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Predict Concentration for Test Samples

Source: R/predict_samples.R
predict_samples_freq.Rd

For each test sample, applies the inverse of the best-fit model to the observed response to get predicted concentration, then propagates uncertainty via the delta method to get se_concentration and pcov.

Usage

predict_samples_freq(
  samples,
  fit,
  model_name,
  response_variable,
  fixed_a = NULL,
  is_log_response = TRUE,
  se_response = 0,
  cv_x_max = 150,
  is_log_independent = TRUE,
  verbose = FALSE
)

Arguments

samples

Data frame of test samples. Must contain the response column and dilution.

fit

The best-fit nlsLM object.

model_name

Character. Best model name.

response_variable

Character. Response column name.

fixed_a

Numeric or NULL. Fixed lower asymptote on fitting scale.

is_log_response

Logical. Was the response log10-transformed?

se_response

Numeric. Residual SE on the fitting scale. Typically summary(fit)$sigma. Default 0.

cv_x_max

Numeric. Cap for pcov. Default 150.

is_log_independent

Logical. Is x on log10 scale? Default TRUE.

verbose

Logical.

Value

Data frame with original sample columns plus: observed_response_fit, predicted_log10_concentration, predicted_concentration, final_concentration, se_concentration, pcov, pcov_rmse.