# Find quantities of interest from generalized linear models ```r qi_builder(obj, newdata, FUN, ci = 0.95, nsim = 1000, slim = FALSE, large_computation = FALSE, original_order = FALSE, b_sims, mu, Sigma, verbose = TRUE, ...) ``` ## Arguments - `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 `obj` or `mu`/`Sigma`. Note that variables not included in `newdata` will be fitted at 0. If `missing` then observations used to fit the model in `obj` will be used. - `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 `missing` then a normal linear regression model is assumed and the predicted values are returned (i.e. the fitted linear systematic component from `linear_systematic`). - `ci`: the proportion of the central interval of the simulations to return. Must be in (0, 1] or equivalently (0, 100]. Note: if `ci = 1` then the full interval (i.e. 100 percent) is assumed. - `nsim`: number of simulations to draw. - `slim`: logical indicating whether to (if `FALSE`) return all simulations in the central interval specified by `ci` for each fitted scenario or (if `TRUE`) just the minimum, median, and maxium values. See `qi_slimmer` for more details. - `large_computation`: logical. If `newdata` is not supplied, whether to allow > 100000 simulated quantities of interest to be found. - `original_order`: logical whether or not to keep the original scenario order when `slim = TRUE`. Choosing `FALSE` can imporove computation time. - `b_sims`: an optional data frame created by `b_sim` of simulated coefficients. Only used if `obj` is not supplied. - `mu`: an optional vector giving the means of the variables. If `obj` or `b_sims` is supplied then `mu` is ignored. - `Sigma`: an optional positive-definite symmetric matrix specifying the covariance matrix of the variables. If `obj` is supplied then `Sigma` is ignored. If your model includes an intercept, this should be given the name `intercept_`. - `verbose`: logical. Whether to include full set of messages or not. - `...`: arguments to passed to `linear_systematic`. ## Returns 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. ## Examples ```r 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) ```

qi_builder function