Looking good so far 👀

library(fixest)
library(Matrix)
library(summclust)
# library(devtools)
# load_all()

nlswork <- haven::read_dta("http://www.stata-press.com/data/r9/nlswork.dta")

# drop NAs at the moment
nlswork <- nlswork[, c("ln_wage", "grade", "age", "birth_yr",
                        "union", "race", "msp", "ind_code")]
nlswork <- na.omit(nlswork)

# 100 groups
nlswork$group <- (seq_len(nrow(nlswork)) - 1) %% 100 + 1

lm_fit <- lm(
  ln_wage ~ 0 + factor(union) + factor(msp) + factor(race),
  data = nlswork
)
feols_fit <- feols(
  ln_wage ~ 0 + i(union) + i(msp) + i(race),
  data = nlswork
)
obj = feols_fit

vcovCR3_feols = vcov_CR3J(feols_fit, ~ group, type = "CRV3")
sqrt(diag(vcovCR3_feols))
#>    union::0    union::1      msp::1     race::2     race::3 
#> 0.006237060 0.009494587 0.007800561 0.007650714 0.032370445

vcovCR3_lm    = vcov_CR3J(lm_fit, ~ group, type = "CRV3")
sqrt(diag(vcovCR3_lm))[c("factor(union)0", "factor(union)1", "factor(msp)1", "factor(race)2", "factor(race)3")]
#> factor(union)0 factor(union)1   factor(msp)1  factor(race)2  factor(race)3 
#>    0.006237060    0.009494587    0.007800561    0.007650714    0.032370445

X = fixest::sparse_model_matrix(feols_fit, type = c("rhs", "fixef"))
y = fixest::sparse_model_matrix(feols_fit, type = "lhs")
cluster_vec = nlswork$group

tictoc::tic("Sparse")
res = summclust:::cluster_jackknife_sparse(y, X, cluster_vec, type = "CRV3")
tictoc::toc()
#> Sparse: 0.331 sec elapsed
tictoc::tic("Dense")
res = summclust:::cluster_jackknife(as.matrix(y), as.matrix(X), data.frame(group = cluster_vec), type = "CRV3")
tictoc::toc()
#> Dense: 2.521 sec elapsed

^{Created on 2023-06-09 with reprex v2.0.2}

fixest: sparse matrix support #18

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