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Find model formula

Source: R/find_formula.R
find_formula.Rd

Returns the formula(s) for the different parts of a model (like fixed or random effects, zero-inflated component, ...). formula_ok() checks if a model formula has valid syntax regarding writing TRUE instead of T inside poly() and that no data names are used (i.e. no data$variable, but rather variable).

Usage

find_formula(x, ...)

formula_ok(
  x,
  checks = "all",
  action = "warning",
  prefix_msg = NULL,
  verbose = TRUE,
  ...
)

# Default S3 method
find_formula(x, verbose = TRUE, ...)

# S3 method for class 'nestedLogit'
find_formula(x, dichotomies = FALSE, verbose = TRUE, ...)

Arguments

x

A fitted model.

...

Currently not used.

checks

Indicates what kind of checks are conducted when checking the formula notation. Currently, four different formula specification that can result in unexpected behaviour of downstream-functions are checked. checks can be one or more of:

  • "dollar": Check if formula contains data name with "$", e.g. mtcars$am.

  • "T": Check if formula contains poly-term with "raw=T", e.g. poly(x, 2, raw=T). In this case, all.vars() returns T as variable, which is not intended.

  • "index": Check if formula contains indexed data frames as response variable (e.g., df[, 5] ~ x).

  • "name": Check if syntactically invalid variable names were used and quoted in backticks.

  • "all": Checks all of the above mentioned options.

action

Should a message, warning or error be given for an invalid formula? Must be one of "message", "warning" (default) or "error".

prefix_msg

Optional string that will be added to the warning/error message. This can be used to add additional information, e.g. about the specific function that was calling formula_ok() and failed.

verbose

Toggle warnings.

dichotomies

Logical, if model is a nestedLogit objects, returns the formulas for the dichotomies.

Value

A list of formulas that describe the model. For simple models, only one list-element, conditional, is returned. For more complex models, the returned list may have following elements:

  • conditional, the "fixed effects" part from the model (in the context of fixed-effects or instrumental variable regression, also called regressors) . One exception are DirichletRegModel models from DirichletReg, which has two or three components, depending on model.

  • random, the "random effects" part from the model (or the id for gee-models and similar)

  • zero_inflated, the "fixed effects" part from the zero-inflation component of the model. for models from brms, this component is named zi.

  • zero_inflated_random, the "random effects" part from the zero-inflation component of the model; for models from brms, this component is named zi_random.

  • dispersion, the dispersion formula

  • instruments, for fixed-effects or instrumental variable regressions like ivreg::ivreg(), lfe::felm() or plm::plm(), the instrumental variables

  • cluster, for fixed-effects regressions like lfe::felm(), the cluster specification

  • correlation, for models with correlation-component like nlme::gls(), the formula that describes the correlation structure

  • scale, for distributional models such as mgcv::gaulss() family fitted with mgcv::gam(), the formula that describes the scale parameter

  • slopes, for fixed-effects individual-slope models like feisr::feis(), the formula for the slope parameters

  • precision, for DirichletRegModel models from DirichletReg, when parametrization (i.e. model) is "alternative".

  • bidrange, for models of class oohbchoice (from package DCchoice), which indicates the right-hand side of the bar (the bid-range).

For models from package brms, distributional parameters are also included.

Note

For models of class lme or gls the correlation-component is only returned, when it is explicitly defined as named argument (form), e.g. corAR1(form = ~1 | Mare)

Examples

data(mtcars)
m <- lm(mpg ~ wt + cyl + vs, data = mtcars)
find_formula(m)
#> $conditional
#> mpg ~ wt + cyl + vs
#> <environment: 0x5639c8675700>
#> 
#> attr(,"class")
#> [1] "insight_formula" "list"           

m <- lme4::lmer(Sepal.Length ~ Sepal.Width + (1 | Species), data = iris)
f <- find_formula(m)
f
#> $conditional
#> Sepal.Length ~ Sepal.Width
#> <environment: 0x5639c8675700>
#> 
#> $random
#> ~1 | Species
#> <environment: 0x5639c8ecf9f0>
#> 
#> attr(,"class")
#> [1] "insight_formula" "list"           
format(f)
#> [1] "Sepal.Length ~ Sepal.Width + (~1 | Species)"