similar to: Fitting a Harmonic Function to Time Series Data

Hi Folks, I think I have searched exhaustively, including, of course R-help (D. Bates, S. Graves, and others) and but I remain uncertain about testing fixed effects with lmer(..., family=binomial). I gather that mcmcsamp does not work with Do we rely exclusively on z values of model parameters, or could we use anova() with likelihood ratios, AIC and BIC, with (or without)

Hi folks, Warning: I don't know if the result I am getting makes sense, so this may be a statistics question. The fitted values from my binomial lmer mixed model seem to consistently overestimate the cell means, and I don't know why. I assume I am doing something stupid. Below I include code, and a binary image of the data is available at this link:

Hi all, I am trying to re-compile some "unmaintained" (it seems) packages, namely rSoNIA and dynamicnetwork from: http://csde.washington.edu/~skyebend/rsonia/rsoniaDemo/ These packages predates R 2.10.0 so they need to be recompile. After split the single big file in /man in each packages into a file for each function + some minor fix, I successfully manage to recompile and load

I am looking for a mailing list for general statistical questions that are not R related. Do you have any suggestions for lists that are busy and helpful and/or lists that you use and recommend? Thanks in advance, Ralf

Hi everyone, I am trying to construct a glm and am running into a couple of questions. The data set I am using consists of 6 categories for the response and 6 independent predictors representing nutrient concentrations at sample point locations. Ultimately I'd like to use the probabilities for each response category in a simulation model such that these probabilities are used to define a

Hello. I'm running a generalized linear model and am interested in using the random effects that are output for further analysis. My random effect is interacting with two different fixed effects (which which are factors with two levels each). When I retrieve the random effects I get something like this: (Intercept) nutrient (Intercept) light (Intercept) Aa-0 0.59679192

Hi, Is there a package to perform a sine function fitting to XY data? Thx, Ashz -- View this message in context: http://r.789695.n4.nabble.com/Sine-function-fitting-tp3000156p3000156.html Sent from the R help mailing list archive at Nabble.com.

Hi, I am interested in ggplot2 and I found this lattice code very interesting (http://addictedtor.free.fr/graphiques/graphcode.php?graph=48). Code: library(lattice) lattice.options(default.theme = canonical.theme(color = FALSE)) tmp <- expand.grid(geology = c("Sand","Clay","Silt","Rock"), species = c("ArisDiff",

Dear R-Experts. I collected different datas about Nitrogen content (mg/ml) in Dung. The dung was eighter fresh (day=0) or had different ages (15,29,47) to observe nutrient changes over time. Now I like to draw a boxplot. boxplot(nmgml~day) abline((nmgml~day) The Problem is, that the boxplot considers the day values as groups and not as time series (neighter when the days are numeric or

Hi, I have 20*60 data matrix (with some NAs) and I wish to perfom a Pearson correlation coefficient matrix as well as simple linear regression equation and coefficient of determination (R2) for every possible combination. Any tip/idea/library/script how do to so. Thanks, As hz -- View this message in context:

Hi, I was trying as well as looking for an answer without success (a bit strange since it should be an easy problem) and therefore I will appreciate you help: My simple script is: # Loadings data of 5 columns and 100 rows of data data1<-read.csv("C:/?/MyPCA.csv") pairs(data1[,1:4]) pca1 <- princomp(data1[,1:4], score=TRUE, cor=TRUE) biplot(pca1) The biplot present the data

I don't know if there is a way to use the scale relation free argument in ggplot2 like in lattice. I have a feeling that there is not, but I would like to make a plea for this feature. It would be nice to be able to plot Total Inorganic Nitrogen Total Phosphorus and the ratio of the two- the numbers on the axis are not related, but the previous two are surely related to the last (this ratio

Hi, I have this script: dat <- data.frame(X = halistat$Date,Y1 = halistat$avg,Y2 = halistat$stdev) ggplot(data = dat, aes(x = X, y = Y1, ymin = Y1 - Y2, ymax = Y1 + Y2)) + geom_point() + # points at the means geom_line() + # if you want lines between pints geom_errorbar() # error bars, Y1 - Y2 and Y1 + Y2 halistat$Date values: 29/1/10 21/2/10 30/3/10 30/4/10 30/5/10 In the resulted

Dear all, I generated a Correlograms and used the panel.ellipse (confidence ellipse and smoothed line) option. Is there a way to get instead of the smoothed line the linear regression? Thanks, As hz -- View this message in context: http://r.789695.n4.nabble.com/Correlograms-and-linear-regression-tp2331071p2331071.html Sent from the R help mailing list archive at Nabble.com.

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

Hi, Based on some modification that I did to the R Cookbook Graphs Scatterplots code, link:http://wiki.stdout.org/rcookbook/Graphs/Scatterplots%20(ggplot2) I have some questions and I will appreciate a help: - How do I change the legend title? - How can I change the for each linear regression its color and linetype? - How can I add for both the linear regression lines their equations and Rseq

Hello, I will try to explain the problem, sorry if it will be a little long... I'm using R to analyze results of cyclic mechanical testing, like this: - apply quasi-sinusoidal load - measure quasi-sinusoidal vertical and horizontal deformations (quasi-sinusoidal load means that load "should be" sinusoidal, but testing machine puts in some noise...) I enclose a sample of data at

Hello, I try to synthesize harmonic functions with a subset of frequencies determined by fast fourier transform. I wrote two different functions, a looped version and a matrix multiplication version. As an example the looped version takes 5 sec and the matrix-version takes 3-4 sec (R 1.4, Athlon 1.2 GHz, 256 MB, Win ME), but the latter needs huge amount of memory due to the matrices. So, none of

Dear R Users, I have a CO2 time series. I want to fit this series seasonal cycle and trend with fourth harmonic function, and then compute residuals. I am doing something like: file<-read.csv("co2data.csv") names(file) attach(file) fit<-lm(co2~1+time+I(time^2)+sin(2*pi*time)+cos(2*pi*time)+sin(4*pi*time)+cos(4*pi*time)+

Dear list members I want to analyze separately the months of a time series. In other words, I want to plot and fit models for each month separately. Taking the example of http://a-little-book-of-r-for-time-series.readthedocs.org/en/latest/src/timeseries.html births <- scan("http://robjhyndman.com/tsdldata/data/nybirths.dat") birthstimeseries <- ts(births, frequency=12,