step1_down {AccelStab}R Documentation

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, the column must contain only 1s and 0s, 1 for validation data and 0 for fit data. (column name) (optional).

draw

Number of simulations used to estimate confidence intervals. When set to NULL the calculus method is used, however this is not recommended.

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).

...

Further arguments to passed to minpack.lm.

Details

Fit the one-step Šesták–Berggren kinetic (non-linear) model using accelerated stability data that has been stored in an R data frame. Additionally, predictions of the mean at each tested temperature are returned, including associated confidence and prediction intervals, which can be subsequently visualised with step1_plot_pred(), step1_plot_CI(), step1_plot_PI() and step1_plot_T(). Kinetic parameters (k1, k2 and, if used, k3) are retained in the model even if one or more of these parameters turn out to be non-significant. Further arguments relating to model fitting, such as setting lower bounds for one or more model parameters, may be passed.

Value

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

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 & Validation data segmented out.
fit2 <- step1_down(data = antigenicity, y = "conc", .time = "time", K = "K",
validation = "validA", 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)

#Use a custom lower bound for k1 (default is 0).
fit5 <- step1_down(data = potency, y = "Potency", .time = "Time",C = "Celsius",
  reparameterisation = TRUE, zero_order = TRUE, draw = 5000, lower = c(-Inf, 0, 0))


[Package AccelStab version 2.2.1 Index]