similar to: RMSE and lm

I'm using 2.10.0 on Linux (64 bit), and I just noticed that random numbers are occasionally added to the text of names in vectors. It's happened to me in two separate, long-running R sessions, but I can't find a way to reproduce it in a smaller setting. The code I'm using is > diag.gam.2 <- mdl.run.diag(fit.gam.2, ds.valid) > diag.gam.2 rmse mdae.1413751

Dear R users,   I need to do a F test on the hypothesis that a 2 by 1 vector (X_1, X_2)' has the mean vector  (M_1, M_2)'. Specifically, I would like to assume the X vector comes from a bivariate Normal distribution (M, Sigma). Then, given 1000 observations on X, I wanted to test if the means of X agree with the means of the target Normal distribution. Any function or package in R could do

Dear all, I cannot find to explicitly get the R-squared or adjusted R-squared from summary(lm()) Thanks a lot! [[alternative HTML version deleted]]

I am using R for Unix and want to make some LaTeX tables. I have already played around in R for Windows and have succeeded in making tables that I want using the following code: latex(Estimates, file='out.tex', rowlabel='',digits=3) However, when I use this code in Unix, I can never find the file "out.tex". I assumed that R would send the file to whatever directory I

Simple question. For a simple linear regression, I obtained the "standard error of predicted means", for both a confidence and prediction interval: x<-1:15 y<-x + rnorm(n=15) model<-lm(y~x) predict.lm(model,newdata=data.frame(x=c(10,20)),se.fit=T,interval="confidence")$se.fit 1 2 0.2708064 0.7254615

Hi, Does anyone know how one could format numbers using 1000 separator in R? For example, format 1000 as 1,000 and 100000 as 100,000, etc. Thanks, -- Tom [[alternative HTML version deleted]]

Hello, I'm trying to plot a fitted lm() line on a plot when the one explanatory variable is log transformed and log="x". I get different lines using abline and predict.lm(). #Example x <- 1:100 y <- rnorm(100) plot(y ~ x, log="x") abline(lm(y ~ log(x))) lines(x, predict(lm(y ~ log(x))), lwd=2) I'm sure I'm missing something but could someone tell me which

If first fit my data column V1 to column V2 using normal "lm" fitting, call it "fit1", then I used "acf(fit1$residuals, type='cov', 40) " function to obtain the autocovariance of the residuals, and then constructed a autocovariance matrix, I chose it to be 40x40. Call this autocovariance matrix B, I then use the following "lm.gls" function to

In the summary of the output of arma, there's a number Pr(>|t|), however, I don't know what is its meaning - at least, it doesn't _seem_ to be a Student's t distribution. Reproducible test case: x <- c(0.5, sin(1:9)) reg <- arma(x, c(1,0)) summary(reg) <output> Call: arma(x = x, order = c(1, 0)) Model: ARMA(1,0) Residuals: Min 1Q Median 3Q

I'm trying to get started with maximum likelihood estimation with a simple regression equivalent out of Bolker (Ecological Models and Data in R, p302). With this code: #Basic example regression library(bbmle) RegData<-data.frame(c(0.3,0.9,0.6),c(1.7,1.1,1.5)) names(RegData)<-c("x", "y") linregfun = function(a,b,sigma) { Y.pred = a+b*x

Dear All, I am wondering if there is an R function to convert a covariance matrix to a correlation matrix. I have a covariance matrix sigma and I want to compute the corresponding correlation matrix R from sigma. Thank you very much, Bernard --------------------------------- [[alternative HTML version deleted]]

Dear all, how can I perform a repeated measures ANOVA using a covariance matrix as input? E.g., I have four repeated measures (N = 200) with mean vector tau (no BS factor): tau <- rbind(1.10, 2.51, 2.76, 3.52) and covariance matrix (sigma): sigma <- matrix(c(0.523, 0.268, 0.267, 0.211, 0.268, 0.444, 0.492, 0.571, 0.267, 0.492, 1.213,

Hi, I am trying to find a function in R that would calculate the critical value of t for a given probability and df. For example, if p=5% and df=20, then does R have a function to calculate the t value? I tried: > dt(0.05,df=20) [1] 0.3934718 That doesn't look right to me. Could someone tell me what I was missing? Thanks, -- Tom [[alternative HTML version deleted]]

I am more than 'a little disappointed' that you expect a detailed explanation of the problems with your 'bug' report, especially as you did not provide any explanation yourself as to your reasoning (nor did you provide any credentials nor references). Note that 1) Your report did not make clear that this was only relevant to prediction intervals, which are not commonly used.

Hi, guys, I am trying to sample from a truncated normal/gamma distribution. But only the far end of the distribution (where the probability is very low) is left. e.g. mu = - 4; sigma = 0.1; The distribution is Normal(mu,sigma^2) truncated on [0,+Inf]; How can I get a sample? I tried to use inverse CDF method, but got Inf as answers. Please help me out. Also, pls help me on the similar

Hi, I've looked around but can't find a clear answer to the difference for these two? Any help? Thanks! -- View this message in context: http://old.nabble.com/dfbetas-vs-dfbeta-tp26331704p26331704.html Sent from the R help mailing list archive at Nabble.com.

Dear friends, regression towards the mean is interesting in medical circles, and a very recent paper (The American Statistician November 2007;61:302-307 by Krause and Pinheiro) treats it at length. An initial example specifies (p 303): "Consider the following example: we draw 100 samples from a bivariate Normal distribution with X0~N(0,1), X1~N(0,1) and cov(X0,X1)=0.7, We then calculate

Hello, I use t.test for normal distributed and wilcox.test for non-normal distributed samples. It is easy to write a function for t.test that calculates the effect size, because all parts of the formula are available from the t.test result: r = sqrt(t*t / (t*t + df)) However, for Wilcoxon tests, the formula for effect sizes is: r = Z / sqrt(N) I wonder how I can calculate the Z-score in R for

i need HELPPP!! how do i calculate the RMSE value for two GEV models?first GEV is where the three parameters are constant.2nd GEV model a 4 parameter model with the location parameter is allowed to vary linearly with respect to time while holding the other parameters at constant. is there any programming code for this? i really really need help. please reply to me as soon as possible. thanks in

Hi, I just started with R and I found that there are many options to rearrange the data to do mixed models. I want to use the reshape function. I have 2 between subject variables and one within. I was able to change the data structure but still - the result of the aov functions are calculating everything as a within subject. the table looks like this: SerialNo breed treatment distance_1