rRec: Calculate the mean recovery rate (Pacioni et al 2017) and...

In vortexR: Post Vortex Simulation Analysis

Description

rRec calculates the mean and standard deviation growth rate between the time yr0 and yrt, which was defined as 'recovery rate' by Pacioni et al (in press). The function then calculates the strictly standardised mean difference (SSMD, Zhang 2007) for each scenario, and each population contained in the data. rRec uses this statistic to compare each scenario (providing associated p-values) with a baseline scenario.

Usage

rRec(
  data,
  project,
  scenario,
  ST = FALSE,
  runs,
  yr0 = 1,
  yrt,
  save2disk = TRUE,
  dir_out = "DataAnalysis/rRec"
)

Arguments

data	A data.frame generated by collate_dat
project	The Vortex project name
scenario	The ST Vortex scenario name or the scenario that should be used as baseline if simulations were not conducted with the ST module
ST	Whether files are from sensitivity analysis (TRUE), or not (FALSE, default)
runs	The number of Vortex simulation runs
yr0	The time window to be considered (first and last year respectively)
yrt	The time window to be considered (first and last year respectively)
save2disk	Whether to save the output to disk, default: TRUE
dir_out	The local path to store the output. Default: DataAnalysis/Pairwise

Details

The means and standard deviations are calculated as: **check how to insert formula in Roxy tags** rRec = sigma(Ni*Mi) / sigma(Ni) (N1*M1+N2*M2+N3*M3)/(N1+N2+N3) SD =N1*S1+N2*S2+N3*S3/(N1+N2+N3)

Where M is the mean growth rate in each year, N is the sample size (number of simulation runs) and S is the standard deviation.

The baseline scenario is selected with the argument scenario. However, if the simulations were part of a sensitivity testing (as indicated by ST) then the baseline scenario is selected using the scenario with the suffix '(Base)'.

Value

A table (data.table) with the mean rRec and its SD, the SSMD and its associated p-value for each scenario and population

References

Zhang, X. D. 2007. A pair of new statistical parameters for quality control in RNA interference high-throughput screening assays. Genomics 89:552-561.

Pacioni, C., and Mayer, F. (2017). vortexR: an R package for post Vortex simulation analysis.

Examples

# Using Pacioni et al. example data. See ?pac.clas for more details.
data(pac.clas)
recov <- rRec(pac.clas, project='Pacioni_et_al', scenario='ST_Classic',
              ST=TRUE, runs=3, yr0=1, yrt=120, save2disk=FALSE,
              dir_out='DataAnalysis/rRec')

vortexR documentation built on April 14, 2020, 7:23 p.m.