Running ‘fixest_tests.R’ [30s/34s]
Running the tests in ‘tests/fixest_tests.R’ failed.
Complete output:
  > #----------------------------------------------#
  > # Author: Laurent Berge
  > # Date creation: Fri Jul 10 09:03:06 2020
  > # ~: package sniff tests
  > #----------------------------------------------#
  > 
  > # Not everything is currently covered, but I'll improve it over time
  > 
  > # Some functions are not trivial to test properly though
  > 
  > library(fixest)
  > library(sandwich)
  > 
  > test = fixest:::test ; chunk = fixest:::chunk
  > vcovClust = fixest:::vcovClust
  > stvec = stringmagic::string_vec_alias()
  > 
  > setFixest_notes(FALSE)
  > 
  > if(fixest:::is_r_check()){
  +   if(requireNamespace("data.table", quietly = TRUE)){
  +     library(data.table)
  +     data.table::setDTthreads(1)
  +   }
  +   setFixest_nthreads(1)
  + }
  > 
  > ####
  > #### ESTIMATIONS ####
  > ####
  > 
  > ####
  > #### ... Main ####
  > ####
  > 
  > 
  > chunk("ESTIMATION")
  ESTIMATION
  
  > 
  > set.seed(0)
  > 
  > base = iris
  > names(base) = c("y", "x1", "x2", "x3", "species")
  > base$fe_2 = rep(1:5, 30)
  > base$fe_3 = sample(15, 150, TRUE)
  > base$constant = 5
  > base$y_int = as.integer(base$y)
  > base$w = as.vector(unclass(base$species) - 0.95)
  > base$offset_value = unclass(base$species) - 0.95
  > base$y_01 = 1 * ((scale(base$x1) + rnorm(150)) > 0)
  > # what follows to avoid removal of fixed-effects (logit is pain in the neck)
  > base$y_01[1:5 + rep(c(0, 50, 100), each = 5)] = 1
  > base$y_01[6:10 + rep(c(0, 50, 100), each = 5)] = 0
  > # We enforce the removal of observations
  > base$y_int_null = base$y_int
  > base$y_int_null[base$fe_3 %in% 1:5] = 0
  > 
  > for(model in c("ols", "pois", "logit", "negbin", "Gamma")){
  +   cat("Model: ", format(model, width = 6), sep = "")
  +   for(use_weights in c(FALSE, TRUE)){
  +     my_weight = NULL
  +     if(use_weights) my_weight = base$w
  + 
  +     for(use_offset in c(FALSE, TRUE)){
  +       my_offset = NULL
  +       if(use_offset) my_offset = base$offset_value
  + 
  +       for(id_fe in 0:9){
  + 
  +         cat(".")
  + 
  +         tol = switch(model, "negbin" = 1e-2, "logit" = 3e-5, 1e-5)
  + 
  +         # Setting up the formula to accommodate FEs
  +         if(id_fe == 0){
  +           fml_fixest = fml_stats = y ~ x1
  +         } else if(id_fe == 1){
  +           fml_fixest = y ~ x1 | species
  +           fml_stats = y ~ x1 + factor(species)
  +         } else if(id_fe == 2){
  +           fml_fixest = y ~ x1 | species + fe_2
  +           fml_stats = y ~ x1 + factor(species) + factor(fe_2)
  +         } else if(id_fe == 3){
  +           # varying slope
  +           fml_fixest = y ~ x1 | species[[x2]]
  +           fml_stats = y ~ x1 + x2:species
  +         } else if(id_fe == 4){
  +           # varying slope -- 1 VS, 1 FE
  +           fml_fixest = y ~ x1 | species[[x2]] + fe_2
  +           fml_stats = y ~ x1 + x2:species + factor(fe_2)
  +         } else if(id_fe == 5){
  +           # varying slope -- 2 VS
  +           fml_fixest = y ~ x1 | species[x2]
  +           fml_stats = y ~ x1 + x2:species + species
  +         } else if(id_fe == 6){
  +           # varying slope -- 2 VS bis
  +           fml_fixest = y ~ x1 | species[[x2]] + fe_2[[x3]]
  +           fml_stats = y ~ x1 + x2:species + x3:factor(fe_2)
  +         } else if(id_fe == 7){
  +           # Combined clusters
  +           fml_fixest = y ~ x1 + x2 | species^fe_2
  +           fml_stats = y ~ x1 + x2 + paste(species, fe_2)
  +         } else if(id_fe == 8){
  +           fml_fixest = y ~ x1 | species[x2] + fe_2[x3] + fe_3
  +           fml_stats = y ~ x1 + species + i(species, x2) + factor(fe_2) + i(fe_2, x3) + factor(fe_3)
  +         } else if(id_fe == 9){
  +           fml_fixest = y ~ x1 | species + fe_2[x2,x3] + fe_3
  +           fml_stats = y ~ x1 + species + factor(fe_2) + i(fe_2, x2) + i(fe_2, x3) + factor(fe_3)
  +         }
  + 
  +         # ad hoc modifications of the formula
  +         if(model == "logit"){
  +           fml_fixest = xpd(y_01 ~ ..rhs, ..rhs = fml_fixest[[3]])
  +           fml_stats = xpd(y_01 ~ ..rhs, ..rhs = fml_stats[[3]])
  + 
  +           # The estimations are OK, conv differences out of my control
  +           if(id_fe %in% 8:9) tol = 0.5
  + 
  +         } else if(model == "pois"){
  +           fml_fixest = xpd(y_int_null ~ ..rhs, ..rhs = fml_fixest[[3]])
  +           fml_stats = xpd(y_int_null ~ ..rhs, ..rhs = fml_stats[[3]])
  + 
  +         } else if(model %in% c("negbin", "Gamma")){
  +           fml_fixest = xpd(y_int ~ ..rhs, ..rhs = fml_fixest[[3]])
  +           fml_stats = xpd(y_int ~ ..rhs, ..rhs = fml_stats[[3]])
  +         }
  + 
  +         adj = 1
  +         if(model == "ols"){
  +           res = feols(fml_fixest, base, weights = my_weight, offset = my_offset)
  +           res_bis = lm(fml_stats, base, weights = my_weight, offset = my_offset)
  + 
  +         } else if(model %in% c("pois", "logit", "Gamma")){
  +           adj = 0
  +           if(model == "Gamma" && use_offset) next
  + 
  +           my_family = switch(model, pois = poisson(), logit = binomial(), Gamma = Gamma())
  + 
  +           res = feglm(fml_fixest, base, family = my_family, weights = my_weight, offset = my_offset)
  + 
  +           if(!is.null(res$obs_selection$obsRemoved)){
  +             qui = res$obs_selection$obsRemoved
  + 
  +             # I MUST do that.... => subset does not work...
  +             base_tmp = base[qui, ]
  +             base_tmp$my_offset = my_offset[qui]
  +             base_tmp$my_weight = my_weight[qui]
  +             res_bis = glm(fml_stats, base_tmp, family = my_family, weights = my_weight, offset = my_offset)
  +           } else {
  +             res_bis = glm(fml_stats, data = base, family = my_family, weights = my_weight, offset = my_offset)
  +           }
  + 
  +         } else if(model == "negbin"){
  +           # no offset in glm.nb + no VS in fenegbin + no weights in fenegbin
  +           if(use_weights || use_offset || id_fe > 2) next
  + 
  +           res = fenegbin(fml_fixest, base, notes = FALSE)
  +           res_bis = MASS::glm.nb(fml_stats, base)
  + 
  +         }
  + 
  +         test(coef(res)["x1"], coef(res_bis)["x1"], "~", tol)
  +         test(se(res, se = "st", ssc = ssc(K.adj = adj))["x1"], se(res_bis)["x1"], "~", tol)
  +         test(pvalue(res, se = "st", ssc = ssc(K.adj = adj))["x1"], pvalue(res_bis)["x1"], "~", tol*10**(model == "negbin"))
  +         # cat("Model: ", model, ", FE: ", id_fe, ", weight: ", use_weights,  ", offset: ", use_offset, "\n", sep="")
  + 
  +       }
  +       cat("|")
  +     }
  +   }
  +   cat("\n")
  + }
  Model: ols   ..........|..........|..........|..........|
  Model: pois  ..........|..........|..........|..........|
  Model: logit ..........|..........|..........|..........|
  Model: negbin..........|..........|..........|..........|
  Model: Gamma ..........|..........|..........|..........|
  There were 36 warnings (use warnings() to see them)
  > 
  > ####
  > #### ... Corner cases ####
  > ####
  > 
  > chunk("Corner cases")
  CORNER CASES
  
  > 
  > 
  > # We test the absence of bugs
  > 
  > base = iris
  > names(base) = c("y", "x1", "x2", "x3", "fe1")
  > base$fe2 = rep(1:5, 30)
  > base$y[1:5] = NA
  > base$x1[4:8] = NA
  > base$x2[4:21] = NA
  > base$x3[110:111] = NA
  > base$fe1[110:118] = NA
  > base$fe2[base$fe2 == 1] = 0
  > base$fe3 = sample(letters[1:5], 150, TRUE)
  > base$period = rep(1:50, 3)
  > base$x_cst = 1
  > 
  > res = feols(y ~ 1 | csw(fe1, fe1^fe2), base)
  > 
  > res = feols(y ~ 1 + csw(x1, i(fe1)) | fe2, base)
  > 
  > res = feols(y ~ csw(f(x1, 1:2), x2) | sw0(fe2, fe2^fe3), base, panel.id = ~ fe1 + period)
  > 
  > res = feols(d(y) ~ -1 + d(x2), base, panel.id = ~ fe1 + period)
  > test(length(coef(res)), 1)
  > 
  > res = feols(c(y, x1) ~ 1 | fe1 | x2 ~ x3, base)
  > 
  > res = feols(y ~ x1 | fe1[x2] + fe2[x2], base)
  > 
  > #
  > # singletons
  > #
  > 
  > # all variables removed bc of singleton
  > base$singletons = 1:nrow(base)
  > test(feols(y ~ x1 | singletons, base, fixef.rm = "singleton"), "err")
  > 
  > # in multiple estimations => no error
  > res = feols(y ~ x1 | sw0(singletons), base, fixef.rm = "singleton")
  > res_bis = feols(y ~ x1, base)
  > test(coef(res[[1]]), coef(res_bis))
  > test(is.null(coef(res[[2]])), TRUE)
  > 
  > #
  > # NA models (ie all variables are collinear with the FEs)
  > #
  > 
  > # Should work when warn = FALSE or multiple est
  > for(i in 1:2){
  +   fun = switch(i, "1" = feols, "2" = feglm)
  + 
  +   res = feols(y ~ x_cst | fe1, base, warn = FALSE)
  +   res         # => no error
  +   etable(res) # => no error
  + 
  +   # error when warn = TRUE
  +   test(feols(y ~ x_cst | fe1, base), "err")
  + 
  +   # multiple est => no error
  +   res = feols(c(y, x1) ~ x_cst | fe1, base)
  +   res         # => no error
  +   etable(res) # => no error
  + }
  > 
  > 
  > # Removing the intercept!!!
  > 
  > res = feols(y ~ -1 + x1 + i(fe1), base)
  > test("(Intercept)" %in% names(res$coefficients), FALSE)
  > 
  > res = feols(y ~ -1 + x1 + factor(fe1), base)
  > test("(Intercept)" %in% names(res$coefficients), FALSE)
  > 
  > res = feols(y ~ -1 + x1 + i(fe1) + i(fe2), base)
  > test("(Intercept)" %in% names(res$coefficients), FALSE)
  > test(is.null(res$collin.var), TRUE)
  > 
  > 
  > # IV + interacted FEs
  > res = feols(y ~ x1 | fe1^fe2 | x2 ~ x3, base)
  > 
  > # IVs no exo var
  > res = feols(y ~ 0 | x2 ~ x3, base)
  > # Same in stepwise
  > res = feols(y ~ 0 | sw0(fe1) | x2 ~ x3, base)
  > 
  > # IVs + lags
  > res = feols(y ~ x1 | fe1^fe2 | l(x2, -1:1) ~ l(x3, -1:1), base, panel.id = ~ fe1 + period)
  > 
  > # functions in interactions
  > res = feols(y ~ x1 | factor(fe1)^factor(fe2), base)
  > res = feols(y ~ x1 | round(x2^2), base)
  > test(feols(y ~ x1 | factor(fe1^fe2), base), "err")
  > 
  > res = feols(y ~ x1 | bin(x2, "bin::1")^fe1 + fe1^fe2, base)
  > 
  > # 1 obs (after FE removal) estimation
  > base_1obs = data.frame(y = c(1, 0), fe = c(1, 2), x = c(1, 0))
  > test(fepois(y ~ x | fe, base_1obs, fixef.rm = "infinite"), "err")
  > # no error
  > res = fepois(y ~ 1 | fe, base_1obs, fixef.rm = "infinite")
  > 
  > # warning when demeaning algo reaches max iterations 
  > data(trade)
  > test(feols(Euros ~ log(dist_km) | Destination + Origin + Product, 
  +            trade, fixef.iter = 1), "warn")
  > 
  > 
  > ####
  > #### ... depvar removal ####
  > ####
  > 
  > chunk("depvar removal")
  DEPVAR REMOVAL
  
  > 
  > # we remove the depvar when it is in the RHS
  > base = setNames(iris, c("y", "x1", "x2", "x3", "species"))
  > 
  > fml_equiv = c(
  +   "y" = "1",
  +   "y + x1" = "x1",
  +   "x1 + y + x2 + I(x1)" = "x1 + x2 + I(x1)"
  + )
  > 
  > all_funs = list(feols, feglm, femlm)
  > 
  > id_fml = 3
  > id_fun = 2
  > 
  > fun = all_funs[[id_fun]]
  > 
  > for(id_fun in seq_along(all_funs)){
  +   fun = all_funs[[id_fun]]
  +   cat("|")
  +   for(id_fml in seq_along(fml_equiv)){
  +     
  +     if(id_fun == 3 && id_fml == 3) next
  +     
  +     cat(".")
  +     rhs_0 = names(fml_equiv)[id_fml]
  +     rhs_1 = fml_equiv[id_fml]
  +     
  +     # simple estimation
  +     est_0 = fun(y ~ .[rhs_0], base)
  +     est_1 = fun(y ~ .[rhs_1], base)
  +     test(coef(est_0), coef(est_1))
  +     test(se(est_0), se(est_1))
  +     
  +     # multiple samples
  +     est_0 = fun(y ~ .[rhs_0], base, split = "species")
  +     est_1 = fun(y ~ .[rhs_1], base, split = "species")
  +     test(coef(est_0[[1]]), coef(est_1[[1]]))
  +     test(se(est_0[[1]]), se(est_1[[1]]))
  +     
  +     # multiple lhs
  +     est_0 = fun(c(y, x3) ~ .[rhs_0] + x3, base)
  +     est_1a = fun(y ~ .[rhs_1] + x3, base)
  +     est_1b = fun(x3 ~ .[rhs_0], base)
  +     test(coef(est_0[[1]]), coef(est_1a))
  +     test(coef(est_0[[2]]), coef(est_1b))
  +     test(se(est_0[[1]]), se(est_1a))
  +     test(se(est_0[[2]]), se(est_1b))
  +     
  +     # multiple rhs + fixef
  +     est_0 = fun(y ~ csw(.[rhs_0], x3) | sw0(species), base)
  +     est_1 = fun(y ~ csw(.[rhs_1], x3) | sw0(species), base)
  +     for(id_mult in n_models(est_0)){
  +       test(coef(est_0[[id_mult]]), coef(est_1[[id_mult]]))
  +       test(se(est_0[[id_mult]]), se(est_1[[id_mult]]))
  +     }
  +     
  +     # in IVs
  +     if(id_fun == 1){
  +       base$inst = rnorm(nrow(base))
  +       base$endo = rnorm(nrow(base))
  +       est_0 = feols(y ~ csw(.[rhs_0], x3) | sw0(species) | endo ~ inst, base)
  +       est_1 = feols(y ~ csw(.[rhs_1], x3) | sw0(species) | endo ~ inst, base)
  +       for(id_mult in n_models(est_0)){
  +         test(coef(est_0[[id_mult]]), coef(est_1[[id_mult]]))
  +       }
  +     }
  +   }
  + }
  |...|...|..> cat("\n")
  
  > 
  > 
  > # model.matrix
  > est = feols(y ~ x1 + y, base)
  > test(ncol(model.matrix(est)), 2)
  > 
  > ####
  > #### ... obs removal ####
  > ####
  > 
  > base_rm = base_did
  > base_rm$y = abs(base_rm$y)
  > # we add 0-values
  > is_only_0 = base_rm$id <= 10
  > base_rm$y[is_only_0] = 0
  > # we add singletons
  > is_single = base_rm$id %in% 50:55
  > base_rm$period[is_single] = runif(sum(is_single))
  > 
  > est_none = fepois(y ~ x1 | id + period, base_rm, fixef.rm = "none")
  > est_single = fepois(y ~ x1 | id + period, base_rm, fixef.rm = "singletons")
  > est_inf = fepois(y ~ x1 | id + period, base_rm, fixef.rm = "infinite")
  > est_perfect = fepois(y ~ x1 | id + period, base_rm, fixef.rm = "perfect")
  > 
  > n = nrow(base_rm)
  > test(nobs(est_none), n)
  > test(nobs(est_single), n - sum(is_single))
  > test(nobs(est_inf), n - sum(is_only_0))
  > test(nobs(est_perfect), n - sum(is_single) - sum(is_only_0))
  > 
  > ####
  > #### ... Fit methods ####
  > ####
  > 
  > chunk("Fit methods")
  FIT METHODS
  
  > 
  > base = setNames(iris, c("y", "x1", "x2", "x3", "species"))
  > base$y_int = as.integer(base$y)
  > base$y_log = sample(c(TRUE, FALSE), 150, TRUE)
  > 
  > res = feglm.fit(base$y, base[, 2:4])
  > res_bis = feglm(y ~ -1 + x1 + x2 + x3, base)
  > test(coef(res), coef(res_bis))
  > 
  > res = feglm.fit(base$y_int, base[, 2:4])
  > res_bis = feglm(y_int ~ -1 + x1 + x2 + x3, base)
  > test(coef(res), coef(res_bis))
  > 
  > res = feglm.fit(base$y_log, base[, 2:4])
  > res_bis = feglm(y_log ~ -1 + x1 + x2 + x3, base)
  > test(coef(res), coef(res_bis))
  > 
  > 
  > 
  > res = feglm.fit(base$y, base[, 2:4], family = "poisson")
  > res_bis = feglm(y ~ -1 + x1 + x2 + x3, base, family = "poisson")
  > test(coef(res), coef(res_bis))
  > 
  > res = feglm.fit(base$y_int, base[, 2:4], family = "poisson")
  > res_bis = feglm(y_int ~ -1 + x1 + x2 + x3, base, family = "poisson")
  > test(coef(res), coef(res_bis))
  > 
  > res = feglm.fit(base$y_log, base[, 2:4], family = "poisson")
  > res_bis = feglm(y_log ~ -1 + x1 + x2 + x3, base, family = "poisson")
  > test(coef(res), coef(res_bis))
  > 
  > ####
  > #### global variables ####
  > ####
  > 
  > chunk("globals")
  GLOBALS
  
  > 
  > est_reg = function(df, yvar, xvar, refgrp) {
  +   feols(.[yvar] ~ i(.[xvar], ref = refgrp), data = df)
  + }
  > 
  > est = est_reg(iris, "Sepal.Length", "Species", refgrp = "setosa")
  > 
  > # checking when it should not work
  > base = setNames(iris, c("y", "x1", "x2", "x3", "species"))
  > 
  > z = base$x1
  > test(feols(y ~ z, base), "err")
  > 
  > 
  > 
  > ####
  > #### ... Collinearity ####
  > ####
  > 
  > chunk("COLLINEARITY")
  COLLINEARITY
  
  > 
  > base = iris
  > names(base) = c("y", "x1", "x2", "x3", "species")
  > base$constant = 5
  > base$y_int = as.integer(base$y)
  > base$w = as.vector(unclass(base$species) - 0.95)
  > 
  > for(useWeights in c(FALSE, TRUE)){
  +   for(model in c("ols", "pois")){
  +     for(use_fe in c(FALSE, TRUE)){
  +       cat(".")
  + 
  +       my_weight = NULL
  +       if(useWeights) my_weight = base$w
  + 
  +       adj = 1
  +       if(model == "ols"){
  +         if(!use_fe){
  +           res = feols(y ~ x1 + constant, base, weights = my_weight)
  +           res_bis = lm(y ~ x1 + constant, base, weights = my_weight)
  +         } else {
  +           res = feols(y ~ x1 + constant | species, base, weights = my_weight)
  +           res_bis = lm(y ~ x1 + constant + species, base, weights = my_weight)
  +         }
  +       } else {
  +         if(!use_fe){
  +           res = fepois(y_int ~ x1 + constant, base, weights = my_weight)
  +           res_bis = glm(y_int ~ x1 + constant, base, weights = my_weight, family = poisson)
  +         } else {
  +           res = fepois(y_int ~ x1 + constant | species, base, weights = my_weight)
  +           res_bis = glm(y_int ~ x1 + constant + species, base, weights = my_weight, family = poisson)
  +         }
  +         adj = 0
  +       }
  + 
  +       test(coef(res)["x1"], coef(res_bis)["x1"], "~")
  +       test(se(res, se = "st", ssc = ssc(K.adj=adj))["x1"], se(res_bis)["x1"], "~")
  +       # cat("Weight: ", useWeights, ", model: ", model, ", FE: ", use_fe, "\n", sep="")
  + 
  +     }
  +   }
  + }
  ........> cat("\n")
  
  > 
  > 
  > ####
  > #### ... Non linear tests ####
  > ####
  > 
  > chunk("NON LINEAR")
  NON LINEAR
  
  > 
  > base = iris
  > names(base) = c("y", "x1", "x2", "x3", "species")
  > 
  > tab = c("versicolor" = 5, "setosa" = 0, "virginica" = -5)
  > 
  > fun_nl = function(a, b, spec){
  +   res = as.numeric(tab[spec])
  +   a*res + b*res^2
  + }
  > 
  > est_nl = feNmlm(y ~ x1, base, NL.fml = ~fun_nl(a, b, species), NL.start = 1, family = "gaussian")
  > 
  > base$var_spec = as.numeric(tab[base$species])
  > 
  > est_lin = feols(y ~ x1 + var_spec + I(var_spec^2), base)
  > 
  > test(coef(est_nl), coef(est_lin)[c(3, 4, 1, 2)], "~")
  > 
  > ####
  > #### ... Lagging ####
  > ####
  > 
  > # Different types of lag
  > # 1) check no error in wide variety of situations
  > # 2) check consistency
  > 
  > chunk("LAGGING")
  LAGGING
  
  > 
  > data(base_did)
  > base = base_did
  > 
  > n = nrow(base)
  > 
  > set.seed(0)
  > base$y_na = base$y ; base$y_na[sample(n, 50)] = NA
  > base$period_txt = letters[base$period]
  > ten_dates = c("1960-01-15", "1960-01-16", "1960-03-31", "1960-04-05", "1960-05-12", "1960-05-25", "1960-06-20", "1960-07-30", "1965-01-02", "2002-12-05")
  > base$period_date = as.Date(ten_dates, "%Y-%m-%d")[base$period]
  > base$y_0 = base$y**2 ; base$y_0[base$id == 1] = 0
  > 
  > # We compute the lags "by hand"
  > base = base[order(base$id, base$period), ]
  > base$x1_lag = c(NA, base$x1[-n]) ; base$x1_lag[base$period == 1] = NA
  > base$x1_lead = c(base$x1[-1], NA) ; base$x1_lead[base$period == 10] = NA
  > base$x1_diff = base$x1 - base$x1_lag
  > 
  > # we create holes
  > base$period_bis = base$period ; base$period_bis[base$period_bis == 5] = 50
  > base$x1_lag_hole = base$x1_lag ; base$x1_lag_hole[base$period %in% c(5, 6)] = NA
  > base$x1_lead_hole = base$x1_lead ; base$x1_lead_hole[base$period %in% c(4, 5)] = NA
  > 
  > # we reshuffle the base
  > base = base[sample(n), ]
  > 
  > #
  > # Checks consistency
  > #
  > 
  > cat("consistentcy...")
  consistentcy...> 
  > test(lag(x1 ~ id + period, data = base), base$x1_lag)
  > test(lag(x1 ~ id + period, -1, data = base), base$x1_lead)
  > 
  > test(lag(x1 ~ id + period_bis, data = base), base$x1_lag_hole)
  > test(lag(x1 ~ id + period_bis, -1, data = base), base$x1_lead_hole)
  > 
  > test(lag(x1 ~ id + period_txt, data = base), base$x1_lag)
  > test(lag(x1 ~ id + period_txt, -1, data = base), base$x1_lead)
  > 
  > test(lag(x1 ~ id + period_date, data = base), base$x1_lag)
  > test(lag(x1 ~ id + period_date, -1, data = base), base$x1_lead)
  > 
  > cat("done.\nEstimations...")
  done.
  Estimations...> 
  > #
  > # Estimations
  > #
  > 
  > # Poisson
  > 
  > for(depvar in c("y", "y_na", "y_0")){
  +   for(p in c("period", "period_txt", "period_date")){
  + 
  +     base$per = base[[p]]
  + 
  +     cat(".")
  + 
  +     base$y_dep = base[[depvar]]
  +     pdat = panel(base, ~ id + period)
  + 
  +     if(depvar == "y_0"){
  +       estfun = fepois
  +     } else {
  +       estfun = feols
  +     }
  + 
  +     est_raw  = estfun(y_dep ~ x1 + x1_lag + x1_lead, base)
  +     est      = estfun(y_dep ~ x1 + l(x1) + f(x1), base, panel.id = "id,per")
  +     est_pdat = estfun(y_dep ~ x1 + l(x1, 1) + f(x1, 1), pdat)
  +     test(coef(est_raw), coef(est))
  +     test(coef(est_raw), coef(est_pdat))
  + 
  +     # Now diff
  +     est_raw  = estfun(y_dep ~ x1 + x1_diff, base)
  +     est      = estfun(y_dep ~ x1 + d(x1), base, panel.id = "id,per")
  +     est_pdat = estfun(y_dep ~ x1 + d(x1, 1), pdat)
  +     test(coef(est_raw), coef(est))
  +     test(coef(est_raw), coef(est_pdat))
  + 
  +     # Now we just check that calls to l/f works without checking coefs
  + 
  +     est = estfun(y_dep ~ x1 + l(x1) + f(x1), base, panel.id = "id,per")
  +     est = estfun(y_dep ~ l(x1, -1:1) + f(x1, 2), base, panel.id = c("id", "per"))
  +     est = estfun(y_dep ~ l(x1, -1:1, fill = 1), base, panel.id = ~ id + per)
  +     if(depvar == "y") test(est$nobs, n)
  +     est = estfun(f(y_dep) ~ f(x1, -1:1), base, panel.id = ~ id + per)
  +   }
  + }
  .........> 
  > cat("done.\n\n")
  done.
  
  > 
  > #
  > # Data table
  > #
  > 
  > cat("data.table...")
  data.table...> # We just check there is no bug (consistency should be OK)
  > 
  > suppressPackageStartupMessages(library(data.table))
  > 
  > base_dt = data.table(id = c("A", "A", "B", "B"),
  +            time = c(1, 2, 1, 3),
  +            x = c(5, 6, 7, 8))
  > 
  > base_dt = panel(base_dt, ~id + time)
  > 
  > base_dt[, x_l := l(x)]
  > test(base_dt$x_l, c(NA, 5, NA, NA))
  > 
  > lag_creator = function(dt) {
  +   dt2 = panel(dt, ~id + time)
  +   dt2[, x_l := l(x)]
  +   return(dt2)
  + }
  > 
  > base_bis = lag_creator(base_dt)
  > 
  > base_bis[, x_d := d(x)]
  > 
  > cat("done.\n\n")
  done.
  
  > 
  > #
  > # Panel
  > #
  > 
  > # We ensure we get the right SEs whether we use the panel() or the panel.id method
  > data(base_did)
  > 
  > # Setting a data set as a panel...
  > pdat = panel(base_did, ~id+period)
  > pdat$fe = sample(15, nrow(pdat), replace = TRUE)
  > 
  > base_panel = unpanel(pdat)
  > 
  > est_pdat = feols(y ~ x1 | fe, pdat)
  > est_panel = feols(y ~ x1 | fe, base_panel, panel.id = ~id+period)
  > 
  > test(attr(vcov(est_pdat, attr = TRUE), "type"),
  +      attr(vcov(est_panel, attr = TRUE), "type"))
  > 
  > #
  > # testing irregularities
  > # 
  > 
  > base_panel = data.frame(
  +   id = rep(letters[1:3], each = 4),
  +   time = c(1, 2, 3, 3, 
  +            1, 2, 4, 6, 
  +            2, 4, 6, 8)
  + )
  > base_panel$x = rnorm(nrow(base_panel))
  > base_panel$y = base_panel$x * 0.5 + rnorm(nrow(base_panel))
  > 
  > est_panel_irr = feols(y ~ l(x), base_panel, panel.id = "id,time", 
  +                       panel.duplicate.method = "first")
  > 
  > x_lag = model.matrix(est_panel_irr)[, 2]
  > test(x_lag, base_panel$x[c(1, 2, 2, 5)])
  > 
  > est_panel_irr_cons = feols(y ~ l(x), base_panel, panel.id = "id,time", 
  +                            panel.time.step = "cons",
  +                            panel.duplicate.method = "first")
  > 
  > x_lag = model.matrix(est_panel_irr_cons)[, 2]
  > test(x_lag, base_panel$x[c(NULL, 1, 2, 2, 
  +                            NULL, 5, NULL, 7,
  +                            NULL, NULL, 10, 11)])
  > 
  > est_panel_irr_with = feols(y ~ l(x), base_panel, panel.id = "id,time", 
  +                            panel.time.step = "within",
  +                            panel.duplicate.method = "first")
  > 
  > x_lag = model.matrix(est_panel_irr_with)[, 2]
  > test(x_lag, base_panel$x[c(NULL, 1, 2, 3, 
  +                            NULL, 5, 6, 7,
  +                            NULL, 9, 10, 11)])
  > 
  > ####
  > #### ... subset ####
  > ####
  > 
  > chunk("SUBSET")
  SUBSET
  
  > 
  > set.seed(5)
  > base = iris
  > names(base) = c("y", "x1", "x2", "x3", "species")
  > base$fe_bis = sample(letters, 150, TRUE)
  > base$x4 = rnorm(150)
  > base$x1[sample(150, 5)] = NA
  > 
  > fml = y ~ x1 + x2
  > 
  > # Errors
  > test(feols(fml, base, subset = ~species), "err")
  > test(feols(fml, base, subset = -1:15), "err")
  > test(feols(fml, base, subset = integer(0)), "err")
  > test(feols(fml, base, subset = c(TRUE, TRUE, FALSE)), "err")
  > 
  > # Valid use
  > for(id_fun in 1:6){
  +   estfun = switch(as.character(id_fun),
  +           "1" = feols,
  +           "2" = feglm,
  +           "3" = fepois,
  +           "4" = femlm,
  +           "5" = fenegbin,
  +           "6" = feNmlm)
  + 
  +   for(id_fe in 1:5){
  + 
  +     cat(".")
  + 
  +     fml = switch(as.character(id_fe),
  +            "1" = y ~ x1 + x2,
  +            "2" = y ~ x1 + x2 | species,
  +            "3" = y ~ x1 + x2 | fe_bis,
  +            "4" = y ~ x1 + x2 + i(fe_bis),
  +            "5" = y ~ x1 + x2 | fe_bis[x3])
  + 
  +     if(id_fe == 5 && id_fun %in% 4:6) next
  + 
  +     if(id_fun == 6){
  +       res_sub_a = estfun(fml, base, subset = ~species == "setosa", NL.fml = ~ a*x4, NL.start = 0)
  +       res_sub_b = estfun(fml, base, subset = base$species == "setosa", NL.fml = ~ a*x4, NL.start = 0)
  +       res_sub_c = estfun(fml, base, subset = which(base$species == "setosa"), NL.fml = ~ a*x4, NL.start = 0)
  +       res       = estfun(fml, base[base$species == "setosa", ], NL.fml = ~ a*x4, NL.start = 0)
  +     } else {
  +       res_sub_a = estfun(fml, base, subset = ~species == "setosa")
  +       res_sub_b = estfun(fml, base, subset = base$species == "setosa")
  +       res_sub_c = estfun(fml, base, subset = which(base$species == "setosa"))
  +       res       = estfun(fml, base[base$species == "setosa", ])
  +     }
  + 
  +     test(coef(res_sub_a), coef(res))
  +     test(coef(res_sub_b), coef(res))
  +     test(coef(res_sub_c), coef(res))
  +     test(se(res_sub_c, cluster = "fe_bis"), se(res, cluster = "fe_bis"))
  +   }
  +   cat("|")
  + }
  .....|.....|.....|.....|.....|.....|There were 12 warnings (use warnings() to see them)
  > cat("\n")
  
  > 
  > 
  > ####
  > #### ... split ####
  > ####
  > 
  > chunk("split")
  SPLIT
  
  > 
  > base = setNames(iris, c("y", "x1", "x2", "x3", "species"))
  > 
  > # simple: formula
  > est = feols(y ~ x.[1:3], base, split = ~species %keep% "@^v")
  > test(length(est), 2)
  > 
  > est = feols(y ~ x.[1:3], base, fsplit = ~species %keep% c("set", "vers"))
  > test(length(est), 3)
  > 
  > est = feols(y ~ x.[1:3], base, split = ~species %drop% "set")
  > test(length(est), 2)
  > 
  > # simple: vector
  > est = feols(y ~ x.[1:3], base, split = base$species %keep% "@^v")
  > test(length(est), 2)
  > 
  > est = feols(y ~ x.[1:3], base, split = base$species %keep% c("set", "vers"))
  > test(length(est), 2)
  > 
  > est = feols(y ~ x.[1:3], base, split = base$species %drop% "set")
  > test(length(est), 2)
  > 
  > # with bin
  > est = feols(y ~ x.[1:2], base,
  +       split = ~bin(x3, c("cut::5", "saint emilion", "pessac leognan",
  +                  "margaux", "saint julien", "entre deux mers")) %keep% c("saint e", "pe"))
  > test(length(est), 2)
  > 
  > est = feols(y ~ x.[1:2], base,
  +       split = ~bin(x3, c("cut::5", "saint emilion", "pessac leognan", NA)) %drop% "@\\d")
  > test(length(est), 2)
  > 
  > # with argument
  > est = feols(y ~ x.[1:3], base, split = ~species, split.keep = "@^v")
  > test(length(est), 2)
  > 
  > est = feols(y ~ x.[1:3], base, fsplit = ~species, split.keep = c("set", "vers"))
  > test(length(est), 3)
  > 
  > est = feols(y ~ x.[1:3], base, split = ~species, split.drop = "set")
  > test(length(est), 2)
  > 
  > 
  > ####
  > #### ... Multiple estimations ####
  > ####
  > 
  > chunk("Multiple")
  MULTIPLE
  
  > 
  > set.seed(2)
  > base = iris
  > names(base) = c("y1", "x1", "x2", "x3", "species")
  > base$y2 = 10 + rnorm(150) + 0.5 * base$x1
  > base$x4 = rnorm(150) + 0.5 * base$y1
  > base$fe2 = rep(letters[1:15], 10)
  > base$fe2[50:51] = NA
  > base$y2[base$fe2 == "a" & !is.na(base$fe2)] = 0
  > base$x2[1:5] = NA
  > base$x3[6] = NA
  > base$x5 = rnorm(150)
  > base$x6 = rnorm(150) + base$y1 * 0.25
  > base$fe3 = rep(letters[1:10], 15)
  > 
  > 
  > for(id_fun in 1:5){
  +   estfun = switch(as.character(id_fun),
  +                   "1" = feols,
  +                   "2" = feglm,
  +                   "3" = fepois,
  +                   "4" = femlm,
  +                   "5" = feNmlm)
  + 
  +   # Following weird bug ASAN on CRAN I cannot replicate, check 4/5 not performed on non Windows
  +   if(Sys.info()["sysname"] != "Windows"){
  +     if(id_fun %in% 4:5) next
  +   }
  + 
  + 
  +   est_multi = estfun(c(y1, y2) ~ x1 + sw(x2, x3), base, split = ~species)
  + 
  +   k = 1
  +   for(s in c("setosa", "versicolor", "virginica")){
  +     for(lhs in c("y1", "y2")){
  +       for(rhs in c("x2", "x3")){
  +         res = estfun(.[lhs] ~ x1 + .[rhs], base[base$species == s, ], notes = FALSE)
  + 
  +         test(coef(est_multi[[k]]), coef(res))
  +         test(se(est_multi[[k]], cluster = "fe3"), se(res, cluster = "fe3"))
  +         k = k + 1
  +       }
  +     }
  +   }
  + 
  +   cat("__")
  + 
  +   est_multi = estfun(c(y1, y2) ~ x1 + csw0(x2, x3) + x4 | species + fe2, base, fsplit = ~species)
  +   k = 1
  +   all_rhs = c("", "x2", "x3")
  +   for(s in c("all", "setosa", "versicolor", "virginica")){
  +     for(lhs in c("y1", "y2")){
  +       for(n_rhs in 1:3){
  +         if(s == "all"){
  +           res = estfun(xpd(..lhs ~ x1 + ..rhs + x4 | species + fe2, ..lhs = lhs, ..rhs = all_rhs[1:n_rhs]), base, notes = FALSE)
  +         } else {
  +           res = estfun(xpd(..lhs ~ x1 + ..rhs + x4 | species + fe2, ..lhs = lhs, ..rhs = all_rhs[1:n_rhs]), base[base$species == s, ], notes = FALSE)
  +         }
  + 
  +         vname = names(coef(res))
  +         test(coef(est_multi[[k]])[vname], coef(res), "~" , 1e-6)
  +         test(se(est_multi[[k]], cluster = "fe3")[vname], se(res, cluster = "fe3"), "~" , 1e-6)
  +         k = k + 1
  +       }
  +     }
  +   }
  + 
  +   cat("|")
  + }
  __|__|__|> cat("\n")
  
  > 
  > 
  > 
  > # No error tests
  > # We test with IV + possible corner cases
  > 
  > base$left = rnorm(150)
  > base$right = rnorm(150)
  > 
  > est_multi = feols(c(y1, y2) ~ sw0(x1) | sw0(species) | x2 ~ x3, base)
  > 
  > # We check a few
  > est_a = feols(y1 ~ 1 | x2 ~ x3, base)
  > est_b = feols(y1 ~ x1 | species | x2 ~ x3, base)
  > est_c = feols(y2 ~ 1 | x2 ~ x3, base)
  > 
  > test(coef(est_multi[lhs = "y1", rhs = "^1", fixef = "1", drop = TRUE]), coef(est_a))
  > test(coef(est_multi[lhs = "y1", rhs = "x1", fixef = "spe", drop = TRUE]), coef(est_b))
  > test(coef(est_multi[lhs = "y2", rhs = "^1", fixef = "1", drop = TRUE]), coef(est_c))
  > 
  > # with fixed covariates
  > est_multi_LR = feols(c(y1, y2) ~ left + sw0(x1*x4) + right | sw0(species) | x2 ~ x3, base)
  > 
  > est_a = feols(y1 ~ left + right | x2 ~ x3, base)
  > est_b = feols(y1 ~ left + x1*x4 + right | species | x2 ~ x3, base)
  > est_c = feols(y2 ~ left + right | x2 ~ x3, base)
  > 
  > test(coef(est_multi_LR[lhs = "y1", rhs = "!x1", fixef = "1", drop = TRUE]), coef(est_a))
  > user_name = c("fit_x2", "left", "x1", "x4", "x1:x4", "right")
  > test(names(coef(est_multi_LR[lhs = "y1", rhs = "x1", fixef = "spe", drop = TRUE])), user_name)
  > test(coef(est_multi_LR[lhs = "y1", rhs = "x1", fixef = "spe", drop = TRUE]), coef(est_b)[user_name])
  > test(coef(est_multi_LR[lhs = "y2", rhs = "!x1", fixef = "1", drop = TRUE]), coef(est_c))
  > 
  > 
  > # mvsw
  > 
  > est_mvsw = feols(y1 ~ mvsw(x1, x2), base)
  > est_mvsw_fe = feols(y1 ~ mvsw(x1, x2) | mvsw(species, fe2), base)
  > est_mvsw_fe_iv = feols(y1 ~ mvsw(x1, x2) | mvsw(species, fe2) | x3 ~ x4, base)
  > 
  > test(length(est_mvsw), 4)
  > test(length(as.list(est_mvsw_fe)), 16)
  > test(length(as.list(est_mvsw_fe_iv)), 16)
  > 
  > # Summary of multiple endo vars
  > est_multi_iv = feols(c(y1, y2) ~ sw0(x1) | sw0(species) | x3 + x4 ~ x5 + x6, base)
  > test(length(est_multi_iv), 8)
  > test(length(summary(est_multi_iv, stage = 1)), 16)
  > 
  > # IV without exo var:
  > est_mult_no_exo = feols(c(y1, y2) ~ 0 | x3 + x4 ~ x5 + x6, base)
  > est_no_exo_y2 = feols(y2 ~ 0 | x3 + x4 ~ x5 + x6, base)
  > test(coef(est_mult_no_exo[[2]]), coef(est_no_exo_y2))
  > 
  > # proper ordering
  > est_multi = feols(c(y1, y2) ~ sw0(x1) | sw0(fe2), base, split = ~species)
  > test(names(models(est_multi[fixef = TRUE, sample = FALSE])),
  +    stvec("id, fixef, lhs, rhs, sample.var, sample"))
  > 
  > test(names(models(est_multi[fixef = "fe2", sample = "seto"])),
  +    stvec("id, fixef, sample.var, sample, lhs, rhs"))
  > 
  > test(names(models(est_multi[fixef = "fe2", sample = "seto", reorder = FALSE])),
  +    stvec("id, sample.var, sample, fixef, lhs, rhs"))
  > 
  > # NA models
  > base$y_0 = base$x1 ** 2 + rnorm(150)
  > base$y_0[base$species == "setosa"] = 0
  > 
  > est_pois = fepois(y_0 ~ csw(x.[,1:4]), base, split = ~species)
  > 
  > base$x1_bis = base$x1
  > est_pois = fepois(y_0 ~ x.[1:3] + x1_bis | sw0(species), base)
  > 
  > # Different ways .[]
  > base = setNames(iris, c("y", "x1", "x2", "x3", "species"))
  > 
  > dep_all = list(stvec("y, x1, x2"), ~y + x1 + x2)
  > for(dep in dep_all){
  +   m = feols(.[dep] ~ x3, base)
  +   test(length(m), 3)
  + 
  +   m = feols(x3 ~ .[dep], base)
  +   test(length(m$coefficients), 4)
  + 
  +   m = feols(x3 ~ csw(.[,dep]), base)
  +   test(length(m), 3)
  + }
  > 
  > # offset in multiple outcomes // no error test
  > 
  > offset_single_ols = feols(am ~ hp, offset = ~ log(qsec), data = mtcars)
  > offset_mult_ols = feols(c(mpg, am) ~ hp, offset = ~ log(qsec), data = mtcars)
  > 
  > test(coef(offset_mult_ols[[2]]), coef(offset_single_ols))
  > 
  > offset_single_glm = feglm(am ~ hp, offset = ~ log(qsec), data = mtcars)
  > offset_mult_glm = feglm(c(mpg, am) ~ hp, offset = ~ log(qsec), data = mtcars)
  > 
  > test(coef(offset_mult_glm[[2]]), coef(offset_single_glm))
  > 
  > # LHS expansion with IVs
  > 
  > lhs = c("mpg", "wt")
  > est_lhs = feols(.[lhs] ~ disp | hp ~ qsec, data = mtcars)
  > test(length(est_lhs), 2)
  > 
  > est_lhs = feols(..("mpg|wt") ~ disp | hp ~ qsec, data = mtcars)
  > test(length(est_lhs), 2)
  > 
  > 
  > 
  > ####
  > #### ... IV ####
  > ####
  > 
  > chunk("IV")
  IV
  
  > 
  > base = iris
  > names(base) = c("y", "x1", "x_endo_1", "x_inst_1", "fe")
  > set.seed(2)
  > base$x_inst_2 = 0.2 * base$y + 0.2 * base$x_endo_1 + rnorm(150, sd = 0.5)
  > base$x_endo_2 = 0.2 * base$y - 0.2 * base$x_inst_1 + rnorm(150, sd = 0.5)
  > 
  > # Checking a basic estimation
  > 
  > setFixest_vcov(all = "iid")
  > 
  > est_iv = feols(y ~ x1 | x_endo_1 + x_endo_2 ~ x_inst_1 + x_inst_2, base)
  > 
  > res_f1 = feols(x_endo_1 ~ x1 + x_inst_1 + x_inst_2, base)
  > res_f2 = feols(x_endo_2 ~ x1 + x_inst_1 + x_inst_2, base)
  > 
  > base$fit_x_endo_1 = predict(res_f1)
  > base$fit_x_endo_2 = predict(res_f2)
  > 
  > res_2nd = feols(y ~ fit_x_endo_1 + fit_x_endo_2 + x1, base)
  > 
  > # the coef
  > test(coef(est_iv), coef(res_2nd))
  > 
  > # the SE
  > resid_iv = base$y - predict(res_2nd, 
  +                             data.frame(x1 = base$x1, fit_x_endo_1 = base$x_endo_1, 
  +                                        fit_x_endo_2 = base$x_endo_2))
  > sigma2_iv = sum(resid_iv**2) / (res_2nd$nobs - res_2nd$nparams)
  > 
  > sum_2nd = summary(res_2nd, vcov = res_2nd$cov.iid / res_2nd$sigma2 * sigma2_iv)
  > 
  > test(se(sum_2nd), se(est_iv))
  > 
  > # check no bug when all exogenous vars are removed bc of collinearity
  > df = data.frame(x = rnorm(8), y = rnorm(8),
  +                 z = rnorm(8), fe = rep(0:1, each = 4))
  > 
  > est_iv = feols(y ~ fe | fe | x ~ z, df)
  > est_iv = feols(y ~ sw(fe, fe) | fe | x ~ z, df)
  > 
  > # check no bug
  > etable(summary(est_iv, stage = 1:2))
                  summary(est_..1 summary(es..2 summary(est_..3 summary(es..4
  Dependent Var.:               x             y               x             y
                                                                             
  z               0.1127 (0.5532)               0.1127 (0.5532)              
  x                               1.110 (6.499)                 1.110 (6.499)
  Fixed-Effects:  --------------- ------------- --------------- -------------
  fe                          Yes           Yes             Yes           Yes
  _______________ _______________ _____________ _______________ _____________
  S.E. type                   IID           IID             IID           IID
  Observations                  8             8               8             8
  R2                      0.35875       0.19449         0.35875       0.19449
  Within R2               0.00823       0.00779         0.00823       0.00779
  ---
  Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
  > 
  > setFixest_vcov(reset = TRUE)
  > 
  > # Check the number of instruments
  > test(
  +   feols(y ~ x1 | x_endo_1 + x_endo_2 ~ x_inst_1, base),
  +   "err"
  + )
  > 
  > 
  > ####
  > #### ... VCOV at estimation ####
  > ####
  > 
  > chunk("vcov at estimation")
  VCOV AT ESTIMATION
  
  > 
  > base = iris
  > names(base) = c("y", "x1", "x2", "x3", "species")
  > base$clu = sample(6, 150, TRUE)
  > base$clu[1:5] = NA
  > 
  > est = feols(y ~ x1 | species, base, cluster = ~clu, ssc = ssc(K.adj = FALSE))
  > 
  > # The three should be identical
  > v1 = est$cov.scaled
  > v1b = vcov(est)
  > v1c = summary(est)$cov.scaled
  > 
  > test(v1, v1b)
  > test(v1, v1c)
  > 
  > # Only ssc change
  > v2 = summary(est, ssc = ssc())$cov.scaled
  > v2b = vcov(est, ssc = ssc())
  > 
  > test(v2, v2b)
  > test(max(abs(v1 - v2)) == 0, FALSE)
  > 
  > # vcov change only
  > v3 = summary(est, se = "hetero")$cov.scaled
  > v3b = vcov(est, se = "hetero")
  > 
  > test(v3, v3b)
  > test(max(abs(v1 - v3)) == 0, FALSE)
  > test(max(abs(v2 - v3)) == 0, FALSE)
  > 
  > # feols.fit
  > 
  > ymat = base$y
  > xmat = base[, 2:3]
  > fe = base$species
  > 
  > for(use_fe in c(TRUE, FALSE)){
  +   all_vcov = stvec("iid, hetero")
  +   if(use_fe){
  +     setFixest_fml(..fe = ~ 1 | species)
  +     all_vcov = c(all_vcov, "cluster")
  +   } else {
  +     setFixest_fml(..fe = ~ 1)
  +   }
  + 
  +   for(v in all_vcov){
  + 
  +     if(use_fe){
  +       est_fit = feols.fit(ymat, xmat, fe, vcov = v)
  +     } else {
  +       est_fit = feols.fit(ymat, cbind(1, xmat), vcov = v)
  +     }
  + 
  +     est = feols(y ~ x1 + x2 + ..fe, base, vcov = v)
  + 
  +     test(vcov(est), vcov(est_fit))
  +   }
  + }
  > 
  > ####
  > #### ... Custom VCOV ####
  > ####
  > 
  > chunk("custom vcov")
  CUSTOM VCOV
  
  > 
  > # Named list stores string
  > est <- feols(mpg ~ i(cyl), mtcars)
  > vcov_HC3 <- sandwich::vcovHC(est, type = "HC3")
  > est_HC3 <- summary(est, vcov = list("HC3" = vcov_HC3))
  > test(attr(est_HC3$se, "type"), "HC3")
  > est_tab <- etable(est_HC3)
  > test(any(grepl("HC3", est_tab[[2]])), TRUE)
  > 
  > # Passing functions
  > test(
  +   summary(est, vcov = function(x) sandwich::vcovHC(x, type = "HC3"))$se,
  +   summary(est, vcov = sandwich::vcovHC, .vcov_args = list(type = "HC3"))$se
  + )
  > # Confirming these work 
  > temp <- etable(est, vcov = function(x) sandwich::vcovHC(x, type = "HC3"))
  > temp <- etable(est, vcov = sandwich::vcovHC, .vcov_args = list(type = "HC3"))
  > 
  > 
  > # deprecated `.vcov` still works
  > est_.vcov <- summary(est, .vcov = vcov_HC3)
  Warning message:
  In summary.fixest(est, .vcov = vcov_HC3) :
    The argument '.vcov' is deprecated. Please use 'vcov' instead.
  > test(est_.vcov$se, est_HC3$se)
  > etable(est, .vcov = vcov_HC3)
                                est
  Dependent Var.:               mpg
                                   
  Constant        26.66*** (0.9718)
  cyl = 6         -6.921*** (1.558)
  cyl = 8         -11.56*** (1.299)
  _______________ _________________
  S.E. type                     IID
  Observations                   32
  R2                        0.73246
  Adj. R2                   0.71401
  ---
  Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
  > 
  > # Custom vcov to fixest multi
  > est_multi <- feols(c(mpg, hp) ~ i(cyl), mtcars)
  > vcovs_HC3 <- lapply(est_multi, function(est) sandwich::vcovHC(est, type = "HC3"))
  > names(vcovs_HC3) <- c("HC3", "HC3")
  > est_multi_HC3 <- summary(est_multi, vcov = vcovs_HC3)
  > 
  > test(vcov(est_multi_HC3[[1]]), vcovs_HC3[[1]])
  > test(vcov(est_multi_HC3[[2]]), vcovs_HC3[[2]])
  > 
  > test(attr(est_multi_HC3[[1]]$se, "type"), "HC3")
  > test(attr(est_multi_HC3[[2]]$se, "type"), "HC3")
  > 
  > est_tab <- etable(est_multi, est_multi_HC3)
  > test(any(grepl("HC3", est_tab$est_multi_HC3.1)), TRUE)
  > test(any(grepl("HC3", est_tab$est_multi_HC3.2)), TRUE)
  > 
  > # deprecated `.vcov` still works
  > est_multi_.vcov <- summary(est_multi, .vcov = vcovs_HC3)
  > test(est_multi_.vcov[[1]]$se, est_multi_HC3[[1]]$se)
  > 
  > 
  > 
  > ####
  > #### ... Argument sliding ####
  > ####
  > 
  > chunk("argument sliding")
  ARGUMENT SLIDING
  
  > 
  > base = setNames(iris, c("y", "x1", "x2", "x3", "species"))
  > 
  > setFixest_estimation(data = base)
  > 
  > raw = feols(y ~ x1 + x2, base, ~species)
  Warning message:
  The VCOV matrix is not positive semi-definite and was 'fixed' (see ?vcov). 
  > slided = feols(y ~ x1 + x2, ~species)
  Warning message:
  The VCOV matrix is not positive semi-definite and was 'fixed' (see ?vcov). 
  > 
  > test(coef(raw), coef(slided))
  > 
  > # Error, with error msg relative to 'data'
  > test(feols(y ~ x1 + x2, 1:5), "err")
  > 
  > # should be another estimation
  > other_est = feols(y ~ x1 + x2, head(base, 50))
  > test(nobs(other_est), 50)
  > 
  > setFixest_estimation(reset = TRUE)
  > 
  > ####
  > #### ... Offset ####
  > ####
  > 
  > chunk("offset")
  OFFSET
  
  > 
  > # we test the different ways to set an offset
  > 
  > base = setNames(iris, c("y", "x1", "x2", "x3", "species"))
  > 
  > o1 = feols(y ~ x1 + offset(x2) + offset(x3^2 + 3), base)
  > o2 = feols(y ~ x1, base, offset = ~x2 + x3^2 + 3)
  > test(coef(o1), coef(o2))
  > 
  > test(predict(o1, newdata = head(base)), 
  +      predict(o2, newdata = head(base)))
  > 
  > # error
  > test(feols(y ~ x1 + offset(x2), base, offset = ~x3), "err")
  > 
  > 
  > ####
  > #### ... Only Coef ####
  > ####
  > 
  > chunk("only.coef")
  ONLY.COEF
  
  > 
  > 
  > base = setNames(iris, c("y", "x1", "x2", "x3", "species"))
  > base$x4 = base$x1 + 5
  > 
  > m = feols(y ~ x1 + x2 + x4, base, only.coef = TRUE)
  > test(length(m), 4)
  > test(sum(is.na(m)), 1)
  > 
  > m = fepois(y ~ x1 + x2 + x4, base, only.coef = TRUE)
  > test(length(m), 4)
  > test(sum(is.na(m)), 1)
  > 
  > m = femlm(y ~ x1 + x2, base, only.coef = TRUE)
  > test(length(m), 3)
  > test(sum(is.na(m)), 0)
  > 
  > test(feols(y ~ sw(x1, x2), base, only.coef = TRUE), "err")
  > 
  > 
  > ####
  > #### Standard-errors ####
  > ####
  > 
  > chunk("STANDARD ERRORS")
  STANDARD ERRORS
  
  > 
  > #
  > # Fixed-effects corrections
  > #
  > 
  > # We create "irregular" FEs
  > set.seed(0)
  > base = data.frame(x = rnorm(20))
  > base$y = base$x + rnorm(20)
  > base$fe1 = rep(rep(1:3, c(4, 3, 3)), 2)
  > base$fe2 = rep(rep(1:5, each = 2), 2)
  > est = feols(y ~ x | fe1 + fe2, base, vcov = "cluster")
  > 
  > # fe1: 3 FEs
  > # fe2: 5 FEs
  > 
  > #
  > # Clustered standard-errors: by fe1
  > #
  > 
  > # Default: K.fixef = "nonnested"
  > #  => adjustment K = 1 + 5 (i.e. x + fe2)
  > test(attr(vcov(est, ssc = ssc(K.fixef = "nonnested"), attr = TRUE), "df.K"), 6)
  > 
  > # K.fixef = FALSE
  > #  => adjustment K = 1 (i.e. only x)
  > test(attr(vcov(est, ssc = ssc(K.fixef = "none"), attr = TRUE), "df.K"), 1)
  > 
  > # K.fixef = TRUE
  > #  => adjustment K = 1 + 3 + 5 - 1 (i.e. x + fe1 + fe2 - 1 restriction)
  > test(attr(vcov(est, ssc = ssc(K.fixef = "full"), attr = TRUE), "df.K"), 8)
  > 
  > # K.fixef = TRUE & fixef.exact = TRUE
  > #  => adjustment K = 1 + 3 + 5 - 2 (i.e. x + fe1 + fe2 - 2 restrictions)
  > test(attr(vcov(est, ssc = ssc(K.fixef = "full", K.exact = TRUE), attr = TRUE), "df.K"), 7)
  > 
  > #
  > # Manual checks of the SEs
  > #
  > 
  > n = est$nobs
  > VCOV_raw = est$cov.iid / ((n - 1) / (n - est$nparams))
  > 
  > # standard
  > for(k_val in c("none", "nonnested", "full")){
  +   for(adj in c(FALSE, TRUE)){
  + 
  +     K = switch(k_val, none = 1, nonnested = 8, full = 8)
  +     my_adj = ifelse(adj, (n - 1) / (n - K), 1)
  + 
  +     test(vcov(est, se = "standard", ssc = ssc(K.adj = adj, K.fixef = k_val)), VCOV_raw * my_adj)
  + 
  +     # cat("adj = ", adj, " ; K.fixef = ", k_val, "\n", sep = "")
  +   }
  + }
  > 
  > # Clustered, fe1
  > VCOV_raw = est$cov.iid / est$sigma2
  > H = vcovClust(est$fixef_id$fe1, VCOV_raw, scores = est$scores, adj = FALSE)
  > n = nobs(est)
  > 
  > for(tdf in c("conventional", "min")){
  +   for(k_val in c("none", "nonnested", "full")){
  +     for(c_adj in c(FALSE, TRUE)){
  +       for(adj in c(FALSE, TRUE)){
  + 
  +         K = switch(k_val, none = 1, nonnested = 6, full = 8)
  +         cluster_factor = ifelse(c_adj, 3/2, 1)
  +         df = ifelse(tdf == "min", 2, 20 - K)
  +         my_adj = ifelse(adj, (n - 1) / (n - K), 1)
  + 
  +         V = H * cluster_factor
  + 
  +         # test SE
  +         test(vcov(est, se = "cluster", ssc = ssc(K.adj = adj, K.fixef = k_val, G.adj = c_adj)), V * my_adj)
  + 
  +         # test pvalue
  +         my_tstat = tstat(est, se = "cluster", ssc = ssc(K.adj = adj, K.fixef = k_val, G.adj = c_adj))
  +         test(pvalue(est, se = "cluster", ssc = ssc(K.adj = adj, K.fixef = k_val, G.adj = c_adj, t.df = tdf)), 2*pt(-abs(my_tstat), df))
  + 
  +         # cat("adj = ", adj, " ; K.fixef = ", k_val, " ; G.adj = ", c_adj, " t.df = ", tdf, "\n", sep = "")
  +       }
  +     }
  +   }
  + }
  > 
  > 
  > # 2-way Clustered, fe1 fe2
  > VCOV_raw = est$cov.iid / est$sigma2
  > M_i  = vcovClust(est$fixef_id$fe1, VCOV_raw, scores = est$scores, adj = FALSE)
  > M_t  = vcovClust(est$fixef_id$fe2, VCOV_raw, scores = est$scores, adj = FALSE)
  > M_it = vcovClust(paste(base$fe1, base$fe2), VCOV_raw, scores = est$scores, adj = FALSE, do.unclass = TRUE)
  > 
  > M_i + M_t - M_it
              [,1]
  [1,] 0.005391594
  > vcov(est, se = "two", ssc = ssc(K.adj = FALSE, G.adj = FALSE))
              x
  x 0.005391594
  > 
  > for(cdf in c("conventional", "min")){
  +   for(tdf in c("conventional", "min")){
  +     for(k_val in c("none", "nonnested", "full")){
  +       for(c_adj in c(FALSE, TRUE)){
  +         for(adj in c(FALSE, TRUE)){
  + 
  +           K = switch(k_val, none = 1, nonnested = 2, full = 8)
  + 
  +           if(c_adj){
  +             if(cdf == "min"){
  +               V = (M_i + M_t - M_it) * 3/2
  +             } else {
  +               V = M_i  * 3/2 + M_t * 5/4 - M_it * 6/5
  +             }
  +           } else {
  +             V = M_i + M_t - M_it
  +           }
  + 
  +           df = ifelse(tdf == "min", 2, 20 - K)
  +           my_adj = ifelse(adj, (n - 1) / (n - K), 1)
  + 
  +           # test SE
  +           test(vcov(est, se = "two", ssc = ssc(K.adj = adj, K.fixef = k_val, G.adj = c_adj, G.df = cdf)),
  +              V * my_adj)
  + 
  +           # test pvalue
  +           my_tstat = tstat(est, se = "two", ssc = ssc(K.adj = adj, K.fixef = k_val, G.adj = c_adj, G.df = cdf))
  +           test(pvalue(est, se = "two", ssc = ssc(K.adj = adj, K.fixef = k_val, G.adj = c_adj, G.df = cdf, t.df = tdf)),
  +              2*pt(-abs(my_tstat), df))
  + 
  +           # cat("adj = ", adj, " ; K.fixef = ", k_val, " ; G.adj = ", c_adj, " t.df = ", tdf, "\n", sep = "")
  +         }
  +       }
  +     }
  +   }
  + }
  > 
  > 
  > #
  > # Comparison with sandwich and plm
  > #
  > 
  > # Data generation
  > set.seed(0)
  > N = 20 ; G = N / 5 ; T = N / G
  > d = data.frame(y = rnorm(N), x = rnorm(N), grp = rep(1:G, T), tm = rep(1:T, each = G))
  > 
  > # Estimations
  > est_lm    = lm(y ~ x + as.factor(grp) + as.factor(tm), data=d)
  > est_feols = feols(y ~ x | grp + tm, data = d, vcov = "cluster")
  > 
  > #
  > # Standard
  > #
  > 
  > test(se(est_feols, se = "st")["x"], se(est_lm)["x"])
  > 
  > #
  > # Heteroskedasticity-robust
  > #
  > 
  > se_white_lm_HC1 = sqrt(vcovHC(est_lm, type = "HC1")["x", "x"])
  > se_white_lm_HC0 = sqrt(vcovHC(est_lm, type = "HC0")["x", "x"])
  > 
  > test(se(est_feols, se = "hetero"), se_white_lm_HC1)
  > test(se(est_feols, se = "hetero", ssc = ssc(K.adj = FALSE, G.adj = FALSE)), se_white_lm_HC0)
  > 
  > 
  > #
  > # Clustered
  > #
  > 
  > # Clustered by grp
  > se_CL_grp_lm_HC1 = sqrt(vcovCL(est_lm, cluster = d$grp, type = "HC1")["x", "x"])
  > se_CL_grp_lm_HC0 = sqrt(vcovCL(est_lm, cluster = d$grp, type = "HC0")["x", "x"])
  > 
  > # How to get the lm
  > test(se(est_feols, ssc = ssc(K.fixef = "full")), se_CL_grp_lm_HC1)
  > test(se(est_feols, ssc = ssc(K.adj = FALSE, K.fixef = "full")), se_CL_grp_lm_HC0)
  > 
  > #
  > # Two way
  > #
  > 
  > # Clustered by grp & tm
  > se_CL_2w_lm    = sqrt(vcovCL(est_lm, cluster = ~ grp + tm, type = "HC1")["x", "x"])
  > se_CL_2w_feols = se(est_feols, se = "twoway")
  > 
  > test(se(est_feols, se = "twoway", ssc = ssc(K.fixef = "full", G.df = "conv")), se_CL_2w_lm)
  > 
  > # 
  > # HC2/HC3
  > #
  > 
  > # HC2/HC3
  > base = iris
  > base$w = runif(nrow(base))
  > 
  > est_feols = feols(Sepal.Length ~ Sepal.Width | Species, base)
  > est_lm = lm(Sepal.Length ~ Sepal.Width + factor(Species), base)
  > 
  > test(
  +   vcov(est_feols, "hc2", ssc = ssc(K.adj = FALSE, G.adj = FALSE)),
  +   sandwich::vcovHC(est_lm, "HC2")["Sepal.Width", "Sepal.Width"]
  + )
  > 
  > test(
  +   vcov(est_feols, "hc3", ssc = ssc(K.adj = FALSE, G.adj = FALSE)),
  +   sandwich::vcovHC(est_lm, "HC3")["Sepal.Width", "Sepal.Width"]
  + )
  > 
  > # HC2/HC3 with weights
  > base = iris
  > base$w = runif(nrow(base))
  > 
  > est_feols = feols(Sepal.Length ~ Sepal.Width | Species, base, weights = ~ w)
  > est_lm = lm(Sepal.Length ~ Sepal.Width + factor(Species), base, weights = base$w)
  > 
  > test(
  +   vcov(est_feols, "hc2", ssc = ssc(K.adj = FALSE, G.adj = FALSE)),
  +   sandwich::vcovHC(est_lm, "HC2")["Sepal.Width", "Sepal.Width"]
  + )
  > 
  > test(
  +   vcov(est_feols, "hc3", ssc = ssc(K.adj = FALSE, G.adj = FALSE)),
  +   sandwich::vcovHC(est_lm, "HC3")["Sepal.Width", "Sepal.Width"]
  + )
  > 
  > # HC2/HC3 with GLM
  > base$Sepal.Length = floor(base$Sepal.Length)
  > est_feglm = feglm(
  +   Sepal.Length ~ Sepal.Width | Species, base, 
  +   "poisson", weights = ~ w
  + )
  > est_glm = glm(
  +   Sepal.Length ~ Sepal.Width + factor(Species), base, 
  +   family = poisson(), weights = base$w
  + )
  > 
  > test(
  +   vcov(est_feglm, "hc2", ssc = ssc(K.adj = FALSE, G.adj = FALSE)),
  +   sandwich::vcovHC(est_glm, "HC2")["Sepal.Width", "Sepal.Width"]
  + )
  > test(
  +   vcov(est_feglm, "hc3", ssc = ssc(K.adj = FALSE, G.adj = FALSE)),
  +   sandwich::vcovHC(est_glm, "HC3")["Sepal.Width", "Sepal.Width"]
  + )
  > 
  > # Fail when P_ii = 1
  > base$Species = as.character(base$Species)
  > base[1, "Species"] = "foo"
  > 
  > est_pii_singular = feols(Sepal.Length ~ Sepal.Width | Species, base, fixef.rm = "none")
  > 
  > test(vcov(est_pii_singular, "hc2"), "err")
  > 
  > #
  > # Newey-west + Driscoll-Kraay
  > #
  > 
  > if(requireNamespace("plm", quietly = TRUE)){
  +   
  +   # library(plm)
  +   data(Grunfeld, package = "plm")
  +   
  +   est_panel_plm = plm::plm(inv ~ capital + as.factor(year), Grunfeld, 
  +                            index = c("firm", "year"), model = "within")
  +   est_panel_feols = feols(inv ~ capital | firm + year, Grunfeld, 
  +                           panel.id = ~firm + year)
  +   
  +   # NW
  +   se_plm_NW = se(plm::vcovNW(est_panel_plm))["capital"]
  +   test(se(est_panel_feols, vcov = NW ~ ssc(adj = FALSE, cluster.adj = FALSE)), se_plm_NW)
  + 
  +   # DK
  +   se_plm_DK = se(plm::vcovSCC(est_panel_plm))["capital"]
  +   test(se(est_panel_feols, vcov = DK ~ ssc(adj = FALSE, cluster.adj = FALSE)), se_plm_DK)
  + }
  > 
  > #
  > # Checking the calls work properly
  > #
  > 
  > data(trade)
  > 
  > est_pois = femlm(Euros ~ log(dist_km)|Origin+Destination, trade)
  > 
  > se_clust = se(est_pois, se = "cluster", cluster = "Product")
  > test(se(est_pois, cluster = trade$Product), se_clust)
  > test(se(est_pois, cluster = ~Product), se_clust)
  > 
  > se_two = se(est_pois, se = "twoway", cluster = trade[, c("Product", "Destination")])
  > test(se_two, se(est_pois, cluster = c("Product", "Destination")))
  > test(se_two, se(est_pois, cluster = ~Product+Destination))
  > 
  > se_clu_comb = se(est_pois, cluster = "Product^Destination")
  > test(se_clu_comb, se(est_pois, cluster = paste(trade$Product, trade$Destination)))
  > test(se_clu_comb, se(est_pois, cluster = ~Product^Destination))
  > 
  > se_two_comb = se(est_pois, cluster = c("Origin^Destination", "Product"))
  > test(se_two_comb, se(est_pois, cluster = list(paste(trade$Origin, trade$Destination), 
  +                                               trade$Product)))
  > test(se_two_comb, se(est_pois, cluster = ~Origin^Destination + Product))
  > 
  > # With cluster removed
  > base = trade
  > base$Euros[base$Origin == "FR"] = 0
  > est_pois = femlm(Euros ~ log(dist_km)|Origin+Destination, base)
  > 
  > se_clust = se(est_pois, se = "cluster", cluster = "Product")
  > test(se(est_pois, cluster = base$Product), se_clust)
  > test(se(est_pois, cluster = ~Product), se_clust)
  > 
  > se_two = se(est_pois, se = "twoway", cluster = base[, c("Product", "Destination")])
  > test(se_two, se(est_pois, cluster = c("Product", "Destination")))
  > test(se_two, se(est_pois, cluster = ~Product+Destination))
  > 
  > se_clu_comb = se(est_pois, cluster = "Product^Destination")
  > test(se_clu_comb, se(est_pois, cluster = paste(base$Product, base$Destination)))
  > test(se_clu_comb, se(est_pois, cluster = ~Product^Destination))
  > 
  > se_two_comb = se(est_pois, cluster = c("Origin^Destination", "Product"))
  > test(se_two_comb, se(est_pois, cluster = list(paste(base$Origin, base$Destination), 
  +                                               base$Product)))
  > test(se_two_comb, se(est_pois, cluster = ~Origin^Destination + Product))
  > 
  > # With cluster removed and NAs
  > base = trade
  > base$Euros[base$Origin == "FR"] = 0
  > base$Euros_na = base$Euros ; base$Euros_na[sample(nrow(base), 50)] = NA
  > base$Destination_na = base$Destination ; base$Destination_na[sample(nrow(base), 50)] = NA
  > base$Origin_na = base$Origin ; base$Origin_na[sample(nrow(base), 50)] = NA
  > base$Product_na = base$Product ; base$Product_na[sample(nrow(base), 50)] = NA
  > 
  > est_pois = femlm(Euros ~ log(dist_km)|Origin+Destination_na, base)
  > 
  > se_clust = se(est_pois, se = "cluster", cluster = "Product")
  > test(se(est_pois, cluster = base$Product), se_clust)
  > test(se(est_pois, cluster = ~Product), se_clust)
  > 
  > se_two = se(est_pois, se = "twoway", cluster = base[, c("Product", "Destination")])
  > test(se_two, se(est_pois, cluster = c("Product", "Destination")))
  > test(se_two, se(est_pois, cluster = ~Product+Destination))
  > 
  > se_clu_comb = se(est_pois, cluster = "Product^Destination")
  > test(se_clu_comb, se(est_pois, cluster = paste(base$Product, base$Destination)))
  > test(se_clu_comb, se(est_pois, cluster = ~Product^Destination))
  > 
  > se_two_comb = se(est_pois, cluster = c("Origin^Destination", "Product"))
  > test(se_two_comb, se(est_pois, cluster = list(paste(base$Origin, base$Destination), 
  +                                               base$Product)))
  > test(se_two_comb, se(est_pois, cluster = ~Origin^Destination + Product))
  corrupted double-linked list
  Aborted