sts (version 1.3)

lgaecpp: Estimate Gradient

Description

Estimates the Gradient needed for the Variational E-step in C++

Usage

lgaecpp(alpha_d, Sigma_Inv, kappa_t, kappa_s, mu_d, doc, V, mv)

Value

The Hessian matrix

Arguments

alpha_d

the estimated alpha variables for the given document

Sigma_Inv

the inverse covariance matrix

kappa_t

the estimated kappa_t coefficients

kappa_s

the estimated kappa_s coefficients

mu_d

the fitted values based on document-level variables * estimated Gamma for each document.

doc

the sparse matrix representation of the document, with two rows, and columns equal to the number of unique vocabulary words in the document.

V

the size of the vocabulary

mv

the baseline log-transformed occurrence rate of each word in the corpus

Examples

# \donttest{
library("tm"); library("stm"); library("sts")
temp<-textProcessor(documents=gadarian$open.ended.response,
metadata=gadarian, verbose = FALSE)
out <- prepDocuments(temp$documents, temp$vocab, temp$meta, verbose = FALSE)
out$meta$noTreatment <- ifelse(out$meta$treatment == 1, -1, 1)
## low max iteration number just for testing
sts_estimate <- sts(~ treatment*pid_rep, ~ noTreatment, out, K = 3, maxIter = 2)
# for document #1: 
gradient <- lgaecpp(alpha_d = sts_estimate$alpha[1,], kappa_t=sts_estimate$kappa$kappa_t, 
kappa_s=sts_estimate$kappa$kappa_s, Sigma_Inv = sts_estimate$sigma_inv, doc = out$documents[[1]], 
V=length(sts_estimate$vocab), mv = sts_estimate$mv, mu_d = sts_estimate$mu[,1])
# }