similar to: extraction of mean square value from ANOVA

Hello, I have a for loop that generates data in R. With the IML program, I would like to analyze data in SAS from each iteration of the for loop in R. It would be helpful if someone could explain to me how to analyze data this way. Thanks [[alternative HTML version deleted]]

Hello, I have two plots on the same screen. I use the command par(mfrow=c(1,2)) in order to do this. When I try to make a legend for both plots, it only puts the legend in the plot on the right side. If I would like a legend that is outside of both of the plots, how would I do this? Thanks [[alternative HTML version deleted]]

Hello, Is there a way to get the Var-Cov matrix from the LMER function? Thanks [[alternative HTML version deleted]]

Hi, I would like to find a way to append data to an excel file through each iteration of a for loop. Is there a way to use a command such as write.table and append each iteration to a different sheet? Thanks [[alternative HTML version deleted]]

Hello, I have a simple question for you: making: mylm<-lm(y~x) summary(mylm) I get the following results: ****************************************************** Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 16.54087 0.19952 82.91 <2e-16 *** x[1:19] -2.32337 0.04251 -54.66 <2e-16 *** ******************************************************

Hi folks, I am running a very simple regression using mylm <- lm(mass ~ tarsus, na.action=na.exclude) I would like the use the residuals from this analysis for more regression but I'm running into a snag when I try cbind(mylm$residuals, mydata) # where my data is the original data set The error tells me that it cannot use cbind because the length of mylm$residuals is

I'd trying to subclass the "lm" class to produce a "mylm" class whose instances behave like lm objects (are accepted by methods like summary.lm) but have additional data or slots of my own design. For starters: setClass("mylm", "lm") produces the somewhat cryptic: Warning message: Old-style (``S3'') class "mylm" supplied as a

Hello list, I have a linear regression: mylm = lm(y~x-1) I've been reading old mail postings as well as the plotmath demo and I came up with a way to print an equation resulting from a linear regression: model = substitute(list("y"==slope%*%"x", R^2==rsq), list(slope=round(mylm$coefficients[[1]],2),rsq=round(summary(mylm)$adj.r.squared, 2))) I have four models and I

Dear all - We perform some measurements with a machine that needs to be recalibrated. The best calibration we get with polynomial regression. The data might look like follows: > true_y <- c(1:50)*.8 > # the real values > m_y <- c((1:21)*1.1, 21.1, 22.2, 23.3 ,c(25:50)*.9)/0.3-5.2 > # the measured data > x <- c(1:50) > # and the x-axes > > # Now I do the following:

Hello, I use the command rnorm, and I feed these results into a lmer command. Since I am using the rnorm command I expect to get different results for each iteration, yet for each iteration I am getting the same answer. If someone understands why I am getting the same answer every time with a random number generator, I would appreciate help in understanding why this is happening. Is the lmer

Greetings, When I try to use the write.table command to save a matrix as a file and then open the file with read.table, if I try to take the mean of the entire matrix, instead each column of the matrix has its mean calculated. I have copied and pasted an example of my code below. When I try to make the header false with the read.table command, I am given an error message. I would appreciate any

Hi All, I had posted a question on a similar topic, but I think it was not focused. I am posting a modification that I think better accomplishes this. I hope this is ok, and I apologize if it is not. :) I am looping through variables and running several regressions. I have reason to believe that the data is being duplicated because I have been monitoring the memory use on unix. How can I avoid

Hi, I wonder if someone has already figured out a way of making summary(mylm) # where mylm is an object of the class lm() to print the "(Intercept)" at the last line, rather than the first line of the output. I don't know about, say, biostatistics, but in economics the intercept is usually the least interesting of the parameters of a regression model. That's why, say, Stata

DeaR list I would like to predict the values of each column of a matrix A by regressing it on all other columns of the same matrix A. I do this with a for loop: A <- B <- matrix(round(runif(10*3,1,10),0),10) A for (i in 1:length(A[1,])) B[,i] <- as.matrix(predict(lm( A[,i] ~ A[,-i] ))) B It works fine, but I need it to be faster. I've looked at *apply but just can't

Dear R-help, I have a four-part question about regression, matrices, and sandwich package. 1) In the sandwich package, I would like to better understand the meat() function. >From the bread() documentation, for a simple OLS regression, bread() returns (1/n * X'X)^(-1) That is, for a simple regression (per the documentation on bread()): MyLM <- lm(y ~ x) bread(MyLM)

Hi, this is my script > anova <- aov(data ~ Ts*Te*t + Error(R/Ts*Te*t)) > results <- summary(anova$Within) this is results Df Sum Sq Mean Sq F value Pr(>F) Ts 2 1232.2 616.11 53.606 3.965e-10 *** Ts:Te 4 4889.5 1222.37 106.356 4.075e-16 *** Ts:t 4 6472.1 1618.01 140.780 < 2.2e-16 *** Ts:Te:t 8 4181.0 522.63 45.473 1.088e-13 ***

Dear r-devel list members, Dirk Eddelbuettel brought the following problem to my attention. The code is abstracted from the appendix on mixed models from my R and S-PLUS Companion: > set.seed(12345) # for reproducibility > library(nlme) Loading required package: lattice > data(MathAchieve) > data(MathAchSchool) > attach(MathAchieve) > mses <- tapply(SES, School,

Hey I'm not really sure what I should put on here, but I am having trouble with my R code. I am trying to get the p-values, R^2s etc for a number of different groups of variables that are all in one dataset. This is the code: #Stand counter st<-1 #Collections stands<-numeric(67) slopes<-numeric(67) intercepts<-numeric(67) mses<-numeric(67) rsquares<-numeric(67)

Dear R-guRus: I am stuck with the following problem in R: Let's say i have a data.frame with columns X,Y & Z. X is a column of numbers, Y is a column of N factors "A", "B", "C", "D", etc and Z is a column of n factors "a", "b", "c", etc if i do MyLm<-lm(X~Y*Z - 1) i get all N coefficients for Y-factors, but only

Hi, I want to plot the residuals of a least-squares regression. plot(lm(y~x), which=1) does this, but it plots the y-axis of my data on the x-axis of the residuals plot. That is, it plots the residual for each y-value in the data. Can I instead use the x-axis of my data as the x-axis of the residuals plot, showing the residual for a given x? Thanks! Jason Priem University of North