optimCheck: Graphical and numerical checks for mode-finding routines.

In mlysy/optimCheck: Graphical and Numerical Checks for Mode-Finding Routines

Description

Graphical and numerical checks for mode-finding routines.

Examples

# example: logistic regression
ilogit <- binomial()$linkinv

# generate data
p <- sample(2:10,1) # number of parameters
n <- sample(1000:2000,1) # number of observations
X <- matrix(rnorm(n*p),n,p) # design matrix
beta0 <- rnorm(p, sd = .1) # true parameter values
y <- rbinom(n, size = 1, prob = ilogit(X %*% beta0)) # response

# fit logistic regression
bhat <- coef(glm(y ~ X - 1, family = binomial))

# check convergence

# likelihood function
loglik <- function(beta, y, X) {
  sum(dbinom(y, size = 1, prob = ilogit(X %*% beta), log = TRUE))
}

# projection plots
bnames <- parse(text = paste0("beta[", 1:p, "]"))
system.time({
  oproj <- optim_proj(xsol = bhat,
                      fun = function(beta) loglik(beta, y, X),
                      xnames = bnames,
                      xlab = "Coefficient", ylab = "Loglikelihood")
})

# numerical summary
oproj # see ?summary.optproj for more information

# elementwise differences between potential and optimal solution
diff(oproj) # same as summary(oproj)$xdiff

# refit general purpose optimizer starting from bhat
# often faster than optim_proj, but less stable
system.time({
  orefit <- optim_refit(xsol = bhat,
                        fun = function(beta) loglik(beta, y, X))
})
orefit

mlysy/optimCheck documentation built on May 25, 2019, 10:31 p.m.