Extract the data used to fit a model.

getData(mod, subset = FALSE, merge = FALSE, env = NULL)

Arguments

mod

A fitted model object, or a list or nested list of such objects.

subset

Logical. If TRUE, only observations used to fit the model(s) are returned (i.e. missing observations (NA) or those with zero weight are removed).

merge

Logical. If TRUE, and mod is a list or nested list, a single dataset containing all variables used to fit models is returned (variables must be the same length).

env

Environment in which to look for data (passed to eval()). Defaults to the formula() environment.

Value

A data frame of the variables used to fit the model(s), or a list or nested list of same.

Details

This is a simple convenience function to return the data used to fit a model, by evaluating the 'data' slot of the model call object. If the 'data' argument of the model call was not specified, or is not a data frame (or coercible to such) containing all variables referenced in the model formula, an error will be thrown – this restriction is largely to ensure that a single coherent dataset of all model variables can be made available for resampling purposes.

If mod is a list of models and merge = TRUE, all (unique) variables used to fit models are merged into a single data frame. This will return an error if subset = TRUE results in datasets with different numbers of observations (rows).

See also

Examples

# Get data used to fit SEM from Shipley (2009) head(getData(shipley.sem[[1]])) # from single model
#> site tree lat year Date DD Growth Survival Live #> 1 1 1 40.38063 1970 115.4956 160.5703 61.36852 0.9996238 1 #> 2 1 2 40.38063 1970 118.4959 158.9896 43.77182 0.8433521 1 #> 3 1 3 40.38063 1970 115.8836 159.9262 44.74663 0.9441110 1 #> 4 1 4 40.38063 1970 110.9889 161.1282 48.20004 0.9568525 1 #> 5 1 5 40.38063 1970 120.9946 157.3778 50.02237 0.9759584 1 #> 6 1 1 40.38063 1972 114.2315 160.6120 56.29615 0.9983398 1
lapply(getData(shipley.sem), head) # from SEM (list)
#> $DD #> site tree lat year Date DD Growth Survival Live #> 1 1 1 40.38063 1970 115.4956 160.5703 61.36852 0.9996238 1 #> 2 1 2 40.38063 1970 118.4959 158.9896 43.77182 0.8433521 1 #> 3 1 3 40.38063 1970 115.8836 159.9262 44.74663 0.9441110 1 #> 4 1 4 40.38063 1970 110.9889 161.1282 48.20004 0.9568525 1 #> 5 1 5 40.38063 1970 120.9946 157.3778 50.02237 0.9759584 1 #> 6 1 1 40.38063 1972 114.2315 160.6120 56.29615 0.9983398 1 #> #> $Date #> site tree lat year Date DD Growth Survival Live #> 1 1 1 40.38063 1970 115.4956 160.5703 61.36852 0.9996238 1 #> 2 1 2 40.38063 1970 118.4959 158.9896 43.77182 0.8433521 1 #> 3 1 3 40.38063 1970 115.8836 159.9262 44.74663 0.9441110 1 #> 4 1 4 40.38063 1970 110.9889 161.1282 48.20004 0.9568525 1 #> 5 1 5 40.38063 1970 120.9946 157.3778 50.02237 0.9759584 1 #> 6 1 1 40.38063 1972 114.2315 160.6120 56.29615 0.9983398 1 #> #> $Growth #> site tree lat year Date DD Growth Survival Live #> 1 1 1 40.38063 1970 115.4956 160.5703 61.36852 0.9996238 1 #> 2 1 2 40.38063 1970 118.4959 158.9896 43.77182 0.8433521 1 #> 3 1 3 40.38063 1970 115.8836 159.9262 44.74663 0.9441110 1 #> 4 1 4 40.38063 1970 110.9889 161.1282 48.20004 0.9568525 1 #> 5 1 5 40.38063 1970 120.9946 157.3778 50.02237 0.9759584 1 #> 6 1 1 40.38063 1972 114.2315 160.6120 56.29615 0.9983398 1 #> #> $Live #> site tree lat year Date DD Growth Survival Live #> 1 1 1 40.38063 1970 115.4956 160.5703 61.36852 0.9996238 1 #> 2 1 2 40.38063 1970 118.4959 158.9896 43.77182 0.8433521 1 #> 3 1 3 40.38063 1970 115.8836 159.9262 44.74663 0.9441110 1 #> 4 1 4 40.38063 1970 110.9889 161.1282 48.20004 0.9568525 1 #> 5 1 5 40.38063 1970 120.9946 157.3778 50.02237 0.9759584 1 #> 6 1 1 40.38063 1972 114.2315 160.6120 56.29615 0.9983398 1 #>
head(getData(shipley.sem, merge = TRUE)) # from SEM (single dataset)
#> site tree lat year Date DD Growth Survival Live #> 1 1 1 40.38063 1970 115.4956 160.5703 61.36852 0.9996238 1 #> 2 1 2 40.38063 1970 118.4959 158.9896 43.77182 0.8433521 1 #> 3 1 3 40.38063 1970 115.8836 159.9262 44.74663 0.9441110 1 #> 4 1 4 40.38063 1970 110.9889 161.1282 48.20004 0.9568525 1 #> 5 1 5 40.38063 1970 120.9946 157.3778 50.02237 0.9759584 1 #> 6 1 1 40.38063 1972 114.2315 160.6120 56.29615 0.9983398 1