Package index • mlmodels

Model Fitting

Main model estimation functions

ml_beta(): Fit Beta Model by Maximum Likelihood
ml_gamma(): Fit Gamma Model by Maximum Likelihood
ml_lm(): Fit linear model by Maximum Likelihood
ml_logit(): Fit Binary Logit Model by Maximum Likelihood
ml_negbin(): Fit negative binomial models by Maximum Likelihood
ml_poisson(): Fit Poisson model by Maximum Likelihood
ml_probit(): Fit Binary Probit Model by Maximum Likelihood

Post-Estimation

Prediction, summaries, and marginal effects

predict(<ml_beta>) predict(<ml_gamma>) predict(<ml_lm>) predict(<ml_logit>) predict(<mlmodel>) predict(<ml_negbin>) predict(<ml_poisson>) predict(<ml_probit>): Predictions for mlmodel models
summary(<ml_beta>) summary(<ml_gamma>) summary(<ml_lm>) summary(<ml_logit>) summary(<mlmodel>) summary(<ml_negbin>) summary(<ml_poisson>) summary(<ml_probit>): Summary for mlmodel objects
coef(<mlmodel>): Extract Model Coefficients
vcov(<mlmodel>): Variance-Covariance Matrix for mlmodel Objects
logLik(<mlmodel>) logLik(<summary.mlmodel>): Extract Log-Likelihood from mlmodel objects
fitted(<mlmodel>) fitted(<values.mlmodel>): Extract Fitted Values from mlmodel
residuals(<mlmodel>): Extract Model Residuals
confint(<mlmodel>): Confidence Intervals for mlmodel Coefficients
update(<mlmodel>) update(<ml_poisson>): Update an mlmodel Call

Hypothesis Testing

Model comparison and specification tests

waldtest(): Wald Test for Linear Restrictions
lrtest(): Likelihood Ratio Test for Nested mlmodel Objects
vuongtest(): Vuong's Test for Non-Nested Models
IMtest(): Information Matrix Test for Model Misspecification
OVDtest(): Overdispersion Tests for Count Models
GOFtest(): Goodness-of-Fit Test for Count Models

Internal & Helper Functions

Not usually called directly by users

AIC(<mlmodel>) AIC(<summary.mlmodel>): Extract AIC from mlmodel objects
BIC(<mlmodel>) BIC(<summary.mlmodel>): Extract BIC from mlmodel objects
GOFtest(): Goodness-of-Fit Test for Count Models
IMtest(): Information Matrix Test for Model Misspecification
OVDtest(): Overdispersion Tests for Count Models
coef(<mlmodel>): Extract Model Coefficients
confint(<mlmodel>): Confidence Intervals for mlmodel Coefficients
docvis: U.S. Medical Expenditure Panel Survey
find_predictors(<mlmodel>): Extract the predictors used in the model (for insight/marginaleffects compatibility)
find_variables.mlmodel(): Extract the variables used in the model (for insight/marginaleffects compatibility)
fitted(<mlmodel>) fitted(<values.mlmodel>): Extract Fitted Values from mlmodel
formula(<mlmodel>): Extract value formula from mlmodel objects
get_data(<mlmodel>) get_modeldata.mlmodel(): Extract data used to fit the model (for insight/marginaleffects compatibility)
gradientObs(): Gradient (Score) by Observation
hessianObs(): Hessian by Observation
logLik(<mlmodel>) logLik(<summary.mlmodel>): Extract Log-Likelihood from mlmodel objects
loglikeObs(): Log-Likelihood by Observation
lrtest(): Likelihood Ratio Test for Nested mlmodel Objects
ml_beta(): Fit Beta Model by Maximum Likelihood
ml_gamma(): Fit Gamma Model by Maximum Likelihood
ml_lm(): Fit linear model by Maximum Likelihood
ml_logit(): Fit Binary Logit Model by Maximum Likelihood
ml_negbin(): Fit negative binomial models by Maximum Likelihood
ml_poisson(): Fit Poisson model by Maximum Likelihood
ml_probit(): Fit Binary Probit Model by Maximum Likelihood
mroz: University of Michigan Panel Study of Income Dynamics (PSID)
nobs(<mlmodel>): Extract the Number of Observations from an mlmodel
`%||%`: Null default operator
predict(<ml_beta>) predict(<ml_gamma>) predict(<ml_lm>) predict(<ml_logit>) predict(<mlmodel>) predict(<ml_negbin>) predict(<ml_poisson>) predict(<ml_probit>): Predictions for mlmodel models
pw401k: 401(k) Participation Rates
residuals(<mlmodel>): Extract Model Residuals
se(): Extract Standard Errors from mlmodel Objects
smoke: 1979 National Health Interview Survey
summary(<ml_beta>) summary(<ml_gamma>) summary(<ml_lm>) summary(<ml_logit>) summary(<mlmodel>) summary(<ml_negbin>) summary(<ml_poisson>) summary(<ml_probit>): Summary for mlmodel objects
update(<mlmodel>) update(<ml_poisson>): Update an mlmodel Call
vcov(<mlmodel>): Variance-Covariance Matrix for mlmodel Objects
vuongtest(): Vuong's Test for Non-Nested Models
waldtest(): Wald Test for Linear Restrictions
mlmodels mlmodels-package: mlmodels: Maximum Likelihood Models and Tools for Estimation, Prediction, and Testing