step1_down_rmse {AccelStab}

Step1 Down Model Root Mean Square Error Calculation

Description

Calculate Root Mean Square Error (RMSE) for the one-step Šesták–Berggren kinetic model.

Usage

step1_down_rmse(
  data,
  y,
  .time,
  K = NULL,
  C = NULL,
  parms,
  reparameterisation = FALSE
)

Arguments

data

Dataframe containing accelerated stability data (required).

y

Name of decreasing variable (e.g. concentration) contained within data (required).

.time

Time variable contained within data (required).

K

Kelvin variable (numeric or column name) (optional).

C

Celsius variable (numeric or column name) (optional).

parms

Values for the parameters as a list - k1, k2, k3, and c0. If multiple are provided all combinations will be used (required).

reparameterisation

Use alternative parameterisation of the one-step model which aims to reduce correlation between k1 and k2.

Details

Calculate RMSE for the one-step Šesták–Berggren kinetic (non-linear) model using user provided parameters.

Value

A data frame containing one row for each RMSE calculation

Examples

#load antigenicity and potency data.
data(antigenicity)
data(potency)

#Basic use of the step1_down_rmse function with C column defined.
rmse1 <- step1_down_rmse(data = antigenicity, y = "conc", .time = "time",
 C = "Celsius", parms = list(c0 = c(96,98,100), k1 = c(42,45),
  k2 = c(12000,12500), k3 = c(8,9,10)))

#Basic use of the step1_down_rmse function with K column defined.
rmse2 <- step1_down_rmse(data = antigenicity, y = "conc", .time = "time",
 K = "K", parms = list(c0 = c(98), k1 = c(42,45), k2 = c(12500), k3 = c(8,9)))

#reparameterisation is TRUE.
rmse3 <- step1_down_rmse(data = antigenicity, y = "conc", .time = "time",
 C = "Celsius", parms = list(c0 = c(100,95), k1 = c(2,2.5), k2 = c(12000,13000),
  k3 = c(9,10)), reparameterisation = TRUE)