similar to: Time Series statistical modeling: about time lab

Hello all R listers, I'm struggling to select an appropriate statistical method for my data set. I have collected soil moisture measurements every hour for 2 years. There are 75 sensors taking these automated measurements, spread evenly across 4 treatments and a control. I'm not interested in being able to predict soil future soil moisture trends, but rather in knowing whether the

Hello, I am trying to collect several global measures or statistics for time-series as well as packages of R that can compute them. I have found several of them in papers and books, but the literature is so big i am sure i am missing several of them. skewness kurtosis min max mean SD trend seasonality periodicity chaos (Lyapunov Exponent) / Largest Lyapunov Exponent (i think is the same

Dear all, I am a statistician doing research in QSAR, building regression models where the dependent variable is a numerical expression of some chemical activity and input variables are chemical descriptors, e.g. molecular weight, number of carbon atoms, etc. I am building regression models and I am confronted with a widely a technique called Y-RANDOMIZATION for which I have difficulties in

Dear list, I hope the topic is of sufficient interest, because it is not R-related. I have N=100 yes/no-responses from a psychophysics paradigm (say Y Yes and 100-Y No-Responses). I want to see whether these yes-no-responses are in line with a model predicting a certain amount p of yes-responses. Standard procedure would be a one-sample binomial test for the observed proportion, chi?(1 df) =

Rational functions (ratios of polynomials) often provide good approximations to many functions. Does anyone know of any literature on nonlinear modeling with rational functions, sequential estimation, diagnostics, etc.? I know I can do it with "nls" and other nonlinear regression functions, but I'm wondering what literature might exist discussing how a search for an

Good afternoon! I'm trying to model a time series on the following data, which represent a monthly consumption of juices: >x<-scan() 1: 2859 3613 3930 5193 4523 3226 4280 3436 3235 3379 3517 6022 13: 4465 4604 5441 6575 6092 6607 6390 6150 6488 5912 6228 10196 25: 7612 7270 8617 9535 8449 8520 9148 8077 7824 7991 7660 12130 37: 9135 9512 9631 12642

Dear R list As suggested by Prof Brian Ripley, I have tried to read acf literature. The main problem is I am not the statistician and hence have some problem in understanding the concepts immediately. I came across one literature (http://www.stat.nus.edu.sg/~staxyc/REG32.pdf) on auto-correlation giving the methodology. As per that literature, the auto-correlation is arrived at as per following.

Dear all, I am having difficulties using the seemingly-simple function lag. I have a dataframe with several weather variables (maxitemp, windspeed, rainfall etc), and the response variable (admissions). The dataset is fairly large (1530 observations). I simply want to model the response against a lag of a couple of the explanatory variables, say maxitemp and rainfall. I would like to look at

I have data for several rings of a left heart chamber, and which I would like to display in concentric rings, with color-encoding of the values. Each ring corresponds to one slice through the heart, and the rings correspond to positions from the base to the apex of the heart as you move from the outermost ring to the innermost one. The data have a circular pattern. These types of displays are

hi, hi, good morning everyone. I have a time series with binary outputs like : 0001011110100.................etc. Now I want to forecast the future values of that. Can anyone please tell me whether there is any tools exist in literature for dealing with this kind of binary observation? If possible please provide me some good references in net as well. rgd, Megh

Hello R folks, I'm hoping the answer to the question in the subject line. I have in the past used SAS PROC NLIN and PROC NLP to carry out nonlinear optimizations. I'm wondering if there is analogous ways for doing this using R. If so, could someone please point me to some literature that would help me examine this further? Thanks very much. [[alternative HTML version deleted]]

Dear all, This is a pure statistical question, not necessarly related to R. I could not find it in literature. Suppose I'm intersted in a parameter rho, say, equal to: r=beta1/beta2, where beta1 and beta2 come from a linear model y=beta0+beta1X1+beta2X2+.... Fitting the model I can get the (biased) estimate of r=b1/b2, where b1 and b2 are the estimates in the regression model; I can get the

I am fitting individual growth models using nlme (multilevel models with repeated measurements nested within the individual), and I am trying to calculate the Pseudo R-squared for the models (an overall summary of the total outcome variability explained). Singer and Willett (2003) recommend calculating Pseudo R-squared in multilevel modeling by squaring the sample correlation between observed and

Hi guys, I am afraid I am stuck with an estimation problem. I have two variables, X and Y. Y is explained by the weighted sum of n lagged values of X. My aim is to estimate the two parameters c(alpha0,alpha1) in: Yt = Sum from j=1 to n of ( ( alpha0 + alpha1 * j ) * Xt-j ) Where Xt-j denotes the jth lag of X. I came up with this approach because I thought it would be a good idea to estimate

I am new to R and have a question about spatial econometrics. I have noticed that you can easily test for spatial autocorrelation with the spdep package, but was wondering if any code has been written to correct for spatial autocorrelation? Or if there is any literature on this? Thanks. -Jill *************************************************** Jill L. Caviglia-Harris, Ph.D. Assistant

Dear R users I am currently investigating time series analysis using an irregular time series. Our study is looking at vegetation change in areas of alien vegetation growth after clearing events. The irregular time series is sourced from Landsat ETM+ data, over a six year period I have 38 scenes. For certain periods I have monthly data while for others, images are up to three months apart. So far

Dear R-users, Is there a function in R that classifies data in more than 2 groups using logistic regression/classification? I want to compare the c-indices of earlier research (lrm, binary response variables) with new c-indices obtained from 'multiple' (more response variables) logistic regression. Best regards, Stephanie Delalieux Department Biosystems M?-BIORES Group of Geomatics

Unrelated to previous email: every time I send an email to this list I get a response such as: Hey Robin Lovelace > > Thanks for your response. Can I have a pic or two to start talking? Please > respond with pics/infos, Hope to hear back from you asap. > I wonder if others have received such emails and, if so, any suggestions how to tackle the spam? Robin [[alternative HTML

Howdy What I 'd like to analyze with a large data on building permits is to find time series effect of urban policy on buildings as well as cross-sectional effects in any. In 1990 the specialZone urban policy was introduced. I guess that the effects of this specialZone policy would be different from countys. There are counties that do not welcome this specialZone forced to design it. One of

Brian Ripley wrote (to somebody asking about "effect sizes"): > ... > Given that, I wonder if you are used to standard terminology. Good point. But I think for many of us there is more behind that. I personally belong to an (apparently fairly large) group of R users who may be enthusiastic, but are statistical laymen due to a lack of formal education in the area. The