# The design and structure of geex In geex: An API for M-Estimation

The details below are for those interested in how geex is organized. It is not necessary for using geex.

## The Estimating Function

The design of geex starts with the key to M-estimation, the estimating function:

[ \psi(O_i, \theta) . ]

geex composes $\psi$ with two R functions: the "outer" estFUN and the "inner" psiFUN. In pseudocode, $\psi(O_i, \theta) =$:

estFUN <- function(O_i){
psiFUN <- function(theta){
psi(O_i, theta)
}
return(psiFUN)
}


The reason for composing the $\psi$ function in this way is that in order to do estimation (finding roots) and inference (computing the empirical sandwich variance estimator), $\psi$ needs to be function of $\theta$. M-estimation theory gives the following instructions:

• To estimate $\hat{\theta}$, we need to find roots of $G_m = \sum_i \psi(O_i, \theta) = 0$.
• To estimate the empirical sandwich variance estimator, we need two quantities for each unit: $A_i = - (\partial \psi(O_i, \theta)/\partial \theta)|_{\theta = \hat{\theta}}$ and $B_i = \psi(O_i, \hat{\theta})\psi(O_i, \theta)^{\intercal}$.

With $\hat{\theta}$ in hand, the quantity $B_i$ is simple to compute. The computational challenges of M-estimation, then, are finding roots of $G_m$ and calculating the derivative $A_i$. By composing $\psi$ of two functions in geex, one can first do all the manipulations of $O_i$ (data) that are independent of $\theta$. In a sense, estFUN "fixes" the data so that numerical routines only need deal with $\theta$ in psiFUN.

## M-estimation basis

Before describing the mechanics of how geex finding roots of $G_m$ and computes derivatives of $\psi$, let's look at the m_estimation_basis S4 object which forms the basis of all computations in geex.

An m_estimation_basis object, at a minimum needs two objects: an estFUN and a data.frame. Let's use a simple estFUN that estimates the mean and variance of Y1 in the geexex dataset.

library(geex)
library(dplyr)

myee <- function(data){
Y1 <- data$Y1 function(theta){ c(Y1 - theta[1], (Y1 - theta[1])^2 - theta[2]) } }  Now we can create a basis: mybasis <- new("m_estimation_basis", .estFUN = myee, .data = geexex)  And look at what this object contains: slotNames(mybasis)  Two slots are worth examining. First, .psiFUN_list is a list of functions: mybasis@.psiFUN_list[1:2]  This object is essentially equivalent to: m <- nrow(geexex) lapply(split(geexex, f = 1:m), function(O_i){ myee(O_i) })  From this list of functions, we can compute$A_i$, and by summing across the list, form$G_m$. The latter is found in: mybasis@.GFUN  ## Finding roots Now that we have$G_m$as a function of theta, we can found its roots using a root-finding algorithm such as rootSolve::multiroot: rootSolve::multiroot( f = mybasis@.GFUN, start = c(0, 0))  Within geex this is done with the estimate_GFUN_roots function. To illustrate, I first need to update the .control slot in mybasis with starting values for multiroot. mycontrol <- new('geex_control', .root = setup_root_control(start = c(1, 1))) mybasis@.control <- mycontrol roots <- mybasis %>% estimate_GFUN_roots() roots  Note that is bad form to assign S4 slot with [email protected] <- something, but I do so here because I have not created a generic function for setting the .control slot. ## Computing the Empirical Sandwich Variance Estimator In the last section, we found$\hat{\theta}$, which we now use to compute the$A_i$and$B_i$matrices. geex uses the numDeriv::jacobian function to numerically evaluate derivatives. For example,$A_1 = - (\partial \psi(O_1, \theta)/\partial \theta)|_{\theta = \hat{\theta}}$for this example is: -numDeriv::jacobian(func = mybasis@.psiFUN_list[[1]], x = roots$root)


geex performs this operation for each $i = 1, \dots, m$ to yield a list of $A_i$ matrices. Then summing across this list yields $A = \sum_i A_i$. The estimate_sandwich_matrices function computes the list of $A_i$, $B_i$ and $A$ and $B$:

mats <- mybasis %>%
estimate_sandwich_matrices(.theta = roots$root) # Compare to the numDeriv computation above grab_bread_list(mats)[[1]]  Finally, computing$\hat{\Sigma} = A^{-1} B (A^{-1})^{\intercal}\$ is accomplished with the compute_sigma function.

mats %>%
{compute_sigma(A = grab_bread(.), B = grab_meat(.))}


## M-estimation with m_estimate

All of the operations described above are wrapped and packaged in the m_estimate function:

m_estimate(
estFUN = myee,
data   = geexex,
root_control = setup_root_control(start = c(0, 0))
)


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geex documentation built on Sept. 24, 2018, 1:04 a.m.