similar to: Coeficients estimation in a repeated measures linear model

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

Hi! I wanted to install Transport Tycoon Deluxe for Windows but the installation does not even start. I have wine-20050930-1fc3winehq.i686 on my system, FC3/x86-64. The installation directory seems strange on the very first window, every backslash character is painted as a strike-through upcase letter W. Browsing for another directory or typing the directory manually gives the same problem.

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

I am still fairly new to R and have a fairly rudimentary question. I am trying to name a vector of coefficients retrieved from a multilevel model using the coef function. I guess the default name is "Intercept" and I cannot figure out how to rename it. I have tried the using the code below to name the column of coefficients ind.y derived from an lme model. Unfortunately, the

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

How can I get the outlier in this boxplot of "Score" to be represented by the corresponding value in "SubNo"? score=c(6,6,7,14,5,7,6,8) SubNo=1:8 mydata=data.frame(SubNo, score) boxplot(mydata$score) Thanks! Kevin [[alternative HTML version deleted]]

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

Hi all, In assessing the concordance of two sets of ANOVA results, I would like to extract the p values from the summaries. I had a look at previous answers to this problem, and the suggestions do not seem to work: # toy example test.df<-data.frame(subno=c(1:50,1:50), ndrinks=c(rpois(50,4.5),rpois(50,4)), b4after=c(rep(1,50),rep(-1,50)))

Hi! I am using the fft() function the base package to transform some 1d signal. If I use this standar fucntion I get a very huge first fourier coeficient. I think this dues to the handling of the borders of the signal. Usually in fft especially in image processing the signal is simulated to be continuous by adding the signal several times periodically. My question is, is there some function

Dear Prof Ripley, First of all, unless you are an english professor, then I do not think you have any business policing language. I'm still very much a student, both in R, and regarding signal analysis. My competence on the subject as compared too your own level of expertise, or my spelling for that matter, may be a contension for you, but it would have been better had you kept that opinion

Hi, I have been dabbling with str() to extract values from outputs such as lmer etc and have found it very helpful sometimes. but only seem to manage to extract the values when the output is one simple table, any more complicated and I'm stumped :-( take this example of the extracted coeficients from a lmer analysis... using str(coef(lmer(resp3~b$age+b$size+b$pcfat+(1|sex), data=b)))

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

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

Hi all. This is my first post in this forum. Finally I find a forum in the web about R, although is not in my language. Now I'm working with Bootstrap CI. I'd like to know how I can calculate a Bootstrap CI for any statistic, in particular, for Kurtosis Coeficient. I have done the following code lines: > library(boot) > x=rnorm(20) > kurtosis=function(x)

hello, i am a bit of a statistical neophyte and currently trying to make some sense of confidence intervals for correlation coefficients. i am using the cor.test() function. the documentation is quite terse and i am having trouble tieing up the output from this function with stuff that i have read in the literature. so, for example, i make two sequences and calculate the correlation coefficient:

Hi I'm using boot package for some analysis on linear regression coeficients. My problem is that I can not compute bca intervals, I get an error message > bca.ci(blm8901,index=1) Error in if (!all(rk > 1 & rk < R)) warning("Extreme Order Statistics used as Endpoints") : missing value where logical needed The problem is the empinf.reg function that is

Hi everyone, I've been trying to find a function that outputs the Pearson and/or Spearman correlation coefficients for several variables with the associated statistics in one single table/matrix. For what I've been able to understand the Stats package is only able to compute these coeficients/statistics only in defined pairs. This becomes time consuming when we want to determine these

I like what smooth.spline does but I am unclear on the output. I can see from the documentation that there are fit.coef but I am unclear what those coeficients are applied to.With spline I understand the "noraml" coefficients applied to a cubic polynomial. But these coefficients I am not sure how to interpret. If I had a description of the algorithm maybe I could figure it out but as it

Hi, I noticed that when I was conducting some calculation involving finding correlation coeficients, R stopped abnormally. So I did some research, and find out that 0/0 was the culprit. For sure 0/0 is not a valid expression, but R should give a warning, an error msg or NaN instead of segmentation fault. I am using R 2.1.0 under Gentoo Linux. My GCC version is 3.3.5. Xing