aple.plot: Approximate profile-likelihood estimator (APLE) scatterplot
In spdep: Spatial Dependence: Weighting Schemes, Statistics and Models
Description
Usage
Arguments
Details
Value
Author(s)
References
See Also
Examples
Description
A scatterplot decomposition of the approximate profile-likelihood estimator, and a local APLE based on the list of vectors returned by the scatterplot function.
Usage
1
2
Arguments
x
a zero-mean detrended continuous variable
listw
a listw object from for example nb2listw
override\_similarity\_check
default FALSE, if TRUE - typically for row-standardised weights with asymmetric underlying general weights - similarity is not checked
useTrace
default TRUE, use trace of sparse matrix W %*% W (Li et al. (2010)), if FALSE, use crossproduct of eigenvalues of W as in Li et al. (2007)
do.plot
default TRUE: should a scatterplot be drawn
...
other arguments to be passed to plot
Details
The function solves a secondary eigenproblem of size n internally, so constructing the values for the scatterplot is quite compute and memory intensive, and is not suitable for very large n.
Value
aple.plot returns list with components:
X
A vector as described in Li et al. (2007), p. 366.
Y
A vector as described in Li et al. (2007), p. 367.
localAple returns a vector of local APLE values.
Author(s)
Roger Bivand Roger.Bivand@nhh.no
References
Li, H, Calder, C. A. and Cressie N. A. C. (2007) Beyond Moran's I: testing for spatial dependence based on the spatial autoregressive model. Geographical Analysis 39, pp. 357-375; Li, H, Calder, C. A. and Cressie N. A. C. (2012) One-step estimation of spatial dependence parameters: Properties and extensions of the APLE statistic, Journal of Multivariate Analysis 105, 68-84.
See Also
Examples
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
## Not run:
example(aple)
plt_out <- aple.plot(as.vector(scale(wheat$yield_detrend, scale=FALSE)),
nb2listw(nbr12, style="W"), cex=0.6)
crossprod(plt_out$Y, plt_out$X)/crossprod(plt_out$X)
lm_obj <- lm(Y ~ X, plt_out)
abline(lm_obj)
abline(v=0, h=0, lty=2)
zz <- summary(influence.measures(lm_obj))
infl <- as.integer(rownames(zz))
points(plt_out$X[infl], plt_out$Y[infl], pch=3, cex=0.6, col="red")
wheat$localAple <- localAple(as.vector(scale(wheat$yield_detrend, scale=FALSE)),
nb2listw(nbr12, style="W"))
mean(wheat$localAple)
hist(wheat$localAple)
spl <- list("sp.text", coordinates(wheat)[infl,], rep("*", length(infl)))
spplot(wheat, "localAple", sp.layout=spl)
## End(Not run)
Example output
Loading required package: sp
Loading required package: Matrix
aple> example(wheat)
wheat> ## Not run:
wheat> ##D data(wheat)
wheat> ##D wheat$lat1 <- 69 - wheat$lat
wheat> ##D wheat$r <- factor(wheat$lat1)
wheat> ##D wheat$c <- factor(wheat$lon)
wheat> ##D wheat_sp <- wheat
wheat> ##D coordinates(wheat_sp) <- c("lon", "lat1")
wheat> ##D wheat_spg <- wheat_sp
wheat> ##D gridded(wheat_spg) <- TRUE
wheat> ##D wheat_spl <- as(wheat_spg, "SpatialPolygons")
wheat> ##D df <- as(wheat_spg, "data.frame")
wheat> ##D row.names(df) <- sapply(slot(wheat_spl, "polygons"),
wheat> ##D function(x) slot(x, "ID"))
wheat> ##D wheat <- SpatialPolygonsDataFrame(wheat_spl, data=df)
wheat> ## End(Not run)
wheat> require(maptools)
Loading required package: maptools
Checking rgeos availability: TRUE
wheat> wheat <- readShapeSpatial(system.file("etc/shapes/wheat.shp",
wheat+ package="spdep")[1])
aple> nbr1 <- poly2nb(wheat, queen=FALSE)
aple> nbrl <- nblag(nbr1, 2)
aple> nbr12 <- nblag_cumul(nbrl)
aple> cms0 <- with(as(wheat, "data.frame"), tapply(yield, c, median))
aple> cms1 <- c(model.matrix(~ factor(c) -1, data=wheat) %*% cms0)
aple> wheat$yield_detrend <- wheat$yield - cms1
aple> isTRUE(all.equal(c(with(as(wheat, "data.frame"),
aple+ tapply(yield_detrend, c, median))), rep(0.0, 25),
aple+ check.attributes=FALSE))
[1] TRUE
aple> moran.test(wheat$yield_detrend, nb2listw(nbr12, style="W"))
Moran I test under randomisation
data: wheat$yield_detrend
weights: nb2listw(nbr12, style = "W")
Moran I statistic standard deviate = 10.305, p-value < 2.2e-16
alternative hypothesis: greater
sample estimates:
Moran I statistic Expectation Variance
0.1935469610 -0.0020040080 0.0003600869
aple> aple(as.vector(scale(wheat$yield_detrend, scale=FALSE)), nb2listw(nbr12, style="W"))
[1] 0.6601805
aple> ## No test:
aple> ##D errorsarlm(yield_detrend ~ 1, wheat, nb2listw(nbr12, style="W"))
aple> ## End(No test)
aple>
aple>
aple>
Warning messages:
1: use rgdal::readOGR or sf::st_read
2: use rgdal::readOGR or sf::st_read
[,1]
[1,] 0.6601805
Potentially influential observations of
lm(formula = Y ~ X, data = plt_out) :
dfb.1_ dfb.X dffit cov.r cook.d hat
34 -0.12 0.01 -0.12 0.98_* 0.01 0.00
50 0.10 0.01 0.11 0.98_* 0.01 0.00
60 0.00 0.00 0.00 1.01_* 0.00 0.01
118 0.01 0.01 0.02 1.01_* 0.00 0.01
137 -0.10 -0.01 -0.10 0.98_* 0.01 0.00
143 -0.02 -0.04 -0.05 1.01_* 0.00 0.01
157 -0.10 -0.05 -0.11 0.99_* 0.01 0.00
166 0.00 0.00 0.00 1.02_* 0.00 0.02_*
168 0.01 0.02 0.03 1.01_* 0.00 0.01
176 -0.10 0.17 -0.20_* 0.99 0.02 0.01
177 0.03 -0.07 0.08 1.01_* 0.00 0.01
191 0.01 0.04 0.04 1.02_* 0.00 0.02_*
192 0.10 0.18 0.20_* 0.99 0.02 0.01
201 -0.10 0.07 -0.12 0.99_* 0.01 0.00
216 0.02 0.05 0.05 1.02_* 0.00 0.01_*
217 0.03 0.08 0.09 1.02_* 0.00 0.01_*
225 -0.11 -0.07 -0.13 0.98_* 0.01 0.00
237 -0.10 0.02 -0.10 0.99_* 0.01 0.00
242 -0.02 -0.04 -0.04 1.01_* 0.00 0.01
287 0.14 -0.23 0.27_* 0.97_* 0.04 0.01
290 -0.18 -0.35 -0.40_* 0.95_* 0.08 0.01
295 -0.01 -0.01 -0.01 1.01_* 0.00 0.01
322 0.00 0.00 0.00 1.01_* 0.00 0.01
325 -0.10 -0.07 -0.12 0.99_* 0.01 0.00
351 0.19 -0.04 0.20_* 0.94_* 0.02 0.00
369 0.01 -0.03 0.03 1.01_* 0.00 0.01
376 -0.05 -0.13 -0.14 1.02_* 0.01 0.02_*
392 -0.04 0.08 -0.09 1.01_* 0.00 0.01
393 -0.03 0.06 -0.07 1.01_* 0.00 0.01
394 -0.01 0.03 -0.03 1.01_* 0.00 0.01
402 0.10 0.02 0.10 0.99_* 0.01 0.00
429 0.13 -0.10 0.16 0.98_* 0.01 0.00
430 -0.11 -0.23 -0.25_* 0.99 0.03 0.01
438 0.00 -0.01 -0.01 1.01_* 0.00 0.01
442 -0.01 0.04 -0.04 1.02_* 0.00 0.02_*
443 -0.01 0.02 -0.02 1.02_* 0.00 0.01_*
461 0.02 0.04 0.04 1.01_* 0.00 0.01
462 0.01 0.03 0.03 1.03_* 0.00 0.02_*
466 0.01 -0.02 0.02 1.02_* 0.00 0.01_*
467 -0.03 0.08 -0.08 1.02_* 0.00 0.01_*
468 0.02 -0.04 0.04 1.01_* 0.00 0.01
480 0.13 0.05 0.14 0.97_* 0.01 0.00
488 0.16 0.09 0.18 0.96_* 0.02 0.00
492 -0.13 0.12 -0.17 0.98_* 0.01 0.00
Warning message:
In (length(aplepl$X) * aplepl$Y * aplepl$X)/crossprod(aplepl$X) :
Recycling array of length 1 in vector-array arithmetic is deprecated.
Use c() or as.vector() instead.
[1] 0.6601805
spdep documentation built on Aug. 19, 2017, 3:01 a.m.
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Description Usage Arguments Details Value Author(s) References See Also Examples
Description
A scatterplot decomposition of the approximate profile-likelihood estimator, and a local APLE based on the list of vectors returned by the scatterplot function.
Usage
1 2 |
Arguments
x |
a zero-mean detrended continuous variable |
listw |
a |
override\_similarity\_check |
default FALSE, if TRUE - typically for row-standardised weights with asymmetric underlying general weights - similarity is not checked |
useTrace |
default TRUE, use trace of sparse matrix |
do.plot |
default TRUE: should a scatterplot be drawn |
... |
other arguments to be passed to |
Details
The function solves a secondary eigenproblem of size n internally, so constructing the values for the scatterplot is quite compute and memory intensive, and is not suitable for very large n.
Value
aple.plot returns list with components:
X |
A vector as described in Li et al. (2007), p. 366. |
Y |
A vector as described in Li et al. (2007), p. 367. |
localAple returns a vector of local APLE values.
Author(s)
Roger Bivand Roger.Bivand@nhh.no
References
Li, H, Calder, C. A. and Cressie N. A. C. (2007) Beyond Moran's I: testing for spatial dependence based on the spatial autoregressive model. Geographical Analysis 39, pp. 357-375; Li, H, Calder, C. A. and Cressie N. A. C. (2012) One-step estimation of spatial dependence parameters: Properties and extensions of the APLE statistic, Journal of Multivariate Analysis 105, 68-84.
See Also
Examples
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 | ## Not run:
example(aple)
plt_out <- aple.plot(as.vector(scale(wheat$yield_detrend, scale=FALSE)),
nb2listw(nbr12, style="W"), cex=0.6)
crossprod(plt_out$Y, plt_out$X)/crossprod(plt_out$X)
lm_obj <- lm(Y ~ X, plt_out)
abline(lm_obj)
abline(v=0, h=0, lty=2)
zz <- summary(influence.measures(lm_obj))
infl <- as.integer(rownames(zz))
points(plt_out$X[infl], plt_out$Y[infl], pch=3, cex=0.6, col="red")
wheat$localAple <- localAple(as.vector(scale(wheat$yield_detrend, scale=FALSE)),
nb2listw(nbr12, style="W"))
mean(wheat$localAple)
hist(wheat$localAple)
spl <- list("sp.text", coordinates(wheat)[infl,], rep("*", length(infl)))
spplot(wheat, "localAple", sp.layout=spl)
## End(Not run)
|
Example output
Loading required package: sp
Loading required package: Matrix
aple> example(wheat)
wheat> ## Not run:
wheat> ##D data(wheat)
wheat> ##D wheat$lat1 <- 69 - wheat$lat
wheat> ##D wheat$r <- factor(wheat$lat1)
wheat> ##D wheat$c <- factor(wheat$lon)
wheat> ##D wheat_sp <- wheat
wheat> ##D coordinates(wheat_sp) <- c("lon", "lat1")
wheat> ##D wheat_spg <- wheat_sp
wheat> ##D gridded(wheat_spg) <- TRUE
wheat> ##D wheat_spl <- as(wheat_spg, "SpatialPolygons")
wheat> ##D df <- as(wheat_spg, "data.frame")
wheat> ##D row.names(df) <- sapply(slot(wheat_spl, "polygons"),
wheat> ##D function(x) slot(x, "ID"))
wheat> ##D wheat <- SpatialPolygonsDataFrame(wheat_spl, data=df)
wheat> ## End(Not run)
wheat> require(maptools)
Loading required package: maptools
Checking rgeos availability: TRUE
wheat> wheat <- readShapeSpatial(system.file("etc/shapes/wheat.shp",
wheat+ package="spdep")[1])
aple> nbr1 <- poly2nb(wheat, queen=FALSE)
aple> nbrl <- nblag(nbr1, 2)
aple> nbr12 <- nblag_cumul(nbrl)
aple> cms0 <- with(as(wheat, "data.frame"), tapply(yield, c, median))
aple> cms1 <- c(model.matrix(~ factor(c) -1, data=wheat) %*% cms0)
aple> wheat$yield_detrend <- wheat$yield - cms1
aple> isTRUE(all.equal(c(with(as(wheat, "data.frame"),
aple+ tapply(yield_detrend, c, median))), rep(0.0, 25),
aple+ check.attributes=FALSE))
[1] TRUE
aple> moran.test(wheat$yield_detrend, nb2listw(nbr12, style="W"))
Moran I test under randomisation
data: wheat$yield_detrend
weights: nb2listw(nbr12, style = "W")
Moran I statistic standard deviate = 10.305, p-value < 2.2e-16
alternative hypothesis: greater
sample estimates:
Moran I statistic Expectation Variance
0.1935469610 -0.0020040080 0.0003600869
aple> aple(as.vector(scale(wheat$yield_detrend, scale=FALSE)), nb2listw(nbr12, style="W"))
[1] 0.6601805
aple> ## No test:
aple> ##D errorsarlm(yield_detrend ~ 1, wheat, nb2listw(nbr12, style="W"))
aple> ## End(No test)
aple>
aple>
aple>
Warning messages:
1: use rgdal::readOGR or sf::st_read
2: use rgdal::readOGR or sf::st_read
[,1]
[1,] 0.6601805
Potentially influential observations of
lm(formula = Y ~ X, data = plt_out) :
dfb.1_ dfb.X dffit cov.r cook.d hat
34 -0.12 0.01 -0.12 0.98_* 0.01 0.00
50 0.10 0.01 0.11 0.98_* 0.01 0.00
60 0.00 0.00 0.00 1.01_* 0.00 0.01
118 0.01 0.01 0.02 1.01_* 0.00 0.01
137 -0.10 -0.01 -0.10 0.98_* 0.01 0.00
143 -0.02 -0.04 -0.05 1.01_* 0.00 0.01
157 -0.10 -0.05 -0.11 0.99_* 0.01 0.00
166 0.00 0.00 0.00 1.02_* 0.00 0.02_*
168 0.01 0.02 0.03 1.01_* 0.00 0.01
176 -0.10 0.17 -0.20_* 0.99 0.02 0.01
177 0.03 -0.07 0.08 1.01_* 0.00 0.01
191 0.01 0.04 0.04 1.02_* 0.00 0.02_*
192 0.10 0.18 0.20_* 0.99 0.02 0.01
201 -0.10 0.07 -0.12 0.99_* 0.01 0.00
216 0.02 0.05 0.05 1.02_* 0.00 0.01_*
217 0.03 0.08 0.09 1.02_* 0.00 0.01_*
225 -0.11 -0.07 -0.13 0.98_* 0.01 0.00
237 -0.10 0.02 -0.10 0.99_* 0.01 0.00
242 -0.02 -0.04 -0.04 1.01_* 0.00 0.01
287 0.14 -0.23 0.27_* 0.97_* 0.04 0.01
290 -0.18 -0.35 -0.40_* 0.95_* 0.08 0.01
295 -0.01 -0.01 -0.01 1.01_* 0.00 0.01
322 0.00 0.00 0.00 1.01_* 0.00 0.01
325 -0.10 -0.07 -0.12 0.99_* 0.01 0.00
351 0.19 -0.04 0.20_* 0.94_* 0.02 0.00
369 0.01 -0.03 0.03 1.01_* 0.00 0.01
376 -0.05 -0.13 -0.14 1.02_* 0.01 0.02_*
392 -0.04 0.08 -0.09 1.01_* 0.00 0.01
393 -0.03 0.06 -0.07 1.01_* 0.00 0.01
394 -0.01 0.03 -0.03 1.01_* 0.00 0.01
402 0.10 0.02 0.10 0.99_* 0.01 0.00
429 0.13 -0.10 0.16 0.98_* 0.01 0.00
430 -0.11 -0.23 -0.25_* 0.99 0.03 0.01
438 0.00 -0.01 -0.01 1.01_* 0.00 0.01
442 -0.01 0.04 -0.04 1.02_* 0.00 0.02_*
443 -0.01 0.02 -0.02 1.02_* 0.00 0.01_*
461 0.02 0.04 0.04 1.01_* 0.00 0.01
462 0.01 0.03 0.03 1.03_* 0.00 0.02_*
466 0.01 -0.02 0.02 1.02_* 0.00 0.01_*
467 -0.03 0.08 -0.08 1.02_* 0.00 0.01_*
468 0.02 -0.04 0.04 1.01_* 0.00 0.01
480 0.13 0.05 0.14 0.97_* 0.01 0.00
488 0.16 0.09 0.18 0.96_* 0.02 0.00
492 -0.13 0.12 -0.17 0.98_* 0.01 0.00
Warning message:
In (length(aplepl$X) * aplepl$Y * aplepl$X)/crossprod(aplepl$X) :
Recycling array of length 1 in vector-array arithmetic is deprecated.
Use c() or as.vector() instead.
[1] 0.6601805
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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