similar to: error message: "is"

Hi, I have been using lm in R to do a linear regression and find the slope coefficients and value for R-squared. The R-squared value reported by R (R^2 = 0.9558) is very different than the R-squared value when I use the same equation in Exce (R^2 = 0.328). I manually computed R-squared and the Excel value is correct. I show my code for the determination of R^2 in R. When I do not set 0 as the

Hi, I used R to run a linear regression and keep getting the following error message. I do not understand it very well. Anyone can help out? Thanks. Error in storage.mode(y) <- "double" : invalid to change the storage mode of a factor In addition: Warning message: In model.response(mf, "numeric") : using type="numeric" with a factor response will be ignored

I am running a multi-level model. I use the following commands with validatedRS6 as the outcome, random as the predictor and clustno as the random effects variable. new<-as.data.frame(read.delim("BABEX.dat", header=TRUE)) install.packages("lme4") library(lme4) model1<- glmer(validatedRS6 ~ random + (1|clustno), data=new, family=binomial("logit"), nAGQ)

Folks, This is probably a "help me google this properly, please"-type of question. In TIBCO Spotfire, there is a procedure called "line similarity". I use this to determine which observations show a growing, stable or declining pattern... sort of like a mini-regression on the time-line for each observation. So of the input is

Dear list Getting very frustrated with this simple-looking problem > m1 <- lm(x~y, data=mydata) > outliers <- abs(stdres(m1))>2 > plot(x~y, data=mydata) I would like to plot a simple x,y scatter plot with labels giving custom information displayed for the outliers only, i.e. I would like to define a column mydata$labels for the mydata dataframe so that the command >

Hi, I have a data set of 10000 rows and ran a linear regression by using the function lmres = lm(formula,data) then got the fitted value by using the value fit=fitted(lmres). but the number of rows in the fitted one is about 9548, What could be the reason for reduction in the number of rows in the fitted one -- View this message in context:

Hello all, I just noticed the following behavior of plot: x <- c(1,2,9) plot(x ~ x) # this is just like doing: plot(x) # when maybe we would like it to give this: plot(x ~ c(x)) # the same as: plot(x ~ I(x)) I was wondering if there is some reason for this behavior. Thanks, Tal ----------------Contact Details:------------------------------------------------------- Contact me:

Should the F statistic be the same when using add1() on models created by lm and glm(family=gaussian)? They are in the single-degree-of-freedom case but not in the multiple-degree-of-freedom case. MASS:addterm shows the same discrepancy. It looks like the deviance (==residual sum of squares) gets divided by the number of degrees of freedom for the term twice in add1.glm. Using anova() on the

Shouldn't the F statistic (and p value) for the x2 term in the following calls to anova() and add1() be the same? I think anova() gets it right and add1() does not. > d <- data.frame(y=1:10, x1=log(1:10), x2=replace(1/(1:10), 2:3, NA)) > anova(lm(y ~ x1 + x2, data=d)) Analysis of Variance Table Response: y Df Sum Sq Mean Sq F value Pr(>F) x1 1

I have two indicator variables ABS and DEFF. I want to create another indicator variable which will take value 1 if either ABS=1 or DEFF=1. Otherwise, it will take value 0. How can I make that? [[alternative HTML version deleted]]

We have an R package which needs to include a JAR file. Is there a canonical directory for it?

If x has an S3 class then sort.int(x) returns a value without an S3 class but which has the is.object flag set, which, I think, causes identical() give a false/misleading report: > x <- structure(1:3, class="unrecognizedClass") > y <- sort.int(x) > t <- 1:3 > identical(y, t) # expect TRUE [1] FALSE > identical(as.vector(y), as.vector(t)) # expect

Hola a tod@s, quiero generar informes con los resultados de R en formato pdf preferiblemente. Actualmente estoy utilizando knitr junto con LaTeX. Y ya conozco el resto de formatos de salida de knitr como: markdown, html, ... ¿Alguien conoce otras alternativas? He visto algo de jasper report por ahí, pero desconozco cómo poder comunicarlo con R. Muchas gracias por adelantado. -- Víctor Nalda

Hello R usuer, The code given below superimposes a pie diagram on another plot containing some points. However, I would like to center the pie diagram on the xy location on the plot, but not on the center. is there any way to re-center pic diagram. Any suggestion or better alternative are highly appreciated. Thank you in advance for your help. Regards, Bibke library(visualFields) library(car)

Hello, I am searching for a function to calculate "partial" cumsums. For example it should calculate the cumulative sums until a NA appears, and restart the cumsum calculation after the NA. this: x <- c(1, 2, 3, NA, 5, 6, 7, 8, 9, 10) should become this: 1 3 6 NA 5 11 18 26 35 45 any ideas? thank you and best regards, stefan

Hello, I am new to R and have created an application using R 2.10, with a graphical UI using TclTk 8.5, running on windows 7, quad core machine. The intention of the application is to launch calculations and display results on a graphical dashboard. I've reached a roadblock, and I need to confirm that the following CANNOT be done. I've been trying to find a mechanism for doing the

Our firm would like to route support requests through a website instead of using email. However R will refuse to install a package if DESCRIPTION's Maintainer field does not have a valid email adress or the special value "ORPHANED". (The check is done with tools:::.valid_maintainer_field_regexp.) I imagine a typical Maintainer line for the package "foo" might be

Greets, I'm trying to use nlminb() to estimate the parameters of a bivariate normal sample and during one of the iterations it passes a parameter vector to the likelihood function resulting in an invalid covariance matrix that causes dmvnorm() to throw an error. Thus, it seems I need to somehow communicate to nlminb() that the final three parameters in my parameter vector are used to

In modelling functions some people like to use a weight of 0 to drop an observation instead of using a subset value of FALSE. E.g., weights=c(0,1,1,...) instead of subset=c(FALSE, TRUE, TRUE, ...) to drop the first observation. lm() and summary.lm() appear to treat these in the same way, decrementing the number of degrees of freedom for each dropped observation. However, predict.lm() does

When weighted.residuals() is given a fitted model object with several responses (class mlm) and some zero weights it returns a vector instead of a matrix. It looks like it is doing resids[ weights != 0 ] instead of resids[ weights != 0, , drop=FALSE] in the multi-response case. E.g., > d4 <- data.frame(y1=1:4, y2=2^(0:3), wt=log(1:4), fac=LETTERS[c(1,1,2,2)]) > fit <-