CPCAT.bMDD {qountstat} | R Documentation |
CPCAT bootstrap MDD (bMDD)
Description
The basic idea of the calculation of bootstrap MDD (bMDD) using the CPCAT approach is to shift the lambda parameter of Poisson distribution until there is a certain proportion of results significantly different from the control.
Usage
CPCAT.bMDD(
groups,
counts,
control.name = NULL,
alpha = 0.05,
shift.step = -0.25,
bootstrap.runs = 200,
power = 0.8,
max.iterations = 1000,
use.fixed.random.seed = NULL,
CPCAT.bootstrap.runs = 200,
show.progress = TRUE,
show.results = TRUE
)
Arguments
groups |
Group vector |
counts |
Vector with count data |
control.name |
Character string with control group name (optional) |
alpha |
Significance level |
shift.step |
Step of shift (negative as a reduction is assumed) |
bootstrap.runs |
Number of bootstrap runs |
power |
Proportion of bootstrap.runs that return significant differences |
max.iterations |
Max. number of iterations to not get stuck in the while loop |
use.fixed.random.seed |
Use fixed seed, e.g. 123, for reproducible results. If NULL no seed is set. |
CPCAT.bootstrap.runs |
Bootstrap runs within CPCAT method |
show.progress |
Show progress for each shift of lambda |
show.results |
Show results |
Value
Data frame with results from bMDD analysis
Examples
Daphnia.counts # example data provided alongside the package
# Test CPCAT bootstrap MDD
CPCAT.bMDD(groups = Daphnia.counts$Concentration,
counts = Daphnia.counts$Number_Young,
control.name = NULL,
alpha = 0.05,
shift.step = -1, # Caution: big step size for testing
bootstrap.runs = 5, # Caution: low number of bootstrap runs for testing
power = 0.8,
max.iterations = 1000,
use.fixed.random.seed = 123, #fixed seed for reproducible results
CPCAT.bootstrap.runs = 10,
show.progress = TRUE,
show.results = TRUE)
[Package qountstat version 0.1.1 Index]