David Scott d.sc...@auckland.ac.nz napsal dne 17.10.2009 03:01:35:
Petr PIKAL wrote:
Hi
r-help-boun...@r-project.org napsal dne 16.10.2009 15:24:05:
hi everybody, I'm a student, and I'm new using R!
I'm looking for statistical
help hoping somebody can answer me!
This is my problem:
I have 2 temporal
series. The firstone is a series of mesured data (height of
monitorated
points), the second is a series of temperature (in Celsius degree).
Using
Matlab I have built the two graphs (Measured Data - Time
Temperature
-
Time).
Looking those graphs I can surely say that there is a clear
correlation beetween theme, and also that the measured data are
surely
influenced by the variations of temperature.
Unfortunately my statistical
knowledges are not that large so using R seems quite difficult to me.
My
question is: is there a code already written the can compare the 2
temporal
series and can find the correlation between the data???
If the relationship is linear than
lm(values~temperature, ...)
shall suffice
if it is nonlinear than you can look e.g. to
?nls
And also: is there a
code that can correct the Measured Data from the influence of
temperature and
return a clean data???
maybe ?predict.
Regards
Petr
This sounds a little dangerous to me. Antonio is wanting to determine
correlations between *time series* if I understand correctly.
Hm, I understood he measured some value and at the same time he recorded a
temperature. He wants to know if there is a relationship between value and
temperature. If he is monitoring some continuous process and there is no
wave or drift of both values with time (the series are stationery) he can
use simple lm. I agree that when the values are drifting or fluctuating
with some time pattern than he shall adjust for it.
Regards
Petr
The time series need to be prewhitened or the correlations between
successive observations modeled in some way. Just using lm can be very
misleading because of the violation of the independence assumption.
If Antonio does not understand these comments he needs to consult a
local statistician.
David Scott
--
_
David Scott Department of Statistics
The University of Auckland, PB 92019
Auckland 1142,NEW ZEALAND
Phone: +64 9 923 5055, or +64 9 373 7599 ext 85055
Email: d.sc...@auckland.ac.nz, Fax: +64 9 373 7018
Director of Consulting, Department of Statistics
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