similar to: [R-sig-ME] account for temporal correlation

Displaying 20 results from an estimated 7000 matches similar to: "[R-sig-ME] account for temporal correlation"

2006 Oct 08
1
Simulate p-value in lme4
Dear r-helpers, Spencer Graves and Manual Morales proposed the following methods to simulate p-values in lme4: ************preliminary************ require(lme4) require(MASS) summary(glm(y ~ lbase*trt + lage + V4, family = poisson, data = epil), cor = FALSE) epil2 <- epil[epil$period == 1, ] epil2["period"] <- rep(0, 59); epil2["y"] <- epil2["base"]
2012 Feb 06
1
lmer with spatial and temporal random factors, not nested
Hi, I am new to this list. I have a question regarding including both spatial and temporal random factors in lmer. These two are not nested, and an example of model I try to fit is model1<-lmer(Richness~Y+Canopy+Veg_cm+Treatment+(1|Site/Block/Plot)+(1|Year), family=poisson, REML=FALSE), where richness = integer Y & Treatment = factor Canopy & Veg_cm = numerical, continous
2005 May 25
1
question: corCAR1 in lme
Hello all, I am trying to use lme to examine how a response variable (Chla) changes over time in different treatments (2 Temp & 2 Light levels). Within each treatment combination, there are two replicate tanks (each with unique TankID) with coral fragments in them. All tanks are subject to the same environment until Time=0, when treatments are imposed, and Chla is measured for each
2005 Jun 28
1
How to extract the within group correlation structure matrix in "lme"
Dear R users, I fitted a repeated measure model without random effects by using lme. I will use the estimates from that model as an initial estimates to do multiple imputation for missing values of the response variable in the model. I am trying to extract the within group correlation matrix or covariance matrix. here is my code: f = lme(y ~x0+x1+trt+tim+x1:tim +tim:trt,random=~-1|subj,
2012 Aug 07
1
Which R function for GLMM with binary response, nested random factors with temporal correlation?
Despite lots of investigation, I haven't found any R packages might be suitable for the following problem. I'd be very grateful for suggestions. I have three-way nested data, with a series of measures (obs) taken in quick succession (equal time spacing) from each subject on different days. The measures taken on the same day are temporally correlated, so I'd like to use an AR1
2012 Jan 13
1
plotting regression line in with lattice
#Dear All, #I'm having a bit of a trouble here, please help me... #I have this data set.seed(4) mydata <- data.frame(var = rnorm(100), temp = rnorm(100), subj = as.factor(rep(c(1:10),5)), trt = rep(c("A","B"), 50)) #and this model that fits them lm <- lm(var ~ temp * subj, data = mydata) #i want to
2011 Aug 08
1
mixed model fitting between R and SAS
Hi al, I have a dataset (see attached), which basically involves 4 treatments for a chemotherapy drug. Samples were taken from 2 biopsy locations, and biopsy were taken at 2 time points. So each subject has 4 data points (from 2 biopsy locations and 2 time points). The objective is to study treatment difference.? I used lme to fit a mixed model that uses "biopsy.site nested within pid"
2012 May 31
1
anova of lme objects (model1, model2) gives different results depending on order of models
Hello- I understand that it's convention, when comparing two models using the anova function anova(model1, model2), to put the more "complicated" (for want of a better word) model as the second model. However, I'm using lme in the nlme package and I've found that the order of the models actually gives opposite results. I'm not sure if this is supposed to be the case
2012 Feb 17
2
Error message in gamm. Problem with temporal correlation structure
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
2009 Dec 01
0
GLM Repeated measures test of assumptions: e.g. test for sphericity e.g. Bartletts and Levenes homogenous variances
Hello and thanks in advance I am running a glm in R the code is as follows with residual diagnostic code below model4<-glm(Biomass~(Treatment+Time+Site)^2, data=bobB, family=quasi(link="log", variance="mu")) par(mfrow=c(2,2)) plot(model2) to test the effect of grazing exclusion of feral horses for a Phd with following factors: Treatment - 3 levels which are grazed
2009 Jan 12
1
help on nested mixed effects ANOVA
Hello, I am trying to run a mixed effects nested ANOVA but none of my codes are giving me any meaningful results and I am not sure what I am doing wrong. I am a new user on R and would appreciate some help. The experimental design is that I have some frogs that have been exposed to three acoustic Treatments and I am measuring neural activity (egr), in 12 brain regions. Some frogs also called
2011 Oct 08
0
Accouting for temporal correlation in linear regression
I measured nitrate concentration and primary production (PP) biweekly for 23 months in one headwater stream. I would like to use linear regression to determine if PP is related to nitrate concentration. My dataframe is called "data" and consists of the vectors Rdate, PP, and nitrate. Rdate is the observation date in class "date" and PP is primary production. I first
2009 Dec 01
0
Amendment to previous post a minute ago, please amend before posting if possible
Sorry, I just posted the email below but realised I did not give a name or details, would it be possible to adjust before posting and send what is below, sorry again, first time user... From: Joanne Lenehan [mailto:jlenehan@une.edu.au] Sent: Tuesday, 1 December 2009 3:51 PM To: 'r-help@r-project.org' Subject: GLM Repeated measures test of assumptions: e.g. test for sphericity e.g.
2008 Jan 25
1
Trouble setting up correlation structure in lme
Hi, I'm trying to set up AR(1) as a correlation structure in modeling some data (attached file data.txt in text format) with lme, but have trouble getting it to work. Incent, Correctness, and Oppor are 3 categorical variables, Beta is a response variable, and Time is an equally-spaced variable with 6 time points (treated as a categorical variable as well). Basically I want to model the
2007 Jun 01
2
Interaction term in lmer
Dear R users, I'm pretty new on using lmer package. My response is binary and I have fixed treatment effect (2 treatments) and random center effect (7 centers). I want to test the effect of treatment by fitting 2 models: Model 1: center effect (random) only Model 2: trt (fixed) + center (random) + trt*center interaction. Then, I want to compare these 2 models with Likelihood Ratio Test.
2011 Apr 07
1
Panel data - replicating Stata's xtpcse in R
Dear list, I am trying to replicate an econometrics study that was orginally done in Stata. (Blanton and Blanton. 2009. A Sectoral Analysis of Human Rights and FDI: Does Industry Type Matter? International Studies Quarterley 53 (2):469 - 493.) The model I try to replicate is in Stata given as xtpcse total_FDI lag_total ciri human_cap worker_rts polity_4 market income econ_growth log_trade
2008 Jul 06
2
Error: cannot use PQL when using lmer
> library(MASS) > attach(bacteria) > table(y) y n y 43 177 > y<-1*(y=="y") > table(y,trt) trt y placebo drug drug+ 0 12 18 13 1 84 44 49 > library(lme4) > model1<-lmer(y~trt+(week|ID),family=binomial,method="PQL") Error in match.arg(method, c("Laplace", "AGQ")) : 'arg' should be one of
2005 Apr 15
1
AR1 in gls function
Dear R-project users I would like to calculate a linear trend versus time taking into account a first order autoregressive process of a single time series (e.g. data$S80 in the following example) using th gls function. gls(S80 ~ tt,data=data,corAR1(value, form, fixed)) My question is what number to set in the position of value within corAR1? Should it be the acf at lag 1? I look forward for
2009 Aug 13
1
R code to reproduce (while studying) Bates & Watts 1988
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
2009 Aug 17
1
[Fwd: Re: R code to reproduce (while studying) Bates & Watts 1988]]]
Kevin Wright wrote: > library(nlme) > m2 <- gnls(conc ~ t1*(1-t2*exp(-k*time)), > data = df.Chloride, > start = list( > t1 = 35, > t2 = 0.91, > k = 0.22)) So my error was to use nls instead that gnls. Thanks a lot, Kevin. > summary(m2) > plot(m2) > lag.plot(resid(m2), do.lines=FALSE) >