similar to: Intercept in model formulae.

I am not sure if this question has been asked before - but is there a procedure in R (in lm or glm?) that is equivalent to ENTER and REMOVE regression commands in SPSS? Thanks a lot! -- Dimitri Liakhovitski Ninah.com Dimitri.Liakhovitski at ninah.com

Hi, I'm trying to get confidence intervals to slopes from a linear model and I can't figure out how to get at them. As a cut 'n' paste example: ################# # dummy dataset - regression data for 3 treatments, each treatment with different (normal) variance x <- rep(1:10, length=30) y <- 10 - (rep(c(0.2,0.5,0.8), each=10)*x)+c(rnorm(10, sd=0.1), rnorm(10,

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,

Hi, I'm fitting a glm to a dataset... > objects() [1] "pop.div.full" "speciation.data" ...and then performing model simplification to give a series of model objects all of which have a common suffix. > objects() [1] "pop.div.full" "pop.div.gp.f.b.hm" [3] "pop.div.gp.fb.hm"

Hi all, Suppose: y<-rnorm(100) x1<-rnorm(100) lm.yx<-lm(y~x1) To predict from a new data source, one can use: # works as expected dum<-data.frame(x1=rnorm(200)) predict(lm.yx, newdata=dum) Suppose lm.yx has been run and we have the lm object. And we have a dataframe that has columns that don't correspond by name to the original regressors. I very! naively assumed that doing

>>>>> 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'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 everyone. I'm using segmented package to find break point in a bi-linear relationship. In a particular case, I find 1 pointcut (so 2 slopes). I would like to know if it is possible to retrieve information in the segmented object that could let me to plot 1 particular segment with a different color. For example, in that folowing example, I would like to plot the second segment in red.

Dear R-users, i have a simple problem maybe, but i don't see the solution. i want to find the entry-wise closest element of an vector compared with another. ind1<-c(1,4,10) ind2<-c(3,5,11) for (i in length(ind2):1) { print(which.min(abs(ind1-ind2[i]))) } for ind2[3] it should be ind1[3] 10, for ind2[2] it should be ind1[2] 4 and for ind2[1] it should be ind1[1] 1. but with the

Hi group, I have this type of data x(predictor), y(response), factor (grouping x into many groups, with 6-20 obs/group) I want to fit a linear regression with one common intercept. 'factor' should only modify the slopes, not the intercept. The intercept is expected to be >0. If I use y~ x + factor, I get a different intercept for each factor level, but one slope only if I use y~ x *

Dear list members, I am working with a multilevel growth curve, that in its simplest form goes like follows: Yit = Ai + Bi t + eit (the error term is assumed to follow an AR(1) autorregressive process) One major topic in my research is the convergence in the values of Y over time. Thus, I am interested in the relationship between the random effects for the intercept and the slope, and I

Hi Everyone, I would like to do sequential subtractions within a group so that I know the time between separate observations for a group of individuals. My data: data <- structure(list(group = c("IND1", "IND1", "IND2", "IND2", "IND2", "IND3", "IND4", "IND5", "IND6", "IND6"), date_obs =

Hello R experts I have two linear regressions for sexes (Male, Female, Unknown). All have a good correlation between body length (response variable) and head length (explanatory variable). I know it is not recommended, but for a good practical reason (the purpose of study is to find a single conversion factor from head length to body length), the regressions need to go through the origin (0

Dear R users, I would like to accumulate objects generated from 'for' loop to a list or array. To illustrate the problem, arbitrary data set and script is shown below, x <- data.frame(a = c(rep("n",3),rep("y",2),rep("n",3),rep("y",2)), b = c(rep("y",2),rep("n",4),rep("y",3),"n"), c =

Hi Everyone, I have the following problem: data <- structure(list(prochi = c("IND1", "IND1", "IND1", "IND2", "IND2", "IND2", "IND2", "IND3", "IND4", "IND5"), date_admission = structure(c(6468, 6470, 7063, 9981, 9983, 14186, 14372, 5129, 9767, 11168), class = "Date")), .Names =

Dear friends of lme, After so many year with lme, I feel ashamed that I cannot get this to work. Maybe it's a syntax problem, but possibly a lack of understanding. We have growth curves of new dental bone that can well be modeled by a linear growth curve, for two different treatments and several subjects as random parameter. By definition, newbone is zero at t=0, so I tried to force the

x0=rnorm(100) y0=rpois(100,3)+1 ind=as.data.frame(table(y0)) ind1=ind[,1] ind2=ind[,2] phi=NULL for (i in 1:length(ind2)){ phi[i]=sum(x0[y0==ind1[i]])/ind2[i] } [[alternative HTML version deleted]]

Hello R-users, I've got a file with individuals as colums and the clusters where they occur in as rows. And I wanted a table which tells me how many times each individual occurs with another. I don't really know how such a table is called...it is not a frequency table....My eventual goal is to make Venn-diagrams from the occurence of my individuals. So I've this: cluster ind1 ind2

I have some data with two categorises plus/minus (p53) and a particular time (Time) and the outcome is a continuous vairable (Result). I set up a maximum model. ancova <- lm(Result~Time*p53) > summary(ancova) .. Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 0.05919 0.55646 0.106 0.916 Time -0.02134 0.01785 -1.195 0.241 p53plus

Dear all, First I apologize if my question is quite simple, but i'm very newbie with R. I have vectors of the form v = c(1,1,-1,-1,-1,1,1,1,1,-1,1) (longer than this one of course). The elements are only +1 or -1. I would like to calculate : - the frequencies of -1 occurences after 2 consecutives -1 - the frequencies of +1 occurences after 2 consecutives +1 It looks probably something like