Power and Sample Size Calculation for Differential Abundance Microbiome Studies


[Up] [Top]

Documentation for package ‘power.nb’ version 0.1.0

Help Pages

contour_plot_fun Contour plot for showing predicted power
countdata_sim_fun Simulate Count Data for Microbiome Studies
deseqfun Estimate log fold changes using 'DESeq2'.
deseq_fun_est Fold change and p-value estimations for simulations
dispersion_fit Fit the non-linear function to dispersion estimates
dispersion_fun Calculate Dispersion for Microbiome Data
dnormmix Density of a Normal Mixture Model
dnormmix0 Density function for the mixture of Gaussian distributions
filter_low_count Filter to remove low abundant taxa
gam_fit Title
genmixpars generate normal mixture parameters (prob vector, mean vector, sd vector for a specified set of 'x' values (logmean)
gen_parnames Generate Parameter Names for Mixture Model
logfoldchange_fit Fit a mixture of Gaussian distributions to log fold change
logfoldchange_sim_fun Simulate Log Fold Change Values
logmean_fit Fit a mixture of Gaussian Distributions to log mean count of taxa.
logmean_sim_fun Simulate Log Means for OTUs
myrnormmix Simulating from a mixture of Gaussian
nllfun Objective function
optimal.comp Computes the optimal number of gaussian components for log mean count
polyfun General-purpose log-likelihood function, vectorized sum(pars*x^i)
power_fun_ss Fit a smooth power model for sample size estimation
read_data Extract specified data from a list of datasets
rnormmix0 general-purpose normal-mixture deviate generator: takes _matrices_ of probabilities, means, sds
sample_size_ss_interp Estimate sample size required to achieve a target statistical power
ss_solver Solve for the sample size required to achieve a target statistical power
uniroot_ss Sample Size estimation function using uniroot