similar to: how do I join two lists of the same type ?

My "R" problem.. I want to join two lists but am so for not having any luck. Can anyone assist ? Variable: myData[1] "Data.Id" "Data.Length" "Data.Weight" "1" 12 12 "2" 45 23 Variable: myData[2] "Data.Id" "Data.Length" "Data.Weight" "3" 25 56 "4" 55

Dear list, I have a problem with defining a function (see below) to read my testfile (see testfile). My function only returns mydata I wish to work with attr(mydata, 'fc') as well (for labelling a plot). Principally it works if I do not insist on this function but it would be much easer if it is possible to return mydata AND attr(mydata, 'fc') by using a function. 1) testfile:

# I have a simple function problem. I thought that I could write a function to modify a couple of vectors but I am doing something wrong #I have a standard cost vector called "fuel" and some adjustments to the #costs called "adjusts". The changes are completely dependend on the length #of the dataframe newdata I then need to take the modifed vectors and use # them later. I

Hi All, I'm trying to understand the difference between do.call and lapply for applying a function to a list. Below is one of the variations of programs (by Marc Schwartz) discussed here recently to select the first and last n observations per group. I've looked in several books, the R FAQ and searched the archives, but I can't find enough to figure out why lapply doesn't do what

Hello all, I have generated a principal components regression model using the pcr() function from the PLS package (R version 2.12.0). I am getting a "non-conformable arguments" error when I try to use the predict() function on new data, but only when I try to read in the new data from a separate file. More specifically, when my data looks like this #########training data

Hi, I have measured two response variables (y1, y2) at each treatment level (x = 0, 1.5 or 3). Now I would like to show the y1 and y2 against x in a bar plot. However, y1 and y2 differ in scale so I need two y-axises, one on the left side and one on the right side (and I dont want to standardize my responses). This is fairly easy if you want to show points,lines etc, but gets more complicated

Hi All, I was adding a new row of data to my data frame using rbind(). I was surprised to see that after adding new row, I lost my data frame level attibute as well as col level attribute. Please help me to insert a new row at frist or middle position so that my custom attribute is not lost. Here is what I did. age<-c(15,20,18) weight<-c(40,42,30) ### creating my data frame

Dear All,I encountered a problem when I use RWeka for prediction. Specifically, I use the following: res=J48(X1~.,data=mydata); predict(res), #it worked fine but when I tried to use a different data set, i.e. predict(res,newdata=mynewdata); all the predictions I get is 0, which apparently is problematic. What is weird is, if I use the old data, but use the newdata option, i.e.

Dear all, I'm having a few problems trying to reshape a data frame. I tried with reshape{stats} and melt{reshape} but I was missing something. Any help is very welcome. Please find details below: ################################# # data in its original shape: indiv <- rep(c("A","B"),c(10,10)) level.1 <- rpois(20, lambda=3) covar.1 <- rlnorm(20, 3, 1) level.2

Dear all, for a cross-validation I have to use predict.glmmPQL() , where the formula of the corresponding glmmPQL call is not given explicitly, but constructed using as.formula. However, this does not work as expected: x1<-rnorm(100); x2<-rbinom(100,3,0.5); y<-rpois(100,2) mydata<-data.frame(x1,x2,y) library(MASS) # works as expected model1<-glmmPQL(y~x1, ~1 | factor(x2),

I have completed a binomial GLM in R (details attached (finalModel.docx)) and I am trying to create a graph of observed and fitted values using the following commands: > MyData<-data.frame(time=seq(from=0,to=1323,by=1)) > Pred<-predict(M2,newdata=MyData,type="response") > plot(x=turtle$time,y=turtle$success) > lines(MyData$time,Pred) However, I get the following

Hi I rbind data frames in a loop in a cumulative way and the performance detriorates very quickly. My code looks like this: for( k in 1:N) { filename <- paste("/tmp/myData_",as.character(k),".txt",sep="") myDataTmp <- read.table(filename,header=TRUE,sep=",") if( k == 1) { myData <- myDataTmp } else{ myData

Hi, I'm using a loop to evaluate several models by taking adjacent variables from my dataframe. When i try to get predictions for new values, i get an error message about a missing variable in my new dataframe. Below is an example adapted from ?gam in mgcv package library(mgcv) set.seed(0) n<-400 sig<-2 x0 <- runif(n, 0, 1) x1 <- runif(n, 0, 1) x2 <- runif(n, 0, 1) x3 <-

Hi Group, I have a data frame below. Within this data frame there are samples (columns) that are measured more than once. Samples are indicated by "idx". So "id1" is present in columns 1, 3, and 5. Not every id is repeated. I would like to create a new data frame so that the repeated ids are averaged. For example, in the new data frame, columns 1, 3, and 5 of the original

Hallo! I would need a code for 10-fold cross validation for the classifiers Naive Bayes and svm (e1071) package. Has there already been done something like that? I tried to do it myself by applying the tune function first: library(e1071) tune.control <- tune.control(random =F, nrepeat=1, repeat.aggregate=min.,sampling=c("cross"),sampling.aggregate=mean, cross=10, best.model=T,

Hi, I am wondering if anyone knows how to perform an F-test on the change in R square between hierarchical models in R? SPSS provides this information and a researcher that I am working with is interested in getting this information. Alternatively, if someone knows how I can calculate the test statistic (SPSS calls it F-change?) and dfs that would be helpful as well. The output and the test I am

Data and code as promised: #Creating a test dataset Year<- c(2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013, 2014) Lake1<- c(2, 4, 5, 2, 1, 1, 2, 3, 4, 5, 6, 2, 3, 1, 2) Lake2<- c(1, 3, -1, 4, -2, 1, 2, 3, 4, 5, 6, 2, 3, 1, 2) Lake3<- c(1, 2, 5, -3, 1, 1, 2, 3, 4, 5, 6, 2, 3, 1, 2) Lake4<- c(1, 1, 1, 1, 1, 1, 1, 250, 240, 240, 240, 240, 240, 239,

I am recoding some data. Many values that should be 1.5 are recorded as 1-2. Some example data and my solution is below. I am curious about better approaches or any other suggestions. Thanks! # example input data myData <- read.table(textConnection("id, v1, v2, v3 a,1,2,3 b,1-2,,3-4 c,,3,4"),header=TRUE,sep=",") closeAllConnections() # the first column is IDs so remove

Hi all, Do any packages implement density estimation in a modelling framework? I want to be able to do something like: dmodel <- density(~ a + b, data = mydata) predict(dmodel, newdata) This isn't how sm or KernSmooth or base density estimation works. Are there other packages that do density estimation? Or is there some reason that this is a bad idea. Hadley -- http://had.co.nz/

No. If your ouput is a numeric "matrix", it cannot include alpha. Columns in a data frame can be of different classes, but each column must be single class. and finally, of course, see ?cat -- I think you are misusing it. If you simply want to return "somestuff", your function should be: function(w) {"somestuff"} not function(w) {cat("somestuff")} As