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.
SMW(yo, ws, dist = "bray", rand = c("shift", "plot"), n.rand = 99)The ordered community matrix.
The window sizes to be analyzed. Either a single value or a vector of values.
The dissimilarity index used in vegan::vegdist. Defaults to 'bray'.
The type of randomization for significance computation (Erd<U+00F6>s 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").
The number of randomizations.
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;
..$params: list with input arguments
Available methods for class "smw" are print, extract and plot.
Erdos, L., Z. B<U+00E1>tori, C. S. T<U+00F6>lgyesi, and L. K<U+00F6>rm<U+00F6>czi. 2014. The moving split window (MSW) analysis in vegetation science - An overview. Applied Ecology and Environmental Research 12:787<U+2013>805.
Cornelius, J. M., and J. F. Reynolds. 1991. On Determining the Statistical Significance of Discontinuities with Ordered Ecological Data. Ecology 72:2057<U+2013>2070.
# NOT RUN {
data(sim1)
sim1o<-OrdData(sim1$envi,sim1$comm)
# }
# NOT RUN {
ws20<-SMW(yo=sim1o$yo,ws=20)
pool<-SMW(yo=sim1o$yo,ws=c(20,30,40))
# }
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