similar to: using lapply()

I am trying to fit a very simple broken stick model using the package "segmented" but I have hit a roadblock. > str(data) 'data.frame': 18 obs. of 2 variables: $ Bin : num 0.25 0.75 1.25 1.75 2.25 2.75 3.25 3.75 4.25 4.75 ... $ LnFREQ: num 5.06 4.23 3.50 3.47 2.83 ... I fit the lm easily: > fit.lm<-lm(LnFREQ~Bin, data=id07) But I keep getting an error

Hi, I'm stuck on creating a plot with x tick labels for every Nth tick mark - how is that done? I don't see a simple solution to this in help(plot) or help(par) and what I've tried is not working, eg, the following does not work, although it seems intuitive to me that it should work: x <- seq(-100,1000,25) y <- x * x % find all the x values that are multiples of 100 tmp <-

Dear R helpers I have following dataset rate_number = matrix(c(5, 15, 60, 15, 5, 0, 20, 60, 20,0, 10, 20, 40, 20, 10), nrow = 5, ncol = 3) range_mat = matrix(c(6.25, 6.75, 7.25, 8.75, 9.25, 9.75, 8.5, 9, 9.5, 10.5, 11, 11.5, 4.25, 4.75, 5.25, 5.75, 6.25, 6.75), nrow = 6, ncol = 3) > rate_number        [,1]   [,2]   [,3] [1,]    5     0      10 [2,]   15   20      20 [3,]   60   60      40

Hi, I am trying to find m breakpoints of a linear regression model. I used the segmented package. It works fine for small number of predicators and breakpoints.(3 r.v. 3 points). However, my model has 14 variables it even would not work even for just one breakpoints!. The error message is always estimated breakpoints are out of range. Since my problem is time related problem. So I

Sorry if this post should be long but I tried to give you a piece of my data to reproduce my error message using hetcor: Fehler in result$rho : $ operator is invalid for atomic vectors Zus?tzlich: Warning messages: 1: In polychor(x, y, ML = ML, std.err = std.err) : 1 row with zero marginal removed 2: In polychor(x, y, ML = ML, std.err = std.err) : the table has fewer than 2 rows Error in

Hi R users, I'm here trying to understand correlated residuals in nonlinear estimation. I'm reading/studying the book Bates, D. M. and D. G. Watts, (1988), /Nonlinear regression analysis and its applications/, Wiley, NY. pages 92-94, trying to reproduce the figures and to find out the code in R to perform the necessary calculations. I also consulted Pinheiro and Bates, but without

I am relatively new to R, so apologize up front for my long question, particularly if there is too much or too little information. I have a large time series data set where each subject's behavior was originally coded on .25s intervals for 3min task. I am trying to determine if the findings are different depending on the coding interval (i.e. Compare .25s to 1s to 5s to 10s). I also need to

Hello, dear R-community. This is a question about TukeyHSD between factor combinations of a Three-Way ANOVA, which is - since it is a multi measure ANOVA - not a simple ANOVA but a Generalised Linear Mixed Model (GLMM), calculated with "lmer". > growth <- groupedData(length~meas|box_id,outer=~spec*comp*water,data=all.spec) > model <-

I am trying to do post-hoc tests associated with a repeated measures analysis with on factor nested within respondents. The factor (SOI) has 17 levels. The overall testing is working fine, but I can''t seem to get the multiple comparisons to work. The first step is to "stack" the data. Then I used "lme" to specify and test the overall model. Finally

uhps!! I forgot to tell you that I'm running R 0.99.0 for windows32 on windows 95 osr2. Thank you very much for the latest R-1.0.0 (even if I don't tried it yet)!!! Andrea Rossetti University of Perugia (Italy) Statistical Science Department. ---------- Forwarded message ---------- Date: Tue, 29 Feb 2000 17:03:51 +0100 (MET) From: Andrea Rossetti <rossetti at markov.stat.unipg.it>

Hello, this forum was very helpful yesterday with a simple question I had on working with tables. What function will I need to use to do the following. Move matrix a: A B C D x 1 2 3 4 y 5 6 7 8 to the mean of B&C and C&D: BC CD x 2.5 3.5 y 6.5 7.5 and then to the mean of B&CB, C&BC, C&CD, and D&CD: AAB BAB

Dear all, I am attempting to convert 10 NetCDF files into a single NetCDF file, due to the data input requirements of a model I hope to use. I am using the ncdf package, version 1.6. The data are global-scale water values, on a monthly basis for 10 years (ie. 120 months of data in total; at present the data are separated by year, with 12 months of data in each file - mrunoff_1986 through to

Dear all, I am attempting to convert 10 NetCDF files into a single NetCDF file, due to the data input requirements of a model I hope to use. I am using the ncdf package, version 1.6. The data are global-scale water values, on a monthly basis for 10 years (ie. 120 months of data in total; at present the data are separated by year, with 12 months of data in each file - mrunoff_1986 through to

I try tu use mob() with my data.frame ('data.frame': 288 obs. of 81 variables; factors, numerics and ordered factors) My response is a binary variable and I should use for modelling a logistic regression (family=binomial). I read in the "MOB" Vignette that I could use a formula like this if I would like to have only partitioning variables apart from the response.

Hi all, I've got object *cumsumtab, *where the first row (1,2,3,4,5,6,8,13,14,17,40) are names (actually, categories), and the second row has numeric values. > cumsumtab 1 2 3 4 5 6 8 13 14 17 40 2 4 6 7 9 11 13 14 15 16 17 I'm trying to find a way of finding to which category ( in *names(cumsumtab) * ) belongs a given value (let's say Q1 <- 4.25). So far, I've

Dear all. I have some data and I seek the way to start their evaluation. The values are from survey of different persons and their opinion about various methods (metoda) and instrument (uzel). I am not sure where to start. I would like to end with combination of method (metoda) and instrument (uzel) sorted to let say three categories according to knowledge (znalost) and influence (vliv)

Hi, i am new to this forum and not sure how it works, I am trying to do deskriptive descripe my data in terms of gender: head(scltotal) pbnr dat dep dys sop ago mis age female messpunkt2 messpunkt1 tage eintrittsjahr 1 10023 1994-02-21 0.75 1.00 0.50 0.50 0.75 35 1 8817 8817 0 1994 2 10023 1994-05-25 0.75 1.00 0.50 0.50 0.75 35 1 8910 8817

Hello all, I'm trying to use confint from the MASS package to compute confidence intervals for an nls object. When I plot the results, however, they don't make sense - lines cross over the fitted model or just don't match the data. Code is : Thanks for help dat<-data.frame(a,b) with(dat, plot(a,b)) model<-(nls(b~(1/exp(a*x))*n, data=dat, start=list(x=.001,n=20),

Dear R users, A have a dataframe (matrix) with two collumns (plot, and diameter (d)). I want all diameters values for different combination of plots. For example I want all d values for all posible combination, 100C2 (all d values for plot 1 with all d values in the plot 2.......with all d values from plot 1 with all d values from plot 100, ...... with all d values from plot 99 with all d values

I am trying to make predictions with se's using a nlme (kew11.nlme below). I get an error indicating levels for a factor are not allowed. I have searched and read Rnews, MEMSS, MASS, R-Help, and other lists in Spanish where I found questions similar to mine but not solution. I do not really care about the method used. Any suggestions to obtain predictions with se's from an nlme