A user had a case of estimating parameters based on a dataset that contained only categorical predictors. The data can be represented either as one row per individual or one row per group defined by unique combinations of categories. In this example, I show how computations in geex
can be massively sped up using the latter data representation and the weights
option in estimate_equation
.
The following code generates two datasets: data1
has one row per unit and data2
has one row per unique combination of the categorical varibles.
library(geex) library(dplyr) set.seed(42) n <- 1000 data1 <- data_frame( ID = 1:n, Y_tau = rbinom(n,1,0.2), S_star = rbinom(n,1,0.6), Y = rbinom(n,1,0.4), Z = rbinom(n,1,0.5)) data2 <- data1 %>% group_by(Y_tau, S_star, Y, Z) %>% count()
This is the estimating equation that the user provided as an example. I have no idea what the target parameters represent, but it nicely illustrates the point.
example <- function(data) { function(theta) { with(data, c( (1 - Y_tau)*(1 -Z )*(Y - theta[1]), (1-Y_tau)*Z*(Y-theta[2]), theta[3] - theta[2]*theta[1])) } }
The timing to find point and variance estimates is compared:
system.time({ results1 <- m_estimate( estFUN = example, data = data1, root_control = setup_root_control(start = c(.5, .5, .5)) )}) system.time({ results2 <- m_estimate( estFUN = example, data = data2, weights = data2$n, root_control = setup_root_control(start = c(.5, .5, .5)) )})
The latter option is clearly preferred.
And the results are basically identical:
roots(results1) roots(results2) vcov(results1) vcov(results2)
Any scripts or data that you put into this service are public.
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.