vario: vario
In synchrony: Methods for Computing Spatial, Temporal, and Spatiotemporal Statistics
Description
Usage
Arguments
Details
Value
Author(s)
References
See Also
Examples
Description
Compute the empirical variogram and determine its significance via Monte Carlo randomizations
Usage
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vario (n.bins = 20, size.bins = NULL, extent = 0.5, data, data2 = NULL,
is.latlon = TRUE, is.centered = FALSE, nrands = 0,
type = c("semivar", "cov", "pearson",
"spearman", "kendall", "moran", "geary"),
alternative = c("one.tailed", "two.tailed"),
mult.test.corr = c("none", "holm", "hochberg", "bonferroni"),
regional = c("all", "extent"),
quiet = FALSE)
Arguments
n.bins
Number of bins or lag distances. This argument is only used when
size.bins=NULL
size.bins
Size of bins in units of distance (e.g., km). When specified, this argument overrides
n.bins. Default is NULL
extent
Proportion of the spatial extent of the data over which to compute
the variogram. Default is 0.5 to limit potentially spurious results due to the
limited number of data points at large lag distances.
data
n x m matrix containing y-coordinates (or latitude),
x-coordinates (or longitude), and values. The values can either be
a single column of observations at each site for univariate variograms
or a matrix of observations at each site for multivariate variograms
(e.g., to compute spatial synchrony).
data2
n x m matrix containing y-coordinates (or latitude),
x-coordinates (or longitude), and values for second variable. The values
can either be a single column of observations at each site for univariate
variograms or a matrix of observations at each site for multivariate
variograms (e.g., to compute spatial synchrony).
is.latlon
Are coordinates latitudes/longitudes? Default is TRUE
is.centered
Should the variogram be centered by subtracting the regional mean
from each value? If so, the zero-line represents the regional mean. Default is FALSE
nrands
Number of randomizations to determine statistical significance
of variogram. Default is 0.
type
Type of variogram to compute. Default is semivar for semivariance.
Other options include cov for covariance, pearson for Pearson
correlation, spearman for Spearman correlation, kendall for Kendall correlation,
moran for Moran's I, and geary for Geary's C
alternative
Conduct a one-tailed or a two-tailed test? Note that the statistical test
is to determine whether the local value within each lag distance is different from the regional
mean. If the variogram is centered, the null hypothesis is that the local values are equal to zero.
If the variogram is not centered, the null hypothesis is that the local values are equal to the
regional mean. Default is one.tailed
mult.test.corr
Correct for multiple tests? Default is "none". Other options include
holm, hochberg and bonferroni
regional
Should the regional average be computed for the entire dataset (all)
or just the extent specified (extent). Default is the entire dataset (all)
quiet
Suppress progress bar when set to TRUE. Default is FALSE
Details
This function can be used to compute univariate correlograms using Moran's I,
Geary's C, and the covariance function or variograms using the semivariance function.
Multivariate (Mantel) correlograms can also be computed using the covariance function,
Pearson's, Spearman's or Kendall's correlation coefficients. Cross-correlograms/variograms
between data1 and data2 can be computed with the covariance function,
Pearson's, Spearman's or Kendall's correlation coefficients for multivariate
variograms and Moran's I, Geary's C, the covariance function, or semivariance
for univariate variograms.
Value
Returns a named list containing the following variables:
bins
Center of each lag/bin
mean.bin.dist
Mean distance of each lag/bin
vario
Variogram values in each lag/bin
npoints
Number of pairs of points in each lag/bin
metric
Type of variogram computed
is.centered
Is the variogram centered?
regional.mean
Regional mean value
pvals
p-value for each lag/bin.
This variable is only returned if nrands > 0.
rands
nrands x n.bins matrix of randomizations.
This variable is only returned if nrands > 0.
alternative
One-tailed or two-tailed test?
This variable is only returned if nrands > 0.
mult.test.corr
Correct for multiple tests?
This variable is only returned if nrands > 0.
is.multivar
Was the analysis performed on multivariate data?
Author(s)
Tarik C. Gouhier (tarik.gouhier@gmail.com)
References
Bjornstad, O. N., and W. Falck. 2001. Nonparametric spatial covariance functions:
Estimation and testing. Environmental and Ecological Statistics 8:53-70.
Bjornstad, O. N., R. A. Ims, and X. Lambin. 1999. Spatial population dynamics:
analyzing patterns and processes of population synchrony. Trends in Ecology & Evolution 14:427-432.
Fortin, M. J., and M. R. T. Dale. 2005. Spatial Analysis: A Guide for Ecologists. Cambridge University Press.
See Also
Examples
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data(pisco.data)
d=subset(pisco.data, subset=year==2000, select=c("latitude", "longitude", "sst"))
semiv=vario(data=d)
moran=vario(data=d, type="moran", nrands=100)
par(mfrow=c(2,1), mar=c(4.2, 4, 1, 1))
plot(semiv$mean.bin.dist, semiv$vario, xlab="Lag distance (km)", ylab="Semivariance")
plot(moran$mean.bin.dist, moran$vario, xlab="Lag distance (km)", ylab="Moran's I", t="l")
points(moran$mean.bin.dist[moran$pvals >= 0.05], moran$vario[moran$pvals >= 0.05],
bg="white", pch=21)
points(moran$mean.bin.dist[moran$pvals < 0.05], moran$vario[moran$pvals < 0.05],
bg="black", pch=21)
abline(h=0, lty=2)
# Compute spatial synchrony
d.upw=subset(pisco.data, select=c("latitude", "longitude", "year", "upwelling"))
d.cov=subset(pisco.data, select=c("latitude", "longitude", "year", "mussel_abund"))
# Reshape the data
d.upw.wide=reshape(data=d.upw, idvar=c("latitude", "longitude"), timevar=c("year"),
direction="wide")
d.cov.wide=reshape(data=d.cov, idvar=c("latitude", "longitude"), timevar=c("year"),
direction="wide")
# Generate variograms
v.upw=vario(n.bins=12, data=d.upw.wide, type="pearson", extent=1, nrands=999)
v.cov=vario(n.bins=12, data=d.cov.wide, type="pearson", extent=1, nrands=999)
## Fit variograms
v.cov.per=vario.fit(v.cov$vario, v.cov$mean.bin.dist, type="period",
start.vals=list(a=1, b=3, c=0))
v.upw.lin=vario.fit(v.upw$vario, v.upw$mean.bin.dist, type="linear")
par(mfrow=c(2,1))
plot(v.cov, xlab="Lag distance (km)", bg.sig="red", col.nonsig="red",
main="Mussel cover",
rug=TRUE, ylim=c(-0.3, 0.3))
lines(v.cov$mean.bin.dist, v.cov.per$fit, col="red")
plot(v.upw, xlab="Lag distance (km)", bg.sig="blue", col.nonsig="blue",
main="Upwelling", rug=TRUE)
lines(v.upw$mean.bin.dist, v.upw.lin$fit, col="blue")
Example output
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synchrony documentation built on March 26, 2020, 7:14 p.m.
R Package Documentation
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Description Usage Arguments Details Value Author(s) References See Also Examples
Description
Compute the empirical variogram and determine its significance via Monte Carlo randomizations
Usage
1 2 3 4 5 6 7 8 | vario (n.bins = 20, size.bins = NULL, extent = 0.5, data, data2 = NULL,
is.latlon = TRUE, is.centered = FALSE, nrands = 0,
type = c("semivar", "cov", "pearson",
"spearman", "kendall", "moran", "geary"),
alternative = c("one.tailed", "two.tailed"),
mult.test.corr = c("none", "holm", "hochberg", "bonferroni"),
regional = c("all", "extent"),
quiet = FALSE)
|
Arguments
n.bins |
Number of bins or lag distances. This argument is only used when
|
size.bins |
Size of bins in units of distance (e.g., km). When specified, this argument overrides
|
extent |
Proportion of the spatial extent of the data over which to compute the variogram. Default is 0.5 to limit potentially spurious results due to the limited number of data points at large lag distances. |
data |
|
data2 |
|
is.latlon |
Are coordinates latitudes/longitudes? Default is |
is.centered |
Should the variogram be centered by subtracting the regional mean
from each value? If so, the zero-line represents the regional mean. Default is |
nrands |
Number of randomizations to determine statistical significance of variogram. Default is 0. |
type |
Type of variogram to compute. Default is |
alternative |
Conduct a one-tailed or a two-tailed test? Note that the statistical test
is to determine whether the local value within each lag distance is different from the regional
mean. If the variogram is centered, the null hypothesis is that the local values are equal to zero.
If the variogram is not centered, the null hypothesis is that the local values are equal to the
regional mean. Default is |
mult.test.corr |
Correct for multiple tests? Default is |
regional |
Should the regional average be computed for the entire dataset ( |
quiet |
Suppress progress bar when set to |
Details
This function can be used to compute univariate correlograms using Moran's I,
Geary's C, and the covariance function or variograms using the semivariance function.
Multivariate (Mantel) correlograms can also be computed using the covariance function,
Pearson's, Spearman's or Kendall's correlation coefficients. Cross-correlograms/variograms
between data1 and data2 can be computed with the covariance function,
Pearson's, Spearman's or Kendall's correlation coefficients for multivariate
variograms and Moran's I, Geary's C, the covariance function, or semivariance
for univariate variograms.
Value
Returns a named list containing the following variables:
bins |
Center of each lag/bin |
mean.bin.dist |
Mean distance of each lag/bin |
vario |
Variogram values in each lag/bin |
npoints |
Number of pairs of points in each lag/bin |
metric |
Type of variogram computed |
is.centered |
Is the variogram centered? |
regional.mean |
Regional mean value |
pvals |
p-value for each lag/bin.
This variable is only returned if |
rands |
|
alternative |
One-tailed or two-tailed test?
This variable is only returned if |
mult.test.corr |
Correct for multiple tests?
This variable is only returned if |
is.multivar |
Was the analysis performed on multivariate data? |
Author(s)
Tarik C. Gouhier (tarik.gouhier@gmail.com)
References
Bjornstad, O. N., and W. Falck. 2001. Nonparametric spatial covariance functions: Estimation and testing. Environmental and Ecological Statistics 8:53-70.
Bjornstad, O. N., R. A. Ims, and X. Lambin. 1999. Spatial population dynamics: analyzing patterns and processes of population synchrony. Trends in Ecology & Evolution 14:427-432.
Fortin, M. J., and M. R. T. Dale. 2005. Spatial Analysis: A Guide for Ecologists. Cambridge University Press.
See Also
Examples
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 | data(pisco.data)
d=subset(pisco.data, subset=year==2000, select=c("latitude", "longitude", "sst"))
semiv=vario(data=d)
moran=vario(data=d, type="moran", nrands=100)
par(mfrow=c(2,1), mar=c(4.2, 4, 1, 1))
plot(semiv$mean.bin.dist, semiv$vario, xlab="Lag distance (km)", ylab="Semivariance")
plot(moran$mean.bin.dist, moran$vario, xlab="Lag distance (km)", ylab="Moran's I", t="l")
points(moran$mean.bin.dist[moran$pvals >= 0.05], moran$vario[moran$pvals >= 0.05],
bg="white", pch=21)
points(moran$mean.bin.dist[moran$pvals < 0.05], moran$vario[moran$pvals < 0.05],
bg="black", pch=21)
abline(h=0, lty=2)
# Compute spatial synchrony
d.upw=subset(pisco.data, select=c("latitude", "longitude", "year", "upwelling"))
d.cov=subset(pisco.data, select=c("latitude", "longitude", "year", "mussel_abund"))
# Reshape the data
d.upw.wide=reshape(data=d.upw, idvar=c("latitude", "longitude"), timevar=c("year"),
direction="wide")
d.cov.wide=reshape(data=d.cov, idvar=c("latitude", "longitude"), timevar=c("year"),
direction="wide")
# Generate variograms
v.upw=vario(n.bins=12, data=d.upw.wide, type="pearson", extent=1, nrands=999)
v.cov=vario(n.bins=12, data=d.cov.wide, type="pearson", extent=1, nrands=999)
## Fit variograms
v.cov.per=vario.fit(v.cov$vario, v.cov$mean.bin.dist, type="period",
start.vals=list(a=1, b=3, c=0))
v.upw.lin=vario.fit(v.upw$vario, v.upw$mean.bin.dist, type="linear")
par(mfrow=c(2,1))
plot(v.cov, xlab="Lag distance (km)", bg.sig="red", col.nonsig="red",
main="Mussel cover",
rug=TRUE, ylim=c(-0.3, 0.3))
lines(v.cov$mean.bin.dist, v.cov.per$fit, col="red")
plot(v.upw, xlab="Lag distance (km)", bg.sig="blue", col.nonsig="blue",
main="Upwelling", rug=TRUE)
lines(v.upw$mean.bin.dist, v.upw.lin$fit, col="blue")
|
Example output
synchrony 0.2.3 loaded.
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