similar to: Using a by() function to process several regression (lm()) functions

Hi there, I am stuck trying to solve what should be a fairly easy problem. I have a data frame that essentially consists of (ID, time as seqMonth, variable, value) and i want to find the regression coefficient of value vs time for each combination of ID and Variable. I have tried several approaches and none of them seems to work as i expected. For example, i have tried:

Dear All, I am a beginner creating R packages. I followed the Leisch (2009) tutorial and the document ?Writing R Extensions? to write an example. I installed R 2.12.2 (I also tried R2.13.2), the last version of Rtools and the recommended packages in a PC with Windows 7 Home Premium. I can run R CMD INSTALL linmod in the command prompt and the R CMD check linmod. The following outputs are

Dear useRs, I have some trouble with the calculation of Cook's distance in R. The formula for Cook's distance can be found for example here: http://en.wikipedia.org/wiki/Cook%27s_distance I tried to apply it in R: > y <- (1:400)^2 > x <- 1:100 > lm(y~x) -> linmod # just for the sake of a simple example >

I am reading Section 5 and 6 of http://cran.r-project.org/doc/contrib/Leisch-CreatingPackages.pdf It seems that I have to do the following two steps in order to make an R package. But when I am testing these package, these two steps will run many times, which may take a lot of time. So when I still develop the package, shall I always source('linmod.R') to test it. Once the code in

I would like to plot 3 best-fit models in a single panel of a lattice plot, superimposed on 3 corresponding datasets in the same panel. My goal is to show the models as lines of 3 different colors, and the data as points whose colors correspond to the model colors. In essence, I have two levels of grouping: 1) model vs. data, and 2) model number. Since there is only one ?groups? variable, I

Hello, I'm wondering how I can merge two featuresets into one. My dataset is two sets of microarray data and it looks like followings: > rawData $v1 TilingFeatureSet (storageMode: lockedEnvironment) assayData: 2197815 features, 59 samples element names: channel1, channel2 protocolData rowNames: LT290677RU_D1_2011-02-16 LT286300LU_D1_2010-07-24 ... LT003990RU_D1_2010-11-04 (59

Hi, folks, As seen in the following codes: x1=rlnorm(10) x2=rlnorm(10,mean=2) y=rlnorm(10,mean=10)### Fake dataset linmod=lm(log(y)~log(x1)+log(x2)) After the regression, I would like to know the mean of y. Since log(y) is normal and y is lognormal, I need to know the mean and variance of log(y) first. I tried mean (y) and mean(linmod), but either one is what I want. Any tips? Thanks in

I am using a multiple additive model (in the quantreg package) and I would like to 'extract' the fitted model formulae ie- for a straight line the formula would be y= 'a+b*c' for my multiple model I would expect somthing more complex because the model is not linear (its a bit like a GAM) but given I can plot the model using # f<-fitted(model) #lines(f) there must be a formula

Hi folks, I created a subset of a dataframe (i.e., selected only men): subdata <- subset(data,data$gender==1) After a residual diagnostic of a regression analysis, I detected three outliers: linmod <- lm(y ~ x, data=subdata) plot(linmod) Say, the cases 11,22, and 33 were outliers. Here comes the problem: When I want to exclude these three cases in a further regression analysis, - for

Dear R-Helpers Given a function like foo <- function(data,var1,var2,var3) { f <- formula(paste(var1,'~',paste(var2,var3,sep='+'),sep='')) linmod <- lm(f) return(linmod) } By typing foo(mydata,'a','b','c') I get the result of the linear model a~b+c. How can I rewrite the function so that the formula can be updated inside the function,

Hi, I'm generating the name of the variable with paste function and then using that variable name further to get the specific position value from the data.frame, here is the snippet from my code: modelResults <- extractModelParameters("C:/PilotStudy/Mplus_Input/Test", recursive=TRUE) #extractModelParameters reads all the output files from the Test folder and create the

Hi, folks linmod=y~x+z summary(linmod) The summary of linmod shows the standard error of the coefficients. How can we get the sd of y and the robust standard errors in R? Thanks! [[alternative HTML version deleted]]

Dear R-Help, I'm new to R and struggling with weighting data when I run regression. I've tried to use search to solve my problem but haven't found anything helpful so far. I (successfully) import data from SPSS (15) and try to run a linear regression on a subset of my data file where WEIGHT is the name of my weighting variable (numeric), e.g.: library(foreign)

Forgive me if this is a trivial question, but I couldn't find it an answer in former forums. I'm trying to reproduce some SAS results where they set two parameters equal. For example: y = b1X1 + b2X2 + b1X3 Notice that the variables X1 and X3 both have the same slope and the intercept has been removed. How do I get an estimate of this regression model? I know how to remove the intercept

Dear R-helpers & bioconductor Sorry for cross-posting, this concerns R-programming stuff applied on Bioconductor context. Also sorry for this long message, I try to be complete in my request. I am trying to write a subset method for a specific class (ExpressionSet from Bioconductor) allowing selection more flexible than "[" method . The schema I am thinking for is the following:

Hi, I'm just starting out with Sweave, and I can't get a plot(linmod) to display all four plots: << bild >>= x1 <- runif(100) x2 <- rexp(100) y <- 3 + 4*x1 + 5*x2 + rnorm(100) mod <- lm(y~x1+x2) plot(mod) @ Some Text <<fig=TRUE>>= <<bild>> @ This plots only the first image of the four-page plot.lm() result. I don't want to use

Hi all, I have this microarray large microarray data set (ALL) from which I would like to subset or extract a set of data based on a factor ($mol.biol). I looked up some example of subsetting in, picked up two commands and tried both but I got error messages as follows > testset <- subset(ALL, ALL$mol.biol %in% c("BCR/ABL","ALL1/AF4")) >> Error in

I want to calculate a statistic on a number of subgroups of a dataframe, then put the results into a dataframe. (What SAS PROC MEANS does, I think, though it's been years since I used it.) This is possible using by(), but it seems cumbersome and fragile. Is there a more straightforward way than this? Here's a simple example showing my current strategy: > dataset <-

Hello, I used the function Fstats (in the package strucchange) and would like to transform the F probability given by Fstats in P value. This transformation can be made while making a plot, but I need to have the numerical P value which are ploted... and I can't find out how to do. Here a is an exemple, to plot the P value. let's take data as a array fs <-fstats(data ~ 1, from = 4,

Hi, I am new to R. It would be kind if I could get some help on this. I am using R to estimate Fstats but I am getting following error. a3 is annual GDP data from 1951 to 2010. > fs<- Fstats(ecm.model, from=1954, to = 1975,data=a3) Error in Fstats(ecm.model, from = 1954, to = 1975, data = a3) : inadmissable change points: 'from' is larger than 'to' In addition: Warning