Description Usage Arguments Value Examples
Nonparametric Microbial Interdependence Test (NMIT)
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otu 
a matrix or data frame of OTU table. 
id.var 
a vector of subjects. 
cov.var 
a vector of covariates. 
time.var 
a vector of time variable. 
method 
an option of the correlation method ("pearson","kendall","spearman"). The default method is "kendall". 
dist.type 
character string, specifying the type of matrix norm to be computed. A character indicating the type of norm desired. The default is

heatmap 
a logical value indicating whether to draw heatmap. The default value is TRUE. 
classify 
a logical value indicating whether to draw classifier tree. The default value is FALSE. 
fill.na 
a number between 0 and 1 to fill the missing value. The default value is 0. 
This function returns typical, but limited, output for analysis of variance (general linear models).
aov.tab 
Typical AOV table showing sources of variation, degrees of freedom, sequential sums of squares, mean squares, F statistics, partial Rsquared and P values, based on N permutations. 
coefficients 
matrix of coefficients of the linear model, with rows representing sources of variation and columns representing species; each column represents a fit of a species abundance to the linear model. These are what you get when you fit one species to your predictors. These are NOT available if you supply the distance matrix in the formula, rather than the site x species matrix 
coef.sites 
matrix of coefficients of the linear model, with rows representing sources of variation and columns representing sites; each column represents a fit of a sites distances (from all other sites) to the linear model. These are what you get when you fit distances of one site to your predictors. 
f.perms 
an N by m matrix of the null F statistics for each source of variation based on N permutations of the data. The permutations can be inspected with permustats and its support functions. 
model.matrix 
The model.matrix for the right hand side of the formula. 
terms 
The terms component of the model. 
1 2 3 4 5 6  #data(mice)
#otu < mice[,  (1:3)]
#id.var < mice$id
#cov.var < mice$group
#time.var < mice$time
#NMIT(otu, id.var, cov.var, time.var)

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