The wildwyoung package computes Westfall-Young
multiple-hypothesis-adjusted p-values for objects of type fixest and
fixest_multi from the fixest package via a wild (cluster) bootstrap.
At its current stage, the package is experimental and it is not
thoroughly tested.
Because the bootstrap-resampling is based on the
fwildclusterboot
package, wildwyoung is usually really fast.
The package is complementary to wildwrolf, which implements the multiple hypothesis adjustment method following Romano and Wolf (2005).
You can install the development version from GitHub with:
# install.packages("devtools")
devtools::install_github("s3alfisc/wyoung")
# from r-universe (windows & mac, compiled R > 4.0 required)
install.packages('wyoung', repos ='https://s3alfisc.r-universe.dev')library(wildwyoung)
library(wildrwolf)
library(fixest)
set.seed(43)
N <- 1000
X1 <- rnorm(N)
X2 <- rnorm(N)
rho <- 0.5
sigma <- matrix(rho, 4, 4); diag(sigma) <- 1
u <- MASS::mvrnorm(n = N, mu = rep(0, 4), Sigma = sigma)
Y1 <- 1 + 1 * X1 + X2
Y2 <- 1 + 0.01 * X1 + X2
Y3 <- 1 + 0.4 * X1 + X2
Y4 <- 1 + -0.02 * X1 + X2
for(x in 1:4){
var_char <- paste0("Y", x)
assign(var_char, get(var_char) + u[,x])
}
# intra-cluster correlation of 0 for all clusters
#numb_clusters <- N / 50
#group_id <- as.character(sample(1:numb_clusters, N, replace = TRUE))
data <- data.frame(Y1 = Y1,
Y2 = Y2,
Y3 = Y3,
Y4 = Y4,
X1 = X1,
X2 = X2,
#group_id = group_id,
splitvar = sample(1:2, N, TRUE))
fit <- feols(c(Y1, Y2, Y3, Y4) ~ csw(X1,X2),
data = data,
se = "hetero",
ssc = ssc(cluster.adj = TRUE))
rm(list= ls()[!(ls() %in% c('fit','data'))])
res_wyoung <- wildwyoung::wyoung(
models = fit,
param = "X1",
B = 9999,
seed = 23
)
#> | | | 0% | |========= | 12% | |================== | 25% | |========================== | 38% | |=================================== | 50% | |============================================ | 62% | |==================================================== | 75% | |============================================================= | 88% | |======================================================================| 100%
res_rwolf <- wildrwolf::rwolf(
models = fit,
param = "X1",
B = 9999,
seed = 23
)
#> | | | 0% | |========= | 12% | |================== | 25% | |========================== | 38% | |=================================== | 50% | |============================================ | 62% | |==================================================== | 75% | |============================================================= | 88% | |======================================================================| 100%
pvals <- lapply(fit, function(x) pvalue(x)["X1"]) |> unlist()
# Westfall-Young Corrected P-values
summary(res_wyoung)
#> model Estimate Std. Error t value Pr(>|t|) WY Pr(>|t|)
#> 1 1 1.068297 0.04609664 23.17516 2.065929e-95 0.0000000
#> 2 2 1.016816 0.03148187 32.29848 3.359662e-157 0.0000000
#> 3 3 0.02840066 0.04533561 0.6264537 0.5311606 0.8793879
#> 4 4 -0.02080291 0.03160324 -0.6582525 0.5105279 0.8793879
#> 5 5 0.4136845 0.04599385 8.994343 1.170761e-18 0.0000000
#> 6 6 0.3617969 0.03049297 11.86493 1.812883e-30 0.0000000
#> 7 7 0.05546035 0.0434969 1.275042 0.2025913 0.5940594
#> 8 8 0.006285337 0.03169741 0.1982918 0.8428572 0.8793879
# Romano-Wolf Corrected P-values
summary(res_rwolf)
#> model Estimate Std. Error t value Pr(>|t|) RW Pr(>|t|)
#> 1 1 1.068297 0.04609664 23.17516 2.065929e-95 0.0001
#> 2 2 1.016816 0.03148187 32.29848 3.359662e-157 0.0001
#> 3 3 0.02840066 0.04533561 0.6264537 0.5311606 0.8850
#> 4 4 -0.02080291 0.03160324 -0.6582525 0.5105279 0.8850
#> 5 5 0.4136845 0.04599385 8.994343 1.170761e-18 0.0001
#> 6 6 0.3617969 0.03049297 11.86493 1.812883e-30 0.0001
#> 7 7 0.05546035 0.0434969 1.275042 0.2025913 0.5943
#> 8 8 0.006285337 0.03169741 0.1982918 0.8428572 0.8850
# Holm Corrected P-values
p.adjust(pvals, method = "holm") |> round(4)
#> X1 X1 X1 X1 X1 X1 X1 X1
#> 0.0000 0.0000 1.0000 1.0000 0.0000 0.0000 0.8104 1.0000The above procedures with S=8 hypotheses, N=5000 observations and
k %in% (1,2) parameters finish each in around 3.5 seconds.
if(requireNamespace("microbenchmark")){
microbenchmark::microbenchmark(
"Westfall-Young" = wildwyoung::wyoung(
models = fit,
param = "X1",
B = 9999,
seed = 23
),
"Romano-Wolf" = wildrwolf::rwolf(
models = fit,
param = "X1",
B = 9999,
seed = 23
),
times = 1
)
# t: seconds
# expr min lq mean median uq max neval
# Westfall-Young 3.625710 3.625710 3.625710 3.625710 3.625710 3.625710 1
# Romano-Wolf 3.382969 3.382969 3.382969 3.382969 3.382969 3.382969 1
}
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