fic (version 1.0.0)

focus_fns: Built-in focus functions and their derivatives

Description

Built-in focus functions and their derivatives

Usage

prob_logistic(par, X)
prob_logistic_deriv(par, X)
mean_normal(par, X)
mean_normal_deriv(par, X)

Value

prob_logistic returns the probability of the outcome in a logistic regression model, and mean_normal returns the mean outcome in a normal linear regression. The _deriv functions return the vector of partial derivatives of the focus with respect to each parameter (or matrix, if there are multiple foci).

Arguments

par

Vector of parameter estimates, including the intercept.

X

Vector or matrix of covariate values, including the intercept. This can either be a vector of length \(p\), or a \(n x p\) matrix, where \(p\) is the number of covariate effects, and \(n\) is the number of alternative sets of covariate values at which the focus function is to be evaluated.

See Also

fic

Examples

## Model and focus from the main vignette 
wide.glm <- glm(low ~ lwtkg + age + smoke + ht + ui +
                smokeage + smokeui, data=birthwt, family=binomial)
vals.smoke <-    c(1, 58.24, 22.95, 1, 0, 0, 22.95, 0)
vals.nonsmoke <- c(1, 59.50, 23.43, 0, 0, 0, 0, 0)
X <- rbind("Smokers" = vals.smoke, "Non-smokers" = vals.nonsmoke)
prob_logistic(coef(wide.glm), X=X)
prob_logistic_deriv(coef(wide.glm), X=X)


## Mean mpg for a particular covariate category in the Motor Trend data
## See the "fic" linear models vignette for more detail 

wide.lm <- lm(mpg ~ am + wt + qsec + disp + hp, data=mtcars)
cmeans <- colMeans(model.frame(wide.lm)[,c("wt","qsec","disp","hp")])
X <- rbind(
  "auto"   = c(intercept=1, am=0, cmeans),
  "manual" = c(intercept=1, am=1, cmeans)
)
mean_normal(coef(wide.lm), X)
mean_normal_deriv(coef(wide.lm), X)