Dear Xochitl, Have a look at gls() from the nlme package. It allows you to fit auto correlated errors.
gls(k ~ NPw, correlation = corAR1(form = ~ Time)) Best regards, ir. Thierry Onkelinx Instituut voor natuur- en bosonderzoek / Research Institute for Nature and Forest team Biometrie & Kwaliteitszorg / team Biometrics & Quality Assurance Kliniekstraat 25 1070 Anderlecht Belgium + 32 2 525 02 51 + 32 54 43 61 85 thierry.onkel...@inbo.be www.inbo.be To call in the statistician after the experiment is done may be no more than asking him to perform a post-mortem examination: he may be able to say what the experiment died of. ~ Sir Ronald Aylmer Fisher The plural of anecdote is not data. ~ Roger Brinner The combination of some data and an aching desire for an answer does not ensure that a reasonable answer can be extracted from a given body of data. ~ John Tukey -----Oorspronkelijk bericht----- Van: R-help [mailto:r-help-boun...@r-project.org] Namens Xochitl CORMON Verzonden: woensdag 28 januari 2015 15:09 Aan: Rlist; Rlist Onderwerp: [R] Prediction of response after glm on whitened data Hi all, Here is a description of my case. I am sorry if my question is also statistic related but it is difficult to disentangle. I will however try to make it only R applied. My response is a growth constant "k" and my descriptor is prey biomass "NP" and time series is of 21 years. I applied a gaussiam GLM (or LM) to this question. After the regression I tested the residuals for autocorrelation using acf(). Because autocorrelation was significant I decided to whiten my data using {car}dwt() in order to obtain rho (an estimation of my correlation) and then applying the following to my data in order to remove autocorrelation: kw_i = k_i - rho * k_i-1 NPw_i = NPw_i - rho * NPw_i-1 (method from Jonathan Taylor, http://statweb.stanford.edu/~jtaylo/courses/stats191/correlated_errors.html). After that I fitted a model on this whitened data (kw_i ~ NPw_i), realised an F-test and obtained classical results such as deviance explained, pvalues and of course the intercept and coefficient of the last regression. However doing that and coming to prediction using predict() I can only obtained predictions of deltaK (kw_i) in function of deltaNP (NPw_i) but I am actually interested in being able to predict k in function of NP... Is there a solution to predict directly k and its associated variance using R without having to detail in the script all the mathematical process necessary to come back to something like k_i = mu + rho * k_i-1 + beta(NPw_i - rho * NPw_i-1) + epsilon with mu being the intercept, beta the regression coefficient and epsilon the error, ? Thank you for your help, Best, Xochitl C. -- <>< <>< <>< <>< Xochitl CORMON +33 (0)3 21 99 56 84 Doctorante en écologie marine et science halieutique PhD student in marine ecology and fishery science <>< <>< <>< <>< IFREMER Centre Manche Mer du Nord 150 quai Gambetta 62200 Boulogne-sur-Mer <>< <>< <>< <>< ______________________________________________ R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code. Disclaimer<https://www.inbo.be/nl/disclaimer-mailberichten-van-het-inbo> ______________________________________________ R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
