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Estimate mean and confidence intervals using a mixed-effects model

Source: R/fit_lme.R
fit_lme.Rd

Estimates mean and standard deviation of energetics or rates with replicates as the random-effect

Usage

fit_lme(
  data_col,
  input,
  group_colname = "exp_group",
  rep_colname = "replicate"
)

Arguments

data_col

The column name of the ATP measure ("ATP_basal_resp", "ATP_max_resp", "ATP_basal_glyc", "ATP_max_glyc") or rate measure ("OCR", "ECAR")

input

The dataset containing data_col from get_energetics or read_data

group_colname

The column containing experimental group names

rep_colname

The column containing replicate IDs

Value

an lme4::lmer mixed effects model

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)
fit_lme("ATP_max_glyc", energetics)
#> boundary (singular) fit: see help('isSingular')
#> Linear mixed model fit by REML ['lmerMod']
#> Formula: ATP_max_glyc ~ exp_group + (1 | replicate)
#>    Data: input
#> REML criterion at convergence: 1014.011
#> Random effects:
#>  Groups    Name        Std.Dev.
#>  replicate (Intercept)  0.00   
#>  Residual              71.61   
#> Number of obs: 92, groups:  replicate, 2
#> Fixed Effects:
#>      (Intercept)  exp_groupGroup_2  exp_groupGroup_3  exp_groupGroup_4  
#>            470.7             462.9             371.2             149.0  
#> optimizer (nloptwrap) convergence code: 0 (OK) ; 0 optimizer warnings; 1 lme4 warnings