wlm: Wavelet linear models
In wsyn: Wavelet Approaches to Studies of Synchrony in Ecology and Other Fields

Description Usage Arguments Details Value Author(s) References See Also Examples

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

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

`dat`	A list of matrices representing the data (or in the case of one location, a list of vectors). All the same dimensions (respectively, lengths)
`times`	The times at which measurements were made, spacing 1
`resp`	Index in dat for the response variable of the model
`pred`	Vector of indices in dat for the predictor variables of the model; must differ from `resp`
`norm`	The normalization of wavelet transforms to use. One of "none", "powall", "powind". See details.
`scale.min`	The smallest scale of fluctuation that will be examined. At least 2.
`scale.max.input`	The largest scale of fluctuation that will be examined. Note that if this is set too high relative to the length of the timeseries it will be truncated.
`sigma`	The ratio of each time scale examined relative to the next timescale. Greater than 1.
`f0`	The ratio of the period of fluctuation to the width of the envelope

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.

wlm returns an object of class wlm. Slots are:

`dat`	The input data list, but reordered and subsetted so the response is first and only used predictors are included
`times`	The times associated with the data
`norm`	The input
`wtopt`	The inputted wavelet transform options scale.min, scale.max.input, sigma, f0 in a list
`wts`	List of transforms, normalized as specified in `norm`. Same length as the output `dat`, each entry a locations x time x timescales array of transforms.
`timescales`	The timescales associated with the wavelet transforms of the data
`coefs`	A list (data frame, actually) of complex vectors, each of length the same as `timescales`. These are the model coefficients (which depend on timescale), and correspond to the `wts`.
`modval`	The model values.
`coher`	Appropriately normalized version of coherence of the model and response transforms. See details.

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

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

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

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)