dpcr_density_table: Calculate Density of Multiple dPCR runs

In michbur/dpcR: Digital PCR Analysis

Calculate Density of Multiple dPCR runs

Description

Calculates the density of the number of positive molecules or the average number of molecules per partition of dpcr objects.

Usage

dpcr_density_table(
  input,
  average = FALSE,
  methods = "wilson",
  conf.level = 0.95
)

Arguments

input	an object of class dpcr.
average	If TRUE, calculates density of the average number of molecules per partition. If FALSE, instead performs calculations for the total number of positive molecules.
methods	Method for calculating the confidence interval. Possible values are: "wilson", "agresti-coull", "exact", "prop.test", "profile", "lrt", "asymptotic", "bayes", "cloglog", "logit", "probit". Default value is "wilson". See Details.
conf.level	The level of confidence to be used in the confidence interval. Values from 0 to 1 and -1 to 0 are acceptable.

Value

A list (with the length equal to the number of runs in input) of data frames containing densities and borders of confidence intervals.

Author(s)

Michal Burdukiewicz, Stefan Roediger.

See Also

dpcr_density for easy analysis and plots of single runs.

Examples

dens <- dpcr_density_table(six_panels)

# create plot using ggplot2
library(ggplot2)

ggplot(dens[["Experiment2.2"]], aes(x = x, y = y)) +
  geom_line() +
  geom_area(aes(fill = !(conf_up | conf_low))) +
  scale_y_continuous("Density") +
  scale_fill_discrete("0.95 CI")

michbur/dpcR documentation built on Nov. 17, 2022, 5:02 a.m.