Description Usage Arguments Details Value Examples
This function tries to choose sensible values of the explanatory variables
from the data used to build a model or any other specified data.
(or from data specified with the data =
argument.)
1 2 |
mod |
either a data frame or a model from which to extract the data to be discretized. If a model is given, then the model output will be appended for all rows. |
formula |
a formula with one variable to identify a variable to be sampled finely (npts over the range) |
nlevels |
the number of discrete levels to use, by default |
pretty |
if TRUE, make the discretized versions of numerical values sensibly spaced for viewing |
npts |
the number of points to evaluate the variable identified by formula (default: 100) |
... |
a more concise mechanism to passing desired values for variables |
For categorical variables, the most populated levels are used. For quantitative
variables, a sequence of pretty()
values is generated.
For categorical variables, will return the nlevels most popular levels, unless
the levels are specified explicitly in an argument. When the model is a classifier, the model outputs will
be the probabilities of each level in the response variable. These will be named prob_[level]
.
Using pretty = TRUE
may cause the number of levels for quantitative variables to be somewhat different
from nlevels
.
A dataframe containing all combinations of the selected values for
the explanatory variables. If there are p explanatory variables,
there will be about nlevels^p
cases.
1 2 3 4 5 6 7 8 | ## Not run:
mod1 <- lm(wage ~ age * sex + sector, data = mosaicData::CPS85)
for_plotting <- mod_eval_grid(mod1, ~ age, nlevels = Inf)
gf_line(model_output ~ age | sector, color = ~ sex, data = for_plotting )
for_plotting2 <- mod_eval_grid(mod1, ~ age, sector = c("const", "service"))
gf_line(model_output ~ age | sector, color = ~ sex, data = for_plotting2 )
## End(Not run)
|
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