Description Usage Arguments Details Value Author(s) Examples
Model selection by comparison of different models using the following criteria: the log likelihood value, Akaike's information criterion (AIC), the estimated residual standard error or the p-value from a lack-of-fit test.
1 2 |
object |
an object of class 'drc'. |
fctList |
a list of dose-response functions to be compared. |
nested |
logical. TRUE results in F tests between adjacent models (in 'fctList'). Only sensible for nested models. |
sorted |
character string determining according to which criterion the model fits are ranked. |
linreg |
logical indicating whether or not additionally polynomial regression models (linear, quadratic, and cubic models) should be fitted (they could be useful for a kind of informal lack-of-test consideration for the models specified, capturing unexpected departures). |
icfct |
function for supplying the information criterion to be used. |
For Akaike's information criterion and the residual standard error: the smaller the better and for lack-of-fit test (against a one-way ANOVA model): the larger (the p-value) the better. Note that the residual standard error is only available for continuous dose-response data.
Log likelihood values cannot be used for comparison unless the models are nested.
A matrix with one row for each model and one column for each criterion.
Christian Ritz
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 | ### Example with continuous/quantitative data
## Fitting initial four-parameter log-logistic model
ryegrass.m1 <- drm(rootl ~ conc, data = ryegrass, fct = LL.4())
## Model selection
mselect(ryegrass.m1, list(LL.3(), LL.5(), W1.3(), W1.4(), W2.4(), baro5()))
## Model selection including linear, quadratic, and cubic regression models
mselect(ryegrass.m1, list(LL.3(), LL.5(), W1.3(), W1.4(), W2.4(), baro5()), linreg = TRUE)
## Comparing nested models
mselect(ryegrass.m1, list(LL.5()), nested = TRUE)
### Example with quantal data
## Fitting initial two-parameter log-logistic model
earthworms.m1 <- drm(number/total~dose, weights=total,
data = earthworms, fct = LL.2(), type = "binomial")
## Comparing 4 models
mselect(earthworms.m1, list(W1.2(), W2.2(), LL.3()))
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