s only to build a smaller map which excludes some regions
plot(swiss.map, axes = FALSE)
plot(submap, add = TRUE, col = colours[findInterval(gino, levels(gino),
all.inside = FALSE)])
legend("topleft", xjust = 0, ncol = 1, legend = levels(gino), fill =
colours, bty ="n", title = &
ity in the size/importance of the regions being analysed),
is there a way to get around this limitation? I struggle to find anything
online relating weighted least squares and LM tests for spatial dependence.
Thanks for the help.
Roberto
********
Roberto Patuelli, Ph.D.
Istitut
onio Silva
********
Roberto Patuelli, Ph.D.
Istituto Ricerche Economiche (IRE) (Institute for Economic Research)
Università della Svizzera Italiana (University of Lugano)
via Maderno 24, CP 4361
CH-6904 Lugano
Switzerland
Phone: +41-(0)58-666-4166
Fax: +39-02-700419665
Email: roberto.patue
(http://ideas.repec.org/p/lug/wpaper/0806.html), which is
forthcoming on the Annals of Regional Science, public.
See my website for more :))
Cheers
Roberto
****
Roberto Patuelli, Ph.D.
Istituto Ricerche Economiche (IRE) (Institute for Economic Research)
Università della Svizzer
ather than creating a new spatial weights matrix, I would like to obtain this
by aggregating the one based on the original regions, or by aggregating
directly the neighbour's set (a listw object for example), if possible.
Can anyone help on these two problems?
Thanks and best regards,
R
bution...
Good luck!
Roberto
********
Roberto Patuelli, Ph.D.
Post-doc researcher
Institute for Economic Research (IRE)
University of Lugano (USI)
via Maderno 24, CP 4361
CH-6904 Lugano
Switzerland
Phone: +41-(0)58-666-4166
Email: [