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tqiantqian
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Add alpha and alpha_se to the output
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‎analysis_code/primary_and_secondary_analysis.R‎

Lines changed: 71 additions & 9 deletions
Original file line numberDiff line numberDiff line change
@@ -37,6 +37,12 @@
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#
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# 1. Changed from one-sided test to two-sided test in all t-tests
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40+
####################################
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# Update by Tianchen Qian, 2019/3/21
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#
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# 1. Add alpha and alpha_se to the output.
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library(rootSolve) # for solver function multiroot()
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@@ -426,7 +432,9 @@ SARA_primary_hypothesis_1 <- function(
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beta_se = as.numeric(result$beta_se_ssa),
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test_stat = as.numeric(result$test_result_t$test_stat),
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critical_value = result$test_result_t$critical_value,
429-
p_value = as.numeric(result$test_result_t$p_value))
435+
p_value = as.numeric(result$test_result_t$p_value),
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alpha = as.numeric(result$alpha_hat),
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alpha_se = as.numeric(result$alpha_se_ssa))
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return(output)
431439
}
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@@ -522,7 +530,9 @@ SARA_primary_hypothesis_2 <- function(
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beta_se = as.numeric(result$beta_se_ssa),
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test_stat = as.numeric(result$test_result_t$test_stat),
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critical_value = result$test_result_t$critical_value,
525-
p_value = as.numeric(result$test_result_t$p_value))
533+
p_value = as.numeric(result$test_result_t$p_value),
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alpha = as.numeric(result$alpha_hat),
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alpha_se = as.numeric(result$alpha_se_ssa))
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return(output)
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}
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@@ -591,14 +601,16 @@ SARA_exploratory_analysis <- function(
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avail_var = avail_var,
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prob_treatment = prob_treatment,
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significance_level = significance_level)
594-
output <- list(beta = result$beta_hat,
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beta_se = result$beta_se_ssa,
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output <- list(beta = as.numeric(result$beta_hat),
605+
beta_se = as.numeric(result$beta_se_ssa),
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test_stat_t = as.numeric(result$test_result_t$test_stat),
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critical_value_t = result$test_result_t$critical_value,
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p_value_t = as.numeric(result$test_result_t$p_value),
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test_stat_f = result$test_result_f$test_stat,
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critical_value_f = result$test_result_f$critical_value,
601-
p_value_f = result$test_result_f$p_value)
611+
p_value_f = result$test_result_f$p_value,
612+
alpha = as.numeric(result$alpha_hat),
613+
alpha_se = as.numeric(result$alpha_se_ssa))
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return(output)
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}
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@@ -704,14 +716,16 @@ SARA_exploratory_analysis_general_F_test <- function(
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p_value_f <- pf(test_stat_f, df1 = p1, df2 = n-q-p, lower.tail = FALSE)
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706718

707-
output <- list(beta = result$beta_hat,
708-
beta_se = result$beta_se_ssa,
719+
output <- list(beta = as.numeric(result$beta_hat),
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beta_se = as.numeric(result$beta_se_ssa),
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test_stat_t = as.numeric(result$test_result_t$test_stat),
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critical_value_t = result$test_result_t$critical_value,
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p_value_t = as.numeric(result$test_result_t$p_value),
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test_stat_f = test_stat_f,
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critical_value_f = critical_value_f,
714-
p_value_f = p_value_f)
726+
p_value_f = p_value_f,
727+
alpha = as.numeric(result$alpha_hat),
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alpha_se = as.numeric(result$alpha_se_ssa))
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return(output)
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}
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@@ -791,6 +805,12 @@ if (0) {
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$p_value
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[1] 1.022584e-14
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$alpha
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[1] -0.4286552699 0.0001338947 0.0022651338
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$alpha_se
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[1] 0.0271394442 0.0292520053 0.0003041147
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794814
>
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> # primary hypothesis 2
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> SARA_primary_hypothesis_2(dta, control_var = c("Y_lag1", "at_tapcount_lag1"), survey_completion_var = "Y")
@@ -809,6 +829,12 @@ if (0) {
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$p_value
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[1] 0.7238992
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832+
$alpha
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[1] -0.1914996030 -0.0078097155 0.0001918152
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$alpha_se
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[1] 0.0287537133 0.0294240517 0.0003142004
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812838
>
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> # exploratory analysis
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> SARA_exploratory_analysis(dta, control_var = c("Y_lag1", "at_tapcount_lag1"), moderator = "Y_lag1")
@@ -838,6 +864,12 @@ if (0) {
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$p_value_f
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[1] 1.81756e-23
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$alpha
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[1] -0.522625830 0.121815023 0.002169604
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$alpha_se
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[1] 0.0449813807 0.0515054456 0.0003145783
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> # this will give the same F-test result as SARA_exploratory_analysis()
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> SARA_exploratory_analysis_general_F_test(dta, control_var = c("Y_lag1", "at_tapcount_lag1"), moderator = "Y_lag1", F_test_L = diag(2))
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$beta
@@ -866,6 +898,12 @@ if (0) {
866898
$p_value_f
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[1] 1.81756e-23
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$alpha
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[1] -0.522625830 0.121815023 0.002169604
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904+
$alpha_se
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[1] 0.0449813807 0.0515054456 0.0003145783
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869907
> SARA_exploratory_analysis_general_F_test(dta, control_var = c("Y_lag1", "at_tapcount_lag1"), moderator = "Y_lag1", F_test_L = rep(1, 2))
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$beta
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Intercept Y_lag1
@@ -893,6 +931,12 @@ if (0) {
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$p_value_f
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[1] 6.113323e-09
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$alpha
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[1] -0.522625830 0.121815023 0.002169604
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937+
$alpha_se
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[1] 0.0449813807 0.0515054456 0.0003145783
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896940
>
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>
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> ### create fake availability indicator, and try the three analysis functions with availability ###
@@ -916,6 +960,12 @@ if (0) {
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$p_value
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[1] 0.03407131
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$alpha
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[1] -0.32417513 -0.03184334 0.00195437
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$alpha_se
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[1] 0.0563985798 0.0640324410 0.0006506912
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919969
> SARA_primary_hypothesis_2(dta2, control_var = c("Y_lag1", "at_tapcount_lag1"), survey_completion_var = "Y", avail_var = "avail")
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$beta
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[1] 0.02120382
@@ -932,6 +982,12 @@ if (0) {
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$p_value
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[1] 0.6068408
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$alpha
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[1] -0.2131429183 0.0033599799 0.0000873171
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988+
$alpha_se
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[1] 0.0485193353 0.0605234082 0.0008457411
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935991
> SARA_exploratory_analysis(dta2, control_var = c("Y_lag1", "at_tapcount_lag1"), moderator = "Y_lag1", avail_var = "avail")
936992
$beta
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Intercept Y_lag1
@@ -958,4 +1014,10 @@ if (0) {
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9591015
$p_value_f
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[1] 0.01252342
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}
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$alpha
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[1] -0.352447929 0.006808953 0.001907070
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1021+
$alpha_se
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[1] 0.0873681644 0.1024315444 0.0006620606
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}

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