.Rhistory

sol_iv <- iv(problem, w, Z, prior_table = 10, M = 1000)
summary(sol_iv)
source('Z:/docs/ProjectsOfficial/papers/WPP/package/distrib/CausalFX/R/iv.R')
source('Z:/docs/ProjectsOfficial/papers/WPP/package/distrib/CausalFX/R/iv.R')
summary(sol_iv)
source('Z:/docs/ProjectsOfficial/papers/WPP/package/distrib/CausalFX/R/iv.R')
source('Z:/docs/ProjectsOfficial/papers/WPP/package/distrib/CausalFX/R/iv.R')
?iv
?summary.iv
source('Z:/docs/ProjectsOfficial/papers/WPP/package/distrib/CausalFX/R/util.R')
source('Z:/docs/ProjectsOfficial/papers/WPP/package/distrib/CausalFX/R/util.R')
2+seq_len(10)
source('Z:/docs/ProjectsOfficial/papers/WPP/package/distrib/CausalFX/R/util.R')
?conditionTable
source('Z:/docs/ProjectsOfficial/papers/WPP/package/distrib/CausalFX/R/util.R')
source('Z:/docs/ProjectsOfficial/papers/WPP/package/distrib/CausalFX/R/util.R')
2^1000
2^20
problem <- simulateWitnessModel(p = 4, q = 4, par_max = 3, M = 1000)
covariate_hat <- covsearch(problem, cred_calc = TRUE, M = 1000)
source('Z:/docs/ProjectsOfficial/papers/WPP/package/distrib/CausalFX/R/util.R')
devtools::load_all(".")
covariate_hat <- covsearch(problem, cred_calc = TRUE, M = 1000)
names(covariate_hat)
names(covariate_hat$witness)
covariate_hat$witness
sol_ACE <- bindagCausalEffectBackdoor(problem, prior_table = 10, S = covariate_hat$Z[[1]])
sol_ACE
cat(sprintf("Estimated ACE = %1.2f\n", sol_ACE))
source('Z:/docs/ProjectsOfficial/papers/WPP/package/distrib/CausalFX/R/util.R')
sol_pop <- covsearch(problem, pop_solve = TRUE)
ffect_pop <- bindagCausalEffect(problem)
effect_pop <- bindagCausalEffect(problem)
effect_pop
source('Z:/docs/ProjectsOfficial/papers/WPP/package/distrib/CausalFX/R/util.R')
source('Z:/docs/ProjectsOfficial/papers/WPP/package/distrib/CausalFX/R/util.R')
source('Z:/docs/ProjectsOfficial/papers/WPP/package/distrib/CausalFX/R/util.R')
bindagCausalEffectBackdoor(problem, prior_table = 10, S = covariate_hat$Z[[1]])
fifi <- bindagCausalEffectBackdoor(problem, prior_table = 10, S = covariate_hat$Z[[1]])
fifi
source('Z:/docs/ProjectsOfficial/papers/WPP/package/distrib/CausalFX/R/util.R')
source('Z:/docs/ProjectsOfficial/papers/WPP/package/distrib/CausalFX/R/util.R')
source('Z:/docs/ProjectsOfficial/papers/WPP/package/distrib/CausalFX/R/util.R')
source('Z:/docs/ProjectsOfficial/papers/WPP/package/distrib/CausalFX/R/util.R')
source('Z:/docs/ProjectsOfficial/papers/WPP/package/distrib/CausalFX/R/util.R')
source('Z:/docs/ProjectsOfficial/papers/WPP/package/distrib/CausalFX/R/util.R')
source('Z:/docs/ProjectsOfficial/papers/WPP/package/distrib/CausalFX/R/wpp.R')
source('Z:/docs/ProjectsOfficial/papers/WPP/package/distrib/CausalFX/R/util.R')
devtools::load_all(".")
cfx
devtools::load_all(".")
library(rje)
library(graph)
library(igraph)
library(rscdd)
library(rcdd)
epsilons <- c(0.2, 0.2, 0.2, 0.2, 0.9, 1.1)
p <- 4
q <- 4
par_max <- 3
sample_size <- 1000
num_monte_carlo <- 1000
hardness_factor <- 0.10
N <- 1000; i <- 1
while (TRUE) {
problem <- simulateWitnessModel(p, q, par_max, sample_size)
effects <- synthetizeCausalEffect(problem)
out <- effects[[1]] - effects[[2]]
if (abs(out) > hardness_factor) break()
printPercentage(i, N)
i <- i + 1
}
sol_pop <- covariateSearch(problem, pop_solve = TRUE)
effect_pop <- synthetizeCausalEffect(problem)
cat(sprintf("ACE (true) = %1.2f\nACE (adjusting for all) = %1.2f\nACE (adjusting for nothing) = %1.2f\n",
effect_pop$effect_real, effect_pop$effect_naive, effect_pop$effect_naive2))
sol_pop <- covsearch(problem, pop_solve = TRUE)
effect_pop <- synthetizeCausalEffect(problem)
cat(sprintf("ACE (true) = %1.2f\nACE (adjusting for all) = %1.2f\nACE (adjusting for nothing) = %1.2f\n",
effect_pop$effect_real, effect_pop$effect_naive, effect_pop$effect_naive2))
covariate_hat <- covsearch(problem, cred_calc = TRUE, M = 1000)
summary(covariate_hat)
sol_iv <- iv(problem, covariate_hat$hw, covariate_hat$hZ, prior_table = 10, M = 1000)
summary(sol_iv)
wpp_sol <- wpp(problem, epsilons, M = 1000, prior_ind = 0.5, prior_table = 10, cred_calc = TRUE, analytical_bounds = TRUE, verbose = FALSE)
problem
class(problem)
wpp_sol <- wpp(problem, epsilons, M = 1000, prior_ind = 0.5, prior_table = 10, cred_calc = TRUE, analytical_bounds = TRUE, verbose = FALSE)
source('Z:/docs/ProjectsOfficial/papers/WPP/package/distrib/CausalFX/R/wpp.R')
wpp_sol <- wpp(problem, epsilons, M = 1000, prior_ind = 0.5, prior_table = 10, cred_calc = TRUE, analytical_bounds = TRUE, verbose = FALSE)
summary(wpp_sol)
summary(wpp_sol)
object$bounds
object$varnames[[object$witness[i]]]
object$varnams
object$varnames
debugSource('Z:/docs/ProjectsOfficial/papers/WPP/package/distrib/CausalFX/R/wpp.R')
summary(wpp_sol)
object$varnames
summary(wpp_sol)
object$varnames
object$problem$varnames
debugSource('Z:/docs/ProjectsOfficial/papers/WPP/package/distrib/CausalFX/R/wpp.R')
summary(wpp_sol)
source('Z:/docs/ProjectsOfficial/papers/WPP/package/distrib/CausalFX/R/wpp.R')
summary(wpp_sol)
summary(wpp_sol)
?CausalFX
?CausalFX
summary(wpp_sol)
?CausalFX
?iv
?covsearch
?wpp
wpp_sol <- wpp(problem, epsilons, M = 1000, prior_ind = 0.5, prior_table = 10, cred_calc = TRUE, analytical_bounds = TRUE, verbose = FALSE)
source('Z:/docs/ProjectsOfficial/papers/WPP/package/distrib/CausalFX/R/wpp.R')
wpp_sol <- wpp(problem, epsilons, M = 1000, prior_ind = 0.5, prior_table = 10, cred_calc = TRUE, analytical_bounds = TRUE, verbose = FALSE)
source('Z:/docs/ProjectsOfficial/papers/WPP/package/distrib/CausalFX/R/wpp.R')
wpp_sol <- wpp(problem, epsilons, M = 1000, prior_ind = 0.5, prior_table = 10, cred_calc = TRUE, analytical_bounds = TRUE, verbose = FALSE)
wpp_sol <- wpp(problem, epsilons, M = 1000, prior_ind = 0.5, prior_table = 10, cred_calc = TRUE, analytical_bounds = TRUE, verbose = TRUE)
wpp_sol <- wpp(problem, epsilons, M = 1000, prior_ind = 0.5, prior_table = 10, cred_calc = TRUE, analytical_bounds = TRUE, verbose = TRUE)
source('Z:/docs/ProjectsOfficial/papers/WPP/package/distrib/CausalFX/R/wpp.R')
wpp_sol <- wpp(problem, epsilons, M = 1000, prior_ind = 0.5, prior_table = 10, cred_calc = TRUE, analytical_bounds = TRUE, verbose = TRUE)
wpp_sol <- wpp(problem, epsilons, M = 1000, prior_ind = 0.5, prior_table = 10, cred_calc = TRUE, analytical_bounds = TRUE, verbose = FALSE)
wpp_sol <- wpp(problem, epsilons, M = 1000, prior_ind = 0.5, prior_table = 10, cred_calc = TRUE, analytical_bounds = TRUE, verbose = FALSE)
debugSource('Z:/docs/ProjectsOfficial/papers/WPP/package/distrib/CausalFX/R/wpp.R')
wpp_sol <- wpp(problem, epsilons, M = 1000, prior_ind = 0.5, prior_table = 10, cred_calc = TRUE, analytical_bounds = TRUE, verbose = FALSE)
debugSource('Z:/docs/ProjectsOfficial/papers/WPP/package/distrib/CausalFX/R/wpp.R')
debugSource('Z:/docs/ProjectsOfficial/papers/WPP/package/distrib/CausalFX/R/wpp.R')
wpp_sol <- wpp(problem, epsilons, M = 1000, prior_ind = 0.5, prior_table = 10, cred_calc = TRUE, analytical_bounds = TRUE, verbose = FALSE)
i
j
debugSource('Z:/docs/ProjectsOfficial/papers/WPP/package/distrib/CausalFX/R/wpp.R')
debugSource('Z:/docs/ProjectsOfficial/papers/WPP/package/distrib/CausalFX/R/wpp.R')
debugSource('Z:/docs/ProjectsOfficial/papers/WPP/package/distrib/CausalFX/R/wpp.R')
debugSource('Z:/docs/ProjectsOfficial/papers/WPP/package/distrib/CausalFX/R/wpp.R')
wpp_sol <- wpp(problem, epsilons, M = 1000, prior_ind = 0.5, prior_table = 10, cred_calc = TRUE, analytical_bounds = TRUE, verbose = FALSE)
debugSource('Z:/docs/ProjectsOfficial/papers/WPP/package/distrib/CausalFX/R/wpp.R')
wpp_sol <- wpp(problem, epsilons, M = 1000, prior_ind = 0.5, prior_table = 10, cred_calc = TRUE, analytical_bounds = TRUE, verbose = FALSE)
debugSource('Z:/docs/ProjectsOfficial/papers/WPP/package/distrib/CausalFX/R/wpp.R')
wpp_sol <- wpp(problem, epsilons, M = 1000, prior_ind = 0.5, prior_table = 10, cred_calc = TRUE, analytical_bounds = TRUE, verbose = FALSE)
debugSource('Z:/docs/ProjectsOfficial/papers/WPP/package/distrib/CausalFX/R/wpp.R')
wpp_sol <- wpp(problem, epsilons, M = 1000, prior_ind = 0.5, prior_table = 10, cred_calc = TRUE, analytical_bounds = TRUE, verbose = FALSE)
debugSource('Z:/docs/ProjectsOfficial/papers/WPP/package/distrib/CausalFX/R/wpp.R')
debugSource('Z:/docs/ProjectsOfficial/papers/WPP/package/distrib/CausalFX/R/wpp.R')
wpp_sol <- wpp(problem, epsilons, M = 1000, prior_ind = 0.5, prior_table = 10, cred_calc = TRUE, analytical_bounds = TRUE, verbose = FALSE)
source('Z:/docs/ProjectsOfficial/papers/WPP/package/distrib/CausalFX/R/wpp.R')
devtools::load_all(".")
library(igraph)
library(rje)
library(rcdd)
epsilons <- c(0.2, 0.2, 0.2, 0.2, 0.9, 1.1)
p <- 4
q <- 4
par_max <- 3
sample_size <- 1000
num_monte_carlo <- 1000
hardness_factor <- 0.10
N <- 1000; i <- 1
while (TRUE) {
problem <- simulateWitnessModel(p, q, par_max, sample_size)
effects <- synthetizeCausalEffect(problem)
out <- effects[[1]] - effects[[2]]
if (abs(out) > hardness_factor) break()
printPercentage(i, N)
i <- i + 1
}
# Test covariate search only
sol_pop <- covsearch(problem, pop_solve = TRUE)
effect_pop <- synthetizeCausalEffect(problem)
cat(sprintf("ACE (true) = %1.2f\nACE (adjusting for all) = %1.2f\nACE (adjusting for nothing) = %1.2f\n",
effect_pop$effect_real, effect_pop$effect_naive, effect_pop$effect_naive2))
covariate_hat <- covsearch(problem, cred_calc = TRUE, M = 1000)
summary(covariate_hat)
sol_iv <- iv(problem, covariate_hat$hw, covariate_hat$hZ, prior_table = 10, M = 1000)
summary(sol_iv)
wpp_sol <- wpp(problem, epsilons, M = 1000, prior_ind = 0.5, prior_table = 10, cred_calc = TRUE, analytical_bounds = TRUE, verbose = FALSE)
epsilons <- c(0.2, 0.2, 0.2, 0.2, 0.9, 1.1)
p <- 4
q <- 4
par_max <- 3
sample_size <- 1000
num_monte_carlo <- 1000
hardness_factor <- 0.10
N <- 1000; i <- 1
while (TRUE) {
problem <- simulateWitnessModel(p, q, par_max, sample_size)
effects <- synthetizeCausalEffect(problem)
out <- effects[[1]] - effects[[2]]
if (abs(out) > hardness_factor) break()
printPercentage(i, N)
i <- i + 1
}
# Test covariate search only
sol_pop <- covsearch(problem, pop_solve = TRUE)
effect_pop <- synthetizeCausalEffect(problem)
cat(sprintf("ACE (true) = %1.2f\nACE (adjusting for all) = %1.2f\nACE (adjusting for nothing) = %1.2f\n",
effect_pop$effect_real, effect_pop$effect_naive, effect_pop$effect_naive2))
covariate_hat <- covsearch(problem, cred_calc = TRUE, M = 1000)
summary(covariate_hat)
# Test IV analysis using result from covariate search
sol_iv <- iv(problem, covariate_hat$hw, covariate_hat$hZ, prior_table = 10, M = 1000)
sol_iv <- iv(problem, covariate_hat$hw, covariate_hat$hZ, prior_table = 10, M = 1000)
sol_iv
sol_iv <- iv(problem, covariate_hat$hw, covariate_hat$hZ, prior_table = 10, M = 1000)
covariate_hat$hw
covariate_hat$hw
sol_iv <- iv(problem, covariate_hat$hw,  covariate_hat$hZ, prior_table = 10, M = 1000)
covariate_hat$hw
covariate_hat$hZ
sol_iv <- iv(problem, covariate_hat$hw,  covariate_hat$hZ, prior_table = 10, M = 1000, verbose = TRUE)
sol_iv <- iv(problem, covariate_hat$hw,  covariate_hat$hZ, prior_table = 10, M = 1000, verbose = TRUE)
devtools::load_all(".")
debugSource('Z:/docs/ProjectsOfficial/papers/WPP/package/distrib/CausalFX/R/iv.R')
sol_iv <- iv(problem, covariate_hat$hw,  covariate_hat$hZ, prior_table = 10, M = 1000, verbose = TRUE)
wpp_sol <- wpp(problem, epsilons, M = 1000, prior_ind = 0.5, prior_table = 10, cred_calc = TRUE, analytical_bounds = TRUE, verbose = FALSE)
problem <- simulateWitnessModel(p, q, par_max, sample_size)
wpp_sol <- wpp(problem, epsilons, M = 1000, prior_ind = 0.5, prior_table = 10, cred_calc = TRUE, analytical_bounds = TRUE, verbose = FALSE)
problem <- simulateWitnessModel(p, q, par_max, sample_size)
wpp_sol <- wpp(problem, epsilons, M = 1000, prior_ind = 0.5, prior_table = 10, cred_calc = TRUE, analytical_bounds = TRUE, verbose = FALSE)
problem <- simulateWitnessModel(p, q, par_max, sample_size)
wpp_sol <- wpp(problem, epsilons, M = 1000, prior_ind = 0.5, prior_table = 10, cred_calc = TRUE, analytical_bounds = TRUE, verbose = FALSE)
problem <- simulateWitnessModel(p, q, par_max, sample_size)
wpp_sol <- wpp(problem, epsilons, M = 1000, prior_ind = 0.5, prior_table = 10, cred_calc = TRUE, analytical_bounds = TRUE, verbose = FALSE)
wpp_sol <- wpp(problem, epsilons, M = 1000, prior_ind = 0.5, prior_table = 10, cred_calc = TRUE, analytical_bounds = TRUE, verbose = TRUE)
source('Z:/docs/ProjectsOfficial/papers/WPP/package/distrib/CausalFX/R/wpp.R')
wpp_sol <- wpp(problem, epsilons, M = 1000, prior_ind = 0.5, prior_table = 10, cred_calc = TRUE, analytical_bounds = TRUE, verbose = TRUE)
source('Z:/docs/ProjectsOfficial/papers/WPP/package/distrib/CausalFX/R/wpp.R')
wpp_sol <- wpp(problem, epsilons, M = 1000, prior_ind = 0.5, prior_table = 10, cred_calc = TRUE, analytical_bounds = TRUE, verbose = TRUE)
wpp_sol <- wpp(problem, epsilons, M = 1000, prior_ind = 0.5, prior_table = 10, cred_calc = TRUE, analytical_bounds = TRUE, verbose = TRUE)
debugSource('Z:/docs/ProjectsOfficial/papers/WPP/package/distrib/CausalFX/R/wpp.R')
wpp_sol <- wpp(problem, epsilons, M = 1000, prior_ind = 0.5, prior_table = 10, cred_calc = TRUE, analytical_bounds = TRUE, verbose = TRUE)
wpp_sol <- wpp(problem, epsilons, M = 1000, prior_ind = 0.5, prior_table = 10, cred_calc = TRUE, analytical_bounds = TRUE, verbose = TRUE)
result_max_relax <- lpcdd(H, lpp_params$C, minimize = FALSE)
capture.output(result_max_relax <- lpcdd(H, lpp_params$C, minimize = FALSE))
try(result_max_relax <- lpcdd(H, lpp_params$C, minimize = FALSE), silent = TRUE)
capture.output(try(result_max_relax <- lpcdd(H, lpp_params$C, minimize = FALSE), silent = TRUE))
sink(try(result_max_relax <- lpcdd(H, lpp_params$C, minimize = FALSE), silent = TRUE))
sink(file = NULL)
try(result_max_relax <- lpcdd(H, lpp_params$C, minimize = FALSE), silent = TRUE)
sink()
cat("oi")
sink(file = NULL)
cat("oi")
sink()
?sink
sink(tempfile())
try(result_max_relax <- lpcdd(H, lpp_params$C, minimize = FALSE), silent = TRUE)
sink()
invisible(force(try(result_max_relax <- lpcdd(H, lpp_params$C, minimize = FALSE), silent = TRUE)))
sink(tempfile())
on.exit(sink())
invisible(force(try(result_max_relax <- lpcdd(H, lpp_params$C, minimize = FALSE), silent = TRUE)))
debugSource('Z:/docs/ProjectsOfficial/papers/WPP/package/distrib/CausalFX/R/wpp.R')
debugSource('Z:/docs/ProjectsOfficial/papers/WPP/package/distrib/CausalFX/R/wpp.R')
debugSource('Z:/docs/ProjectsOfficial/papers/WPP/package/distrib/CausalFX/R/wpp.R')
debugSource('Z:/docs/ProjectsOfficial/papers/WPP/package/distrib/CausalFX/R/wpp.R')
devtools::use_vignette("CausalFX")
install.packages("rmarkdown")
devtools::use_vignette("CausalFX")
devtools::load_all(".")
?CausalFX
?CausalFX
?rje
?CausalFX
source('Z:/docs/ProjectsOfficial/papers/WPP/package/distrib/CausalFX/R/wpp.R')
source('Z:/docs/ProjectsOfficial/papers/WPP/package/distrib/CausalFX/R/wpp.R')
?CausalFX
ff <- runif(3)
ff[1:3]
source('Z:/docs/ProjectsOfficial/papers/WPP/package/distrib/CausalFX/R/wpp.R')
devtools::load_all(".")
epsilons <- c(0.2, 0.2, 0.2, 0.2, 0.9, 1.1)
p <- 4
q <- 4
par_max <- 3
sample_size <- 1000
num_monte_carlo <- 1000
hardness_factor <- 0.10
N <- 1000; i <- 1
while (TRUE) {
problem <- simulateWitnessModel(p, q, par_max, sample_size)
effects <- synthetizeCausalEffect(problem)
out <- effects[[1]] - effects[[2]]
if (abs(out) > hardness_factor) break()
printPercentage(i, N)
i <- i + 1
}
wpp_sol <- wpp(problem, epsilons, M = 1000, prior_ind = 0.5, prior_table = 10, cred_calc = TRUE, analytical_bounds = TRUE, verbose = FALSE)
source('Z:/docs/ProjectsOfficial/papers/WPP/package/distrib/CausalFX/R/wpp.R')
wpp_sol <- wpp(problem, epsilons, M = 1000, prior_ind = 0.5, prior_table = 10, cred_calc = TRUE, analytical_bounds = TRUE, verbose = FALSE)
summary(wpp_sol)
?summary
?print
source('Z:/docs/ProjectsOfficial/papers/WPP/package/distrib/CausalFX/R/wpp.R')
devtools::load_all(".")
?print.summary
devtools::load_all(".")
?summary
source('Z:/docs/ProjectsOfficial/papers/WPP/package/distrib/CausalFX/R/wpp.R')
devtools::load_all(".")
library(igraph)
source('Z:/docs/ProjectsOfficial/papers/WPP/package/distrib/CausalFX/R/wpp.R')
?print
source('Z:/docs/ProjectsOfficial/papers/WPP/package/distrib/CausalFX/R/wpp.R')
source('Z:/docs/ProjectsOfficial/papers/WPP/package/distrib/CausalFX/R/wpp.R')
devtools::load_all(".")
source('Z:/docs/ProjectsOfficial/papers/WPP/package/distrib/CausalFX/R/wpp.R')
source('Z:/docs/ProjectsOfficial/papers/WPP/package/distrib/CausalFX/R/wpp.R')
source('Z:/docs/ProjectsOfficial/papers/WPP/package/distrib/CausalFX/R/wpp.R')
source('Z:/docs/ProjectsOfficial/papers/WPP/package/distrib/CausalFX/R/wpp.R')
devtools::load_all(".")
devtools::run_examples()
devtools::load_all(".")
devtools::load_all(".")
devtools::run_examples()
sol_iv <- iv(problem, w, Z, prior_table = 10, M = 1000)
while (TRUE) {
+   problem <- simulateWitnessModel(p = 4, q = 4, par_max = 3, M = 1000)
+   s <- covsearch(problem, pop_solve = TRUE)
+   if (length(s$witness) > 0) {
+     w <- s$witness[1]
+     Z <- s$Z[[1]]
+     break
+   }
+ }
validateData
sol_iv <- iv(problem, w, Z, prior_table = 10, M = 1000)
devtools::run_examples()
devtools::run_examples()
devtools::run_examples(iv)
devtools::run_examples("iv")
source('Z:/docs/ProjectsOfficial/papers/WPP/package/distrib/CausalFX/R/iv.R')
devtools::run_examples("iv")
devtools::run_examples(3)
devtools::run_examples(5)
?require
library(CausalFX)
devtools::run_examples()
library(igraph)
library(rje)
library(rcdd)
devtools::run_examples()
devtools::load_all(".")
?covsearch
p2 <- simulateWitnessModel(p = 4, q = 4, par_max = 3, M = 1000)
problem
sol_pop <- covsearch(p2, pop_solve = TRUE)
covariate_hat <- covsearch(p2, cred_calc = TRUE, M = 1000)
summary(p2)
p2
summary.covsearch(p2)
class(p2)
summary.covsearch(covariate_hat)
names(covariate_hat)
traceback()
summary.covsearch(covariate_hat)
source('Z:/docs/ProjectsOfficial/papers/WPP/package/distrib/CausalFX/R/search.R')
summary.covsearch(covariate_hat)
devtools::load_all(".")
devtools::run_examples()
validateDta
validateData
covariate_hat <- covsearch(problem, cred_calc = TRUE, M = 1000)
covariate_hat <- covsearch(p2, cred_calc = TRUE, M = 1000)
devtools::run_examples()
devtools::load_all(".")
devtools::run_examples()
?dev_tools:run_examples
?run_examples
library(CausalFX)
library(CausalFX)
?dev_tools:run_examples
?dev_tools:run_examples()
dev_tools:run_examples()
devtools:run_examples()
library(dev_tools)
library(devtools)
devtools:run_examples()
dev_tools:run_examples()
devtools::run_examples()
library(igraph)
library(rje)
library(rcdd)
devtools::run_examples()
p2 <- simulateWitnessModel(p = 4, q = 4, par_max = 3, M = 1000)
probl
epsilons <- c(0.2, 0.2, 0.2, 0.2, 0.95, 1.05)
sol_wpp <- wpp(problem, epsilons, M = 500)
sol_wpp <- wpp(p2, epsilons, M = 500)
source('Z:/docs/ProjectsOfficial/papers/WPP/package/distrib/CausalFX/R/wpp.R')
devtools::run_examples()
library(rje)
library(igraph)
library(rcdd)
devtools::run_examples()
devtools::run_examples()
devtools::run_examples()
devtools::run_examples()
devtools::run_examples()
devtools::run_examples()
source('Z:/docs/ProjectsOfficial/papers/WPP/package/distrib/CausalFX/R/wpp.R')
devtools::run_examples()
library(CausalFX)
devtools::run_examples()
devtools::run_examples()
devtools::run_examples()
source('Z:/docs/ProjectsOfficial/papers/WPP/package/distrib/CausalFX/R/iv.R')
source('Z:/docs/ProjectsOfficial/papers/WPP/package/distrib/CausalFX/R/iv.R')
source('Z:/docs/ProjectsOfficial/papers/WPP/package/distrib/CausalFX/R/iv.R')
source('Z:/docs/ProjectsOfficial/papers/WPP/package/distrib/CausalFX/R/iv.R')
return()
return
quantile(NULL, probs = seq(0, 1, 0.25), na.rm = TRUE)
source('Z:/docs/ProjectsOfficial/papers/WPP/package/distrib/CausalFX/R/iv.R')
source('Z:/docs/ProjectsOfficial/papers/WPP/package/distrib/CausalFX/R/iv.R')
devtools::run_examples()
devtools::run_examples()
source('Z:/docs/ProjectsOfficial/papers/WPP/package/distrib/CausalFX/R/iv.R')
devtools::run_examples()
devtools::load_all(".")
devtools::load_all(".")
devtools::run_examples()
source('Z:/docs/ProjectsOfficial/papers/WPP/package/distrib/CausalFX/R/iv.R')
source('Z:/docs/ProjectsOfficial/papers/WPP/package/distrib/CausalFX/R/search.R')
library(CausalFX)
f <- randn(10, 100)
f <- rnorm(10, 100)
f
f <- rnorm(1000)
f <- matrix(f, ncol = 1000)
f
f <- matrix(rnorm(1000), ncol = 100)
f
M <- f * t(f)
dim(f)
t(f)
M <- f %*% t(f)
M
m <- diag(M)
m
M / (t(m) %*% m)
M / (m %*% t(m)
)
M / sqrt(m %*% t(m)
)
M1 <- M / sqrt(m %*% t(m))
M1
inv(M1)
solve(M1)
R <- matrix(c(1, 0.5, 0.5, 1), ncol=2)
R
solve(R)
0.5^2 / (1 - 0.5^2)
R %*% solve(R)
solve(R) - matrix(c(1, 0, 0, 1), ncol=2)
det(solve(R) - matrix(c(1, 0, 0, 1), ncol=2))
68 * 0.4 + 0.4 * 72
68 * 0.6 + 0.4 * 72
0.6 * 85 + 0.4 * 81
0.6 * 85 + 0.4 * 82
source('Z:/docs/ProjectsOfficial/papers/WPP/package/CausalFX/R/search.R')
source('Z:/docs/ProjectsOfficial/papers/WPP/package/CausalFX/R/search.R')
source('Z:/docs/ProjectsOfficial/papers/WPP/package/CausalFX/R/search.R')
source('Z:/docs/ProjectsOfficial/papers/WPP/package/CausalFX/R/search.R')
source('Z:/docs/ProjectsOfficial/papers/WPP/package/CausalFX/R/search.R')
source('Z:/docs/ProjectsOfficial/papers/WPP/package/CausalFX/R/search.R')
devtools::build()
devtools::load_all(".")
library(ggplot2)
pwd()
setwd()
dir()
app_data <- read.table("../../WPP_final/dat/flu_data_tsr.dat", header = T, row.names = NULL)
app_data
cat("Data loaded, number of variables =", ncol(app_data), "\n")
var_names <- colnames(app_data)
app_data <- as.matrix(app_data)
app_data[which(app_data[, 4]  < 60), 4] <- 0 # "Age" is in column 4, we dichotomize it
app_data[which(app_data[, 4] >= 60), 4] <- 1
flu_cfx <- cfx(x = 2, y = 3, dat = app.data)
flu_cfx <- cfx(x = 2, y = 3, dat = app_data)
flu_cfx
var_names
num_monte_carlo <- 5000
epsilons <- c(0.2, 0.2, 0.2, 0.2, 0.9, 1.1)
sol_wpp <- wpp(flu_cfx, epsilons, M = num_monte_carlo, verbose = TRUE)
summary(sol_wpp)
data_alt <- as.numeric(matrix(runif(50000) > 0.5, nrow = 1000))
data_alt
data_alt <- (matrix(runif(50000) > 0.5, nrow = 1000))
data_alt
data_alt <- numeric(matrix(runif(50000) > 0.5, nrow = 1000))
data_alt <- (matrix(runif(50000) > 0.5, nrow = 1000) == TRUE
)
data_alt
dim(data_alt)
as.numeric(TRUE)
as.numeric(data_alt)
data_alt <- matrix(as.numeric(runif(50000) > 0.5), nrow = 1000)
data_alt
problem <- cfx(1, 2, dat = data_alt)
sol_wpp <- wpp(problem, epsilons, M = num_monte_carlo, verbose = TRUE)
summary(sol_wpp)
?CausalFX
?CausalFX
library(CausalFX)
?CausalFX
?wpp
ver()
version()
R.Version()