similar to: scoping problem?

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

Hello I have a rather simple code and for some reason it produces an error message. If someone can tell me why and how to fix it, I would be very greatful. Thank you in advance. ##### create data set.seed(10) n <- 200 # number of training points n.test <- 200 # number of test points p<-2 # dimension of input space z <-

Dear R users, I ran into this curious problem: > d <- rnorm(100) > d.density <- density(d) > sum( d.density$x * d.density$y) [1] 2.517502 Admittedly the method of computing the mass under the density curve at line 3 is crude. But 2.5 is pretty far from 1, the value it should be. I tried a few other dataset and got similar result. Am I missing something obvious? Or is the return

When I import a simple dataset, run LDA, and then try to use the model to forecast out of sample data, I get a forecast for the training set not the out of sample set. Others have posted this question, but I do not see the answers to their posts. Here is some sample data: Date Names v1 v2 v3 c1 1/31/2009 Name1 0.714472361 0.902552278 0.783353694 a 1/31/2009 Name2 0.512158919 0.770451596

I've been training randomForest models on 7 million rows of data (41 features). Here's an example call: myModel <- randomForest(RESPONSE~., data=mydata, ntree=50, maxnodes=30) I thought surely with only 50 trees and 30 terminal nodes that the memory footprint of "myModel" would be small. But it's 65 megs in a dump file. The object seems to be holding all sorts of

Hi, I must be missing something here...Essentially, a short piece of code works if it's standalone, but doesn't work if it's divided into two functions. The code that works is: ################### WORKS ############### library(pamr) set.seed(120) x <- matrix(rnorm(1000*20),ncol=20) y <- sample(c(1:4),size=20,replace=TRUE) mydata <- list(x=x,y=y)

Hi, I am would like to ask few questions. I am trying to forecast hourly electricity prices by 24 hours ahead. I have hourly data starting from 2015*12*18 to 2017-10-24 and I have defined the data as time series as written in the code below. Then I am trying do neural network with 23 non-seasonal dummies and 1 seasonal dummy. But I don?t know whether training set is enough.( Guess it is 50

hi there!! i am trying to implement the code of the e1071 package for naive bayes, but it doens't really work, any ideas?? i am very glad about any help!! i need a naive bayes with 10-fold cross validation: code: library(e1071) model <- naiveBayes(code ~ ., mydata) tune.control <- tune.control(random = FALSE, nrepeat = 1, repeat.aggregate = min, sampling = c("cross"),

Hi, I posted the message below last week, but no answers, so I'm giving it another attempt in case somebody who would be able to help might have missed it and it has now dropped off the end of the list of mails. I am fairly new to R and still trying to figure out how it all works, and I have run into a few issues. I apologize in advance if my questions are a bit basic, I'm also no

Dear users, I'll appreciate your help with this (hopefully) simple problem. I have a model object which was fitted to inputs X1, X2, X3. Now, I'd like to use this object to make predictions on a new data set where only X1 and X2 are available (just use the estimated coefficients for these variables in making predictions and ignoring the coefficient on X3). Here's my attempt but, of

hi I am trying to write a function around a glm or similar function, in which I can pass the subsetting constraint as an argument to the function, but I am having trouble. the following commands (if I wanted SEX==0 to be my subset) in the global environment work fine: subexpr <- expression(SEX==0) subtest <- with(mydata, eval(subexpr)) test.glm <- glm(y~x1+x2, data=mydata,

Hi, I'm working with support vector machine for the classification purpose, and I have a problem about the accuracy of prediction. I divided my data set in train (1/3 of enteire data set) and test (2/3 of data set) using the "sample" function. Each time I perform the svm model I obtain different result, according with the result of the "sample" function. I would like

Thankyou for your help with subsetting within a function. I have now tried to apply the same theory in the framework of an lme as follows: fit1.lme <- eval(substitute(lme(fixed=fixed, data=dataframe, random=random, correlation=corCAR1(form= corr), na.action=na.omit, subset=subset),list(subset=subs))) but I get the following error: Error in switch(mode(object), name = , numeric = , call =

Dear all, I am trying to train a randomForest using all my control data (12,000 cases, ~ 20 explanatory variables, 2 classes). Because of memory constraints, I have split my data into 7 subsets and trained a randomForest for each, hoping that using combine() afterwards would solve the memory issue. Unfortunately, combine() still runs out of memory. Is there anything else I can do? (I am not using

Hello, I am working with a matrix of multilocus genotypes for ~180 individual snail samples, with substantial missing data. I am trying to calculate the pairwise genetic distance between individuals using the stats package 'dist' function, using euclidean distance. I took a subset of this dataset (3 samples x 3 loci) to test how euclidean distance is calculated: 3x3 subset used

On 18/03/2023 1:57 p.m., akshay kulkarni wrote: > Dear members, > The documentation for source() says: > > Input is read and parsed from that file until the end of the file is reached, then the parsed expressions are evaluated sequentially in the chosen environment. > > What does this mean? I presume that any objects that are CREATED by the script

On 19/03/2023 2:33 p.m., akshay kulkarni wrote: > Dear Duncun, > ? ? ? ? ? ? ? ? ? ? ? ? ?thanks for the reply.... > > So when I run a script in the system command line by R CMD BATCH, the > objects created in the script cannot be stored in the workspace ,right? > If yes, how to save them? Moreover, the only way to save the objects > CREATED from the script permanently is

Hello, 1) What is the difference between a "data frame" (J H Maindonald, Using R, p. 12) and a "vector"? In Using R, the author asks the reader to enter the following data in a data frame, which I will call "mydata": year snow.cover 1970 6.5 1971 12.0 1972 14.9 1973 10.0 1974 10.7 1975 7.9 ... mydata=data.frame(year=c(1970,...),snow.cover=c(6.5,...)) 2) How to

Hello, Could someone please tell me what I am thinking about incorrectly: f <- function(y) { g <- function(x) x + y g } In the following, I get what I expect based on my understanding of lexical scoping: (f(1))(3) # 4 (f(2))(3) # 5 But now, fs <- lapply(c(1, 2), f) fs[[1]](3) # 5 (Why not 4 ?) fs[[2]](3) # 5 Checking the environments of these functions, I see that

On 19/03/2023 2:55 p.m., akshay kulkarni wrote: > Dear Duncun, > ? ? ? ? ? ? ? ? ? ? ? ? ?What if there is no interactive "session" > running? I will be running my scripts automatically from crontab in Linux. I was talking about the session that is created for the duration of the BATCH run, not some other session that may be running in another process. Sorry for the