cbq {cbq} | R Documentation |

The main function for running the conditional binary quantile model. The function returns a cbq `cbq`

object that can be further investigated using standard functions such as `plot`

, `print`

, `coef`

, and `predict`

.

cbq( formula, data, q = NULL, vi = FALSE, nsim = 1000, grad_samples = 1, elbo_samples = 100, tol_rel_obj = 0.01, output_samples = 2000, burnin = NULL, thin = 1, CIsize = 0.95, nchain = 1, seeds = 12345, inverse_distr = FALSE, offset = 1e-20, mc_core = TRUE )

`formula` |
An object of class "Formula" (or one that can be coerced to that class): a symbolic description of the model to be fitted. |

`data` |
A data frame containing the variables in the model. |

`q` |
The quantile value. |

`vi` |
Indicating whether variantional inference should be used instead of MCMC sampling procedure. |

`nsim` |
The number of iterations. |

`grad_samples` |
Passed to |

`elbo_samples` |
Passed to |

`tol_rel_obj` |
Passed to |

`output_samples` |
Passed to |

`burnin` |
The number of burnin iterations. |

`thin` |
Thinning parameter. |

`CIsize` |
The size of confidence interval. |

`nchain` |
The number of parallel chains. |

`seeds` |
Random seeds to replicate the results. |

`inverse_distr` |
If FALSE, the ALD will not be reversed. The default is FALSE. |

`offset` |
Offset values to enhance sampling stability. The default value is 1e-20. |

`mc_core` |
Indicating whether the estimation will be run in multiple parallel chains. The default is TRUE. |

The model can be passed either as a combination of a `formula`

and a data frame `data`

, as in `lm()`

.

Convergence diagnotics can be performed using either `print(object, "mcmc")`

or `plot(object, "mcmc")`

.

A `cbq`

object, which can be further analyzed with its associated `plot.cbq`

, `coef.cbq`

and `print.cbq`

functions.

An object of class `cbq`

contains the following elements

`Call`

The matched call.

`formula`

Symbolic representation of the model.

`q`

The quantile value.

`nsim`

The number of MCMC iterations.

`burnin`

The number of burnin periods.

`thin`

Thinning.

`seeds`

Random seeds.

`CIsize`

The size of confidence interval.

`data`

Data used.

`x`

Covaraites used.

`y`

The dependent variable.

`xnames`

Names of the covariates.

`stanfit`

Outputs from stan.

`sampledf`

A matrix of posterior samples.

`summaryout`

A summary based on posterior samples.

`npars`

Number of covariates.

`ulbs`

Lower and upper confidence bounds.

`means`

Estimates at the mean.

`vi`

Indicating whether variational inference has been performed.

`output_samples`

Sample outputs.

`fixed_var`

Variables estimated using fixed effects.

`random_var`

Variables estimated using random effects.

`xq`

Variables indicating the choice sets.

Xiao Lu

Lu, Xiao. (2020). Discrete Choice Data with Unobserved Heterogeneity: A Conditional Binary Quantile Model. Political Analysis, 28(2), 147-167. https://doi.org/10.1017/pan.2019.29

# Simulate the data x <- rnorm(50) y <- ifelse(x > 0, 1, 0) dat <- as.data.frame(cbind(y, x)) # Estimate the CBQ model model <- cbq(y ~ x, dat, 0.5, nchain = 1, mc_core = FALSE) # Show the results print(model) coef(model) plot(model)

[Package *cbq* version 0.2.0.2 Index]