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Optic Model

Source: R/optic-model-class.R
optic_model.Rd

Generates model object to apply to each simulated dataset

Usage

optic_model(name, type, call, formula, se_adjust, ...)

Arguments

name

Name of the model object, used to identify the model when reviewing simulation results

type

Estimator used to identify the treatment effect using simulated data. Specified as a string, which can either be 'reg' (regression), 'autoreg' (autoregression, which adds a lag for the outcome variable to a regression model), 'drdid' (doubly-robust difference-in-difference estimator), or 'multisynth' (augmented synthetic control)

call

String which specifies the R function to call for applying the estimator. Package currently supports either 'lm' (linear model), 'feols' (fixed-effect OLS), 'multisynth' (pooled synthetic controls), or 'glm.nb' (negative-binomial generalized nearlized linear model)

formula

Model specification, using R formula formatting. Must include a variable labeled 'treatment' for the 'nonconf' & 'selbias' simulation method or variables labeled 'treatment1' & 'treatment2' for the simulation method 'concurrent'

se_adjust

Adjustments applied to standard errors following model estimation. Specified as a string, OPTIC currently support 'none' for no adjustment or 'cluster' for clustered standard errors. Clustered standard errors will use the 'unit_var' specified in optic_simulation for determining unit used for clustering standard errors.

...

Additional arguments that are passed to the model call. Please refer to documentation for each model call for additional details. If the model call expects a name, you may need to pass your parameter using param = as.name("variable_name") as opposed to param = variable_name.

Value

optic_model An optic_model object to be used as an input within optic_simulations. Details model calls and parameters.

Examples


# Load the overdoses example dataset
data(overdoses)

# Set up a simple linear model with fixed effects
lm_fe <- optic_model(
  name = 'fixed_effect_linear', 
  type = 'reg', 
  call = 'lm', 
  formula = crude.rate ~ as.factor(year) + as.factor(state) + treatment_level, 
  se_adjust = 'cluster'
)

# Deploy an auto-regressive model.
# type = "autoreg" will make AR term 
# automatically when the model is deployed; also note
# in formula the use of "treatment_change" as the treatment variable 
# rather than "treatment_level" like in the previous example:

lm_ar <- optic_model(
  name = "auto_regressive_linear", 
  type = "autoreg", 
  call = "lm", 
  formula = crude.rate ~ unemploymentrate + as.factor(year) + treatment_change,
  se_adjust = "none"
)

# Fixed effects model with covariate adjustment
lm_fe_adj <- optic_model(
  name = "fixed_effect_linear_adj",
  type = "reg",
  call = "lm",
  formula = crude.rate ~ unemploymentrate + as.factor(year) + 
            as.factor(state) + treatment_level,
  se_adjust = "cluster"
)