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.
