knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  fig.path = "man/figures/README-",
  out.width = "100%",
  dev.args = list(png = list(type = "cairo")),
  echo = TRUE,
  dpi = 150, 
  fig.align = "center")

aqua - assay qualification and validation tools

AppVeyor build status

The (future, long term, eventual) goal of aqua is to provide tools that help with different assay qualification and validation project analysis and reporting.

Installation

You can install aqua. I hope. You're on your own.

devtools::install_github("cgtc/aqua")    # fingers crossed

Examples

library(aqua)

sd(c(2,3,2))   # boring
sd.u(c(2,3,2)) # amazing

sd.u produces a very unbiased standard deviation estimate. Makes every small sample size data look horrible. The sad but true reality. Uses a chi-distributed correction factor.

library(lme4)

funky_model <- lmer(total.fruits ~ 1 + (1|amd) + (1|status), data = Arabidopsis)

get_intermed_levels(funky_model) |>
  knitr::kable(escape = F) |>
  kable_style()

Boom. Clearly the contribution of amd and status to variations in fruit.growth (in a vacuum) in this model I just invented that makes no sense is what it says there.

You can get repeatability and overall intermediate precision / reproducibility of an assay if you turned it into an lme4 random model with the only fixed term being an intercept. Just describe your model like 1 + (1|Lab/Operator/Day) + (1|SampleType) or whatever your assay design structure is and fire it up.

You can also do the whole TCID50 assay calculations. There's a lot of functions for that that tend to even work! Does anyone use TCID50 anymore? Hello?

Version

Currently 0.2 ALPHA



dmarginean/aqua documentation built on March 16, 2024, 1:03 a.m.