The hardware and bandwidth for this mirror is donated by METANET, the Webhosting and Full Service-Cloud Provider.
If you wish to report a bug, or if you are interested in having us mirror your free-software or open-source project, please feel free to contact us at mirror[@]metanet.ch.

Comparing fitted predmicror models

This vignette shows a compact workflow for comparing alternative predictive microbiology models with the predmicror_fit interface.

Fit candidate models

library(predmicror)

data(growthfull)

huang <- fit_growth(
  growthfull,
  model = "HuangFM",
  time = "Time",
  response = "lnN",
  start = list(Y0 = 0, Ymax = 22, MUmax = 1.7, lag = 5)
)

baranyi <- fit_growth(
  growthfull,
  model = "BaranyiFM",
  time = "Time",
  response = "lnN",
  start = list(Y0 = 0, Ymax = 22, MUmax = 1.7, lag = 5)
)

Extract fitted values and residuals

head(predmicror_augment(huang))

The returned data frame keeps the original columns and adds:

.fitted: predicted response on the fitted response scale;
.resid: observed minus fitted response, when the response column is available;
.model: fitted model name;
.type: model family.

Calculate diagnostics for one model

fit_metrics(huang)

fit_metrics() reports residual and information-criterion diagnostics on the response scale used for fitting.

Compare models

compare_models(
  huang = huang,
  baranyi = baranyi,
  sort_by = "AIC"
)

For models fitted to the same response variable and dataset, lower AIC, BIC, RMSE, and MAE values usually indicate a better fit. These criteria should be interpreted together with residual plots, biological plausibility, and parameter uncertainty.

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.