similar to: R package: autocorrelation in gamm

HELLO ALL, I AM GETTING AN ERROR MESSAGE WHEN TRYING TO RUN A GAMM MODEL LIKE THE ONE BELOW. I AM USING R VERSION 2.14.1 (2011-12-22) AND MGCV 1.7-12. M1 <-gamm(DepVar ~ Treatment + s(Year, by =Treatment), random=list(Block=~1), na.action=na.omit, data = mydata, correlation = corARMA(form =~ Year|Treatment, p = 1, q = 0)) THIS IS THE ERROR MESSAGE Error in `*tmp*`[[k]] : attempt to

Dear R_help list members!   I’m studding forest carnivores and I have data on resting site selection and use!   I’m trying to model the probability that a forest carnivore might have in be located in a tree hollow (1) (the main resting site selected) rather than elsewhere (0) (other resting site types, dens, nests, etc.). The model should be related with several variables such as for instance:  

Hi, I am looking at the effects of two explanatory variables on chlorophyll. The data are an annual time-series (so are autocorrelated) and the relationships are non-linear. I want to account for autocorrelation in my model. The model I am trying to use is this: Library(mgcv) gam1 <-gam(Chl~s(wintersecchi)+s(SST),family=gaussian, na.action=na.omit, correlation=corAR1(form =~

Hi, I am trying out a generalized least squares method of forecasting that corrects for autocorrelation. I downloaded daily stock data from Yahoo Finance, and am trying to predict Close (n=7903). I have learned to use date functions to extract indicator variables for Monday - Friday (and Friday is missing in the model to prevent it from becoming full rank). When I run the following code...

I am working with panel data. I am using the plm package to do this. I would like to do be able to adjust for autocorrelation, as one does with glm models and correlation structures (eg corr=corARMA(q=4)) . In particular, I want to employ MA(4) error structure. Is there a way of doing this with the plm package? (Note: I do not really want to use the pggls function for various

Sorry for disturbing you with another newbie question! I have a data frame about coastal waters quality parameters: for some parameters (e.g. NH3) I have only 1 observation for each sampling station and each sampling date, while in other cases (chlorophyll) I have 1 obs for each meter-depth for each station and date. How can I select only the max chlorophyll value for each station/date? example

Hi, I would be very grateful for some advice. I have read the help pages for Date, strptime, etc. All examples seem to use some version of day month year as date format. However I have Weekly composite data so ONLY want to input the dates as Week.Year (eg 35.2011). strptime seems to show this is possible using %W for week (UK convention) and %Y for year. My data is in a df called chlorophyll and

Hi, I have a problem with a regression I try to run. I did an estimation of the market model with daily data. You can see to output below: /> summary(regression_resn) Time series regression with "ts" data: Start = -150, End = -26 Call: dynlm(formula = ror_resn ~ ror_spi_resn) Residuals: Min 1Q Median 3Q Max -0.0255690 -0.0030378 0.0002787

Hi, I'm a new member here in the list. I am a graduate from University of Georgia. Recently in doing analysis using lme on a dataset, I found several questions: 1. How to express the equation when the correlation structure is very complicated. For exmaple, if the fixed is y(t)=0.03x1(t)+1.5x2(t)(I omitted "hat" and others). And the model with corARMA(p=2,q=3) is proper. What will be

Hello, I would like to evaluate the relationship between flows and phytoplankton abundance (or Chlorophyll a concentrations) using a changepoint analysis.? Specifically, I have two study questions: Study Question 1: Are there certain flow thresholds that result in spikes in phytoplankton abundance? Study Question 2: Are the duration of certain flows important for phytoplankton abundance (e.g.,

Dear R-help, I don't understand why Gls gives me an error when trying to fit a model with AR(2) errors, while gls (from nlme) does not. For example: library(nlme) library(rms) set.seed(1) d <- data.frame(x = rnorm(50), y = rnorm(50)) gls(y ~ x, data=d, correlation = corARMA(p=2)) #This works Gls(y ~ x, data=d, correlation = corARMA(p=2)) # Gives error # Error in

I have a data analysis job for which lme may be used. Prof. Spencer Graves had helped me much on that. I'm really appreciated for that. Could anybody else in the list give me some hints from other perspectives? I hope I can learn as much as possible for this complicated real data. Thanks in advance. Hanhan To briefly describe my data: My data is health effect measurements (y) and personal

Dear R-users, To obtain the percentage of deviance explained when fitting a gam model using the mgcv library is straightforward: summary(object.gam) $dev.expl or alternatively, using the deviance (deviance(object.gam)) of the null and the fitted models, and then using 1 minus the quotient of deviances. However, when a gamm (generalizad aditive mixed model) is fitted, the

Hi all, I would like to fit a gamm model of the form: Y~X+X*f(z) Where f is the smooth function and With random effects on X and on the intercept. So, I try to write it like this: gam.lme<- gamm(Y~ s(z, by=X) +X, random=list(groups=pdDiag(~1+X)) ) but I get the error message : Error in MEestimate(lmeSt, grps) : Singularity in backsolve at level 0, block 1

Dear Madams/Sirs, Hello. I am using the gls function to specify an arma correlation during estimation in my model. The parameter values which I am sending the corARMA function are from a previous fit using arima. I have had some success with the method, however in other cases I get the following error from gls: "All parameters must be less than 1 in absolute value". None of

I am having difficulty fitting a mgcv::gamm model that includes both a random smooth term (i.e. 'fs' smooth) and autoregressive errors. Standard smooth terms with a factor interaction using the 'by=' option work fine. Both on my actual data and a toy example (below) I am getting the same error so am inclined to wonder if this is either a bug or a model that gamm is simply unable

Dear R users, I am trying to use "gamm" from package "mgcv" to model results from a mesocosm experiment. My model is of type M1 <- gamm(Resp ~ s(Day, k=8) + s(Day, by=C, k=8) + Flow + offset(LogVol), data=MyResp, correlation = corAR1(form= ~ Day|Mesocosm), family=poisson(link=log)) where the response variable is counts, offset by the

Try to fit a longitudinal dataset using generalized mixed effects models via the R function gamm() as follows: library(mgcv) gamm0.fit<- gamm(y ~ x+s(z,bs="cr"), random=list( x=~1, s(z,bs="cr")=~1 ), family = binomial, data =raw) the data is

Hello, I am trying to fit a gamm (package mgcv) model with a smooth term, a linear term, and an interaction between the two. The reason I am using gamm rather than gam is that there are repeated measures in time (which is the smooth term x1), so I am including an AR1 autocorrelation term. The model I have so far ended up with is of the type gamm(y ~ s(x1) + s(x1, by=x2), correlation =

I want to use gamm to generate smoothed trend line for three groups identified by dummy variable genea and geneb. My question is how to add in an interaction term between the time and another dummy variable such as gender? fitm<-gamm(change_gfr~ genea+geneb+s(timea_n,bs="ps")+s(timeb_n,bs="ps")+s(timec_n,bs="ps"),data=mm,random=list(time_n=~1|PID)) -- View this