iCAMP (version 1.5.12)

qpen.test: Summary and comparison of QPEN results based on bootstrapping

Description

Bootstrapping analysis of the results from QPEN (quantifying assembly processes based on entire-community null model analysis, Stegen et al 2013, 2015), to estimate the mean and variation of each index and each process influence in each group, and calculate the significance of the difference between groups.

Usage

qpen.test(qpen.result, treat, rand.time = 1000,
          between.group = FALSE, out.detail = TRUE,
          silent = FALSE)

Arguments

qpen.result

the output of the function 'qpen', or the element 'result' (data.frame) in qpen output.

treat

matrix or data.frame, each column indicates the group or treatment of each sample, rownames are sample IDs.

rand.time

integer, bootstrapping times. default is 1000.

between.group

logic. if True, the turnovers between each pair of treatments will also be calculated as a group.

out.detail

logic. if True, the 'qpen' results and the bootstrapping results in each group will also be output.

silent

logic. if FALSE, some messages will show during calculation.

Value

Output is a list.

obs.summary

the mean, standarded deviation, quartile, and boxplot elements for each observed index (e.g. bMNTD, bNTI, etc.) in each group.

boot.summary

the mean, standarded deviation, quartile, and boxplot elements of the average level of each index and the estimated relative importance of each process in each group.

compare

The relative difference, Cohen's d, and P value of the difference of each index or each process importance between different groups.

group.results.detail

the qpen results in each group.

boot.detail

the average value of each index or estimated relative importanc of each process in each group in each time of bootstrapping.

Details

Basically use bootstrapping of samples to estimate the variation of each index's mean and relative importance of each process in each group, as well as the effect size and signficance of the difference between different groups.

References

Ning, D., Yuan, M., Wu, L., Zhang, Y., Guo, X., Zhou, X. et al. (2020). A quantitative framework reveals ecological drivers of grassland microbial community assembly in response to warming. Nature Communications, 11, 4717.

Stegen, J.C., Lin, X., Fredrickson, J.K., Chen, X., Kennedy, D.W., Murray, C.J. et al. (2013). Quantifying community assembly processes and identifying features that impose them. ISME J, 7, 2069.

Stegen, J.C., Lin, X., Fredrickson, J.K. & Konopka, A.E. (2015). Estimating and mapping ecological processes influencing microbial community assembly. Front Microbiol, 6, 370.

See Also

qpen

Examples

# NOT RUN {
data("example.data")
comm=example.data$comm
tree=example.data$tree
treat=example.data$treat
# since pdist.big need to save output to a certain folder,
# the following code is set as 'not test'.
# but you may test the code on your computer
# after change the folder path for 'save.wd'.
# }
# NOT RUN {
  wd0=getwd()
  nworker=2 # parallel computing thread number
  rand.time=5 # usually use 1000 for real data.
  
  # for a big dataset, pdist.big may be used
  save.wd=paste0(tempdir(),"/pdbig.qpen.test")
  # please change to the folder you want to save the pd.big output.
  
  pd.big=pdist.big(tree = tree, wd=save.wd, nworker = nworker)
  qp2=qpen(comm=comm, pd=pd.big$pd.file, pd.big.wd=pd.big$pd.wd,
           pd.big.spname=pd.big$tip.label, tree=tree,
           rand.time=rand.time, nworker=nworker)

  qptest=qpen.test(qpen.result=qp2, treat=treat)
  setwd(wd0)
# }