Displaying 4 results from an estimated 4 matches for "undistort".
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2010 Mar 15
1
map2poly - map lat/long cannot be unconstrained?
...o not line up correctly
on the map.
In the code below, notice that I have set the same axis ranges on each plot
for x (longitude) and y (latitude), and yet it has no effect on the map
plot. For example, if I stretch the plot window vertically, the y data
spread out accordingly, but the map remains undistorted. No matter how much
I try to stretch the y axis, the y:x ratio scale remains 1:1. How do I force
the map to fit the ranges that I set like I can for the data plot?
*# make the map object "lakes"
tmp <- read.shape('D:/R/great lakes map/grtlakes/grtlakes')
lakes <- Map2poly...
2011 Jun 16
0
Update: Is there an implementation of loess with more than 3 parametric predictors or a trick to a similar effect?
...oordinates x,y) for local regression but a number of global
("parametric") offsets that I need to consider.
Essentially, we have a spatial distortion s(x,y) overlaid over a
number of measurements z:
z_i = s(x_i,y_i) + v_{g_i}
These measurements z can be grouped by the same underlying undistorted
measurement value v for each group g. The group membership g_i is
known for each measurement, but the underlying undistorted measurement
values v_g for the groups are not known and should be determined by
(global, not local) regression.
We need to estimate the two-dimensional spatial trend s(x,y...
2011 Jun 11
0
Is there an implementation of loess with more than 3 parametric predictors or a trick to a similar effect? [re-posting as plain text to pass char-set filter]
...fferent in that I have only 2 predictors
(coordinates x,y) for local regression but a number of global
("parametric") offsets that I need to consider.
Essentially, I have a spatial distortion overlaid over a number of
measurements. These measurements can be grouped by the same underlying
undistorted measurement value for each group. The groups are known,
but the values are not. We need to estimate the spatial trend, which
we then want to remove. In our application, the spatial trend is
two-dimensional (x,y), and there are about 20 groups of about 50
measurements each, in the most simple scen...
2011 Jun 11
1
Is there an implementation loess with more than 4 parametric predictors or a trick to similar effect?
Dear R experts,
I have a problem that is a related to the question raised in this earlier
post
https://stat.ethz.ch/pipermail/r-help/2007-January/124064.html
My situation is different in that I have only 2 predictors (coordinates x,y)
for local regression but a number of global ("parametric") offsets that I
need to consider.
Essentially, I have a spatial distortion overlaid over a