similar to: Time series example in Koop

Hello, I am trying to do a logistic regression and have one predictor variable (x) that is ratio and two predictor variables (y and z) that are categorical. These have three levels each which I have called "High", "Medium" and "Low". My question: do I need to use a numerical coding scheme for the categorical variables as required by some statistical software

Hi List, I recently upgraded one of our high-volume SMB servers from samba 3.x to samba 4.1.12 (also changing the OS). Now I have a strange problem. The CPU load on the server is very high, processes that are causing it are nslcd or since I changed to it sssd and the smbd processes themselves. ? Top looks like this: --------------------------------- PID USER????? PR? NI? VIRT? RES? SHR S

Hi All, I am interested in aggregating a data frame based on 2 categories--mean effect size (r) for each 'id's' 'mod1'. The 'with' function works well when aggregating on one category (e.g., based on 'id' below) but doesnt work if I try 2 categories. How can this be accomplished? # sample data id<-c(1,1,1,rep(4:12)) n<-c(10,20,13,22,28,12,12,36,19,12,

Hi All, I'm conducting a meta-analysis and have taken a data.frame with multiple rows per study (for each effect size) and performed a weighted average of effect size for each study. This results in a reduced # of rows. I am particularly interested in simply reducing the additional variables in the data.frame to the first row of the corresponding id variable. For example:

Hello,   I am using mod1 <- lrm(y~x1+x2,na.action=na.pass,method="lrm.fit") summary(mod1) and I've got the following error: Error in summary.Design(mod1) : adjustment values not defined here or with datadist for x1 x2   Many thank, Amor [[alternative HTML version deleted]]

Hi, All: I am new to R and tm package. I'm trying to do the stemming using tm_map() and it doesn't seem to work: *I used:* > stemDocument(t_cmts[[100]]) *Where t_cmts is the corpus object, the results is:* bottle loose box abt airpak sections top plastic bottle squashed nearly flush neck previous shipments bottle wrapped securely bubble wrap wno bottle damage packaging poor

I tried the following ordered logistic regression in R: mod1 <- polr(altitude~sp + wind_dir + wind_speed + hr, data=altioot) But when I asked The summary of my regression I got the folloing error message: > summary (mod1) Re-fitting to get Hessian Error in optim(start, fmin, gmin, method = "BFGS", hessian = Hess, ...) : the initial value of 'vmin' is not

Hi All, I am interested in creating a function that will take x number of lm objects and automate the comparison of each model (using anova). Here is a simple example (the actual function will involve more than what Im presenting but is irrelevant for the example): # sample data: id<-rep(1:20) n<-c(10,20,13,22,28,12,12,36,19,12,36,75,33,121,37,14,40,16,14,20)

Dear list, I would like to simulate individual survival times from a model that has been fitted using the survreg procedure (library survival). Output shown below. My plan is to extract the shape and scale arguments for use with rweibull() since my error terms are assumed to be Weibull, but it does not make any sense. The mean survival time is easy to predict, but I would like to simulate

>> The tomshardware-guys (no gals would do this...) have removed the >> fans, and immersed the innards of the computer in a sealed cabinet >> filled with cooking oil. So they have a completely silent machine >> in 40C warm oil. Amazing... It certainly is. And, I suppose, this will work, for a while, as long as: The "sealed cabinet", has enough expansion

I have the following questions about the variance of the random effects in the survreg() function in the survival package: 1) How can I extract the variance of the random effects after fitting a model? For example: set.seed(1007) x <- runif(100) m <- rnorm(10, mean = 1, sd =2) mu <- rep(m, rep(10,10)) test1 <- data.frame(Time = qsurvreg(x, mean = mu, scale= 0.5, distribution =

I need to make nonlinear regression with the posterior script, but how is the problem? I have error in library (nls), package 'nls' has been merged into 'stats'. I need help? What other forms I have to make nonlinear regression? and how I find to calculate statistics y residuals, scatterplot. thanks SCRIPT ros<-read.table("Dataset.csv",header=T,sep=",")

Hello, I'm a complete newbie to R so sorry if this is too basic..:-S I have to modify some scripts someone else did to make it work with my data. For some reason, one of the scripts which were supposed to work is not, and I get the error message "number of items to replace is not a multiple of replacement length". The script is this one: *open_lpj_nc_gpp <-

I have a model: mod1<-lmer( x ~ (1|rtr)+ trth/(1|cs) , data=dtf) # Here, cs and rtr are crossed random effects. cs 1-5 are of type TRUE, cs 6-10 are of type FALSE, so cs is nested in trth, which is fixed. So for cs I should get a fit for 1-5 and 6-10. This appears to be the case from the random effects: > mean( ranef(mod1)$cs[[1]][1:5] ) [1] -2.498002e-16 > var(

Dear all, I want to draw ticks on the 3rd and 4th row of a lattice. How do I do this ? In my search of the help, I discovered a parameter alternating,which kind of says where the ticks will be but does not suffice for me. I am running this command : - barchart(X03/1000~time|Company, data=df1[which(df1$time!=1),], horiz=F,

Dear All, I have a time series daily data with date are stored ( %dd-%mm-%yy format ) from 22-01-20 to 03-08-21. In total I have 560 observations. I am using the following command to declare as a time series object. Here the the data set is 7 days a week. oil <- read_xlsx("crudefinal.xlsx") pricet=ts(oil$price, start = c(2020, 22), freq = 365)

Hi All, I was wondering if someone could help me to solve this issue with lmer. In order to understand the best mixed effects model to fit my data, I compared the following options according to the procedures specified in many papers (i.e. Baayen <http://www.google.it/url?sa=t&rct=j&q=&esrc=s&source=web&cd=1&ved=0CDsQFjAA

I have spent hours on R in Windows 7. Just installed 2 days ago so the R package should be current. Currently I am using the RGui (64-bit) for Windows. I can not read an Excel file into R from my computer. Have hours on this. Completely crazy!! I have the XLConnect package loaded. I think it is loaded because when I enter: > loadedNamespaces() [1] "base"

When I fit a multivariate linear model, and the formula is defined outside the call to lm(), the method summary.mlm() fails. This works well: > y <- matrix(rnorm(20),nrow=10) > x <- matrix(rnorm(10)) > mod1 <- lm(y~x) > summary(mod1) ... But this does not: > f <- y~x > mod2 <- lm(f) > summary(mod2) Error en object$call$formula[[2L]] <- object$terms[[2L]]

Hi, I am running a logistic regression model using lrm library and I get the following error when I run the command: mod1 <- lrm(death ~ factor(score), x=T, y=T, data = env1) Unable to fit model using ?lrm.fit? where score is a numeric variable from 0 to 6. LRM executes fine for the following commands: mod1 <- lrm(death ~ score, x=T, y=T, data = env1) mod1<- lrm(death ~