From cbb68acc50846ab6a79bef13eb0460b985fe3172 Mon Sep 17 00:00:00 2001 From: Patrick Kimes Date: Tue, 30 Oct 2018 20:08:04 -0400 Subject: [PATCH] Update text at top of some simulation Rmds --- .../simulations-informative-cosine.Rmd | 24 +++++++++-------- .../simulations-informative-cubic.Rmd | 24 +++++++++-------- .../simulations-informative-sine.Rmd | 26 ++++++++++--------- .../simulations-informative-step.Rmd | 24 +++++++++-------- .../simulations/simulations-uasettings-t.Rmd | 2 +- .../simulations/simulations-uasettings.Rmd | 2 +- .../simulations/simulations-varyingntests.Rmd | 4 +-- ...simulations-informative-step-nullAdaPT.Rmd | 6 ++--- .../simulations-uasettings-nonnull25.Rmd | 2 +- 9 files changed, 60 insertions(+), 54 deletions(-) diff --git a/datasets/simulations/simulations-informative-cosine.Rmd b/datasets/simulations/simulations-informative-cosine.Rmd index 5a40820..4e9f1fa 100644 --- a/datasets/simulations/simulations-informative-cosine.Rmd +++ b/datasets/simulations/simulations-informative-cosine.Rmd @@ -13,9 +13,11 @@ output: # Summary In this set of simulations, we consider settings with both null and non-null -tests with an informative covariate. The covariate is sampled uniformly from +tests with an informative and uninformative covariate. The informative covariate is sampled uniformly from the interval [0, 1], and the conditional probability of a test being non-null -is a smooth (cosine) function of the covariate. We include simulation results +is a smooth (cosine) function of the covariate. The uninformative covariate is simply +uninformly sampled independently from the interval [0, 1]. The uninformative covariate is included +as a baseline to compare the informative covariate against. We include simulation results again with Gaussian, t-distributed, and Chi-Squared distributed test statistics. # Workspace Setup @@ -94,8 +96,8 @@ First, we consider the setting with Gaussian test statistics. ## Data Simulation ```{r gauss-parameters} -es_dist <- rnorm_generator(3) # functional: dist of alternative test stats -ts_dist <- rnorm_perturber(1) # functional: sampling dist/noise for test stats +es_dist <- rnorm_generator(3) # functional: dist of alternative test stats +ts_dist <- rnorm_perturber(1) # functional: sampling dist/noise for test stats null_dist <- rnorm_2pvaluer(1) # functional: dist to calc p-values seed <- 608 ``` @@ -215,8 +217,8 @@ Next, we consider the setting with t-distributed test statistics. ## Data Simulation ```{r t5-parameters} -es_dist <- rnorm_generator(3) # functional: dist of alternative test stats -ts_dist <- rt_perturber(5) # functional: sampling dist/noise for test stats +es_dist <- rnorm_generator(3) # functional: dist of alternative test stats +ts_dist <- rt_perturber(5) # functional: sampling dist/noise for test stats null_dist <- rt_2pvaluer(5) # functional: dist to calc p-values seed <- 815 ``` @@ -345,9 +347,9 @@ Next, we consider a second setting with t-distributed test statistics. ## Data Simulation ```{r t11-parameters} -es_dist <- rnorm_generator(3) # functional: dist of alternative test stats -ts_dist <- rt_perturber(11) # functional: sampling dist/noise for test stats -null_dist <- rt_2pvaluer(11) # functional: dist to calc p-values +es_dist <- rnorm_generator(3) # functional: dist of alternative test stats +ts_dist <- rt_perturber(11) # functional: sampling dist/noise for test stats +null_dist <- rt_2pvaluer(11) # functional: dist to calc p-values seed <- 9158 ``` @@ -475,9 +477,9 @@ Finally, we consider the setting with chi-squared distributed test statistics. ## Data Simulation ```{r chisq4-parameters} -es_dist <- rnorm_generator(15) # functional: dist of alternative test stats +es_dist <- rnorm_generator(15) # functional: dist of alternative test stats ts_dist <- rchisq_perturber(4) # functional: sampling dist/noise for test stats -null_dist <- rchisq_pvaluer(4) # functional: dist to calc p-values +null_dist <- rchisq_pvaluer(4) # functional: dist to calc p-values seed <- 1023 ``` diff --git a/datasets/simulations/simulations-informative-cubic.Rmd b/datasets/simulations/simulations-informative-cubic.Rmd index 379474a..7700b34 100644 --- a/datasets/simulations/simulations-informative-cubic.Rmd +++ b/datasets/simulations/simulations-informative-cubic.Rmd @@ -13,9 +13,11 @@ output: # Summary In this set of simulations, we consider settings with both null and non-null -tests with an informative covariate. The covariate is sampled uniformly from +tests with an informative and uninformative covariate. The informative covariate is sampled uniformly from the interval [0, 1], and the conditional probability of a test being non-null -is a smooth (cubic) function of the covariate. We include simulation results +is a smooth (cubic) function of the covariate. The uninformative covariate is simply +uninformly sampled independently from the interval [0, 1]. The uninformative covariate is included +as a baseline to compare the informative covariate against. We include simulation results again with Gaussian, t-distributed, and Chi-Squared distributed test statistics. # Workspace Setup @@ -94,8 +96,8 @@ First, we consider the setting with Gaussian test statistics. ## Data Simulation ```{r gauss-parameters} -es_dist <- rnorm_generator(3) # functional: dist of alternative test stats -ts_dist <- rnorm_perturber(1) # functional: sampling dist/noise for test stats +es_dist <- rnorm_generator(3) # functional: dist of alternative test stats +ts_dist <- rnorm_perturber(1) # functional: sampling dist/noise for test stats null_dist <- rnorm_2pvaluer(1) # functional: dist to calc p-values seed <- 608 ``` @@ -215,8 +217,8 @@ Next, we consider the setting with t-distributed test statistics. ## Data Simulation ```{r t5-parameters} -es_dist <- rnorm_generator(3) # functional: dist of alternative test stats -ts_dist <- rt_perturber(5) # functional: sampling dist/noise for test stats +es_dist <- rnorm_generator(3) # functional: dist of alternative test stats +ts_dist <- rt_perturber(5) # functional: sampling dist/noise for test stats null_dist <- rt_2pvaluer(5) # functional: dist to calc p-values seed <- 815 ``` @@ -345,9 +347,9 @@ Next, we consider a second setting with t-distributed test statistics. ## Data Simulation ```{r t11-parameters} -es_dist <- rnorm_generator(3) # functional: dist of alternative test stats -ts_dist <- rt_perturber(11) # functional: sampling dist/noise for test stats -null_dist <- rt_2pvaluer(11) # functional: dist to calc p-values +es_dist <- rnorm_generator(3) # functional: dist of alternative test stats +ts_dist <- rt_perturber(11) # functional: sampling dist/noise for test stats +null_dist <- rt_2pvaluer(11) # functional: dist to calc p-values seed <- 9158 ``` @@ -475,9 +477,9 @@ Finally, we consider the setting with chi-squared distributed test statistics. ## Data Simulation ```{r chisq4-parameters} -es_dist <- rnorm_generator(15) # functional: dist of alternative test stats +es_dist <- rnorm_generator(15) # functional: dist of alternative test stats ts_dist <- rchisq_perturber(4) # functional: sampling dist/noise for test stats -null_dist <- rchisq_pvaluer(4) # functional: dist to calc p-values +null_dist <- rchisq_pvaluer(4) # functional: dist to calc p-values seed <- 1023 ``` diff --git a/datasets/simulations/simulations-informative-sine.Rmd b/datasets/simulations/simulations-informative-sine.Rmd index 26dc07b..a839241 100644 --- a/datasets/simulations/simulations-informative-sine.Rmd +++ b/datasets/simulations/simulations-informative-sine.Rmd @@ -13,9 +13,11 @@ output: # Summary In this set of simulations, we consider settings with both null and non-null -tests with an informative covariate. The covariate is sampled uniformly from +tests with an informative and uninformative covariate. The informative covariate is sampled uniformly from the interval [0, 1], and the conditional probability of a test being non-null -is a smooth (sine) function of the covariate. We include simulation results +is a smooth (sine) function of the covariate. The uninformative covariate is simply +uninformly sampled independently from the interval [0, 1]. The uninformative covariate is included +as a baseline to compare the informative covariate against. We include simulation results again with Gaussian, t-distributed, and Chi-Squared distributed test statistics. # Workspace Setup @@ -94,9 +96,9 @@ First, we consider the setting with Gaussian test statistics. ## Data Simulation ```{r gauss-parameters} -es_dist <- rnorm_generator(3) # functional: dist of alternative test stats -ts_dist <- rnorm_perturber(1) # functional: sampling dist/noise for test stats -null_dist <- rnorm_2pvaluer(1) # functional: dist to calc p-values +es_dist <- rnorm_generator(3) # functional: dist of alternative test stats +ts_dist <- rnorm_perturber(1) # functional: sampling dist/noise for test stats +null_dist <- rnorm_2pvaluer(1) # functional: dist to calc p-values seed <- 608 ``` @@ -215,8 +217,8 @@ Next, we consider the setting with t-distributed test statistics. ## Data Simulation ```{r t5-parameters} -es_dist <- rnorm_generator(3) # functional: dist of alternative test stats -ts_dist <- rt_perturber(5) # functional: sampling dist/noise for test stats +es_dist <- rnorm_generator(3) # functional: dist of alternative test stats +ts_dist <- rt_perturber(5) # functional: sampling dist/noise for test stats null_dist <- rt_2pvaluer(5) # functional: dist to calc p-values seed <- 815 ``` @@ -345,9 +347,9 @@ Next, we consider a second setting with t-distributed test statistics. ## Data Simulation ```{r t11-parameters} -es_dist <- rnorm_generator(3) # functional: dist of alternative test stats -ts_dist <- rt_perturber(11) # functional: sampling dist/noise for test stats -null_dist <- rt_2pvaluer(11) # functional: dist to calc p-values +es_dist <- rnorm_generator(3) # functional: dist of alternative test stats +ts_dist <- rt_perturber(11) # functional: sampling dist/noise for test stats +null_dist <- rt_2pvaluer(11) # functional: dist to calc p-values seed <- 9158 ``` @@ -475,9 +477,9 @@ Finally, we consider the setting with chi-squared distributed test statistics. ## Data Simulation ```{r chisq4-parameters} -es_dist <- rnorm_generator(15) # functional: dist of alternative test stats +es_dist <- rnorm_generator(15) # functional: dist of alternative test stats ts_dist <- rchisq_perturber(4) # functional: sampling dist/noise for test stats -null_dist <- rchisq_pvaluer(4) # functional: dist to calc p-values +null_dist <- rchisq_pvaluer(4) # functional: dist to calc p-values seed <- 1023 ``` diff --git a/datasets/simulations/simulations-informative-step.Rmd b/datasets/simulations/simulations-informative-step.Rmd index b9f281a..b4af92a 100644 --- a/datasets/simulations/simulations-informative-step.Rmd +++ b/datasets/simulations/simulations-informative-step.Rmd @@ -13,9 +13,11 @@ output: # Summary In this set of simulations, we consider settings with both null and non-null -tests with an informative covariate. The covariate is sampled uniformly from +tests with an informative and uninformative covariate. The informative covariate is sampled uniformly from the interval [0, 1], and the conditional probability of a test being non-null -is a non-smooth (monotone) step function of the covariate. We include simulation results +is a non-smooth monotone step function of the covariate. The uninformative covariate is simply +uninformly sampled independently from the interval [0, 1]. The uninformative covariate is included +as a baseline to compare the informative covariate against. We include simulation results again with Gaussian, t-distributed, and Chi-Squared distributed test statistics. # Workspace Setup @@ -94,8 +96,8 @@ First, we consider the setting with Gaussian test statistics. ## Data Simulation ```{r gauss-parameters} -es_dist <- rnorm_generator(3) # functional: dist of alternative test stats -ts_dist <- rnorm_perturber(1) # functional: sampling dist/noise for test stats +es_dist <- rnorm_generator(3) # functional: dist of alternative test stats +ts_dist <- rnorm_perturber(1) # functional: sampling dist/noise for test stats null_dist <- rnorm_2pvaluer(1) # functional: dist to calc p-values seed <- 608 ``` @@ -215,8 +217,8 @@ Next, we consider the setting with t-distributed test statistics. ## Data Simulation ```{r t5-parameters} -es_dist <- rnorm_generator(3) # functional: dist of alternative test stats -ts_dist <- rt_perturber(5) # functional: sampling dist/noise for test stats +es_dist <- rnorm_generator(3) # functional: dist of alternative test stats +ts_dist <- rt_perturber(5) # functional: sampling dist/noise for test stats null_dist <- rt_2pvaluer(5) # functional: dist to calc p-values seed <- 815 ``` @@ -345,9 +347,9 @@ Next, we consider a second setting with t-distributed test statistics. ## Data Simulation ```{r t11-parameters} -es_dist <- rnorm_generator(3) # functional: dist of alternative test stats -ts_dist <- rt_perturber(11) # functional: sampling dist/noise for test stats -null_dist <- rt_2pvaluer(11) # functional: dist to calc p-values +es_dist <- rnorm_generator(3) # functional: dist of alternative test stats +ts_dist <- rt_perturber(11) # functional: sampling dist/noise for test stats +null_dist <- rt_2pvaluer(11) # functional: dist to calc p-values seed <- 9158 ``` @@ -475,9 +477,9 @@ Finally, we consider the setting with chi-squared distributed test statistics. ## Data Simulation ```{r chisq4-parameters} -es_dist <- rnorm_generator(15) # functional: dist of alternative test stats +es_dist <- rnorm_generator(15) # functional: dist of alternative test stats ts_dist <- rchisq_perturber(4) # functional: sampling dist/noise for test stats -null_dist <- rchisq_pvaluer(4) # functional: dist to calc p-values +null_dist <- rchisq_pvaluer(4) # functional: dist to calc p-values seed <- 1023 ``` diff --git a/datasets/simulations/simulations-uasettings-t.Rmd b/datasets/simulations/simulations-uasettings-t.Rmd index 14ca11b..2ad8468 100644 --- a/datasets/simulations/simulations-uasettings-t.Rmd +++ b/datasets/simulations/simulations-uasettings-t.Rmd @@ -14,7 +14,7 @@ output: In this set of simulations, we consider settings with both null and non-null tests with varying distribution of effect sizes under the non-null (alternative) -setting. An informative covariate is included in the setting as described in +setting. Both informative and uninformative covariates are included in the setting as described in `simulations-informative-cubic.Rmd`. The effect sizes for non-null tests are sampled from unimodal distributions composed of a mixture of normal distributions, as described in the original adaptive shrinkage (ASH) manuscript (Stephens, 2016). diff --git a/datasets/simulations/simulations-uasettings.Rmd b/datasets/simulations/simulations-uasettings.Rmd index 558703b..8df4928 100644 --- a/datasets/simulations/simulations-uasettings.Rmd +++ b/datasets/simulations/simulations-uasettings.Rmd @@ -14,7 +14,7 @@ output: In this set of simulations, we consider settings with both null and non-null tests with varying distribution of effect sizes under the non-null (alternative) -setting. An informative covariate is included in the setting as described in +setting. Both informative and uninformative covariates are included in the setting as described in `simulations-informative-cubic.Rmd`. The effect sizes for non-null tests are sampled from unimodal distributions composed of a mixture of normal distributions, as described in the original adaptive shrinkage (ASH) manuscript (Stephens, 2016). diff --git a/datasets/simulations/simulations-varyingntests.Rmd b/datasets/simulations/simulations-varyingntests.Rmd index b4a79ca..71b5292 100644 --- a/datasets/simulations/simulations-varyingntests.Rmd +++ b/datasets/simulations/simulations-varyingntests.Rmd @@ -14,8 +14,8 @@ output: In this set of simulations, we consider settings with varying number of hypothesis tests. Since many newer FDR controlling approaches require fitting some model to -the independent covariates, they may be sensitive to lower number of tests. An -informative covariate is included in the setting as described in +the independent covariates, they may be sensitive to lower numbers of tests. Both +informative and uninformative covariates are included in these settings as described in `simulations-informative-sine.Rmd`. # Workspace Setup diff --git a/datasets/simulations/supplementary/simulations-informative-step-nullAdaPT.Rmd b/datasets/simulations/supplementary/simulations-informative-step-nullAdaPT.Rmd index 0eb8ca7..dbb4cf1 100644 --- a/datasets/simulations/supplementary/simulations-informative-step-nullAdaPT.Rmd +++ b/datasets/simulations/supplementary/simulations-informative-step-nullAdaPT.Rmd @@ -13,10 +13,8 @@ output: # Summary In this set of simulations, we consider settings with both null and non-null -tests with an informative covariate. The covariate is sampled uniformly from -the interval [0, 1], and the conditional probability of a test being non-null -is a non-smooth (monotone) step function of the covariate. We include simulation -results again with Gaussian, t-distributed, and Chi-Squared distributed test statistics. +tests with informative and non-informative covariates as described in +`simulations-informative-step.Rmd`. This set of simulations differs from `simulations-informative-step.Rmd` only in the implementation of the AdaPT method for multiple testing correction. diff --git a/datasets/simulations/supplementary/simulations-uasettings-nonnull25.Rmd b/datasets/simulations/supplementary/simulations-uasettings-nonnull25.Rmd index d0bb0bf..314a283 100644 --- a/datasets/simulations/supplementary/simulations-uasettings-nonnull25.Rmd +++ b/datasets/simulations/supplementary/simulations-uasettings-nonnull25.Rmd @@ -14,7 +14,7 @@ output: In this set of simulations, we consider settings with both null and non-null tests with varying distribution of effect sizes under the non-null (alternative) -setting. An informative covariate is included in the setting as described in +setting. Both informative and uninformative covariates are included in the setting as described in `simulations-informative-cubic.Rmd`. The effect sizes for non-null tests are sampled from unimodal distributions composed of a mixture of normal distributions, as described in the original adaptive shrinkage (ASH) manuscript (Stephens, 2016).