Dunnett.GLM.bMDD {qountstat} | R Documentation |
Dunnett.GLM bootstrap MDD (bMDD)
Description
The basic idea of the calculation of bootstrap MDD (bMDD) using the Dunnett.GLM approach is to shift the lambda parameter of Poisson distribution until there is a certain proportion of results significantly different from the control.
Usage
Dunnett.GLM.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,
Dunnett.GLM.zero.treatment.action = "log(x+1)",
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. |
Dunnett.GLM.zero.treatment.action |
Dunnett.GLM method to be used for treatments only containing zeros |
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 Dunnett.GLM bootstrap MDD
Dunnett.GLM.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
Dunnett.GLM.zero.treatment.action = "log(x+1)",
show.progress = TRUE,
show.results = TRUE)