similar to: renaming intercept column when retrieving coeficients from lme using coef function

Hello All: I am very new to R and am trying to import some (107) text files into R while simultaneously manipulating the data into a structure that I can use. Below is a description of what I am trying to ultimately accomplish. However, I find that I am unable to even read one file into R using the read.table function. I get the following error: > test<-read.table("test4.txt")

Hi. I am very new to R and have been diligently working my way through the manual and various tutorials. I am now trying to work with some of my own data and have encountered a problem that I need to fix. I have a dataframe with 8 columns and approximately 850 rows. I have provided an excerpt of the dataframe below. Within column 6 (Question) the numbers 1:33 repeat down the entire column.

I am trying to write a function that will run a linear model and plot the regression coeficients with their corresponding means. I am having two problems. I can get the plot with the function below, but I am having trouble labeling the points. function(y,x1,x2,x3,x4){ outlm<-lm(y~x1+x2+x3+x4) imp<-as.data.frame(outlm$coef[-1]) meanvec<-c(mean(x1),mean(x2),mean(x3),mean(x4))

Hi! I am trying to calculate HPD for the coeficients of regression models fitted with lm or lmrob in R, pretty much in the same way that can be accomplished by the association of mcmcsamp and HPDinterval functions for multilevel models fitted with lmer. Can anyone point me in the right direction on which packages/how to implement this? Thanks for your time! R. [[alternative HTML version

Dear Users, I am trying to understand the inner workings of a repeated measures linear model. Take for example a situation with 6 individuals sampled twice for two conditions (control and treated). set.seed(12) ctrl <- rnorm(n = 6, mean = 2) ttd <- rnorm(n = 6, mean = 10) dat <- data.frame(vals = c(ctrl, ttd), group = c(rep("ctrl", 6), rep("ttd",

Dear list, i'm trying to test if a linear combination of coefficients of glm is equal to 0. For example : class 'cl' has 3 levels (1,2,3) and 'y' is a response variable. We want to test H0: mu1 + mu2 - mu3 =0 where mu1,mu2, and mu3 are the means for each level. for me, the question is how to get the covariance matrix of the estimated parameters from glm. but perhaps there

Dear listers, I can't find the variance or se of the coefficients in a multilevel model using lme. I want to calculate a Chi square test statistics for the variability of the coefficients across levels. I have a simple 2-level problem, where I want to check weather a certain covariate varies across level 2 units. Pinheiro Bates suggest just looking at the intervals or doing a rather

Hi, is there an easy way to get the calculated weights in a regression equation? for e.g. if my model has 2 variables 1 and 2 with coefficient .05 and .6 how can I get the computed values for a test dataset for each coefficient? data var1,var2 10,100 so I want to get .5, 60 back in a vector. This is a one row example but I would want to get a matrix of multiplied out coefficients

Hello! I would like to use constrain to sum coeficients of a factor to 0 instead of classical corner contraint i.e. I would like to fit a model like lm(y ~ 1 + effectA + effectB) and say get parameters intercept effectA_1 effectA_2 effectB_1 effectB_2 effectB_3 where effectA_1 represents deviation of level A_1 from intercept and sum(effectA_1, effectA_2) = 0 and the same for factor B. Is

I wonder if someone could explain how the following R idiom works (it's used in glm.fit). start <- coef start[fit$pivot] <- coef coef is a vector of coefficients, set by .Fortran("dqrls", ...). fit$pivot is a vector of integer indexes (indicating how dqrls permuted the columns of x). If coef has n elements, fit$pivot is a permutation of seq(1,5). start[fit$pivot]

Dear Martin, I think that your plan makes sense. It's too bad that aov() behaved differently in this respect from lm(), and thus created more work, but it's not be a bad thing that the difference is now explicit and documented. I expect that that other problems like this will surface, particularly with contributed packages (and I know that you're aware that this has already happened

Hi, What I'm trying to achieve is very fast algorithm for fitting logistic regression model. I have to estimate regression coeficients using about 10k observations. Once I have coefficients estimated, new 100 rows of data becomes available.... Now I need to reestimate coeficients using 100 newly arrived observations and removing 100 oldest observations. So, my question is would it be

Dear list, I was wondering if it is possible to get the p-values for one-sided tests on the parameters of a linear regression. For instance, I use lm() and store the result in an object. lm() gives me a matrix, using summary() and coef() on which gives me a matrix containing the coefficients, the standard errors, the t-statistics and the two-sided p-values by default. Can I get it to provide me

Dear List, Must be a silly question, but I was wondering whether there is a direct way of calculating "standard error of a HR or exp(coef)" from coxph objects x <- coxph(Surv(time, status) ~ age + inst, lung)> x coef exp(coef) se(coef) z page 0.0190 1.02 0.00925 2.06 0.04inst -0.0104 0.99 0.01028 -1.01 0.31 cheers, Ehsan [[alternative HTML

Dear all, I?m having trouble getting a list of regression variables back into a dataframe. mydf <- data.frame(x1=rnorm(100), x2=rnorm(100), x3=rnorm(100)) mydf$fac<-factor(sample((0:2),replace=T,100)) mydf$y<- mydf$x1+0.01+mydf$x2*3-mydf$x3*19+rnorm(100) dlply(mydf,.(fac),function(df) lm(y~x1+x2+x3,data=df))->dl here I?d like to use ldply(dl,coef(summary)) or something

I was wondering if there is anyway to have Add1() display the coefficient estimates for each candidate predictor along with the F test. This is for lm() btw. Thanks -- View this message in context: http://r.789695.n4.nabble.com/Add1-w-coef-estimates-tp2279662p2279662.html Sent from the R help mailing list archive at Nabble.com.

Hi, how do I create a coef.table as for example returned by summary.glm? I don't see where the significance codes are assigned during summary.glm. Thank you, Benjamin

Hello< I routinely use the following without a problem: fm1<-lme(score~time,data,random=~time|ID) tmp<-coef(fm1, augFrame=T) However, in my current situation, I am running into the following error when I execute the coef() call Error in tapply(as.character(object[[nm]]), groups, FUN[[dClass]]) : arguments must have same length There are some cases with missing data, but I

Hi Everyone, I am using ASReml to fit a spatial model. I do not have all the components of ASReml when I call; names(summary()) e.g. names(summary(fcov.asr2)) [1] "call" "loglik" "nedf" "sigma" "varcomp" I am trying to get the coefs but I get "NULL". Does anybody know the reason? Any help would be much appreciated. Regards

Hello, 1. It appears that "coef<-.varIdent" method does not work properly in some instances. Execution error: Error in `coef<-.varIdent`(`*tmp*`, value = c(11, 12)) : Cannot change the length of the varIdent parameter after initialization occurs when "coef<-.varIdent" is applied to an initialized object of class varIdent with some of the coefficients