Description Usage Arguments Details Value References See Also Examples
Multiresponse sequence procedure (MRSP) is a special case of MRPP where first-order sequential pattern of data is tested against the null hypothesis of no sequential pattern. Univariate analyses are analogous to the Durbin-Watson test for first-order serial pattern and bivariate analyses are analogous to Schoener's t^2/r^2 statistic (Solow 1989). Permutation versions of these two tests can be done. Options allow you to select the sequencing variable and to turn off multivariate commensuration.
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data |
an object of class matrix (or an object coercible by |
expon |
allows selection of alternative exponents in distance calculations. |
commens |
a logical value indicating whether to perform multivariate commensuration. |
number.perms |
number of permutations used if a Monte Carlo resampling procedure is to be used. |
exact |
logical indicating whether to perform an exact test or use a Monte Carlo resampling procedure. |
save.test |
logical indicating whether to return Monte Carlo resampled test statistic values. |
sequence |
a numeric vector specifying how the data should be ordered for the analysis. |
variables |
a character string of the names to be used in the analysis. These should match names found in the |
In this analysis of ungrouped data, the agreement measure (1 - average Euclidean distance
between ordered observations/average Euclidean distance among all possible pairs of observations)
is a statistic describing first-order serial dependency. Significance of the null hypothesis of no
first-order serial dependency is provided by the Pearson Type III approximation on the first 3
exact moments of the permutation distribution by default, optionally by exact enumeration for small
samples by specifying the exact
option, or by a Monte Carlo resampling procedure by specifying
the number.perms
option.
sp returns an object of class MRSPObj.
The functions summary
as well as print
can be used to obtain a summary of the test.
Generic accessor functions pvalue
and ResampVals
can be used to obtain the p-value and Monte Carlo resampled test statistic values respectively.
Solow, A.R. 1989. A randomization test for independence of animal locations. Ecology 70, 1546–1549.
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