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Dose-response model selection

Source: R/mselect.R
mselect.Rd

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.

Usage

mselect(
  object,
  fctList = NULL,
  nested = FALSE,
  sorted = c("IC", "Res var", "Lack of fit", "no"),
  linreg = FALSE,
  icfct = AIC
)

Arguments

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.

icfct

function for supplying the information criterion to be used. AIC and BIC are two options.

Value

A matrix with one row for each model and one column for each criterion.

Details

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.

Author

Christian Ritz

Examples

### 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()))
#>          logLik       IC Lack of fit   Res var
#> W2.4  -15.91352 41.82703  0.94507131 0.2646283
#> LL.4  -16.15514 42.31029  0.86648304 0.2700107
#> baro5 -15.86422 43.72844  0.86239408 0.2774141
#> LL.5  -15.87828 43.75656  0.85384758 0.2777393
#> W1.4  -17.46720 44.93439  0.45056762 0.3012075
#> LL.3  -18.60413 45.20827  0.35316787 0.3153724
#> W1.3  -22.22047 52.44094  0.04379149 0.4262881

## 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)
#>          logLik        IC Lack of fit   Res var
#> W2.4  -15.91352  41.82703  0.94507131 0.2646283
#> LL.4  -16.15514  42.31029  0.86648304 0.2700107
#> baro5 -15.86422  43.72844  0.86239408 0.2774141
#> LL.5  -15.87828  43.75656  0.85384758 0.2777393
#> W1.4  -17.46720  44.93439  0.45056762 0.3012075
#> LL.3  -18.60413  45.20827  0.35316787 0.3153724
#> W1.3  -22.22047  52.44094  0.04379149 0.4262881
#> Cubic -25.53428  61.06856          NA 0.5899609
#> Quad  -35.11558  78.23116          NA 1.2485122
#> Lin   -50.47554 106.95109          NA 4.2863247

## Comparing nested models
mselect(ryegrass.m1, list(LL.5()), nested = TRUE)
#>         logLik       IC Lack of fit   Res var Nested F test
#> LL.4 -16.15514 42.31029   0.8664830 0.2700107            NA
#> LL.5 -15.87828 43.75656   0.8538476 0.2777393     0.5134602