step1_down {AccelStab}

Step1 Down Model

Description

Fit the one-step Šesták–Berggren kinetic model.

Usage

step1_down(
  data,
  y,
  .time,
  K = NULL,
  C = NULL,
  validation = NULL,
  draw = 10000,
  parms = NULL,
  temp_pred_C = NULL,
  max_time_pred = NULL,
  confidence_interval = 0.95,
  by = 101,
  reparameterisation = FALSE,
  zero_order = 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).

validation

Validation dummy variable (column name) (optional).

draw

Number of simulations used to estimate confidence intervals.

parms

Starting values for the parameters as a list - k1, k2, k3, and c0.

temp_pred_C

Integer or numeric value to predict the response for a given temperature (in Celsius).

max_time_pred

Maximum time to predict the response variable.

confidence_interval

Confidence level for the confidence and prediction intervals around the predictions (default 0.95).

by

Number of points (on the time scale) to smooth the statistical intervals around the predictions.

reparameterisation

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

zero_order

Set kinetic order, k3, to zero (straight lines).

Details

Fit the one-step Šesták–Berggren kinetic (non-linear) model using accelerated stability data.

Value

An SB class object, a list including the following elements:

• fit - The non-linear fit.

• data - The data set.

• prediction - A data frame containing the predictions with the confidence and prediction intervals.

• user_parameters - List of users input parameters which is utilised by other functions in the package.

Examples

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

#Basic use of the step1.down function with C column defined.
fit1 <- step1_down(data = antigenicity, y = "conc", .time = "time", C = "Celsius", draw = 5000)

#Basic use of the step1.down function with K column defined.
fit2 <- step1_down(data = antigenicity, y = "conc", .time = "time", K = "K", draw = 5000)

#When zero_order = FALSE, the output suggests using zero_order = TRUE for Potency dataset.
fit3 <- step1_down(data = potency, y = "Potency", .time = "Time",C = "Celsius",
  reparameterisation = FALSE, zero_order = TRUE, draw = 5000)

#reparameterisation is TRUE.
fit4 <- step1_down(data = antigenicity, y = "conc", .time = "time",C = "Celsius",
  reparameterisation = TRUE, draw = 5000)