`find_terms.Rd`

Returns a list with the names of all terms, including
response value and random effects, "as is". This means, on-the-fly
tranformations or arithmetic expressions like `log()`

, `I()`

,
`as.factor()`

etc. are preserved.

find_terms(x, flatten = FALSE, ...)

x | A fitted model. |
---|---|

flatten | Logical, if |

... | Currently not used. |

A list with (depending on the model) following elements (character vectors):

`response`

, the name of the response variable`conditional`

, the names of the predictor variables from the*conditional*model (as opposed to the zero-inflated part of a model)`random`

, the names of the random effects (grouping factors)`zero_inflated`

, the names of the predictor variables from the*zero-inflated*part of the model`zero_inflated_random`

, the names of the random effects (grouping factors)`dispersion`

, the name of the dispersion terms`instruments`

, the names of instrumental variables

The difference to `find_variables`

is that `find_terms()`

may return a variable multiple times in case of multiple transformations
(see examples below), while `find_variables()`

returns each variable
name only once.

library(lme4) data(sleepstudy) m <- lmer( log(Reaction) ~ Days + I(Days^2) + (1 + Days + exp(Days) | Subject), data = sleepstudy )#> Warning: Model failed to converge with max|grad| = 14.7145 (tol = 0.002, component 1)#> Warning: Model is nearly unidentifiable: very large eigenvalue #> - Rescale variables?;Model is nearly unidentifiable: large eigenvalue ratio #> - Rescale variables?find_terms(m)#> $response #> [1] "log(Reaction)" #> #> $conditional #> [1] "Days" "I(Days^2)" #> #> $random #> [1] "Days" "exp(Days)" "Subject" #>