SMW {segRDA}

Split moving window analysis

Description

Function SMW performs split moving window analysis (SMW) with randomizations tests. It may compute dissimilarities for a single window size or for several windows sizes.

Usage

SMW(yo, ws, dist = "bray", rand = c("shift", "plot"), n.rand = 99)

Arguments

yo

The ordered community matrix.

ws

The window sizes to be analyzed. Either a single value or a vector of values.

dist

The dissimilarity index used in vegan::vegdist. Defaults to 'bray'.

rand

The type of randomization for significance computation (Erdös et.al, 2014):

• "shift": restricted randomization in which data belonging to the same species are randomly shifted along the data series ("Random shift");

• "plot": unrestricted randomization: each sample is randomly repositioned along the data series ("Random plot").

n.rand

The number of randomizations.

Value

A two-level list object (class smw) describing the SMW results for each window w analyzed. The smw object is of length ws, and each of the w slots is a list of SMW results:

• ..$dp: The raw dissimilarity profile (DP). The DP is a data frame giving the positions, labels, values of dissimilarity and z-scores for each sample;

• ..$rdp: data frame containing the randomized DP;

• ..$md: mean dissimilarity of the randomized DP;

• ..$sd: standard deviation for each sample position;

• ..$oem: overall expected mean dissimilarity;

• ..$osd: average standard deviation for the dissimilarities;

• ..$params: list with input arguments

Available methods for class "smw" are print, extract and plot.

Author(s)

Danilo Candido Vieira

References

• Erdos, L., Z. Bátori, C. S. Tölgyesi, and L. Körmöczi. 2014. The moving split window (MSW) analysis in vegetation science - An overview. Applied Ecology and Environmental Research 12:787–805.

• Cornelius, J. M., and J. F. Reynolds. 1991. On Determining the Statistical Significance of Discontinuities with Ordered Ecological Data. Ecology 72:2057–2070.

See Also

plot.smw, extract.

Examples

data(sim1)
sim1o<-OrdData(sim1$envi,sim1$comm)


ws20<-SMW(yo=sim1o$yo,ws=20)
pool<-SMW(yo=sim1o$yo,ws=c(20,30,40))