Get ordinary least squares mean and confidence intervals from energetics

Helper function to calculate mean and standard deviation of ATP production from glycolysis and OXPHOS at points defined in partition_data and with values calculated using the get_energetics function. Should only be called from get_energetics_summary as the function itself only operaes on a vector without any of the grouping that get_energetics does.

Usage

energetics_ols_summary(atp_col, error_metric, conf_int)

Arguments

atp_col: The column name of the ATP measure - one of "ATP_basal_resp", "ATP_max_resp", "ATP_basal_glyc", "ATP_max_glyc"
error_metric: Whether to calculate error as standard deviation ("sd") or confidence intervals ("ci")
conf_int: The confidence interval percentage. Should be between 0 and 1

Value

a data.table with mean and the confidence interval bounds by experimental group

Examples

rep_list <- system.file("extdata", package = "ceas") |>
  list.files(pattern = "*.xlsx", full.names = TRUE)
seahorse_rates <- read_data(rep_list, sheet = 2)
partitioned_data <- partition_data(seahorse_rates)
energetics <- get_energetics(partitioned_data, ph = 7.4, pka = 6.093, buffer = 0.1)
# Only for one row and across all groups and replicates.
# For the full correctly grouped energetics table run
# `get_energetics_summary` with `model = "ols"`.
energetics_ols_summary(energetics$ATP_max_resp, error_metric = "ci", conf_int = 0.95)
#> $mean
#> [1] 1085.137
#> 
#> $sd
#> [1] 280.1745
#> 
#> $se
#> [1] 29.21021
#> 
#> $lower_bound
#> [1] 1027.886
#> 
#> $higher_bound
#> [1] 1142.388
#>