gsDesignTune uses {future} (via {future.apply}) for parallel
evaluation. You control parallelism using future::plan();
gsDesignTune does not set a global plan.
plan(multisession, workers = 2)
job <- gsSurvTune(
k = 3,
test.type = 4,
alpha = 0.025,
beta = 0.10,
timing = tune_values(list(c(0.33, 0.67, 1), c(0.5, 0.75, 1))),
hr = tune_seq(0.60, 0.75, length_out = 5),
upper = SpendingFamily$new(
SpendingSpec$new(sfLDOF, par = tune_fixed(0)),
SpendingSpec$new(sfHSD, par = tune_seq(-4, 4, length_out = 5))
),
lower = SpendingSpec$new(sfLDOF, par = tune_fixed(0)),
lambdaC = log(2) / 6,
eta = 0.01,
gamma = c(2.5, 5, 7.5, 10),
R = c(2, 2, 2, 6),
T = 18,
minfup = 6,
ratio = 1
)
job$run(strategy = "grid", parallel = TRUE, seed = 1, cache_dir = "gstune_cache")plan(sequential)
job <- gsDesignTune(
k = 3,
test.type = 2,
alpha = 0.025,
beta = 0.10,
timing = tune_values(list(c(0.33, 0.67, 1), c(0.5, 0.75, 1))),
upper = SpendingFamily$new(
SpendingSpec$new(sfLDOF, par = tune_fixed(0)),
SpendingSpec$new(sfHSD, par = tune_seq(-4, 4, length_out = 5))
)
)
job$run(strategy = "random", n = 5, parallel = FALSE, seed = 123)
head(job$results())
#> upper_setting timing upper_fun upper_par config_id status error_message
#> 1 function.... 0.5, 0.75, 1 sfHSD -2 1 ok <NA>
#> 2 function.... 0.5, 0.75, 1 sfHSD -2 2 ok <NA>
#> 3 function.... 0.5, 0.75, 1 sfHSD -4 3 ok <NA>
#> 4 function.... 0.33, 0..... sfHSD -2 4 ok <NA>
#> 5 function.... 0.5, 0.75, 1 sfHSD 0 5 ok <NA>
#> warnings cache_key design_rds call_args k test.type
#> 1 <NA> 5f3477818e4b3d8aa311e1fbb7a6a7b1 <NA> 3, 2, 0..... 3 2
#> 2 <NA> 5f3477818e4b3d8aa311e1fbb7a6a7b1 <NA> 3, 2, 0..... 3 2
#> 3 <NA> 7219e796e829ee3dada9e4a89f402313 <NA> 3, 2, 0..... 3 2
#> 4 <NA> 9840c7e70c1c5d8e2bb9d61598a94588 <NA> 3, 2, 0..... 3 2
#> 5 <NA> 3eef87b53a12633d40cfa1626d1792c5 <NA> 3, 2, 0..... 3 2
#> alpha beta n_I final_n_I upper_z lower_z upper_p
#> 1 0.025 0.1 0.523934.... 1.047869 2.4717, .... -2.4717,.... 0.0067, ....
#> 2 0.025 0.1 0.523934.... 1.047869 2.4717, .... -2.4717,.... 0.0067, ....
#> 3 0.025 0.1 0.509227.... 1.018456 2.75, 2..... -2.75, -.... 0.003, 0....
#> 4 0.025 0.1 0.344287.... 1.043295 2.6821, .... -2.6821,.... 0.0037, ....
#> 5 0.025 0.1 0.555438.... 1.110878 2.2414, .... -2.2414,.... 0.0125, ....
#> lower_p power en upper_name lower_name
#> 1 0.0067, .... 0.8999999 1.037208.... Hwang-Shih-DeCani Hwang-Shih-DeCani
#> 2 0.0067, .... 0.8999999 1.037208.... Hwang-Shih-DeCani Hwang-Shih-DeCani
#> 3 0.003, 0.... 0.9000000 1.012404.... Hwang-Shih-DeCani Hwang-Shih-DeCani
#> 4 0.0037, .... 0.9000000 1.033104.... Hwang-Shih-DeCani Hwang-Shih-DeCani
#> 5 0.0125, .... 0.9000000 1.093520.... Hwang-Shih-DeCani Hwang-Shih-DeCani
#> bound_summary final_n max_n upper_z1 lower_z1
#> 1 c("IA 1:.... 1.047869 1.047869 2.4717 -2.4717
#> 2 c("IA 1:.... 1.047869 1.047869 2.4717 -2.4717
#> 3 c("IA 1:.... 1.018456 1.018456 2.7500 -2.7500
#> 4 c("IA 1:.... 1.043295 1.043295 2.6821 -2.6821
#> 5 c("IA 1:.... 1.110878 1.110878 2.2414 -2.2414