Adds rank information to a coh or wlmtest object

Description

When a coh or wlmtets object is created, the ranks slot is NA. This function fills it in.

Usage

addranks(obj)

Arguments

Value

addranks returns another coh or wlmtest object with ranks slot now included. If obj$ranks was not NA, the object is returned as is.

Note

Internal function, no error checking performed

Author(s)

Thomas Anderson, anderstl@gmail.com, Jon Walter, jaw3es@virginia.edu; Lawrence Sheppard, lwsheppard@ku.edu; Daniel Reuman, reuman@ku.edu

See Also

coh, wlmtest, bandtest, browseVignettes("wsyn")

Adds wavelet mean field information to a clust object

Description

When a clust object is created, the wmfs slot is NA. This function fills it in.

Usage

addwmfs(obj)

Arguments

Details

This function uses the values of scale.min, scale.max.input, sigma and f0 stored in obj$methodspecs. It is possible to create a clust object with bad values for these slots. This function throws an error in that case. You can use a correlation-based method for calculating the synchrony matrix and still pass values of scale.min, scale.max.input, sigma and f0 to clust (in fact, this happens by default) - they won't be used by clust, but they will be there for later use by addwmfs and addwpmfs.

Value

addwmfs returns another clust object with wmfs slot now included. If obj$wmfs was not NA, the object is returned as is.

Author(s)

Daniel Reuman, reuman@ku.edu

See Also

clust, addwpmfs, browseVignettes("wsyn")

Examples

sig<-matrix(.8,5,5)
diag(sig)<-1
lents<-50
if (requireNamespace("mvtnorm",quietly=TRUE))
{
  dat1<-t(mvtnorm::rmvnorm(lents,mean=rep(0,5),sigma=sig))
  dat2<-t(mvtnorm::rmvnorm(lents,mean=rep(0,5),sigma=sig))
}else
{
  dat1<-t(matrix(rep(rnorm(lents),times=5),lents,5))
  dat2<-t(matrix(rep(rnorm(lents),times=5),lents,5))
}
dat<-rbind(dat1,dat2)
times<-1:lents
dat<-cleandat(dat,times,clev=1)$cdat
coords<-data.frame(Y=rep(0,10),X=1:10)
method<-"coh.sig.fast"
clustobj<-clust(dat,times,coords,method,nsurrogs = 100)
res<-addwmfs(clustobj)

Adds wavelet phasor mean field information to a clust object

Description

When a clust object is created, the wpmfs slot is NA. This function fills it in, or adds to it.

Usage

addwpmfs(
  obj,
  level = 1:length(obj$clusters),
  sigmethod = "quick",
  nrand = 1000
)

Arguments

Details

This function uses the values of scale.min, scale.max.input, sigma and f0 stored in obj$methodspecs. It is possible to create a clust object with bad values for these slots. This function throws an error in that case. You can use a correlation-based method for calculating the synchrony matrix and still pass values of scale.min, scale.max.input, sigma and f0 to clust (in fact, this happens by default) - they won't be used by clust, but they will be there for later use by addwmfs and addwpmfs.

Value

addwpmfs returns another clust object with wpmfs slot now included, or more filled in than it was previously. With values of sigmethod other than "quick", this function can be slow, particularly with large nrand. So in that case the user may want to set level equal only to one clustering level of interest. Unlike wmf, old values in obj$wpmfs are overwritten.

Author(s)

Daniel Reuman, reuman@ku.edu

See Also

clust, addwmfs, browseVignettes("wsyn")

Examples

sig<-matrix(.8,5,5)
diag(sig)<-1
lents<-50
if (requireNamespace("mvtnorm",quietly=TRUE))
{
  dat1<-t(mvtnorm::rmvnorm(lents,mean=rep(0,5),sigma=sig))
  dat2<-t(mvtnorm::rmvnorm(lents,mean=rep(0,5),sigma=sig))
}else
{
  dat1<-t(matrix(rep(rnorm(lents),times=5),lents,5))
  dat2<-t(matrix(rep(rnorm(lents),times=5),lents,5))
}
dat<-rbind(dat1,dat2)
times<-1:lents
dat<-cleandat(dat,times,clev=1)$cdat
coords<-data.frame(Y=rep(0,10),X=1:10)
method<-"coh.sig.fast"
clustobj<-clust(dat,times,coords,method,nsurrogs = 100)
res<-addwpmfs(clustobj)

Aggregate significance across a timescale band

Description

Computes the aggregate significance of coherence (coh) or of a wavelet linear model test object (wlmtest) across a timescale band, accounting for non-independence of timescales. Also gets the average phase across the band, in the case of coherence.

Usage

bandtest(object, ...)

## Default S3 method:
bandtest(object, ...)

## S3 method for class 'coh'
bandtest(object, band, ...)

## S3 method for class 'wlmtest'
bandtest(object, band, ...)

Arguments

Value

bandtest returns an object of the same class as its first input but with a bandp slot added. Or if there was already a bandp slot, the output has a bandp slot with an additional row. For a coh object, the bandp slot is a data frame with four columns, the first two indicating the timescale band and the third an associated p-value for the test of coherence over that band. The fourth column is the average phase over the band. For a wlmtest object, the result is only the first three of the above columns.

Author(s)

Thomas Anderson, anderstl@gmail.com, Jon Walter, jaw3es@virginia.edu; Lawrence Sheppard, lwsheppard@ku.edu; Daniel Reuman, reuman@ku.edu

References

Sheppard, L.W., et al. (2016) Changes in large-scale climate alter spatial synchrony of aphid pests. Nature Climate Change. DOI: 10.1038/nclimate2881

See Also

coh, wlm, wlmtest, browseVignettes("wsyn")

Examples

#Example for a coh object
times<-(-3:100)
ts1<-sin(2*pi*times/10)
ts2<-5*sin(2*pi*times/3)
artsig_x<-matrix(NA,11,length(times)) #the driver
for (counter in 1:11)
{
  artsig_x[counter,]=ts1+ts2+rnorm(length(times),mean=0,sd=1.5)
}
times<-0:100
artsig_y<-matrix(NA,11,length(times)) #the driven
for (counter1 in 1:11)
{
  for (counter2 in 1:101)
  {
    artsig_y[counter1,counter2]<-mean(artsig_x[counter1,counter2:(counter2+2)])
  }
}
artsig_y<-artsig_y+matrix(rnorm(length(times)*11,mean=0,sd=3),11,length(times))
artsig_x<-artsig_x[,4:104]
artsig_x<-cleandat(artsig_x,times,1)$cdat
artsig_y<-cleandat(artsig_y,times,1)$cdat
cohobj<-coh(dat1=artsig_x,dat2=artsig_y,times=times,norm="powall",sigmethod="fast",nrand=1000,
            f0=0.5,scale.max.input=28)
cohobj<-bandtest(cohobj,c(2,4))
  
#Example for a wlmtest object - see vignette
  

The one-parameter family of Box-Cox transformations

Description

The one-parameter family of Box-Cox transformations

Usage

bctrans(y, lambda)

Arguments

Details

Internal function. No error checking done. It is assumed the entries of y are positive.

Value

bctrans gives ((y^lambda)-1)/lambda for lambda not 0 or ln(y) for lambda equal to 0.

Author(s)

Daniel Reuman, reuman@ku.edu

References

Box, GEP and Cox, DR (1964) An analysis of transformations (with discussion). Journal of the Royal Statistical Society B, 26, 211–252.

Venables, WN and Ripley, BD (2002) Modern Applied Statistics with S. Fourth edition. Springer.

See Also

cleandat, browseVignettes("wsyn")

Clean (spatio)temporal data matrices to make them ready for analyses using the wsyn package

Description

A data cleaning function for optimal Box-Cox transformation, detrending, standarizing variance, de-meaning

Usage

cleandat(dat, times, clev, lambdas = seq(-10, 10, by = 0.01), mints = NA)

Arguments

Details

NAs, Infs, etc. in dat trigger an error. If clev==1, time series are (individually) de-meaned. If clev==2, time series are (individually) linearly detrended and de-meaned. If clev==3, time series are (individually) linearly detrended and de-meaned, and variances are standardized to 1. If clev==4, an optimal Box-Cox normalization procedure is applied jointly to all time series (so the same Box-Cox transformation is applied to all time series after they are individually shifted depending on the value of mints). Transformed time series are then individually linearly detrended, de-meaned, and variances are standardized to 1. If clev==5, an optimal Box-Cox normalization procedure is applied to each time series individually (again after individually shifting according to mints), and transformed time series are then individually linearly detrended, de-meaned, and variances are standardized to 1. Constant time series and perfect linear trends trigger an error for clev>=3. If clev>=4 and the optimal lambda for one or more time series is a boundary case or if there is more than one optimal lambda, it triggers a warning. A wider range of lambda should be considered in the former case.

Value

cleandat returns a list containing the cleaned data, clev, and the optimal lambdas from the Box-Cox procedure (NA for clev<4, see details).

Author(s)

Jonathan Walter, jaw3es@virginia.edu; Lawrence Sheppard, lwsheppard@ku.edu; Daniel Reuman, reuman@ku.edu; Lei Zhao, lei.zhao@cau.edu.cn

References

Box, GEP and Cox, DR (1964) An analysis of transformations (with discussion). Journal of the Royal Statistical Society B, 26, 211–252.

Venables, WN and Ripley, BD (2002) Modern Applied Statistics with S. Fourth edition. Springer.

Sheppard, LW, et al. (2016) Changes in large-scale climate alter spatial synchrony of aphid pests. Nature Climate Change. DOI: 10.1038/nclimate2881

See Also

wt, wmf, wpmf, coh, wlm, wlmtest, clust, browseVignettes("wsyn")

Examples

times<-1:100
dat<-rnorm(100)
res1<-cleandat(dat,times,1) #this removes the mean
res2<-cleandat(dat,times,2) #detrends and removes the mean
res3<-cleandat(dat,times,3) #variances also standardized
res4<-cleandat(dat,times,4) #also joint Box-Cox applied
res5<-cleandat(dat,times,5) #1-3, also indiv Box-Cox

Community structure detection in networks

Description

Community structure detection in networks based on the leading eigenvector of the community matrix

Usage

cluseigen(adj)

Arguments

Details

The difference between this function and the algorithm described by Newman is that this function can be used on an adjacency matrix with negative elements, which is very common for correlation matrices and other measures of pairwise synchrony of time series.

Value

cluseigen returns a list with one element for each of the splits performed by the clustering algorithm. Each element is a vector with entries corresponding to rows and columns of adj and indicating the module membership of the node, following the split. The last element of the list is the final clustering determined by the algorithm when its halting condition is satisfied. The first element is always a vector of all 1s (corresponding to before any splits are performed).

Author(s)

Lei Zhao, lei.zhao@cau.edu.cn; Daniel Reuman, reuman@ku.edu

References

Gomez S., Jensen P. & Arenas A. (2009). Analysis of community structure in networks of correlated data. Phys Rev E, 80, 016114.

Newman M.E.J. (2006). Finding community structure in networks using the eigenvectors of matrices. Phys Rev E, 74, 036104.

Newman M.E.J. (2006) Modularity and community structure in networks. PNAS 103, 8577-8582.

See Also

clust, modularity, browseVignettes("wsyn")

Examples

adj<-matrix(0, 10, 10) # create a fake adjacency matrix
adj[lower.tri(adj)]<-runif(10*9/2, -1, 1)
adj<-adj+t(adj)
colnames(adj)<-letters[1:10]
z<-cluseigen(adj)

Detection and description of clusters of synchronous locations

Description

Generator function for the clust S3 class, which supports tools for detecting clusters (aka, modules, sub-networks, communities, etc.) of especially synchronous locations.

Usage

clust(
  dat,
  times,
  coords,
  method,
  tsrange = c(0, Inf),
  nsurrogs = 1000,
  scale.min = 2,
  scale.max.input = NULL,
  sigma = 1.05,
  f0 = 1,
  weighted = TRUE,
  sigthresh = 0.95
)

Arguments

Details

The following values are valid for method: "pearson", "pearson.sig.std", "pearson.sig.fft", "pearson.sig.aaft", "spearman", "spearman.sig.std", "spearman.sig.fft", "spearman.sig.aaft", "kendall", "kendall.sig.std", "kendall.sig.fft", "kendall.sig.aaft", "ReXWT", "ReXWT.sig.fft", "ReXWT.sig.aaft", "ReXWT.sig.fast", "coh", "coh.sig.fft", "coh.sig.aaft", "coh.sig.fast", "phasecoh", "phasecoh.sig.fft", and "phasecoh.sig.aaft". The first portions of these identifiers correspond to the Pearson, Spearman, and Kendall correlations, the real part of the cross-wavelet transform, the wavelet coherence, and the wavelet phase coherence. The second portions of these identifiers, when present, indicates that significance of the measure specified in the first portion of the identifies is to be used for establishing the synchrony matrix. Otherwise the value itself is used. The third part of the method identifier indicates what type of significance is used.

Significance testing is performed using standard approaches (method flag containg std; for correlation coefficients, although these are inappropriate for autocorrelated data), or surrogates generated using the Fourier (method flag containing "fft") or amplitude adjusted Fourier surrogates ("aaft"). For "coh" and "ReXWT", the fast testing algorithm of Sheppard et al. (2017) is also implemented ("fast"). That method uses implicit Fourier surrogates. The choice of wavelet coherence (method flag containing "coh") or the real part of the cross-wavelet transform (method flag containing "ReXWT") depends mainly on treatment of out-of-phase relationships. The "ReXWT" is more akin to a correlation coefficient in that strong in-phase relationships approach 1 and strong antiphase relationships approach -1. Wavelet coherence allows any phase relationship and ranges from 0 to 1. Power normalization is applied for "coh" and for "ReXWT". All significance tests are one-tailed. Synchrony matrices for significance-based methods when weighted is TRUE contain 1 minus the p-values.

Clustering is performed using the the eigenvector-based modularity method of Newman (2006).

Value

clust returns an object of class clust. Slots are:

Author(s)

Jonathan Walter, jaw3es@virginia.edu; Daniel Reuman, reuman@ku.edu; Lei Zhao, lei.zhao@cau.edu.cn

References

Walter, J. A., et al. (2017) The geography of spatial synchrony. Ecology Letters. doi: 10.1111/ele.12782

Newman M.E.J. (2006). Finding community structure in networks using the eigenvectors of matrices. Phys Rev E, 74, 036104.

Newman M.E.J. (2006) Modularity and community structure in networks. PNAS 103, 8577-8582.

See Also

cluseigen, modularity, addwmfs, addwpmfs,clust_methods, synmat, plotmap, browseVignettes("wsyn")

Examples

sig<-matrix(.8,5,5)
diag(sig)<-1
lents<-50
if (requireNamespace("mvtnorm",quietly=TRUE))
{
  dat1<-t(mvtnorm::rmvnorm(lents,mean=rep(0,5),sigma=sig))
  dat2<-t(mvtnorm::rmvnorm(lents,mean=rep(0,5),sigma=sig))
}else
{
  dat1<-t(matrix(rep(rnorm(lents),times=5),lents,5))
  dat2<-t(matrix(rep(rnorm(lents),times=5),lents,5))
}
dat<-rbind(dat1,dat2)
times<-1:lents
dat<-cleandat(dat,times,clev=1)$cdat
coords<-data.frame(Y=rep(0,10),X=1:10)
method<-"coh.sig.fast"
res<-clust(dat,times,coords,method,nsurrogs = 50)
#nsurrogs should be much higher for a real application

Basic methods for the clust class

Description

Set, get, summary, and print methods for the clust class.

Usage

## S3 method for class 'clust'
summary(object, ...)

## S3 method for class 'clust'
print(x, ...)

## S3 method for class 'clust'
set_times(obj, newval)

## S3 method for class 'clust'
set_adj(obj, newval)

## S3 method for class 'clust'
set_clusters(obj, newval)

## S3 method for class 'clust'
set_modres(obj, newval)

## S3 method for class 'clust'
set_mns(obj, newval)

## S3 method for class 'clust'
set_dat(obj, newval)

## S3 method for class 'clust'
set_coords(obj, newval)

## S3 method for class 'clust'
set_methodspecs(obj, newval)

## S3 method for class 'clust'
set_wmfs(obj, newval)

## S3 method for class 'clust'
set_wpmfs(obj, newval)

## S3 method for class 'clust'
get_times(obj)

## S3 method for class 'clust'
get_adj(obj)

## S3 method for class 'clust'
get_clusters(obj)

## S3 method for class 'clust'
get_modres(obj)

## S3 method for class 'clust'
get_mns(obj)

## S3 method for class 'clust'
get_dat(obj)

## S3 method for class 'clust'
get_coords(obj)

## S3 method for class 'clust'
get_methodspec(obj)

## S3 method for class 'clust'
get_wmfs(obj)

## S3 method for class 'clust'
get_wpmfs(obj)

Arguments

Value

summary.clust produces a summary of a clust object. A print.clust method is also available. For clust objects, set_* and get_* methods are available for all slots (see the documentation for clust for a list). The set_* methods just throw an error, to prevent breaking the consistency between the slots of a clust object.

Author(s)

Daniel Reuman, reuman@ku.edu

See Also

clust

Examples

sig<-matrix(.8,5,5)
diag(sig)<-1
lents<-50
if (requireNamespace("mvtnorm",quietly=TRUE))
{
  dat1<-t(mvtnorm::rmvnorm(lents,mean=rep(0,5),sigma=sig))
  dat2<-t(mvtnorm::rmvnorm(lents,mean=rep(0,5),sigma=sig))
}else
{
  dat1<-t(matrix(rep(rnorm(lents),times=5),lents,5))
  dat2<-t(matrix(rep(rnorm(lents),times=5),lents,5))
}
dat<-rbind(dat1,dat2)
times<-1:lents
dat<-cleandat(dat,times,clev=1)$cdat
coords<-data.frame(Y=rep(0,10),X=1:10)
method<-"coh.sig.fast"
h<-clust(dat,times,coords,method,nsurrogs = 50)
#nsurrogs should be much higher for a real application
get_times(h)
summary(h)
print(h)

Coherence

Description

Wavelet coherence and wavelet phase coherence, spatial or for single time series. Also the generator function for the coh class, which inherits from the list class.

Usage

coh(
  dat1,
  dat2,
  times,
  norm,
  sigmethod = "none",
  nrand = 1000,
  scale.min = 2,
  scale.max.input = NULL,
  sigma = 1.05,
  f0 = 1
)

Arguments

Details

If the dimensions of dat1 and dat2 are N by T (N is 1 for vector dat1 and dat2), and if the wavelet transform of the nth row of dati is denoted W_{i,n,\sigma}(t), then the coherence is the average, over all locations n and times t for which wavelet transforms are available, of the quantity w_{1,n,\sigma}(t)w_{2,n,\sigma}(t)^{*}, where the * represents complex conjugation and w_{i,n,\sigma}(t) is a normalization of the wavelet transform. The normalization used depends on norm. If norm is "none" then raw wavelet transforms are used. If norm is "phase" then w_{i,n,\sigma}(t)=W_{i,n,\sigma}(t)/|W_{i,n,\sigma}(t)|, which gives the wavelet phase coherence, or the spatial wavelet phase coherence if N>1. If norm is "powall" then the normalization is that descibed in the "Wavelet mean field" section of the Methods of Sheppard et al. (2016), giving the version of the coherence that was there called simply the wavelet coherence, or the spatial wavelet coherence if N>1. If norm is "powind", then w_{i,n,\sigma}(t) is obtained by dividing W_{i,n,\sigma}(t) by the square root of the average of W_{i,n,\sigma}(t)W_{i,n,\sigma}(t)^{*} over the times for which it is defined; this is done separately for each i and n.

The slot signif is NA if sigmethod is "none". Otherwise, and if sigmethod is not "fast", then signif$coher is the same as coher, and signif$scoher is a matrix of dimensions nrand by length(coher) with rows with magnitudes equal to coherences of surrogate datasets, computed using the normalization specified by norm. The type of surrogate used (Fourier surrogates or amplitude adjusted Fourier surrogates, see surrog), as well as which of the datasets surrogates are computed on (dat1, dat2, or both) is determined by sigmethod. The first part of the value of sigmethod specifies the type of surrogate used, and the numbers in the second part (1, 2, or 12) specify whether surrogates are applied to dat1, dat2, or both, respectively. Synchrony-preserving surrogates are used. A variety of statements of significance (or lack thereof) can be made by comparing signif$coher with signif$scoher (see the plotmag, plotrank, and bandtest methods for the coh class). If sigmethod is "fast", the fast algorithm of Sheppard et al. (2017) is used. In that case signif$coher can be compared to signif$scoher to make significance statements about the coherence in exactly the same way, but signif$coher will no longer precisely equal coher, and coher should not be compared directly to signif$scoher. Statements about significance of the coherence should be made using signif$coher and signif$scoher, whereas coher should be used whenever the actual value of the coherence is needed. No fast algorithm exists for norm equal to "phase" (the phase coherence; Sheppard et al, 2017), so if norm is "phase" and sigmethod is "fast", the function throws an error.

The slots ranks and bandp are empty on an initial call to coh. They are made to compute and hold aggregate significance results over any timescale band of choice. These are filled in when needed by other methods, see plotrank and bandtest.

Regardless of what the variables represent, the normalized transform of dat1 is multiplied by the conjugate of the normalized transform of dat2. Thus, a positive phase of the coherence indicates dat1 would be leading dat2.

Value

coh returns an object of class coh. Slots are:

Author(s)

Thomas Anderson, anderstl@gmail.com, Jon Walter, jaw3es@virginia.edu; Lawrence Sheppard, lwsheppard@ku.edu; Daniel Reuman, reuman@ku.edu

References

Sheppard, L.W., et al. (2016) Changes in large-scale climate alter spatial synchrony of aphid pests. Nature Climate Change. DOI: 10.1038/nclimate2881

Sheppard, L.W., et al. (2017) Rapid surrogate testing of wavelet coherences. European Physical Journal, Nonlinear and Biomedical Physics, 5, 1. DOI: 10.1051/epjnbp/2017000

See Also

cleandat, coh_methods, bandtest, plotmag, plotphase, plotrank, browseVignettes("wsyn")

Examples

times<-1:100
dat1<-matrix(rnorm(1000),10,100)
dat2<-matrix(rnorm(1000),10,100)
dat1<-cleandat(dat1,times,1)$cdat
dat2<-cleandat(dat2,times,1)$cdat
norm<-"powall"
sigmethod<-"fast"
nrand<-10
res<-coh(dat1,dat2,times,norm,sigmethod,nrand)
#for real applications, use a much bigger nrand

Basic methods for the coh class

Description

Set, get, summary, and print methods for the coh class.

Usage

## S3 method for class 'coh'
summary(object, ...)

## S3 method for class 'coh'
print(x, ...)

## S3 method for class 'coh'
set_times(obj, newval)

## S3 method for class 'coh'
set_timescales(obj, newval)

## S3 method for class 'coh'
set_coher(obj, newval)

## S3 method for class 'coh'
set_dat1(obj, newval)

## S3 method for class 'coh'
set_dat2(obj, newval)

## S3 method for class 'coh'
set_wtopt(obj, newval)

## S3 method for class 'coh'
set_norm(obj, newval)

## S3 method for class 'coh'
set_sigmethod(obj, newval)

## S3 method for class 'coh'
set_signif(obj, newval)

## S3 method for class 'coh'
set_ranks(obj, newval)

## S3 method for class 'coh'
set_bandp(obj, newval)

## S3 method for class 'coh'
get_times(obj)

## S3 method for class 'coh'
get_timescales(obj)

## S3 method for class 'coh'
get_coher(obj)

## S3 method for class 'coh'
get_dat1(obj)

## S3 method for class 'coh'
get_dat2(obj)

## S3 method for class 'coh'
get_wtopt(obj)

## S3 method for class 'coh'
get_norm(obj)

## S3 method for class 'coh'
get_sigmethod(obj)

## S3 method for class 'coh'
get_signif(obj)

## S3 method for class 'coh'
get_ranks(obj)

## S3 method for class 'coh'
get_bandp(obj)

Arguments

Value

summary.coh produces a summary of a coh object. A print.coh method is also available. For coh objects, set_* and get_* methods are available for all slots (see the documentation for coh for a list). The set_* methods just throw an error, to prevent breaking the consistency between the slots of a coh object.

Author(s)

Daniel Reuman, reuman@ku.edu

See Also

coh

Examples

times<-1:100
dat1<-matrix(rnorm(1000),10,100)
dat2<-matrix(rnorm(1000),10,100)
dat1<-cleandat(dat1,times,1)$cdat
dat2<-cleandat(dat2,times,1)$cdat
norm<-"powall"
sigmethod<-"fast"
nrand<-10
h<-coh(dat1,dat2,times,norm,sigmethod,nrand)
get_times(h)
summary(h)
print(h)

Error check for appropriate spatio-temporal data

Description

Error checking whether a times vector and a matrix with each row a time series make a legitimate spatio-temporal data set for wavelet analysis

Usage

errcheck_stdat(times, dat, callfunc)

Arguments

Value

errcheck_stdat returns nothing but throws and error if inputs not appropriate

Author(s)

Daniel Reuman, reuman@ku.edu

Error check times

Description

Error check whether a vector can represent times at which data suitable for wavelet transforms were measured

Usage

errcheck_times(times, callfunc)

Arguments

Value

errcheck_times returns nothing but throws and error if the conditions are not met

Author(s)

Daniel Reuman, reuman@ku.edu

Error check for appropriate temporal data

Description

Error checking whether a times vector and t.series vector make a legitimate time series for wavelet analysis

Usage

errcheck_tsdat(times, t.series, callfunc)

Arguments

Value

errcheck_tsdat returns nothing but throws and error if inputs not appropriate

Author(s)

Daniel Reuman, reuman@ku.edu

Error check whether inputs are suitable for a tts object

Description

Error check whether inputs are suitable for a tts object

Usage

errcheck_tts(times, timescales, values, callfunc)

Arguments

Value

errcheck_tts returns nothing but throws and error if inputs not appropriate

Author(s)

Daniel Reuman, reuman@ku.edu

Error check wavelet transform parameters

Description

Error check the parameters scale.min, scale.max.input, sigma, f0

Usage

errcheck_wavparam(scale.min, scale.max.input, sigma, f0, times, callfunc)

Arguments

Value

errcheck_wavparam returns nothing but throws and error if the conditions are not met

Author(s)

Daniel Reuman, reuman@ku.edu

Fast algorithm for significance testing coherence using Fourier surrogates

Description

This is the algorithm of Sheppard et al. (2017) (see references).

Usage

fastcohtest(
  dat1,
  dat2,
  scale.min,
  scale.max.input,
  sigma,
  f0,
  nrand,
  randnums,
  randbits,
  norm
)

Arguments

Value

fastcohtest returns a list with these elements:

Note

Internal function, minimal error checking.

Author(s)

Lawrence Sheppard, lwsheppard@ku.edu; Daniel Reuman, reuman@ku.edu

References

Sheppard, L.W., et al. (2017) Rapid surrogate testing of wavelet coherences. European Physical Journal, Nonlinear and Biomedical Physics, 5, 1. DOI: 10.1051/epjnbp/2017000

Surrogate time series using Fourier surrogates

Description

Creates surrogate time series using Fourier surrogates

Usage

fftsurrog(dat, nsurrogs, syncpres)

Arguments

Value

fftsurrog returns a list of nsurrogs surrogate datasets

Note

For internal use, no error checking

Author(s)

Jonathan Walter, jaw3es@virginia.edu; Lawrence Sheppard, lwsheppard@ku.edu; Daniel Reuman, reuman@ku.edu

References

Sheppard, LW, et al. (2016) Changes in large-scale climate alter spatial synchrony of aphid pests. Nature Climate Change. DOI: 10.1038/nclimate2881

Schreiber, T and Schmitz, A (2000) Surrogate time series. Physica D 142, 346-382.

Prichard, D and Theiler, J (1994) Generating surrogate data for time series with several simultaneously measured variables. Physical Review Letters 73, 951-954.

Tests if a graph is connected

Description

Tests if a graph represented by an adjacency matrix is connected.

Usage

is.connected(adj)

Arguments

Details

Idea by Ed Scheinerman, circa 2006. Source: http://www.ams.jhu.edu/~ers/matgraph/; routine: matgraph/@graph/isconnected.m

Value

is.connected returns TRUE or FALSE depending on whether the graph represented in adj is a connected graph.

Author(s)

Lei Zhao, lei.zhao@cau.edu.cn

See Also

cluseigen, clust, browseVignettes("wsyn")

Examples

g1<-matrix(c(0,0,0,1,1,0,0,0,0,1,0,0,0,0,1,0),4,4)
is.connected(g1)
g2<-matrix(c(0,1,0,0,1,0,0,0,0,0,0,1,0,0,1,0),4,4)
is.connected(g2)

For converting certain synchrony matrices to unweighted versions

Description

Convenience function for converting certain synchrony matrices to unweighted versions

Usage

makeunweighted(mat, sigthresh)

Arguments

Value

makeunweighted converts to an unweighted version of the input. Entries of mat less than sigthresh become a 1, other entries become a 0. The diagonal is NA.

Note

Internal function, no error checking

Author(s)

Lei Zhao, lei.zhao@cau.edu.cn, Daniel Reuman reuman@ku.edu

Mean phase of coherence

Description

Gets the mean phase of a bunch of complex numbers

Usage

mnphase(nums)

Arguments

Value

mnphase returns the mean phase

Note

Internal funcion, no error catching

Author(s)

Daniel Reuman, reuman@ku.edu

Modularity of a community structure of a graph

Description

Computes the modularity of partitioning of a graph into sub-graphs. Similar to the modularity function in the igraph package, but allows negative edge weights.

Usage

modularity(adj, membership, decomp = FALSE)

Arguments

Details

The difference between this function and the function modularity in the package igraph is that this function can be used with an adjacency matrix with negative elements. This is a common case for matrices arrising from a for correlation matrix or another synchrony matrix. If the matrix is non-negative, the result of this function should be exactly the same as the result from modularity in the igraph package.

Value

modularity returns a list containing the following:

Note

Adapted from code developed by Robert J. Fletcher, Jr.

Author(s)

Jonathan Walter, jonathan.walter@ku.edu; Lei Zhao, lei.zhao@cau.edu.cn; Daniel Reuman, reuman@ku.edu

References

Fletcher Jr., R.J., et al. (2013) Network modularity reveals critical scales for connectivity in ecology and evolution. Nature Communications. doi: 10.1038//ncomms3572.

Gomez S., Jensen P. & Arenas A. (2009). Analysis of community structure in networks of correlated data. Phys Rev E, 80, 016114.

Newman M.E. (2006). Finding community structure in networks using the eigenvectors of matrices. Phys Rev E, 74, 036104.

See Also

clust, cluseigen, browseVignettes("wsyn")

Examples

adj<-matrix(0, 10, 10) # create a fake adjacency matrix
adj[lower.tri(adj)]<-runif(10*9/2, -1, 1)
adj<-adj+t(adj)
colnames(adj)<-letters[1:10]
m<-cluseigen(adj)
z<-modularity(adj, m[[length(m)]], decomp=TRUE)

Normalization for the coh function

Description

A convenience function for performing the normalization step for the coh function.

Usage

normforcoh(W, norm)

Arguments

Value

normforcoh returns an array the same dimensions as W of normalized transforms

Note

Internal function, no error checking

Author(s)

Daniel Reuman, reuman@ku.edu

For plotting the magnitude of values in tts, coh and wlmtest objects

Description

For plotting the magnitude of values in tts objects (and derived classes) against time and timescale, and coh and wlmtest objects against timescale

Usage

plotmag(object, ...)

## S3 method for class 'tts'
plotmag(
  object,
  zlims = NULL,
  neat = TRUE,
  colorfill = NULL,
  colorbar = TRUE,
  title = NULL,
  filename = NA,
  ...
)

## S3 method for class 'wt'
plotmag(
  object,
  zlims = NULL,
  neat = TRUE,
  colorfill = NULL,
  colorbar = TRUE,
  title = NULL,
  filename = NA,
  ...
)

## S3 method for class 'wmf'
plotmag(
  object,
  zlims = NULL,
  neat = TRUE,
  colorfill = NULL,
  colorbar = TRUE,
  title = NULL,
  filename = NA,
  ...
)

## S3 method for class 'wpmf'
plotmag(
  object,
  zlims = NULL,
  neat = TRUE,
  colorfill = NULL,
  sigthresh = 0.95,
  colorbar = TRUE,
  title = NULL,
  filename = NA,
  ...
)

## S3 method for class 'coh'
plotmag(
  object,
  sigthresh = c(0.95, 0.99),
  bandprows = "all",
  filename = NA,
  ...
)

## S3 method for class 'wlmtest'
plotmag(
  object,
  sigthresh = c(0.95, 0.99),
  bandprows = "all",
  filename = NA,
  ...
)

## Default S3 method:
plotmag(object, ...)

Arguments

Details

For coh (respectively, wlmtest) objects, the modulus of object$coher (respectively, object$wlmobj$coher) is plotted using a solid red line, and the modulus of object$signif$coher is plotted using a dashed red line. The two coherences agree except for sigmethod="fast", for which they are close. The dashed line is what should be compared to the distribution of surrogate coherences (black lines, which only appear for coh objects if signif is not NA). Horizontal axis ticks are labeled as timescales, but are spaced on the axis as log(1/timescale), i.e., log frequencies.

Author(s)

Thomas Anderson, anderstl@gmail.com, Jon Walter, jaw3es@virginia.edu; Lawrence Sheppard, lwsheppard@ku.edu; Daniel Reuman, reuman@ku.edu

References

Sheppard, L.W., et al. (2016) Changes in large-scale climate alter spatial synchrony of aphid pests. Nature Climate Change. DOI: 10.1038/nclimate2881

Sheppard, LW et al. (2019) Synchrony is more than its top-down and climatic parts: interacting Moran effects on phytoplankton in British seas. Plos Computational Biology 15, e1006744. doi: 10.1371/journal.pcbi.1006744

See Also

tts, wt, wmf, wpmf, coh, wlmtest, plotphase, bandtest, plotrank, browseVignettes("wsyn")

Examples

#For a wt object
time1<-1:100
time2<-101:200
ts1p1<-sin(2*pi*time1/15)
ts1p2<-0*time1
ts2p1<-0*time2
ts2p2<-sin(2*pi*time2/8)
ts1<-ts1p1+ts1p2
ts2<-ts2p1+ts2p2
ts<-c(ts1,ts2)
ra<-rnorm(200,mean=0,sd=0.5)
t.series<-ts+ra
t.series<-t.series-mean(t.series)
times<-c(time1,time2)
res<-wt(t.series, times)
plotmag(res)

#For a wmf object
x1<-0:50
x2<-51:100
x<-c(x1,x2)
ts1<-c(sin(2*pi*x1/10),sin(2*pi*x2/5))+1.1
dat<-matrix(NA,11,length(x))
for (counter in 1:dim(dat)[1])
{
  ts2<-3*sin(2*pi*x/3+2*pi*runif(1))+3.1
  ts3<-rnorm(length(x),0,1.5)
  dat[counter,]<-ts1+ts2+ts3    
  dat[counter,]<-dat[counter,]-mean(dat[counter,])
}
times<-x
res<-wmf(dat,times)
plotmag(res)

#similar calls for wpmf, coh, wlm, wlmtest objects
#see documentation 

Map clusters from a clust object

Description

Produces a map of the locations of sampling for a clust object, with colors indicating module (cluster) identity. The sizes of nodes (locations) are scaled according to the strength of membership in its module.

Usage

plotmap(
  inclust,
  spltlvl = length(inclust$clusters),
  nodesize = c(1, 3),
  filename = NA
)

Arguments

Value

plotmap produces a map.

Author(s)

Jonathan Walter, jaw3es@virginia.edu

References

Walter, J. A., et al. (2017) The geography of spatial synchrony. Ecology Letters. doi: 10.1111/ele.12782

See Also

clust, browseVignettes("wsyn")

Examples

Tmax<-500
tim<-1:Tmax
ts1<-sin(2*pi*tim/5)
ts1s<-sin(2*pi*tim/5+pi/2)
ts2<-sin(2*pi*tim/12)
ts2s<-sin(2*pi*tim/12+pi/2)
gp1A<-1:2
gp1B<-3:4
gp2A<-5:6
gp2B<-7:8
d<-matrix(NA,Tmax,8)
d[,c(gp1A,gp1B)]<-ts1
d[,c(gp2A,gp2B)]<-ts1s
d[,c(gp1A,gp2A)]<-d[,c(gp1A,gp2A)]+matrix(ts2,Tmax,4)
d[,c(gp1B,gp2B)]<-d[,c(gp1B,gp2B)]+matrix(ts2s,Tmax,4)
d<-d+matrix(rnorm(Tmax*8,0,2),Tmax,8)
d<-t(d)
d<-cleandat(d,1:Tmax,1)$cdat
coords<-data.frame(X=c(rep(1,4),rep(2,4)),Y=rep(c(1:2,4:5),times=2))
cl5<-clust(dat=d,times=1:Tmax,coords=coords,method="ReXWT",tsrange=c(4,6))
plotmap(cl5)
cl12<-clust(dat=d,times=1:Tmax,coords=coords,method="ReXWT",tsrange=c(11,13))
plotmap(cl12)

For plotting the phases of values in tts and coh objects

Description

For plotting the phases of values in tts objects (and derived classes) against time and timescale, and coh objects against timescale

Usage

plotphase(object, ...)

## S3 method for class 'tts'
plotphase(object, filename = NA, ...)

## S3 method for class 'wt'
plotphase(object, filename = NA, ...)

## S3 method for class 'wmf'
plotphase(object, filename = NA, ...)

## S3 method for class 'wpmf'
plotphase(object, filename = NA, ...)

## S3 method for class 'coh'
plotphase(object, bandprows = "all", filename = NA, ...)

## Default S3 method:
plotphase(object, ...)

Arguments

Author(s)

Thomas Anderson, anderstl@gmail.com, Jon Walter, jaw3es@virginia.edu; Lawrence Sheppard, lwsheppard@ku.edu; Daniel Reuman, reuman@ku.edu

References

Sheppard, L.W., et al. (2016) Changes in large-scale climate alter spatial synchrony of aphid pests. Nature Climate Change. DOI: 10.1038/nclimate2881

See Also

tts, wt, wmf, wpmf, coh, plotmag, plotrank, browseVignettes("wsyn")

Examples

#For a tts object
times<-1:100
timescales<-1:100
cplx<-complex(modulus=1,argument=seq(from=-pi,to=pi,length.out=100))
values1<-matrix(cplx,length(times),length(timescales))
tts1<-tts(times,timescales,values1)  
plotphase(tts1)

#For a coh oject
times<-(-3:100)
ts1<-sin(2*pi*times/10)
ts2<-5*sin(2*pi*times/3)
artsig_x<-matrix(NA,11,length(times)) #the driver
for (counter in 1:11)
{
  artsig_x[counter,]=ts1+ts2+rnorm(length(times),mean=0,sd=1.5)
}
times<-0:100
artsig_y<-matrix(NA,11,length(times)) #the driven
for (counter1 in 1:11)
{
  for (counter2 in 1:101)
  {
    artsig_y[counter1,counter2]<-mean(artsig_x[counter1,counter2:(counter2+2)])
  }
}
artsig_y<-artsig_y+matrix(rnorm(length(times)*11,mean=0,sd=3),11,length(times))
artsig_x<-artsig_x[,4:104]
artsig_x<-cleandat(artsig_x,times,1)$cdat
artsig_y<-cleandat(artsig_y,times,1)$cdat
res<-coh(dat1=artsig_x,dat2=artsig_y,times=times,norm="powall",sigmethod="fast",nrand=50,
         f0=0.5,scale.max.input=28)
res<-bandtest(res,c(2,4))
res<-bandtest(res,c(4,30))
res<-bandtest(res,c(8,12))
plotphase(res)

Plots ranks slot for coh and wlmtest objects

Description

Plots the ranks slot for coh and wlmtest objects to help identify statistical significance of coherence

Usage

plotrank(object, ...)

## S3 method for class 'coh'
plotrank(object, sigthresh = 0.95, bandprows = "all", filename = NA, ...)

## S3 method for class 'wlmtest'
plotrank(object, sigthresh = 0.95, bandprows = "all", filename = NA, ...)

## Default S3 method:
plotrank(object, ...)

Arguments

Details

The plot shows the modulus of object$ranks$coher versus log(1/object$timescales). Horizontal axis ticks are labeled as timescales, but are spaced on the axis as log(1/timescale), i.e., log frequencies. p-values from object$bandp are displayed above the rank plot.

Author(s)

Thomas Anderson, anderstl@gmail.com, Jon Walter, jaw3es@virginia.edu; Lawrence Sheppard, lwsheppard@ku.edu; Daniel Reuman, reuman@ku.edu

References

Sheppard, L.W., et al. (2016) Changes in large-scale climate alter spatial synchrony of aphid pests. Nature Climate Change. DOI: 10.1038/nclimate2881

Sheppard, LW et al. (2019) Synchrony is more than its top-down and climatic parts: interacting Moran effects on phytoplankton in British seas. Plos Computational Biology 15, e1006744. doi: 10.1371/journal.pcbi.1006744

See Also

coh, wlmtest, bandtest, plotphase, plotmag, browseVignettes("wsyn")

Examples

#For a coh object
times<-(-3:100)
ts1<-sin(2*pi*times/10)
ts2<-5*sin(2*pi*times/3)
artsig_x<-matrix(NA,11,length(times)) #the driver
for (counter in 1:11)
{
  artsig_x[counter,]=ts1+ts2+rnorm(length(times),mean=0,sd=1.5)
}
times<-0:100
artsig_y<-matrix(NA,11,length(times)) #the driven
for (counter1 in 1:11)
{
  for (counter2 in 1:101)
  {
    artsig_y[counter1,counter2]<-mean(artsig_x[counter1,counter2:(counter2+2)])
  }
}
artsig_y<-artsig_y+matrix(rnorm(length(times)*11,mean=0,sd=3),11,length(times))
artsig_x<-artsig_x[,4:104]
artsig_x<-cleandat(artsig_x,times,1)$cdat
artsig_y<-cleandat(artsig_y,times,1)$cdat
res<-coh(dat1=artsig_x,dat2=artsig_y,times=times,norm="powall",sigmethod="fast",
nrand=100,f0=0.5,scale.max.input=28)
#use larger nrand for a real application
res<-bandtest(res,c(2,4))
res<-bandtest(res,c(8,12))
plotrank(res)
  
#For a wlmtest object, see vignette

Power of a tts object

Description

Returns the power of a tts object, i.e., the mean over time of the squared magnitude (which is a function of timescale)

Usage

power(object)

## S3 method for class 'tts'
power(object)

Arguments

Value

power returns a data frame with columns timescales and power

Author(s)

Daniel Reuman, reuman@ku.edu

See Also

tts, wt, wmf, wpmf, browseVignettes("wsyn")

Examples

times<-1:10
timescales<-1:10
values<-matrix(rep(complex(modulus=1,argument=2*pi*c(0:9)/10),times=10),10,10)
ttsobj<-tts(times,timescales,values)
res<-power(ttsobj)
 

Predicted synchrony of a wavelet linear model

Description

Predicted synchrony of a wlm object. This is described in the first paragraph of Appendix S15 of Sheppard et al (2019).

Usage

predsync(wlmobj)

## S3 method for class 'wlm'
predsync(wlmobj)

Arguments

Value

predsync returns a tts object. Plotting the magnitude (see plotmag) displays a picture of predicted synchrony versus time and timescale that is comparable with the wavelet mean field (see wmf) of the response variable of the model. Calling the power function on that tts object should give the same results as one of the columns of output of syncexpl. Only norm="powall" implemented so far.

Author(s)

Thomas Anderson, anderstl@gmail.com, Jon Walter, jaw3es@virginia.edu; Lawrence Sheppard, lwsheppard@ku.edu; Daniel Reuman, reuman@ku.edu

References

Sheppard, LW et al. (2019) Synchrony is more than its top-down and climatic parts: interacting Moran effects on phytoplankton in British seas. Plos Computational Biology 15, e1006744. doi: 10.1371/journal.pcbi.1006744

See Also

wlm, tts, plotmag, wmf, power, syncexpl, browseVignettes("wsyn")

Examples

times<-(-3:100)
ts1<-sin(2*pi*times/10)
ts2<-5*sin(2*pi*times/3)
artsig_x<-matrix(NA,11,length(times)) #the driver
for (counter in 1:11)
{
  artsig_x[counter,]<-ts1+ts2+rnorm(length(times),mean=0,sd=.5)
}
times<-0:100
artsig_y<-matrix(NA,11,length(times)) #the driven
for (counter1 in 1:11)
{
  for (counter2 in 1:101)
  {
    artsig_y[counter1,counter2]<-mean(artsig_x[counter1,counter2:(counter2+2)])
  }
}
artsig_y<-artsig_y+matrix(rnorm(length(times)*11,mean=0,sd=1),11,length(times))
artsig_x<-artsig_x[,4:104]
artsig_i<-matrix(rnorm(11*length(times)),11,length(times)) #the irrelevant
artsig_x<-cleandat(artsig_x,times,1)$cdat
artsig_y<-cleandat(artsig_y,times,1)$cdat
artsig_i<-cleandat(artsig_i,times,1)$cdat
dat<-list(driven=artsig_y,driver=artsig_x,irrelevant=artsig_i)
resp<-1
pred<-2:3
norm<-"powall"
wlmobj<-wlm(dat,times,resp,pred,norm)

res<-predsync(wlmobj)

Print method for summary_wsyn class

Description

Print method for summary_wsyn class

Usage

## S3 method for class 'summary_wsyn'
print(x, ...)

Arguments

Value

print.summary_wsyn is called for its effect of printing to the screen.

Author(s)

Daniel Reuman, reuman@ku.edu

See Also

tts_methods, wt_methods, wmf_methods, wpmf_methods, coh_methods, wlm_methods, wlmtest_methods, clust_methods, browseVignettes("wsyn")

Examples

times<-1:10
timescales<-1/c(1:10)
values<-matrix(1,length(times),length(timescales))
h<-tts(times,timescales,values)
print(summary(h))

Set and get methods for classes in the wsyn package

Description

Set and get methods for classes in the wsyn package. There are methods for each slot of each class, named set_* and get_* for * the slot name. Below are listed function specs for the generics and the default methods.

Usage

set_adj(obj, newval)

## Default S3 method:
set_adj(obj, newval)

set_clusters(obj, newval)

## Default S3 method:
set_clusters(obj, newval)

set_modres(obj, newval)

## Default S3 method:
set_modres(obj, newval)

set_mns(obj, newval)

## Default S3 method:
set_mns(obj, newval)

set_coords(obj, newval)

## Default S3 method:
set_coords(obj, newval)

set_methodspecs(obj, newval)

## Default S3 method:
set_methodspecs(obj, newval)

set_wmfs(obj, newval)

## Default S3 method:
set_wmfs(obj, newval)

set_wpmfs(obj, newval)

## Default S3 method:
set_wpmfs(obj, newval)

get_adj(obj)

## Default S3 method:
get_adj(obj)

get_clusters(obj)

## Default S3 method:
get_clusters(obj)

get_modres(obj)

## Default S3 method:
get_modres(obj)

get_mns(obj)

## Default S3 method:
get_mns(obj)

get_coords(obj)

## Default S3 method:
get_coords(obj)

get_methodspec(obj)

## Default S3 method:
get_methodspec(obj)

get_wmfs(obj)

## Default S3 method:
get_wmfs(obj)

get_wpmfs(obj)

## Default S3 method:
get_wpmfs(obj)

set_coher(obj, newval)

## Default S3 method:
set_coher(obj, newval)

set_dat1(obj, newval)

## Default S3 method:
set_dat1(obj, newval)

set_dat2(obj, newval)

## Default S3 method:
set_dat2(obj, newval)

set_norm(obj, newval)

## Default S3 method:
set_norm(obj, newval)

set_sigmethod(obj, newval)

## Default S3 method:
set_sigmethod(obj, newval)

set_ranks(obj, newval)

## Default S3 method:
set_ranks(obj, newval)

set_bandp(obj, newval)

## Default S3 method:
set_bandp(obj, newval)

get_coher(obj)

## Default S3 method:
get_coher(obj)

get_dat1(obj)

## Default S3 method:
get_dat1(obj)

get_dat2(obj)

## Default S3 method:
get_dat2(obj)

get_norm(obj)

## Default S3 method:
get_norm(obj)

get_sigmethod(obj)

## Default S3 method:
get_sigmethod(obj)

get_ranks(obj)

## Default S3 method:
get_ranks(obj)

get_bandp(obj)

## Default S3 method:
get_bandp(obj)

set_times(obj, newval)

## Default S3 method:
set_times(obj, newval)

set_timescales(obj, newval)

## Default S3 method:
set_timescales(obj, newval)

set_values(obj, newval)

## Default S3 method:
set_values(obj, newval)

get_times(obj)

## Default S3 method:
get_times(obj)

get_timescales(obj)

## Default S3 method:
get_timescales(obj)

get_values(obj)

## Default S3 method:
get_values(obj)

set_coefs(obj, newval)

## Default S3 method:
set_coefs(obj, newval)

set_modval(obj, newval)

## Default S3 method:
set_modval(obj, newval)

set_wts(obj, newval)

## Default S3 method:
set_wts(obj, newval)

get_coefs(obj)

## Default S3 method:
get_coefs(obj)

get_modval(obj)

## Default S3 method:
get_modval(obj)

get_wts(obj)

## Default S3 method:
get_wts(obj)

set_wlmobj(obj, newval)

## Default S3 method:
set_wlmobj(obj, newval)

set_drop(obj, newval)

## Default S3 method:
set_drop(obj, newval)

get_wlmobj(obj)

## Default S3 method:
get_wlmobj(obj)

get_drop(obj)

## Default S3 method:
get_drop(obj)

set_signif(obj, newval)

## Default S3 method:
set_signif(obj, newval)

get_signif(obj)

## Default S3 method:
get_signif(obj)

set_dat(obj, newval)

## Default S3 method:
set_dat(obj, newval)

set_wtopt(obj, newval)

## Default S3 method:
set_wtopt(obj, newval)

get_dat(obj)

## Default S3 method:
get_dat(obj)

get_wtopt(obj)

## Default S3 method:
get_wtopt(obj)

Arguments

Details

There are methods for the tts, wt, wmf, wpmf, coh, wlm, wlmtest, and clust classes. See documentation for the generator functions for these classes (which in all cases have the same name as the class) for lists of slots for each class.

Value

set_* methods throw an error - setting of individual slots is not allowed, as it breaks consistency with the other slots. get_* just returns the value in question.

Author(s)

Daniel Reuman, reuman@ku.edu

Examples

times<-1:10
timescales<-1/c(1:10)
values<-matrix(1,length(times),length(timescales))
h<-tts(times,timescales,values)
get_times(h)

Shifts a vector according to the argument mints

Description

Shifts a vector according to the argument mints

Usage

setmints(ts, mints)

Arguments

Value

setmints returns the shifted vector.

Daniel Reuman, reuman@ku.edu

Note

This is an internal function, and no error checking is done.

Creates surrogate time series, either Fourier surrogates or amplitude adjusted Fourier surrogates

Description

For significance testing wavelet coherence and other purposes

Usage

surrog(dat, nsurrogs, surrtype, syncpres)

Arguments

Details

Fourier surrogates are somewhat faster than aaft surrogates, and may be much faster when some of the time series in the data have ties. Prenormalization (e.g., using cleandat) can make it possible to use fft surrogates.

Value

surrog returns a list of nsurrogs surrogate datasets

Author(s)

Jonathan Walter, jaw3es@virginia.edu; Lawrence Sheppard, lwsheppard@ku.edu; Daniel Reuman, reuman@ku.edu

References

Sheppard, LW, et al. (2016) Changes in large-scale climate alter spatial synchrony of aphid pests. Nature Climate Change. DOI: 10.1038/nclimate2881

Schreiber, T and Schmitz, A (2000) Surrogate time series. Physica D 142, 346-382.

Prichard, D and Theiler, J (1994) Generating surrogate data for time series with several simultaneously measured variables. Physical Review Letters 73, 951-954.

See Also

wpmf, coh, wlmtest, synmat, browseVignettes("wsyn")

Examples

times<-1:100
dat<-sin(2*pi*times/10)
nsurrogs<-10
surrtype<-"fft"
syncpres<-TRUE
res<-surrog(dat,nsurrogs,surrtype,syncpres)

Amount of synchrony explained, and related quantities

Description

Gives amount of synchrony explained by a wavelet linear model, as a function of timescale, and related quantities (see details)

Usage

syncexpl(object)

## S3 method for class 'wlm'
syncexpl(object)

Arguments

Details

This function only works for norm="powall" at present. See Sheppard et al (2018) for details of the meaning and computation of the columns.

Value

syncexpl returns a data frame with columns for timescales, sync (the time-averaged square magnitude of the wavelet mean field of the response transforms), syncexpl (synchrony explained by the model predictors), columns named for each predictor (synchrony explained by that predictor), interactions (synchrony explained by all interaction effects), columns named for each pair of predictors (synchrony explained by individual pairwise interactions). There are also columns for crossterms and resids (residuals). The cross terms must be small for a given timescale band for the other results to be meaningful. All columns are functions of timescales.

Author(s)

Thomas Anderson, anderstl@gmail.com, Jon Walter, jaw3es@virginia.edu; Lawrence Sheppard, lwsheppard@ku.edu; Daniel Reuman, reuman@ku.edu

References

Sheppard, LW et al. (2019) Synchrony is more than its top-down and climatic parts: interacting Moran effects on phytoplankton in British seas. Plos Computational Biology 15, e1006744. doi: 10.1371/journal.pcbi.1006744

See Also

wlm, predsync, wlmtest, browseVignettes("wsyn")

Examples

times<-(-3:100)
ts1<-sin(2*pi*times/10)
ts2<-5*sin(2*pi*times/3)
artsig_x<-matrix(NA,11,length(times)) #the driver
for (counter in 1:11)
{
  artsig_x[counter,]=ts1+ts2+rnorm(length(times),mean=0,sd=1.5)
}
times<-0:100
artsig_y<-matrix(NA,11,length(times)) #the driven
for (counter1 in 1:11)
{
  for (counter2 in 1:101)
  {
    artsig_y[counter1,counter2]<-mean(artsig_x[counter1,counter2:(counter2+2)])
  }
}
artsig_y<-artsig_y+matrix(rnorm(length(times)*11,mean=0,sd=3),11,length(times))
artsig_x<-artsig_x[,4:104]
artsig_i<-matrix(rnorm(11*length(times)),11,length(times)) #the irrelevant
artsig_x<-cleandat(artsig_x,times,1)$cdat
artsig_y<-cleandat(artsig_y,times,1)$cdat
artsig_i<-cleandat(artsig_i,times,1)$cdat

dat<-list(driven=artsig_y,driver=artsig_x,irrelevant=artsig_i)
resp<-1
pred<-2:3
norm<-"powall"
wlmobj<-wlm(dat,times,resp,pred,norm)

res<-syncexpl(wlmobj)
 

Synchrony matrices

Description

Calculate synchrony matrices using a variety of methods

Usage

synmat(
  dat,
  times,
  method,
  tsrange = c(0, Inf),
  nsurrogs = 1000,
  scale.min = 2,
  scale.max.input = NULL,
  sigma = 1.05,
  f0 = 1,
  weighted = TRUE,
  sigthresh = 0.95
)

Arguments

Details

The following values are valid for method: "pearson", "pearson.sig.std", "pearson.sig.fft", "pearson.sig.aaft", "spearman", "spearman.sig.std", "spearman.sig.fft", "spearman.sig.aaft", "kendall", "kendall.sig.std", "kendall.sig.fft", "kendall.sig.aaft", "ReXWT", "ReXWT.sig.fft", "ReXWT.sig.aaft", "ReXWT.sig.fast", "coh", "coh.sig.fft", "coh.sig.aaft", "coh.sig.fast", "phasecoh", "phasecoh.sig.fft", and "phasecoh.sig.aaft". The first portions of these identifiers correspond to the Pearson, Spearman, and Kendall correlations, the real part of the cross-wavelet transform, the wavelet coherence, and the wavelet phase coherence. The second portions of these identifiers, when present, indicates that significance of the measure specified in the first portion of the identifies is to be used for establishing the synchrony matrix. Otherwise the value itself is used. The third part of the method identifier indicates what type of significance is used.

Significance testing is performed using standard approaches (method flag containg std; for correlation coefficients, although these are inappropriate for autocorrelated data), or surrogates generated using the Fourier (method flag containing "fft") or amplitude adjusted Fourier surrogates ("aaft"). For "coh" and "ReXWT", the fast testing algorithm of Sheppard et al. (2017) is also implemented ("fast"). That method uses implicit Fourier surrogates. The choice of wavelet coherence (method flag containing "coh") or the real part of the cross-wavelet transform (method flag containing "ReXWT") depends mainly on treatment of out-of-phase relationships. The "ReXWT" is more akin to a correlation coefficient in that strong in-phase relationships approach 1 and strong antiphase relationships approach -1. Wavelet coherence allows any phase relationship and ranges from 0 to 1. Power normalization is applied for "coh" and for "ReXWT". All significance tests are one-tailed. Synchrony matrices for significance-based methods when weighted is TRUE contain 1 minus the p-values.

Value

synmat returns a synchrony matrix, of type depending on the method argument. See details. Diagonal entries are left as NA.

Author(s)

Jonathan Walter, jaw3es@virginia.edu; Daniel Reuman, reuman@ku.edu; Lei Zhao, lei.zhao@cau.edu.cn

References

Walter, J. A., et al. (2017) The geography of spatial synchrony. Ecology Letters. doi: 10.1111/ele.12782

See Also

clust, coh, surrog, browseVignettes("wsyn")

Examples

sig<-matrix(.9,5,5)
diag(sig)<-1
if (requireNamespace("mvtnorm",quietly=TRUE))
{
  dat1<-t(mvtnorm::rmvnorm(30,mean=rep(0,5),sigma=sig))
  dat2<-t(mvtnorm::rmvnorm(30,mean=rep(0,5),sigma=sig))
}else
{
  dat1<-t(matrix(rep(rnorm(30),times=5),30,5))
  dat2<-t(matrix(rep(rnorm(30),times=5),30,5))
}
dat<-rbind(dat1,dat2)
times<-1:30
dat<-cleandat(dat,times,clev=2)$cdat
method<-"pearson.sig.fft"
res<-synmat(dat,times,method,nsurrogs=100,weighted=FALSE,
            sigthresh=0.95)
  

Creator function for the tts class

Description

The tts (time/timescale) class is for matrices for which the rows correspond to times and the columns correspond to timescales. This is a general class from which other classes inherit (e.g., wt, wmf, wpmf). tts inherits from the list class.

Usage

tts(times, timescales, values)

Arguments

Value

tts returns an object of class tts. Slots are:

Author(s)

Daniel Reuman, reuman@ku.edu

See Also

tts_methods, wt, wmf, wpmf, browseVignettes("wsyn")

Examples

times<-1:10
timescales<-1/c(1:10)
values<-matrix(1,length(times),length(timescales))
h<-tts(times,timescales,values)

Basic methods for the tts class

Description

Set, get, summary, and print methods for the tts class.

Usage

## S3 method for class 'tts'
summary(object, ...)

## S3 method for class 'tts'
print(x, ...)

## S3 method for class 'tts'
set_times(obj, newval)

## S3 method for class 'tts'
set_timescales(obj, newval)

## S3 method for class 'tts'
set_values(obj, newval)

## S3 method for class 'tts'
get_times(obj)

## S3 method for class 'tts'
get_timescales(obj)

## S3 method for class 'tts'
get_values(obj)

Arguments

Value

summary.tts produces a summary of a tts object. A print.tts method is also available. For tts objects, set_* and get_* methods are available for all slots, i.e., * equal to times, timescales, and values. The set_* methods just throw an error. Although class tts is flexible enough that setting of individual slots could have been allowed, because wt and other classes are based on it and because individual slots of those classes should not be changed, for consistency the same is forced for the tts class.

Author(s)

Daniel Reuman, reuman@ku.edu

See Also

tts

Examples

times<-1:10
timescales<-1/c(1:10)
values<-matrix(1,length(times),length(timescales))
h<-tts(times,timescales,values)
get_times(h)
summary(h)
print(h)

Creates an array of wavelet transforms from input timeseries

Description

Creates an array of wavelet transforms from input timeseries

Usage

warray(dat, times, scale.min = 2, scale.max.input = NULL, sigma = 1.05, f0 = 1)

Arguments

Value

warray returns a list containing:

Note

Important for interpreting the phase: the phases grow through time, i.e., they turn anti-clockwise. This function is internal, no error checking.

Author(s)

Lauren Hallett, hallett@uoregon.edu; Lawrence Sheppard, lwsheppard@ku.edu; Daniel Reuman, reuman@ku.edu

Facilitates the computations in synmat for coherence and ReXWT methods

Description

Worker/utility function serving the analysis carried out in synmat for methods based on coherence or real part of the cross-wavelet transform.

Usage

wavmatwork(dat, times, scale.min, scale.max.input, sigma, f0, norm, treatment)

Arguments

Value

wavmatwork returns a list consisting of:

Note

Internal function, no error checking done.

Author(s)

Daniel Reuman, reuman@ku.edu

Wavelet linear models

Description

Fits wavelet linear models. Also the generator function of the wlm class, which inherits from the list class.

Usage

wlm(
  dat,
  times,
  resp,
  pred,
  norm,
  scale.min = 2,
  scale.max.input = NULL,
  sigma = 1.05,
  f0 = 1
)

Arguments

Details

Normalization is as specified in the documentation for coh, HOWEVER, only the "powall" option is currently implemented, other choices throw an error. Details are specified in appendices S7 and S9 of Sheppard et al, 2018. The output modval is v in appendix S7, and coefs are the betas in equation 12 in that appendix.

Value

wlm returns an object of class wlm. Slots are:

Author(s)

Thomas Anderson, anderstl@gmail.com, Jon Walter, jaw3es@virginia.edu; Lawrence Sheppard, lwsheppard@ku.edu; Daniel Reuman, reuman@ku.edu

References

Sheppard, LW et al. (2019) Synchrony is more than its top-down and climatic parts: interacting Moran effects on phytoplankton in British seas. Plos Computational Biology 15, e1006744. doi: 10.1371/journal.pcbi.1006744

See Also

wlm_methods, wlmtest, syncexpl, predsync, browseVignettes("wsyn")

Examples

times<-1:30
dat<-list(v1=matrix(rnorm(300),10,30),v2=matrix(rnorm(300),10,30),v3=matrix(rnorm(300),10,30),
          v4=matrix(rnorm(300),10,30),v5=matrix(rnorm(300),10,30))
dat<-lapply(FUN=function(x){cleandat(x,times,1)$cdat},X=dat)
resp<-2
pred<-c(1,3,4)
norm<-"powall"
res<-wlm(dat,times,resp,pred,norm)

Basic methods for the wlm class

Description

Set, get, summary, and print methods for the wlm class.

Usage

## S3 method for class 'wlm'
summary(object, ...)

## S3 method for class 'wlm'
print(x, ...)

## S3 method for class 'wlm'
set_times(obj, newval)

## S3 method for class 'wlm'
set_timescales(obj, newval)

## S3 method for class 'wlm'
set_coefs(obj, newval)

## S3 method for class 'wlm'
set_modval(obj, newval)

## S3 method for class 'wlm'
set_coher(obj, newval)

## S3 method for class 'wlm'
set_dat(obj, newval)

## S3 method for class 'wlm'
set_wtopt(obj, newval)

## S3 method for class 'wlm'
set_norm(obj, newval)

## S3 method for class 'wlm'
set_wts(obj, newval)

## S3 method for class 'wlm'
get_times(obj)

## S3 method for class 'wlm'
get_timescales(obj)

## S3 method for class 'wlm'
get_coefs(obj)

## S3 method for class 'wlm'
get_modval(obj)

## S3 method for class 'wlm'
get_coher(obj)

## S3 method for class 'wlm'
get_dat(obj)

## S3 method for class 'wlm'
get_wtopt(obj)

## S3 method for class 'wlm'
get_norm(obj)

## S3 method for class 'wlm'
get_wts(obj)

Arguments

Value

summary.wlm produces a summary of a wlm object. A print.wlm method is also available. For wlm objects, set_* and get_* methods are available for all slots (see the documentation for wlm for a list). The set_* methods just throw an error, to prevent breaking the consistency between the slots of a wlm object.

Author(s)

Daniel Reuman, reuman@ku.edu

See Also

wlm

Examples

times<-1:30
dat<-list(v1=matrix(rnorm(300),10,30),v2=matrix(rnorm(300),10,30),v3=matrix(rnorm(300),10,30),
          v4=matrix(rnorm(300),10,30),v5=matrix(rnorm(300),10,30))
dat<-lapply(FUN=function(x){cleandat(x,times,1)$cdat},X=dat)
resp<-2
pred<-c(1,3,4)
norm<-"powall"
h<-wlm(dat,times,resp,pred,norm)
get_times(h)
summary(h)
print(h)

Fits a wavelet linear model

Description

Stripped down internal function for doing the fitting

Usage

wlmfit(wts, norm)

Arguments

Details

Only norm="powall" works now, other options throw an error.

Value

wlmfit returns a list with these elements:

Note

Internal function, no error checking done.

Author(s)

Thomas Anderson, anderstl@gmail.com, Jon Walter, jaw3es@virginia.edu; Lawrence Sheppard, lwsheppard@ku.edu; Daniel Reuman, reuman@ku.edu

References

Sheppard, LW et al. (2019) Synchrony is more than its top-down and climatic parts: interacting Moran effects on phytoplankton in British seas. Plos Computational Biology 15, e1006744. doi: 10.1371/journal.pcbi.1006744

Statistical comparison of wavelet linear models

Description

Compares a wavelet linear model with a nested model. Also the generator function for the wlmtest class.

Usage

wlmtest(wlmobj, drop, sigmethod, nrand = 1000)

Arguments

Details

The slot signif provides the core information on significance. If sigmethod is not "fast", then signif$coher is the same as wlmobj$coher, and signif$scoher is a matrix of dimensions nrand by length(signif$coher) with rows equal to coherences between refitted models and the response-variable transforms, for datasets where the variables specified in drop have been replaced by surrogates. Normalization as specified in norm is used. The type of surrogate used (Fourier surrogates or amplitude adjusted Fourier surrogates, see surrog) is determined by sigmethod ("fft" or "aaft"). Synchrony-preserving surrogates are used. A variety of statements of significance (or lack thereof) can be made by comparing signif$coher with signif$scoher (see the plotmag, plotrank, and bandtest methods for the wlmtest class). If sigmethod is "fast", a fast algorithm of Lawrence Sheppard is used which is a generalization to wavelet linear models of the fast algorithm for coherence described in Sheppard et al (2017). In that case signif$coher can be compared to signif$scoher to make significance statements about the coherence in exactly the same way, but signif$coher will no longer precisely equal wlmobj$coher, and wlmobj$coher should not be compared directly to signif$scoher. Statements about significance of the coherence should be made using signif$coher and signif$scoher, whereas wlmobj$coher should be used whenever the actual value of the coherence is needed.

The slots ranks and bandp are empty on an initial call to wlmtest. They are made to compute and hold aggregate significance results over any timescale band of choice. These are filled in when needed by other methods, see plotrank and bandtest.

Value

wlmtest returns an object of class wlmtest. Slots are:

Author(s)

Thomas Anderson, anderstl@gmail.com, Jon Walter, jaw3es@virginia.edu; Lawrence Sheppard, lwsheppard@ku.edu; Daniel Reuman, reuman@ku.edu

References

Sheppard, L.W., et al. (2016) Changes in large-scale climate alter spatial synchrony of aphid pests. Nature Climate Change. DOI: 10.1038/nclimate2881

Sheppard, L.W., et al. (2017) Rapid surrogate testing of wavelet coherences. European Physical Journal, Nonlinear and Biomedical Physics, 5, 1. DOI: 10.1051/epjnbp/2017000

Sheppard, LW et al. (2019) Synchrony is more than its top-down and climatic parts: interacting Moran effects on phytoplankton in British seas. Plos Computational Biology 15, e1006744. doi: 10.1371/journal.pcbi.1006744

See Also

wlm, plotrank, bandtest, coh, wlmtest_methods, browseVignettes("wsyn")

Examples

times<-1:30
dat<-list(v1=matrix(rnorm(300),10,30),v2=matrix(rnorm(300),10,30),v3=matrix(rnorm(300),10,30),
          v4=matrix(rnorm(300),10,30),v5=matrix(rnorm(300),10,30))
dat<-lapply(FUN=function(x){cleandat(x,times,1)$cdat},X=dat)
resp<-1
pred<-2:3
norm<-"powall"
wlmobj<-wlm(dat,times,resp,pred,norm)
drop<-3
sigmethod<-"fft"
res<-wlmtest(wlmobj,drop,sigmethod,nrand=10)

Basic methods for the wlmtest class

Description

Set, get, summary, and print methods for the wlmtest class.

Usage

## S3 method for class 'wlmtest'
summary(object, ...)

## S3 method for class 'wlmtest'
print(x, ...)

## S3 method for class 'wlmtest'
set_wlmobj(obj, newval)

## S3 method for class 'wlmtest'
set_drop(obj, newval)

## S3 method for class 'wlmtest'
set_signif(obj, newval)

## S3 method for class 'wlmtest'
set_ranks(obj, newval)

## S3 method for class 'wlmtest'
set_bandp(obj, newval)

## S3 method for class 'wlmtest'
get_wlmobj(obj)

## S3 method for class 'wlmtest'
get_drop(obj)

## S3 method for class 'wlmtest'
get_signif(obj)

## S3 method for class 'wlmtest'
get_ranks(obj)

## S3 method for class 'wlmtest'
get_bandp(obj)

Arguments

Value

summary.wlmtest produces a summary of a wlmtest object. A print.wlmtest method is also available. For wlmtest objects, set_* and get_* methods are available for all slots (see the documentation for wlmtest for a list). The set_* methods just throw an error, to prevent breaking the consistency between the slots of a wlmtest object.

Author(s)

Daniel Reuman, reuman@ku.edu

See Also

wlmtest

Examples

times<-1:30
dat<-list(v1=matrix(rnorm(300),10,30),v2=matrix(rnorm(300),10,30),v3=matrix(rnorm(300),10,30),
          v4=matrix(rnorm(300),10,30),v5=matrix(rnorm(300),10,30))
dat<-lapply(FUN=function(x){cleandat(x,times,1)$cdat},X=dat)
resp<-1
pred<-2:3
norm<-"powall"
wlmobj<-wlm(dat,times,resp,pred,norm)
drop<-3
sigmethod<-"fft"
h<-wlmtest(wlmobj,drop,sigmethod,nrand=10)
get_times(get_wlmobj(h))
summary(h)
print(h)

Computes the wavelet mean field from a matrix of spatiotemporal data. Also the creator function for the wmf class.

Description

Computes the wavelet mean field from a matrix of spatiotemporal data. Also the creator function for the wmf class. The wmf class inherits from the tts class, which inherits from the list class.

Usage

wmf(dat, times, scale.min = 2, scale.max.input = NULL, sigma = 1.05, f0 = 1)

Arguments

Value

wmf returns an object of class wmf. Slots are:

Author(s)

Jonathan Walter, jaw3es@virginia.edu; Lawrence Sheppard, lwsheppard@ku.edu; Daniel Reuman, reuman@ku.edu

References

Sheppard, L.W., et al. (2016) Changes in large-scale climate alter spatial synchrony of aphid pests. Nature Climate Change. DOI: 10.1038/nclimate2881

See Also

wmf_methods, tts, wpmf, plotmag, browseVignettes("wsyn")

Examples

times<-1:30 #generate time steps
#generate fake count data for 20 locations
dat<-matrix(rpois(20*length(times),20),nrow=20,ncol=length(times)) 
dat<-cleandat(dat=dat,times=times,clev=2)$cdat #detrend and demean
wmf<-wmf(dat,times)

Basic methods for the wmf class

Description

Set, get, summary, and print methods for the wmf class.

Usage

## S3 method for class 'wmf'
summary(object, ...)

## S3 method for class 'wmf'
print(x, ...)

## S3 method for class 'wmf'
set_times(obj, newval)

## S3 method for class 'wmf'
set_timescales(obj, newval)

## S3 method for class 'wmf'
set_values(obj, newval)

## S3 method for class 'wmf'
set_dat(obj, newval)

## S3 method for class 'wmf'
set_wtopt(obj, newval)

## S3 method for class 'wmf'
get_times(obj)

## S3 method for class 'wmf'
get_timescales(obj)

## S3 method for class 'wmf'
get_values(obj)

## S3 method for class 'wmf'
get_dat(obj)

## S3 method for class 'wmf'
get_wtopt(obj)

Arguments

Value

summary.wmf produces a summary of a wmf object. A print.wmf method is also available. For wmf objects, set_* and get_* methods are available for all slots, i.e., * equal to times, timescales, wtopt, values, and dat. The set_* methods just throw an error, to prevent breaking the consistency between the slots of a wmf object.

Author(s)

Daniel Reuman, reuman@ku.edu

See Also

wmf

Examples

times<-1:30 #generate time steps
#generate fake count data for 20 locations
dat<-matrix(rpois(20*length(times),20),nrow=20,ncol=length(times)) 
dat<-cleandat(dat=dat,times=times,clev=2)$cdat #detrend and demean
h<-wmf(dat,times)
get_times(h)
summary(h)
print(h)

Wavelet phasor mean field

Description

Computes the wavelet phasor mean field from a matrix of spatiotemporal data. Also the creator function for the wpmf class. The wpmf class inherits from the tts class, which inherits from the list class.

Usage

wpmf(
  dat,
  times,
  scale.min = 2,
  scale.max.input = NULL,
  sigma = 1.05,
  f0 = 1,
  sigmethod = "none",
  nrand = 1000
)

Arguments

Details

For sigmethod equal to quick, the empirical wpmf is compared to a distribution of magnitudes of sums of random phasors, using the same number of phasors as there are time series. The signif output is a list with first element "quick" and second element a vector of nrand magnitudes of sums of random phasors. For sigmethod equal to fft, the empirical wpmf is compared to wmpfs of Fourier surrogate datasets. The signif output is a list with first element "fft", second element equal to nrand, and third element the fraction of surrogate-based wpmf magnitudes that the empirical wpmf magnitude is greater than (times by timescales matrix). For sigmethod equal to aaft, aaft surrogates are used instead. Output has similar format to the fft case. Values other than quick, fft, and aaft for sigmethod result in no significance testing.

Value

wpmf returns an object of class wpmf. Slots are:

Author(s)

Thomas Anderson, anderstl@gmail.com, Jon Walter, jaw3es@virginia.edu; Lawrence Sheppard, lwsheppard@ku.edu; Daniel Reuman, reuman@ku.edu

References

Sheppard, L.W., et al. (2016) Changes in large-scale climate alter spatial synchrony of aphid pests. Nature Climate Change. DOI: 10.1038/nclimate2881

See Also

wpmf_methods, wmf, tts, plotmag, browseVignettes("wsyn")

Examples

times<-1:30 #generate time steps
#generate fake count data for 20 locations
dat<-matrix(rpois(20*length(times),20),nrow=20,ncol=length(times)) 
dat<-cleandat(dat=dat,times=times,clev=2)$cdat #detrend and demean
res<-wpmf(dat,times)

Basic methods for the wpmf class

Description

Set, get, summary, and print methods for the wpmf class.

Usage

## S3 method for class 'wpmf'
summary(object, ...)

## S3 method for class 'wpmf'
print(x, ...)

## S3 method for class 'wpmf'
set_times(obj, newval)

## S3 method for class 'wpmf'
set_timescales(obj, newval)

## S3 method for class 'wpmf'
set_values(obj, newval)

## S3 method for class 'wpmf'
set_dat(obj, newval)

## S3 method for class 'wpmf'
set_wtopt(obj, newval)

## S3 method for class 'wpmf'
set_signif(obj, newval)

## S3 method for class 'wpmf'
get_times(obj)

## S3 method for class 'wpmf'
get_timescales(obj)

## S3 method for class 'wpmf'
get_values(obj)

## S3 method for class 'wpmf'
get_dat(obj)

## S3 method for class 'wpmf'
get_wtopt(obj)

## S3 method for class 'wpmf'
get_signif(obj)

Arguments

Value

summary.wpmf produces a summary of a wpmf object. A print.wpmf method is also available. For wpmf objects, set_* and get_* methods are available for all slots, i.e., * equal to times, timescales, wtopt, values, dat, and signif. The set_* methods just throw an error, to prevent breaking the consistency between the slots of a wpmf object.

Author(s)

Daniel Reuman, reuman@ku.edu

See Also

wpmf

Examples

times<-1:30 #generate time steps
#generate fake count data for 20 locations
dat<-matrix(rpois(20*length(times),20),nrow=20,ncol=length(times)) 
dat<-cleandat(dat=dat,times=times,clev=2)$cdat #detrend and demean
h<-wpmf(dat,times)
get_times(h)
summary(h)
print(h)

Computes the wavelet transform of a timeseries. Also the creator function for the wt class.

Description

Computes the wavelet transform of a timeseries. Also the creator function for the wt class. The wt class inherits from the tts class, which inherits from the list class.

Usage

wt(
  t.series,
  times,
  scale.min = 2,
  scale.max.input = NULL,
  sigma = 1.05,
  f0 = 1
)

Arguments

Value

wt returns an object of class wt. Slots are:

Note

Important for interpreting the phase: the phases grow through time, i.e., they turn anti-clockwise.

Author(s)

Lawrence Sheppard lwsheppard@ku.edu, Jonathan Walter jaw3es@virginia.edu, Daniel Reuman reuman@ku.edu

See Also

wt_methods, tts, plotmag, plotphase, browseVignettes("wsyn")

Examples

time1<-1:100
time2<-101:200
ts1p1<-sin(2*pi*time1/15)
ts1p2<-0*time1
ts2p1<-0*time2
ts2p2<-sin(2*pi*time2/8)
ts1<-ts1p1+ts1p2
ts2<-ts2p1+ts2p2
ts<-c(ts1,ts2)
ra<-rnorm(200,mean=0,sd=0.5)
t.series<-ts+ra
t.series<-t.series-mean(t.series)
times<-c(time1,time2)
res<-wt(t.series, times)

Basic methods for the wt class

Description

Set, get, summary, and print methods for the wt class.

Usage

## S3 method for class 'wt'
summary(object, ...)

## S3 method for class 'wt'
print(x, ...)

## S3 method for class 'wt'
set_times(obj, newval)

## S3 method for class 'wt'
set_timescales(obj, newval)

## S3 method for class 'wt'
set_values(obj, newval)

## S3 method for class 'wt'
set_dat(obj, newval)

## S3 method for class 'wt'
set_wtopt(obj, newval)

## S3 method for class 'wt'
get_times(obj)

## S3 method for class 'wt'
get_timescales(obj)

## S3 method for class 'wt'
get_values(obj)

## S3 method for class 'wt'
get_dat(obj)

## S3 method for class 'wt'
get_wtopt(obj)

Arguments

Value

summary.wt produces a summary of a wt object. A print.wt method is also available. For wt objects, set_* and get_* methods are available for all slots, i.e., * equal to times, timescales, wtopt, values, and dat. The set_* methods just throw an error, to prevent breaking the consistency between the slots of a wt object.

Author(s)

Daniel Reuman, reuman@ku.edu

See Also

wt

Examples

time1<-1:100
time2<-101:200
ts1p1<-sin(2*pi*time1/15)
ts1p2<-0*time1
ts2p1<-0*time2
ts2p2<-sin(2*pi*time2/8)
ts1<-ts1p1+ts1p2
ts2<-ts2p1+ts2p2
ts<-c(ts1,ts2)
ra<-rnorm(200,mean=0,sd=0.5)
t.series<-ts+ra
t.series<-t.series-mean(t.series)
times<-c(time1,time2)
h<-wt(t.series, times)
get_times(h)
summary(h)
print(h)