similar to: predicting values into the future

 Hi:  Using nls how can I increase the numbers of iterations to go beyond 50.  I just want to be able to predict for the last two weeks of the year.  This is what I have:  weight_random <- runif(50,1,24)  weight <- sort(weight_random);weight weightData <- data.frame(weight,week=1:50)                          weightData plot(weight ~ week, weightData) M_model <- nls(weight ~ alpha +

Hi: Can anyone advice me on how to loop and perform a calculation through all the columns. here's my data xd<- c(2.2024,2.4216,1.4672,1.4817,1.4957,1.4431,1.5676) pd<- c(0.017046,0.018504,0.012157,0.012253,0.012348,0.011997,0.012825) td<- c(160524,163565,143973,111956,89677,95269,81558) mydf<-data.frame(xd,pd,td) trans<-t(mydf) trans I have these values that I need to

Hi: I have the following dataset in R( thanks Gabor for your help) but now the problem is that all the dashes are converted to questions marks ("?") I?am trying to get the dashes back using: mydf <- data.frame(lapply(mydf,function(x) replace(mydf,"?"(x),"-"))) but isn't working. I also tried: mydf <- replace(mydf,mydf=="?","-)")

Hi: # How can I extract the 'Forecasts' from the 'summary(predicted)' from the example below? library(forecast) weightData <- data.frame(weight = c(2.1,2.4,2.8,3.6,4.1,5.2,6.3),week= 1:7) weight <- as.numeric(weightData$weight) predicted <- forecast(weight,h=3,level=95) # see the predicted summary(predicted) # I would like to extract the three predicted values and #

A new version of the ggplot2 web application is available at http://www.yeroon.net/ggplot2. New features include 1D geom?s (histogram,?density, freqpoly), syntax mode (by clicking the tiny arrow at the?bottom), and some additional facet options. Furthermore some minor?improvements and fixes, most notably for Internet Explorer. As usual, a little demo video that shows how to use the new features:

Hi: I have a dataset and need to interpolate for missing days. In Excel I either average from sampled days from above and below the missing days or use the TREND function to make up for the missing values. I have been reading about na.approx, is this function similar to the TREND function? Which is the best recommendable way to make up for missing data? Here's my dataset: weeks 17,18,26 and 46

Hello, all. I wanted to use the "survey" package to analyze data from the National Ambulatory Medical Care Survey, and am having some difficulty translating the analysis keywords from one package (Stata) to the other (R). The data were collected using a multistage probability sampling, and there are variables included to identify the sampling units and weights. Documentation from the

Dear R Users. This seems like a simple task, but I'm stuck. I have a list with 3 elements: (2 vectors and 1 matrix). I wish to extract each of these data elements using index subscripts and multiply them with a vector multiplier. What I have: > betas [1] 0.01 0.01 0.01 > LData[1] $int [1] 1 1 1 1 1 1 1 1 1 1 $date [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10] [1,]

Dear R-users, This is a loop which is part of a bigger script. I managed to isolate the error in this loop and simplified it to the bare minimum and made it self-contained. a<-c(2,3,4,5,5,5,6,6,6,7) for(n in 1:10) { print(paste("n: ",n)) z1<-a[n] #make a list container ldata<-list() t=1 while(z1==a[n]) { #add dataframes to list ldata[[t]]<-paste("hello") n=n+1

Hello: I have 5 years of weekly passage data and want to predict fish passage for the following year. I don't have a model to use to predict data for the sixth year. Can I somehow still predict based on these five years? I just want to see on the graph what the predicted year would look like and how those?new values are generated. Gracias? ? ?fall <- structure(list(week = c(48L, 49L, 50L,

Hello, I have used nlme to fit a model, the R syntax is like fmla0<-as.formula(paste("~",paste(colnames(ldata[,9:13]),collapse="+"),"-1")) > fmla1<-as.formula(paste("~",paste(colnames(ldata[,14:18]),collapse="+"),"-1")) >

Rafael Espíndola <rafael.espindola at gmail.com> writes: > Now that aliases can have any expressions, can't you use something like > > @data = private global [2 x i32] [i32 42, i32 43] > @symbol = alias getelementptr ([2 x i32]* @data, i32 0, i32 1) > > This produces > > .Ldata: > .long 42 # 0x2a > .long 43

 Hi, What's the equivalent of "Save to File" from the R console File menu on an R routine? Just trying to capture the whole R console into a text file when my code fails. Felipe D. Carrillo Supervisory Fishery Biologist Department of the Interior US Fish & Wildlife Service California, USA http://www.fws.gov/redbluff/rbdd_jsmp.aspx [[alternative HTML version deleted]]

Alguien me podría sugerir un paquete en R para generar pronóticos con modelos robusto de series de tiempo.   Saludos Enrique RAMOS [[alternative HTML version deleted]]

Somehow this cover letter was dropped from my symbol offsets patch set: 1. http://lists.cs.uiuc.edu/pipermail/llvmdev/2014-May/073200.html 2. http://lists.cs.uiuc.edu/pipermail/llvmdev/2014-May/073201.html Original message ----------------- About a year ago a proposal suggesting symbol offsets was brought to this list[1]. This proposal goes hand-in-hand with the prefix data proposal[2]

Manuel: I changed your variable names from x to 'long' and y to 'lat' on the riqueza_out.csv file. The code below should do what you want. Also, since the legend title is kind of long, I broke it down into three lines so you can see more plot area. I am cc'ing the other groups so more people use it if needed. library(rgdal) library(ggplot2) library(sp) library(maptools)

I am currently puzzled by a seach path behavior. I have a library of a dozen routines getlabs(), getssn(), getecg(), ... that interface to local repositories and pull back patient information. All have a the first 6 arguments in common, and immediately call a second routine to do initial processing of these 6. The functions "joe" and "fred" below capture the relevant

On converting character variables to ordered factors, regression result has strange names. Is it possible to obtain same variable names with and without intercept? Thanks, Naresh mydf <- data.frame(date = seq.Date(as.Date("2024-01-01"), as.Date("2024-03-31"), by = 1)) mydf[, "wday"] <- weekdays(mydf$date, abbreviate = TRUE) mydf.work <- subset(mydf, !(wday

Given this data frame (a simplified, essential reproducible example) A<-c(8,7,10,1,5) A_flag<-c(10,0,1,0,2) B<-c(5,6,2,1,0) B_flag<-c(12,9,0,5,0) mydf<-data.frame(A, A_flag, B, B_flag) # this is my initial df mydf I want to get to this final situation i<-which(mydf$A_flag==0) mydf$A[i]<-NA ii<-which(mydf$B_flag==0) mydf$B[ii]<-NA

Hi all, Can anyone explain why the following use of the subset() function produces a different outcome than the use of the "[" extractor? The subset() function as used in density(subset(mydf, ht >= 150.0 & wt <= 150.0, select = c(age))) appears to me from documentation to be equivalent to density(mydf[mydf$ht >= 150.0 & mydf$wt <= 150.0, "age"])