[R] Prediction of response after glm on whitened data

[Xochitl CORMON]

Thanks Thierry for your quick answer. Indeed this simplifies a lot my 
method so I decided to apply it.

However I will be curious to check in which extend the coefficients 
obtained with the gls function are similar to the ones obtained using 
glm and whitening. It seems to me thant the method are indeed pretty 
similar.

So if someone knows a function which allows me to predict my response 
and its associated variance using R after whitening and glm (see 
original question), I am still eager to know it.

Best,

Xo


<>< <>< <>< <><

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

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Le 28/01/2015 16:19, ONKELINX, Thierry a écrit :
> 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.Onkelinx at inbo.be
> www.inbo.be
>
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>
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>
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>
> -----Oorspronkelijk bericht-----
> Van: R-help [mailto:r-help-bounces at 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
>
> <>< <>< <>< <><
>
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