similar to: piecewise linear regression

Hello RoR! I''m building a rails app for hosting multi-page surveys. I set up questionnaire, question, and question_component models. Now I''m initializing them with some data. survey.rb q1 = Question.new "q1" q1.quote "Welcome to a test of the new interviewing system." q2 = Question.new "q2" q2.quote "Do you like pie?"

Is a 95% CI on a breakpoint fixed effect legitimate when the nonlinear equation is continuous but not differentiable at the breakpoint? I used the nlme to generate a non-linear mixed-effects piecewise model with initial slope zero prior to an unknown breakpoint. It did give a 95% CI for the breakpoint. Thanks Erik whole model is complex, but here is the simple equation used:

Hi R users, Is there a Package in R to - set up a questionnaire? - enter data? Best regards -- View this message in context: http://www.nabble.com/A-package-to-set-up-a-questionnaire---enter-data-tp20947237p20947237.html Sent from the R help mailing list archive at Nabble.com.

Hi everyone, I'm stuck on my dissertation which is due next week. I'm a business major student and my project is a comparative research on corporate social responsibility (CSR) between Chinese and German firms. According to my literature review, it's quite obvious that German firms have much better CSR initiatives and strategies than the Chinese ones and I wanted to test whether it was

Hi - I'd like to construct and plot the percents by year in a small data set (d) that has values between 1988 and 2007. I'd like to have a breakpoint (buy no discontinuity) at 1996. Is there a better way to do this than in the code below? > d year percent se 1 1988 30.6 0.32 2 1989 31.5 0.31 3 1990 30.9 0.30 4 1991 30.6 0.28 5 1992 29.3 0.25 6 1994 30.3

Hi, I need to fit a piecewise linear regression. x = c(6.25,6.25,12.50,12.50,18.75,25.00,25.00,25.00,31.25,31.25,37.50,37.50,50.00,50.00,62.50,62.50,75.00,75.00,75.00,100.00,100.00) y = c(0.328,0.395,0.321,0.239,0.282,0.230,0.273,0.347,0.211,0.210,0.259,0.186,0.301,0.270,0.252,0.247,0.277,0.229,0.225,0.168,0.202) there are two change points. so the fitted curve should look like \ \ /\

Hi everyone I have the following problem: My dataframe has 3 variables: ID, Year and and an outcome variable. The dataframe contains repeated measurements because the subjects filled out a questionnaire every year. The time span covers 2 years. Now I want to check if there is a significant change in the outcome over the 2 years with a paired wilcox.test. The problem: Not every subject

Dear All, I have worked with R for some time. It's a great tool for data analysis. But it's too hard to inputing raw data manually with R (I don't mean importing data. R is good at importing data). Maybe it's not a focused topic in this list, but I don't know other place where I can ask the question. How do you do when inputing data from a paper material, such as

Asterisk 1.4 r181990 given the dialplan snippet below, can anyone tell me why the h exten is not being run ? ============================================================================ console output: [Mar 29 10:33:49] -- Executing [s at questionnaire-menu:1] Set("Zap/1-1", "TIMEOUT(digit)=3") in new stack [Mar 29 10:33:49] -- Digit timeout set to 3 [Mar 29

Hey, all. I'm working on a data set with a broken stick linear regression where I know one of the two slopes. It is a negative linear function until the line intersects with the x-axis, at which point it becomes 0. It is not a nonlinear asymptotic function, and, indeed, using negative exponential or logistic types of fits as an approximation has tended to lead to an under or

Dear list, I have a series of data points which I want to approximate with exactly two linear functions. I would like to choose the intervals so that the total deviation from my fitted lines is minimal. How do I best do this? Thanks! Kamila The information transmitted in this electronic communication...{{dropped}}

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,

Dear all, I'm struggling to find the best (set of?) function(s) to do repeated measures logistic regression on some data from a psychology experiment. An artificial version of the data I've got is as follows. Firstly, each participant filled in a questionnaire, the result of which is a score. > questionnaire ID Score 1 1 6 2 2 5 3 3 6 4 4 2 ...

>>>>> 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

Hi, I have a series of id's with multiple visits and questionnaire scores. This is a clinical trial that will be analyzed using the last observation carried forward method. In other words, in order to comply with intent to treat analysis when many subjects withdraw, data points for the last visit must be generated and filled in with the last observation. The ultimate goal is to tabulate the

Dear all, I want to do piecewise CAPM linear regression in R: RRiskArb−Rf = (1−δ)[αMktLow+βMktLow(RMkt−Rf)] + δ[αMkt High +βMkt High(RMkt −Rf )] where δ is a dummy variable if the excess return on the value-weighted CRSP index is above a threshold level and zero otherwise. and at the same time add the restriction: αMkt Low + βMkt Low · Threshold = αMkt High + βMkt High · Threshold to ensure

Dear all, I am just wondering if anybody has implemented a function that can give a piecewise linear interpolation in more than 2 dimensions? I have looked at the akima package, but I would rather like a piecewise linear interpolation rather than a spline and while it did the job quite satisfactory for 2 dimensions, I need to interpolate in at least three dimensions. If anybody has implemented

Hi, Let me pick up this old thread. How does one extract the locations of the knots (ends of the segments) from the fit object below? Thanks, Vadim >From : roger koenker < roger_at_ysidro.econ.uiuc.edu > Date : Tue 31 May 2005 - 10:23:19 EST It is conventional to fit piecewise linear models by assuming Gaussian error and using least squares methods, but one can argue that

Dear friends, I used the B-spline (degree=1) method to fit the piecewise linear function and the results are listed below. m.glm<-glm(mark~x+poly(elevation,2)+bs(distance,degree=1,knots=c(16.13,24)) +bs(y,degree=1,knots=c(-0.4357,-0.3202 )),family=binomial(logit),data=point) summary(m.glm) Coefficients: Estimate Std.

I Dears, if that wouldn't take u too much effort I'd like to ask for a swift opinion: I have a alinear (mixed effect) model that I wish to run as a piecewise one. When I predict the values Iget quite some odds and disturbing results: The predicted stright line after the break point is not straight at all, instead behaves like if it was a hig order polynomial or something similar!!!! I