plot.geodata | R Documentation |
This function produces a 2 \times 2
display
with the following plots:
the first indicates the spatial locations assign different
colors to data
in different quartiles,
the next two shows data against the X and
Y coordinates and the last is an histogram of the data values or optionally,
a 3-D plot with spatial locations and associated data values.
## S3 method for class 'geodata'
plot(x, coords=x$coords, data = x$data,
borders, trend="cte", lambda = 1, col.data = 1,
weights.divide = "units.m", lowess = FALSE, scatter3d = FALSE,
density = TRUE, rug = TRUE, qt.col, ...)
x |
a list containing elements |
coords |
an |
data |
a vector with data values. By default it takes the
element |
borders |
If an |
trend |
specifies the mean part of the model. The options are:
|
lambda |
value of the Box-Cox transformation parameter. Two particular cases
are |
col.data |
indicates the column number for the data
to be plotted. Only valid if more than one data-set is available
i.e., if the argument |
weights.divide |
if a vector of weights with the same length as
the data is provided each data is
divided by the corresponding element in this vector.
Defaults divides the data by the element |
lowess |
logical. Indicates whether the function
|
scatter3d |
logical. If |
density |
logical. If |
rug |
logical. If |
qt.col |
colors for the quartiles in the first plot. If missing defaults to blue, green, yellow and red. |
... |
further arguments to be passed to the function
|
A plot is produced on the graphics device. No values are returned.
Paulo J. Ribeiro Jr. paulojus@leg.ufpr.br,
Peter J. Diggle p.diggle@lancaster.ac.uk.
Further information on the package geoR can be found at:
http://www.leg.ufpr.br/geoR/.
points.geodata
,
scatterplot3d
, lowess
,
density
, rug
.
require(geoR)
plot(s100)
plot(s100, scatter3d=TRUE)
plot(s100, qt.col=1)
plot(ca20) # original data
plot(ca20, trend=~altitude+area) # residuals from an external trend
plot(ca20, trend='1st') # residuals from a polynomial trend
plot(sic.100, bor=sic.borders) # original data
plot(sic.100, bor=sic.borders, lambda=0) # logarithm of the data
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