similar to: strange PREDICTIONS from a PIECEWISE LINEAR (mixed) MODEL

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

Does anybody have any insight into how to make this faster? I suspect, that the rounding going on may be an issue, as is the stepping through data frame rows using integers ... If you have the patience to teach a noob, he will highly appreciate it ;0) Joh digit <- 4 for (minute in seq(from=25,to=lrange[2])){ # Extract all data associtaed with the current time (minute) frame <-

This is a continuing issue with the one on the list a long time ago (I couldn't find a solution to it from the web): -------------------------------------------------------------------------- > Using a formula converted with as.formula with lme leads > to an error message. Same works ok with lm, and with > lme and a fixed formula. > > # demonstrates problems with lme and

Using a formula converted with as.formula with lme leads to an error message. Same works ok with lm, and with lme and a fixed formula. # demonstrates problems with lme and as.formula demo<-data.frame(x=1:20,y=(1:20)+rnorm(20),subj=as.factor(rep(1:2,10))) demo.lm1<-lme(y~x,data=demo,random=~1|subj) print(summary(demo.lm1)) newframe<-data.frame(x=1:5,subj=rep(1,5))

Hello, can someone give me an example of how to fit a linear model where the response is a matrix, please? (other than a loop over lm calls) The ?lm points me to model.matrix but it is not clear to me how to use a model matrix in an lm call. fit<-lm(headmatrix ~ tailmatrix) causes problems as soon as I use predict(fit,newframe). The variables in tailmatrix are not found in

If I create an aggregation like this: aggregate(lastYear[,8:10],list(Stadium=lastYear$STAD),mean) I'll get a new data frame, which I can order if I assign it like this: newFrame <- aggregate(lastYear[,8:10],list(Stadium=lastYear$STAD),mean) newFrame[order(newFrame$TEMP),] But.. if I just want to do this in one statement, what can I do? If this was perl, I'd be using something like

Hello Masters, I run the loess() function to obtain local weighted regressions, given lowess() can't handle NAs, but I don't improve significantly my situation......, actually loess() performance leave me much puzzled.... I attach my easy experiment below #------SCRIPT---------------------------------------------- #I explore the functionalities of lowess() & loess() #because I have

Dear R helpers I have following input f = c(257, 520, 110). I need to generate a decreasing sequence (decreasing by 100) which will give me an input (in a tabular form) like 257, 157, 57 520, 420, 320, 220, 120, 20 110, 10 I tried the following R code f = c(257, 520, 110) yy = matrix(data = NA, nrow = 3, ncol = 6) for (i in 1:3)      {      value = NULL      for (j in 1 : 6)           {

Colleages I am running R 2.1.0 on a Mac (same problem occurs in Linux). In some situations, I have mixed text/numeric data that is stored as characters in a matrix. If I convert this matrix to a dataframe, the numeric data becomes factors, not what I intend. TEXT <- paste("Text", 1:4, sep="") NUMBERS <- 10 + 4:1 MATRIX <- cbind(TEXT,

On some systems (alpha), tick marks don't appear on plots. The easiest way to see the problem is something like: > plot(0:1,axes=FALSE) > axis(1,1:2) The problem is in X11_Line(...) from .../src/unix/devX11.c, which is so short I've included the whole function below: static void X11_Line(double x1, double y1, double x2, double y2, int coords, DevDesc *dd) {

R-help, I'm using the code below to plot a shaded area graph. At the same time I want to plot a second series on the y-axis (from par(new=T) on) but as the two series have different x-axis range (first 1994:2007 and second 1996:2007) the corresponding x's do not match. How can this be sorted out? Thanks in advance ############################################# plot.new()

Hi All, I was wondering if someone could help me to solve this issue with lmer. In order to understand the best mixed effects model to fit my data, I compared the following options according to the procedures specified in many papers (i.e. Baayen <http://www.google.it/url?sa=t&rct=j&q=&esrc=s&source=web&cd=1&ved=0CDsQFjAA

Hello R users, I'm working with a time-series of several years and to analyze it, I?m using GAM smoothers from the package mgcv. I?m constructing models where zooplankton biomass (bm) is the dependent variable and the continuous explanatory variables are: -time in Julian days (t), to creat a long-term linear trend -Julian days of the year (t_year) to create an annual cycle - Mean temperature

Greetings, I am interested in creating a stepwise fixed order regression function. There's a function for this already called add1( ). The F statistics are calculated using type 2 anova (the SS and the F changes don't match SPSS's). You can see my use of this at the very end of the email. What I want: a function to make an anova table with f changes and delt R^2. I ran into

I can use stepAIC on an lme object in 1.6.2, but I get the following error if I try to do the same in 1.7.0: Error in lme(fixed = resp ~ cov1 + cov2, data = a, random = structure(list( : unused argument(s) (formula ...) Does anybody know why? Here's an example: library(nlme) library(MASS) a <- data.frame( resp=rnorm(250), cov1=rnorm(250), cov2=rnorm(250),

Dear Thierry, thank you for the quick reply. I have only one question about the approach you proposed. As you suggested, imagine that the model we end up after the model selection procedure is: mod2.1 <- lmer(dT_purs ~ T + Z + (1 +T+Z| subject), data =x, REML= FALSE) According to the common procedures specified in many manuals and recent papers, if I want to compute the p_values relative to

Dear R help, I am trying to work out if I am justified in log-transforming data and specifying Gamma in the same glm. Does it have to be one or the other? I have attached an R script and the datafile to show what I mean. Also, I cannot find a mixed-model that allows Gamma errors (so I cannot find a way of including random effects). What should I do? Many thanks, Pete --------------

I couldn't find any reference to this in the FAQ, but is it possible to sort a dataframe by multiple columns? I've created some code, similar to the following: nspr.code <- sp.results$sp.code[order( sp.results$sp.code )] nspr.tpa <- sp.results$tpa[order( sp.results$sp.code )] nspr.code <- as.character( levels( nspr.code ) )[nspr.code] nspr.tpa <- as.numeric( levels( nspr.tpa

Dear List-members, I found a strange behaviour in the lqs function. Suppose I have the following data: y <- c(7.6, 7.7, 4.3, 5.9, 5.0, 6.5, 8.3, 8.2, 13.2, 12.6, 10.4, 10.8, 13.1, 12.3, 10.4, 10.5, 7.7, 9.5, 12.0, 12.6, 13.6, 14.1, 13.5, 11.5, 12.0, 13.0, 14.1, 15.1) x1 <- c(8.2, 7.6,, 4.6, 4.3, 5.9, 5.0, 6.5, 8.3, 10.1, 13.2, 12.6, 10.4, 10.8, 13.1, 13.3, 10.4, 10.5, 7.7, 10.0, 12.0,

Hello guys, I was some years ago when I sent something to this list. NUT is a great tool but lacks SNMP management. It can access a device using SNMP but I found no way to read NUT values using SNMP. It would be very useful in order to integrate with network management software based on SNMP. As I did not find a solution, I wrote it myself. I wrote a MIB file and a SNMP agent for net-snmp