search for: mylme

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

...rror in eval(expr, envir, enclos) : object 'splineDF' not found" # # First make sure splineDF does not exist in .GlobalEnv, else we would be in WhichApproach==2. if(exists("splineDF", where=".GlobalEnv")) remove(list="splineDF", pos=".GlobalEnv") mylme<-lme(fixed= y ~ ns(x, df=splineDF) , random= ~ 1 | ID , correlation = corAR1() , data=longdat ) } else if(WhichApproach==2) { # returns: "Error in model.frame.default(formula = ~ID + y + x + splineDF, data = list( : # variable lengths differ (found for 'splineDF')&qu...

...rror in eval(expr, envir, enclos) : object 'splineDF' not found" # # First make sure splineDF does not exist in .GlobalEnv, else we would be in WhichApproach==2. if(exists("splineDF", where=".GlobalEnv")) remove(list="splineDF", pos=".GlobalEnv") mylme<-lme(fixed= y ~ ns(x, df=splineDF) , random= ~ 1 | ID , correlation = corAR1() , data=longdat ) } else if(WhichApproach==2) { # returns: "Error in model.frame.default(formula = ~ID + y + x + splineDF, data = list( : # variable lengths differ (found for 'splineDF'...

Hello, I am randomly generating values and then using an ANOVA table to find the mean square value. I would like to form a loop that extracts the mean square value from ANOVA in each iteration. Below is an example of what I am doing. a<-rnorm(10) b<-factor(c(1,1,2,2,3,3,4,4,5,5)) c<-factor(c(1,2,1,2,1,2,1,2,1,2)) mylm<-lm(a~b+c) anova(mylm) Since I would like to use a loop to

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:

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

...and $deviance elements as in glm objects... I tried to find out an answer on R-help & Pineihro & Bates (2000). Partial success only: - null deviance: Response: possibly yes: see http://tolstoy.newcastle.edu.au/R/help/05/12/17796.html (Spencer Graves). The (null?) deviance is -2*logLik(mylme), but a personnal trial with some glm objects did not led to the same numbers that the one given by the print.glm method... - the deviance due to the the random effect(s). I was supposing that the coefficients given by ranef(mylme) may be an entry... but beyond this, I guess those coefficients...

Hello list, I am used to give a lot of attention to the standardized regression coefficients, which in SPSS are listed automatically. Is there alternative to running the last two lines in the following example to get all the information? ctl <- c(4.17,5.58,5.18,6.11,4.50,4.61,5.17,4.53,5.33,5.14) trt <- c(4.81,4.17,4.41,3.59,5.87,3.83,6.03,4.89,4.32,4.69) summary( lm(ctl ~ trt) )

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)

Hello, I've had no luck finding an R package that has the ability to estimate a Tobit model allowing for heteroskedasticity (multiplicative, for example). Am I missing something in survReg? Is there another package that I'm unaware of? Is there an add-on package that will test for heteroskedasticity? Thanks for your help. Cheers, Alan Spearot -- Alan Spearot Department of Economics

...0L)) ## this is what I really want to be able to do, but without contrast ## complaining about the imbalance in the number of rows cat("### With scanner\n") fixedFormula=as.formula("fmri ~ group * task + scanner") randomFormula = as.formula("random = ~ 1 | subject") mylme = lme(fixed=fixedFormula, random=randomFormula, data=my.model) ## now look at the risky choices (40outcome and 80outcome) versus the ## safe choices (20outcome) con=contrast( mylme, a=list(group="Group1", task=c("40outcome", "80outcome"), scanner=levels(my.model$s...

Hello, I am trying to generate a confidence interval (90 or 95%) of a regression line. This is primarily just for illustration on a scatter plot (i.e. I am trying to make this http://www.ast.cam.ac.uk/~rgm/scratch/statsbook/graphics/anima4.gif). I have been trying to use the predict.lm function, with interval set as "confidence", but this still seems to be giving me a prediction

Hi Martin, When I attended the LinuxWorld Expo in NYC back in January, I chatted with some folks at the AMD booth, as well as guys from Penguin Computing (where we bought our Opteron box). I was told that the Operton has this somewhat strange setup that the memory is controlled by one CPU. The net effect of this being that when both CPUs are running, one might only be running at around 90%

...will be " , inNumberOfOutputBriks, " stats briks\n") outStats <- vector(mode="numeric", length=inNumberOfOutputBriks) ##cat (inData, "\n") if ( ! all(inData == 0 ) ) { ##try( ##if( inherits( print(inModelFormula) print(inRandomFormula) mylme <- lme(fixed=inModelFormula, random=inRandomFormula, data = inModel) print(mylme) ##, ## silent=FALSE), ##"try-error") ) { ##temp <- 0 ## cat (paste("Error on slice", inZ, "\n")) ##} else { temp <- as.vector(unlist...

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