similar to: Trying to use segmented in a function

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

Hi, I have a small dataset that I'm fitting a segmented regression using nls on. I get a step below minimum factor error, which I presume is because residual sum of square is still "not small enough" when steps in the parameter space is already below specified/default value. However, when I look at the trace, the convergence seems to have been reached. I initially thought I might

Hi R experts, I'm trying to use nls() for a piecewise linear regression with the first slope constrained to 0. There are 10 data points and when it does converge the second slope is almost always over estimated for some reason. I have many sets of these 10-point datasets that I need to do. The following segment of code is an example, and sorry for the overly precise numbers, they are just

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

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 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:

------------------- Version 2 of my problem improving the definition of what the optimal solution would be. Dear all, I'm working on the following problem: Assume two datasets: Y, Y that represent the same physical quantity Q. Dataset X contains values of Q after an event A while dataset Y contains values of Q after an event B. In R X, Y are vectors of the same length, containing

Le 31/05/2017 ? 17:30, Serguei Sokol a ?crit : > > More thorough reading revealed that I have overlooked this phrase in the > line's doc: "left and right /thirds/ of the data" (emphasis is mine). Oops. I have read the first ref returned by google and it happened to be tibco's doc, not the R's one. The layout is very similar hence my mistake. The latter does not

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]]

People I apologise for asking a general stats question, but I'm at a bit of a loss as to what to do following some hostile referees' comments. If I have a fully randomised blocked design, with only three blocks, should I treat block as a random or fixed effect? I have read comments about not treating block as a random effect if the number of blocks is less than 6 or 7: is this

Hello all I'm trying to do a simulation that involves identifying the minimum point between two peaks of a (usually) bimodal distribution. I can do this easily if there are only two peaks: CnBdens<-density(Ys/Xs) #probability density function for ratio of Ys to Xs for(p in 1:512) ifelse(CnBdens$y[p]>CnBdens$y[p-1],peak1<-p,break) #identifies first peak in probability

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 everyone Not strictly an R question but close... hopefully someone will be able to help. I wish to compare the switchpoints in two switchpoint regressions. The switchpoints were estimated using the segmented library running in R, and I have standard errors for the estimates. I initially thought I could just bootstrap confidence intervals for the difference between the switchpoints,

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

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-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)

Hello people I am doing some thinking about how to analyse data on dimorphic animals - where different individuals of the same species have rather different morphology. An example of this is that some male beetles have large horns and small wings, and rely on beating the other guys up to get access to mates, whereas others have smaller horns and larger wings, and rely on mobility to

Hello Can anyone let me know how I might change the size of the numbers on the axes in a pairs plot? The normal cex.axis call doesn't do anything, cex.labels only seems to change the font size in the diagonal labels. Using R 1.8.0 on a Mac with OS X.3. Thanks Rob Knell

Dear R-helpers Can anyone direct me to a method for plotting what you might call a partial wireframe plot? I have two explanatory variables in a dataset which give me a significant interaction term when I fit a model. The two variables are correlated with each other to a moderate degree, and if I plot the predicted values from the model as a surface in a 3D wireframe plot there are some