Description Usage Arguments Value Examples

Find quantities of interest from generalized linear models

1 2 3 |

`obj` |
a fitted model object from which to base coefficient simulations on. |

`newdata` |
an optional data frame of fitted values with column names
corresponding to coefficient names in |

`FUN` |
a function for calculating how to find the quantity of interest
from a vector of the fitted linear systematic component. It must return
a numeric vector. If |

`ci` |
the proportion of the central interval of the simulations to
return. Must be in (0, 1] or equivalently (0, 100]. Note: if |

`nsim` |
number of simulations to draw. |

`slim` |
logical indicating whether to (if |

`large_computation` |
logical. If |

`original_order` |
logical whether or not to keep the original scenario
order when |

`b_sims` |
an optional data frame created by |

`mu` |
an optional vector giving the means of the variables. If |

`Sigma` |
an optional positive-definite symmetric matrix specifying the
covariance matrix of the variables. If |

`verbose` |
logical. Whether to include full set of messages or not. |

`...` |
arguments to passed to |

If `slimmer = FALSE`

a data frame of fitted values supplied in
`newdata`

and associated simulated quantities of interest for all
simulations in the central interval specified by `ci`

. The quantities
of interest are in a column named `qi_`

.

If `slimmer = TRUE`

a data frame of fitted values supplied in
`newdata`

and the minimum, median, and maximum values of the central
interval specified by `ci`

for each scenario are returned in three
columns named `qi_min`

, `qi_median`

, and `qi_max`

,
respectively.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 | ```
library(car)
## Normal linear model
m1 <- lm(prestige ~ education + type, data = Prestige)
# Using observed data as scenarios
linear_qi_obs <- qi_builder(m1)
# Create fitted values
fitted_df_1 <- expand.grid(education = 6:16, typewc = 1)
linear_qi <- qi_builder(m1, newdata = fitted_df_1)
# Manually supply coefficient means and covariance matrix
coefs <- coef(m1)
vcov_matrix <- vcov(m1)
linear_qi_custom_mu_Sigma <- qi_builder(mu = coefs, Sigma = vcov_matrix,
newdata = fitted_df_1)
## Logistic regression
# Load data
data(Admission)
Admission$rank <- as.factor(Admission$rank)
# Estimate model
m2 <- glm(admit ~ gre + gpa + rank, data = Admission, family = 'binomial')
# Specify fitted values
m2_fitted <- expand.grid(gre = seq(220, 800, by = 10), gpa = c(2, 4),
rank = '4')
# Function to find predicted probabilities from logistic regression models
pr_function <- function(x) 1 / (1 + exp(-x))
# Find quantity of interest
logistic_qi_1 <- qi_builder(m2, m2_fitted, FUN = pr_function)
logistic_qi_2 <- qi_builder(m2, m2_fitted, FUN = pr_function,
slim = TRUE)
``` |

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