similar to: Function with multiple indices

Hello all, R2.1.1, W2k I try to make a plot of a simple regression model in this way: > with(njfA_bcd, { + plot(TC_OS.G31,Prot,cex = 2, col = "red", xlab= "TC/OS at GS32", + ylab="Grain crude protein (CP)") + }) This part works well and produces the datapoints as red circles. When I try to add a line, using a fitted linear model in a way that works perfect

I want to split my whole dateset to training set and test set, building model in training set, and validate model using test set. Now, How can I split my dataset to them reasonally. Please give me a hand, It is better to give me some R code. and I see some ways like using SOM to project whole independents to 2-dimensions and find some to be training set and others are test set. like below. I

Dear all, I would like to conduct a Pearson's correlation using cor.test separately for each individual in my data set. Each individual has nine observations of the measurement variable 'Delta13C'. I can figure our how to do it manually one 'Individual_ID' at a time, but I cannot figure out how to automate the process (a loop I am guessing). In addition, I would like to

http://r.789695.n4.nabble.com/file/n4619765/survival_file.png Hi everyone, This is what my data looks like, I haven't included the covariates, the example would get too large. uid id date feverstart dumfever 130 75 346465 2011-04-11 <NA> 0 131 75 360287 2011-04-18 <NA> 0 132 75

Good day. 1. We have configured two domain controllers on Windows 2003 R2. We named them TEST.LOCAL and CHILD.TEST.LOCAL respectively and made a trust relationships between them. 2. We have installed Samba 3.5.3 on Ubuntu 9.10, kernel 2.6.31-14 and configured it for using winbindd. We have encountered a problem with results that winbind returns upon a command GETGRENT. We

I having problem with dendrogram leaf names when I read a tab delimited file into dataframe; I have a text file, tab delimited, using read.table into a data frame as follows: > test1<-read.table("c:\\R\\data\\Tremont4.txt", header=TRUE, sep="\t") When I do this the "test1" data frame is picking up my first column names as part of the data and not the case

Hello, When using write.table I am getting two variables pasted together (not by choice). Has anyone else had this happen? Specifically, I have the following on a RH7.2/R1.4 box: d _ read.dta(paste('/montecarlo/forecast/off/',F,'.dta',sep='')) write.table(d,file=paste('/montecarlo/forecast/off/csv/',F,'.csv',sep=''), row.names=FALSE,

Hello, When using write.table I am getting two variables pasted together (not by choice). Has anyone else had this happen? Specifically, I have the following: d _ read.dta(paste('/montecarlo/forecast/off/',F,'.dta',sep='')) write.table(d,file=paste('/montecarlo/forecast/off/csv/',F,'.csv',sep=''), row.names=FALSE, col.names=FALSE,

Hello, I hesitate to call this a bug, because I could have forgotten something important, but the MarkowitzPortfolio example in fPortfolio does not work for me. Here's my code: > library(fPortfolio) > >xmpPortfolio("\nStart: Load monthly data set of returns > ") > data(berndtInvest) > # Exclude Date, Market and Interest Rate columns from data

I'm fitting a regression tree with rpart and I want to write the values for every leaf in a dataset. As an example take the variable turnover. Let's suppose my tree for turnover has 30 leaves and I want to have 30 datasets with dataset 1 containing the turnover values of the units in leaf 1, dataset 2 containing turnover values for the observations in leaf 2 and so on. How can I do

Asterisk work does not pay all of my bills, so I have joined up with a company that has a very good payment plan. I have recently become a Mona Vie Independent Distributor. I am not going to go into a sales pitch. This is just an FYI to this opportunity. The company has grown into a Billion dollar company in just 2 years. This company's compensation plan is the best and quickest that I have

Hi,. We have 2 values (first formant F1, second formant F2) for a given phoneme for six languages. We want to see whether the languages are significantly different one from another for this given phoneme. We have done a manova on our data and it works well, but we doesn't allow us to see which pair of languages are different. If we have only one formant for the phoneme, we would use

Hi r-users,   I would like to extract the data that match.  Attached is my data: I'm interested in matchind the value in column 'intg' with value in column 'rand_no' > cbind(z=z,intg=dd,rand_no = rr)             z  intg rand_no    [1,]  0.00 0.000   0.001    [2,]  0.01 0.000   0.002    [3,]  0.02 0.000   0.002    [4,]  0.03 0.000   0.003    [5,]  0.04 0.000   0.003    [6,] 

Dear R helpers, I have been using the loadings function from the multiv library and I get the typical output (see below). When I try to export these results to a file using a write.table() I get the following error message "Error in as.data.frame.default(x[[i]], optional = TRUE) : can't coerce loadings into a data.frame" Any idea why write.table is doing that and any

Hi list, I have the following code to compute the acf of a time series acfresid <- acf(residfit), where residfit is the series when I type acfresid at the prompt the follwoing is displayed Autocorrelations of series ?residfit?, by lag 0.0000 0.0833 0.1667 0.2500 0.3333 0.4167 0.5000 0.5833 0.6667 0.7500 0.8333 1.000 -0.015 0.010 0.099 0.048 -0.014 -0.039 -0.019 0.040 0.018

Hi, I have a question about how I should report the results for a linear mixed effects model where the model includes as predictors three factors (facA, facB and facC), one of which (facA) interacts with the other two. facA and facB have two levels and facC has 3 levels. There are also several other continuous predictors (e.g. varA, varB, varC). My mixed model is specified with the following

Hi R users! I will try to state my question again. I have longitudinal data and fitted the following model with lme: Y = X*beta + U + W(t) + Z where U ~ N(0, nu*I) I is the identity matrix, so this is the random intercept W(t)~ N(0, sigma*H) and H is a matrix which incorporates a Gaussian serial correlation (covariance) in the offdiagonal

Dear all, I am so ashamed to pollute the list with a trivial question, but it is a long time I have not used R, and I need a result in the next one or two hour... I have a table which I have loaded with read.table, and I want to make the mean of its columns. > slides <- read.table("slides.txt") > slides [1:5,] V1 V2 V3 V4 V5 V6 V7 V8 1

Hi there, I am doing multiple comparisons for data that is not normally distributed. For this purpose I tried both functions kruskal{agricolae} and kruskalmc{pgirmess}. It confuses me that these functions do not yield the same results although they are doing the same thing, don't they? Can anyone tell my why this happens and which function I can trust? kruskalmc() tells me that there are no

I can't get my simultaneous equations to work using system fit. Please help. #Reproducible script Empdata<- read.csv("/Users/ngwinuiazenui/Documents/UPLOADemp.csv") View(Empdata) str(Empdata) Empdata$gnipc<-as.numeric(Empdata$gnipc) install.packages("systemfit") library("systemfit") pdata <- plm.data(Empdata,