similar to: setting parameters equal in lm

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 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

Hi all, Can you please tell me what is the problem here. My regression eq is y = B0 + B1X1 +B2X2 +e And i am interested in coefficient B1 I am doing regression with two cases: 1) reg<-lm(y ~ X1 + X2, sam) where sam is the data 2) reg<-lm(y ~ X1 + X2, sam, na.action= na.exclude) . I have missing values in X1 but the values of coefficient is not consistent in two cases. Actually B1 in

Hi everyone: ¡¡I am new to R and I have a really basic question. I have already got a generalized linear model from some dataset, say y=b0 + b1X1 + b2X2. Then I want to get the value of y provided, say, X1=1, X2=2. And the confidence Intervals of this y. I know I can just calculate that since I know the model already. But is there some code that can calculate those automatically?

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:

Hi everyone, I'm trying to perform a linear regression y = b1x1 + b2x2 + b3x3 + b4x4 + b5x5 while constraining the coefficients such that -3 <= bi <= 3, and the sum of bi =1. I've searched R-help and have found solutions for constrained regression using quadratic programming (solve.QP) where the coefficients are between 0 and 1 and sum to 1, but unfortunately do not understand

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 everyone, I'm sure this is pretty basic but I couldn't find a clear example of how to do this. I'm running a regression, say: reg <- lm(Y ~ x1 + year) where x1 is a continuous variable and year is a factor with various year levels. Individually, each year factor variable is not significant, but I have a suspicion they are jointly significant. I can't figure out how to run

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]]

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

Hello, I am trying to learn how to perform Multiple Regression Analysis in R. I decided to take a simple example given in this PDF: http://www.utdallas.edu/~herve/abdi-prc-pretty.pdf I created a small CSV called, students.csv that contains the following data: s1 14 4 1 s2 23 4 2 s3 30 7 2 s4 50 7 4 s5 39 10 3 s6 67 10 6 Col headers: Student id, Memory span(Y), age(X1), speech rate(X2) Now

I am working with a RCB 2x2x3 ANCOVA, and I have noticed a difference in the calculation of sum of squares in a Type III calculation. Anova output is a follows: > Anova(aov(MSOIL~Forest+Burn*Thin*Moisture+ROCK,data=env3l),type=3) Anova Table (Type III tests) Response: MSOIL Sum Sq Df F value Pr(>F) (Intercept) 22.3682 1 53.2141 3.499e-07 *** Forest

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)

For some time I have been looking for a convenient way of performing post-hoc analysis to Repeated Measures ANOVA, that would be acceptable if sphericity is violated (i.e. leaving aside post-hoc to lme models). The best solution I found was John Fox's proposal to similar requests in R-help: http://tolstoy.newcastle.edu.au/R/e2/help/07/09/26518.html

Dear All, I am currently working with the coxph function within the package survival. I have the model h_ij = h_0(t) exp(b1x1 + b2x2) where the indicator variables are as follows: x1 x2 VPS 0 0 LTG 1 0 TPM 0 1 [[alternative HTML version deleted]]

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

Hello, Thank you very much for looking at this. I have a "seasonal" user for R. I teach my undergrads and graduates students statistics using R and often find myself trying to solve problems to process student collected data in an efficient way. In this case, I have a data.frame with multiple observations. These are gas concentrations in a chamber and are used to measure into rates,

Stephen, thanks for your effort. The more appropriate list for this discussion is probably R-devel (as far as I understand it) so I've moved the discussion there. Related topics have already been discussed in the past. Specifically, I remember contributions by Paul Johnson ("rockchalk" package) and John Fox ("effects" and "car" package) as their packages