similar to: --delete-excluded by default?

Hello, I would like to get a correlation coefficient (R-squared) for my model. I don't know how to calculate it in R. What I've done so far: x<-8.5:32.5 #Vektor x y<-c(NA ,5.88 , 6.95 , 7.2 , 7.66 , 8.02 , 8.44 , 9.06, 9.65, 10.22 , 10.63 ,11.06, 11.37, 11.91 ,12.28, 12.69 ,13.07 , 13.5 , 13.3 ,14.14 , NA , NA , NA , NA , NA) #Vektor y

Dear all, Could anyone help me with the following? DATA <- data.frame(rbind(c("Red1", 1, 1, 1), c("Blue1", 1, 1, 1), c("Red2", 1, 1, 1), c("Red3", 1, 1, 1))) colnames(DATA) <- c("A", "B","C", "D") #Option 1 DATA <- DATA[-2, ] #Same result I would like to achieve with Option 2 #Option 2 - I would like to do

Hi gurus! First time post: Scenario want to talk to daemon locked behind a firewall using encrypted transfer of files. Have daemon setup on port 10001. Do not have NC, do not have root access. Client command : rsync -avzul -e "ssh -vvv -l username" /home/users/blah/ rsync://lrem02:10001::/live As you can see the connection is set to talk to lrem02 on port 10001 however i want to

> Dear expeRts, > > Currently, my colors are as follows: > mycol <- > c("blue1","blue2","blue3","blue4","black","yellow4","yellow3","yellow2","y > ellow1") > heatmap(snp, Rowv=NA, Colv=NA, col=mycol) > > However, I would like to have the following colors: > bright blue ->

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:

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 <-

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,

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

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(

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)

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]]

I'm trying to classify some observations using lda and I'm getting a strange error. I loaded the MASS package and created a model like so: >train <- mod1[mod1$rand < 1.7,] >classify <- mod1[mod1$rand >= 1.7,] >lda_res <- lda(over_win ~ t1_scrd_a + t1_alwd_a, data=train, CV=TRUE) That works, and all is well until I try to do a prediction for the holdouts:

Hi All, I am interested in printing column names without quotes and am struggling to do it properly. The tough part is that I am interested in using these column names for a function within a function (e.g., lm() within a wrapper function). Therefore, cat() doesnt seem appropriate and print() is not what I need. Ideas? # sample data mod1 <- rnorm(20, 10, 2) mod2 <- rnorm(20, 5, 1) dat

Hi all and thanks for your time in advance, I can't figure out why summary.lmrob complains when lmrob is used on a zooreg object. If the zooreg object is converted to vector before calling lmrob, no problems appear. Let me clarify this with an example: >library(robustbase) >library(zoo) >dad<-c(801.4625,527.2062,545.2250,608.2313,633.8875,575.9500,797.0500,706.4188,

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

hello. help with effects plots. here's the last bit of code before running the model and then the effects, then the error. nor.dem <- norway$v162 ## nor.dem is my DV & it is continuous. nor.dem <- as.numeric(nor.dem)-5 str(nor.dem) (i had to do a great deal of coding here so i am snipping down to the end) tmp[which(norway$v128 == "trust completely" & norway$v127

Hopefully the attachment will make it this time... Hi: I am practicing with the attached shapefile and was wondering if I can get some help. Haven't used 'rgdal' and 'maptools' much but it appears to be a great way bring map data into R. Please take a look at the comments and let me know if I need to explain better what I am trying to accomplish. library(rgdal)

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 ~