dpcr_density_table: Calculate Density of Multiple dPCR runs
In dpcR: Digital PCR Analysis

Description Usage Arguments Value Author(s) See Also Examples

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

1 2	dpcr_density_table(input, average = FALSE, methods = "wilson", conf.level = 0.95)

`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.

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

Michal Burdukiewicz, Stefan Roediger.

dpcr_density for easy analysis and plots of single runs.

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")