECTING the heck out of

Dear All, I have a question about package "ismev", its function "gpd.fit", and interpretation of the results. I used the package ismev to do an extreme value analysis on a fire dataset. Two variables are used in the analysis. The focal variable is acreage burned per fire, ranging from 1 to 5000 acres per fire. In total, there are 69,980 observations. The date covers

Hello, I am using fitdistr function for parameter estimation. When I use fd<-fitdistr(V2,"gamma") I get following error: Error in optim(x = c(0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, : initial value in 'vmmin' is not finite fd<-fitdistr(V2,"weibull") Error in optim(x = c(0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, :

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Hello, I am using fitdistr function for parameter estimation. When I use fd<-fitdistr(V2,"gamma") I get following error: Error in optim(x = c(0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, : initial value in 'vmmin' is not finite fd<-fitdistr(V2,"weibull") Error in optim(x = c(0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, :

Hello, I am trying to fit distribution for data consisting of 421 readings.It is basically no of requests arrived per minute.It contains many 0 entries as no of requests.When i use fd<-fitdistr(V2,"gamma") I get following error: Error in optim(x = c(0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, : initial value in 'vmmin' is not finite What should I do ? I need

Hi, My asterisk server was working with a 4-FXO analog card (TDM400P). I recently added two digital cards: a TE120P (1 PRI) and a B410P (4 BRI). The B410P is still unconfigured but inserted in a PCI slot. The TE120P's jumper is set to E1 as it will connect to a commercial PBX's PRI card also configured as E1. My analog channels used to be 1-4 but since I added the new cards I changed