NST (version 3.1.10)

nst.panova: Permutational multivariate ANOVA test for ST and NST

Description

Permutational multivariate ANOVA test for stochasticity ratio and normalized stochasticity ratio between treatments

Usage

nst.panova(nst.result, group=NULL, rand=999, trace=TRUE, SES=FALSE, RC=FALSE)

Arguments

nst.result

list object, the output of nsto, must have "details"

group

nx1 matrix, if the grouping is different from the nst.result. default is NULL, means to use the grouping in nst.result.

rand

integer, randomization times for permuational test

trace

logic, whether to show message when randomizing.

SES

Logic, whether to perform the test for standardized effect size (SES). SES is (observed dissimilarity - mean of null dissimilarity)/standard deviation of null dissimilarity. default is FALSE.

RC

Logic, whether to perform the test for modified Raup-Crick metric (RC). RC is percentage of null dissimilarity lower than observed dissimilarity x 2 - 1. default is FALSE.

Value

Output is a data.frame object.

index

name of index

group1

treatment/group name

group2

treatment/group name

Index.group1

index value in group1

Index.group2

index value in group2

Difference

index.group1 - index.group2

F.obs

F value

P.anova

P value of parametric ANOVA test

P.panova

P value of permutational ANOVA test

P.perm

P value of permutational test of the difference

Details

PERMANOVA for stochasticity ratio (ST or NST or MST) or SES or RC is based on the comparison of F values between observed pattern and the permutated patterns where samples are randomly shuffled regardless of treatments. However, it is a bit different from PERMANOVA for dissimilarity. The PERMANOVA of stochasticity ratio here is to ask whether the ST values within a group is higher than those within another group. But the PERMANOVA of dissimilarity is to ask whether the between-group dissimilarity is higher than within-group dissimilarity.

References

Ning D., Deng Y., Tiedje J.M. & Zhou J. (2019) A general framework for quantitatively assessing ecological stochasticity. Proceedings of the National Academy of Sciences 116, 16892-16898. doi:10.1073/pnas.1904623116.

See Also

tNST, nst.boot

Examples

# NOT RUN {
data(tda)
comm=tda$comm
group=tda$group
tnst=tNST(comm=comm, group=group, rand=20,
          output.rand=TRUE, nworker=1)
# rand is usually set as 1000, here set rand=20 to save test time.

nst.pova=nst.panova(nst.result=tnst, rand=99)
# rand is usually set as 999, here set rand=99 to save test time.
# }