similar to: Piecewise Lasso Regression

dear all, I apologize for my delay in replying you. Here my contribution, maybe just for completeness: Similar to "earth", "segmented" also fits piecewise linear relationships with the number of breakpoints being selected by the AIC or BIC (recommended). #code (example and code from Martin Maechler previous email) library(segmented) o<-selgmented(y, ~x, Kmax=20,

>>>>> Anupam Tyagi >>>>> on Tue, 9 Jul 2024 16:16:43 +0530 writes: > How can I do automatic knot selection while fitting piecewise linear > splines to two variables x and y? Which package to use to do it simply? I > also want to visualize the splines (and the scatter plot) with a graph. > Anupam NB: linear splines, i.e. piecewise

Hello, I'm trying to find an algorithm to estimate a switching regression model based on the 1990 Economics Letters paper by Ohtani/Kakimoto/Abe or the earlier version from 1985 (Ohtani/Katayama, Economic Studies Quarterly; assuming as a transition path a polynomial of order 1). I found an idea for using nls here: http://www.biostat.wustl.edu/archives/html/s-news/2000-04/msg00223.html.

Dear All, I have a matrix say, X ( 100 X 40,000) and a vector say, y (100 X 1) . I want to perform linear regression. I have scaled X matrix by using scale () to get mean zero and s.d 1 . But still I get very high values of regression coefficients. If I scale X matrix, then the regression coefficients will bahave as a correlation coefficient and they should not be more than 1.

Dear all, It appears that MASS::polr() and Design::lrm() return the same point estimates but different st.errs when fitting proportional odds models, grade<-c(4,4,2,4,3,2,3,1,3,3,2,2,3,3,2,4,2,4,5,2,1,4,1,2,5,3,4,2,2,1) score<-c(525,533,545,582,581,576,572,609,559,543,576,525,574,582,574,471,595, 557,557,584,599,517,649,584,463,591,488,563,553,549) library(MASS) library(Design)

dear all, When I use all.vars(), I am interest in extracting only the variable names.. Here a simple example all.vars(as.formula(y~poly(x,k)+z)) returns [1] "y" "x" "k" "z" and I would like to obtain "y" "x" "z" Where is the trick? many thanks vito -- ==================================== Vito M.R. Muggeo Dip.to Sc

dear all, I would like to get the lme call without fitting the relevant model. library(nlme) data(Orthodont) fm1 <- lme(distance ~ age, random=list(Subject=~age),data = Orthodont) To get fm1$call without fitting the model I use call(): my.cc<-call("lme.formula", fixed= distance ~ age, random = list(Subject = ~age)) However the two calls are not the same (apart from the data

dear all, This is a probably a silly question. If I type > grep("x",c("a.x" ,"b.x","a.xx"),value=TRUE) [1] "a.x" "b.x" "a.xx" Instead, I would like to obtain only "a.x" "b.x" How is it possible to get this result with grep()? many thanks for your attention, best, vito --

Dear List I hope you can help me: I?ve got a dataframe (df) within which I am looking for Peak Over Threshold values as well as the length of the events. An event starts when walevel equals 5.8 and it should end when walevel equals the lower threshold value (5.35). I tried ?clusters (?)? from ?evd package?, and varied r (see example) but it did not work for all events (again

Hello, I am currently working with the betareg package, which allows the fitting of a variable dispersion beta regression model (Simas et al. 2010, Computational Statistics & Data Analysis). I was wondering whether there is any package in R that allows me to fit variable dispersion parameters in the standard logistic regression model, that is to make the dispersion parameter contingent upon

Dear R, Is there an efficient way to make a list that each element is from the corresponding column of a matrix. For example, if I have a matrix "a" > a <- matrix(1:10, 5, 2) > a [,1] [,2] [1,] 1 6 [2,] 2 7 [3,] 3 8 [4,] 4 9 [5,] 5 10 I would like to have a list "b" like this > b <- list(a[, 1], a[, 2]) > b [[1]] [1] 1 2 3

Hi I was wondering whether anyone can help me with this problem....it's been driving me nuts, I've been trying to figure it out for months and months without success!! Basically I have a group of participants who attended 2 experimental sessions a few months apart. I took measures of the way they approach two tasks at Time 1 and the same two tasks at Time 2. I have categorical data (a

Hello list. I was trying to see some of the code for plot.glmnet in package glmnet (this function name is in the documentation). After loading the library, I tried the obvious typing in the name, but I received a message telling me it could not be found. So I fiddled around a little, and noticed that R does not recognize ''plot'' as a generic function, and as such,

Hi everyone. I'm trying to use the segmented function with the following data: For instance, I use segmented package as follow: myreg2 = lm(xy$y ~ xy$x) mysegmented = segmented(myreg2, seg.Z=~x, psi=c(245000), control = seg.control(display=FALSE)) Which get me to the following error : As a break point, a starting guess of 245000 seems fair. Anyone has an idea why I'm getting such

Dear all, can somebody give me some pointer how I can fit a "linear-by-linear association model" (i.e. loglinear model for the ordinal variables) in R? A brief description can be found here 'https://onlinecourses.science.psu.edu/stat504/node/141'. Thanks for your help

Hi everyone, I have encountered this problem while using 'segmented' plugin for R i386 2.15.0 (for Windows 32bit OS) and I just cannot find neither explanation nor solution for it. I am trying to run this data gpp temp 1.661 5 5.028 10 9.772 15 8.692 20 5.693 25 6.293 30 7.757 5 4.604 10 8.763 15 8.134 20 4.616 25 8.417 30 3.483 5 5.046 10 8.306 15 9.142 20 4.686 25 7.301 30 and with

Hi I want to compare the survival curves in two groups. Because the hazards are not proportional (the curves cross) the log rank test or Cox proportional hazard test are not suitable. How should such curves be compared? Comands are welcome.... Thanks in advance

Hello, I'm attempting to find multiple breakpoints in an association of my response variable (R.AUC) with two explanatory variables ('s.size' and 'bedekking'). The association between 's.size' and 'R.AUC' shows a plateau, but the value when this plateau is reached is differs for different values of 'bedekking'. Initially I thought these different

Hello, I would like to test some data to see whether it has the shape of a step function (i.e. y1 up until x_th and then y2 where x_th is the threshold). The threshold x_th is unknown and the x values can only take discrete values (0,1,2,3,4). An example would be: data<- data.frame(x=1:20,y=c(rnorm(10),rnorm(10,10))) I was thinking along the lines of fitting some sort of piiecewise linear

I?m trying to run a logistic joinpoint regression utilising the ljr package. I?ve been using the forward selection technique to get the number of knots for the analysis, but I?m uncertain as to my results and the interpretation. The documentation is rather brief ( in the package and the stats in medicine article is quite technical) and without any good examples. At the moment I?m thinking 1)find