Dear Jean-Paul,

Have a look at the weights argument in ?lm. This should do the trick:

lm(agri ~poly(x, y, degree = 2), data = yourDataFrameName, weights =
var_agri)

HTH,

Thierry

------------------------------------------------------------------------
----
ir. Thierry Onkelinx
Instituut voor natuur- en bosonderzoek / Research Institute for Nature
and Forest
Cel biometrie, methodologie en kwaliteitszorg / Section biometrics,
methodology and quality assurance
Gaverstraat 4
9500 Geraardsbergen
Belgium
tel. + 32 54/436 185
[EMAIL PROTECTED]
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: [EMAIL PROTECTED]
[mailto:[EMAIL PROTECTED] Namens Jean-Paul Kibambe
Lubamba
Verzonden: dinsdag 2 september 2008 15:21
Aan: [email protected]
Onderwerp: [R-sig-Geo] Weighted linear regression

Hi,

I am trying to explain some of my variables using a weighted linear
regression, with x and y-coordinates as my explanatory variables.

For example, I want to explain my variable 'agri' in the following way :
agri = x + y + x*y+ x^2 + y^2 where x and y are my coordinates and the
regression must be weighted by the variance.

How can I do it ? Thanks in advance !

JP

For illustration, here is a part of my database (var_agri = variance)

    agri             se_agri    var_agri           x             y
1  -1.04E-05  4.47E-06  2.00E-11          23.78230555   2.968836238
2  -1.01E-05  4.36E-06  1.90E-11          26.05798185   4.476217345
3  -9.47E-06  4.55E-06  2.07E-11          30.5248107    3.058706843
4  -1.00E-05  4.22E-06  1.78E-11          26.97325452   0.737615804
5  -9.96E-06  3.74E-06  1.40E-11          17.67392327   -3.908029603
6  -1.05E-05  4.44E-06  1.97E-11          25.92926863   3.114662312
7  -1.01E-05  4.35E-06  1.89E-11          25.4312434    1.572565497
8  -6.54E-06  4.38E-06  1.92E-11          20.07104333   1.048456276
9  -9.65E-06  4.38E-06  1.91E-11          23.92598192   1.728975609
10 -7.39E-06  4.20E-06  1.76E-11          21.2291592    0.489260168

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