## ----setup, include = FALSE--------------------------------------------------- knitr::opts_chunk$set( collapse = TRUE, comment = "#>" ) knitr::include_graphics("stream-diagram.png") ## ----------------------------------------------------------------------------- sample.df <- data.frame( id = c('A15432', 'B90969', 'C18705', 'B49731', 'E99902', 'C38292', 'A30619', 'D46627', 'E29198', 'A41418', 'D51456', 'C88669', 'E03673', 'A63155', 'B66033'), date = as.Date(c(rep("2014-12-01",3), rep("2015-09-21",5), rep("2016-05-19",2), "2016-11-12", rep("2017-03-27",4))), pval = c(2.90e-14, 0.00143, 0.06514, 0.00174, 0.00171, 3.61e-05, 0.79149, 0.27201, 0.28295, 7.59e-08, 0.69274, 0.30443, 0.000487, 0.72342, 0.54757)) ## ----------------------------------------------------------------------------- library(onlineFDR) set.seed(1) LOND_results <- LOND(sample.df) LOND_results ## ----------------------------------------------------------------------------- sum(LOND_results$R) ## ----------------------------------------------------------------------------- set.seed(1) LORD_results <- LORD(sample.df) set.seed(1) Bonf_results <- Alpha_spending(sample.df) # Bonferroni-like test x <- seq_len(nrow(LOND_results)) par(mar=c(5.1, 4.1, 4.1, 9.1)) plot(x, log(LOND_results$alphai), ylim = c(-9.5, -2.5), type = 'l', col = "green", xlab = "Index", ylab = "log(alphai)", panel.first = grid()) lines(x, log(LORD_results$alphai), col = "blue") # LORD lines(x, log(Bonf_results$alphai), col = "red") # Bonferroni-like test lines(x, rep(log(0.05),length(x)), col = "purple") # Unadjusted legend("right", legend = c("Unadjusted", "Bonferroni", "LORD", "LOND"), col = c("purple", "red", "blue", "green"), lty = rep(1,4), inset = c(-0.35,0), xpd = TRUE) ## ----------------------------------------------------------------------------- # Initial experimental data sample.df <- data.frame( id = c('A15432', 'B90969', 'C18705'), date = as.Date(c(rep("2014-12-01",3))), pval = c(2.90e-14, 0.06743, 0.01514)) set.seed(1) LOND_results <- LOND(sample.df) ## ----------------------------------------------------------------------------- # After you've completed more experiments sample.df <- data.frame( id = c('A15432', 'B90969', 'C18705', 'B49731', 'E99902', 'C38292', 'A30619', 'D46627', 'E29198', 'A41418', 'D51456', 'C88669', 'E03673', 'A63155', 'B66033'), date = as.Date(c(rep("2014-12-01",3), rep("2015-09-21",5), rep("2016-05-19",2), "2016-11-12", rep("2017-03-27",4))), pval = c(2.90e-14, 0.06743, 0.01514, 0.08174, 0.00171, 3.61e-05, 0.79149, 0.27201, 0.28295, 7.59e-08, 0.69274, 0.30443, 0.000487, 0.72342, 0.54757)) set.seed(1) LOND_results <- LOND(sample.df) ## ----------------------------------------------------------------------------- sample.df <- data.frame( id = c('A15432', 'B90969', 'C18705', 'B49731', 'E99902', 'C38292', 'A30619', 'D46627', 'E29198', 'A41418', 'D51456', 'C88669', 'E03673', 'A63155', 'B66033'), pval = c(2.90e-08, 0.06743, 0.01514, 0.08174, 0.00171, 3.60e-05, 0.79149, 0.27201, 0.28295, 7.59e-08, 0.69274, 0.30443, 0.00136, 0.72342, 0.54757), batch = c(rep(1,5), rep(2,6), rep(3,4))) batchprds_results <- BatchPRDS(sample.df) ## ----------------------------------------------------------------------------- sample.df <- data.frame( id = c('A15432', 'B90969', 'C18705', 'B49731', 'E99902', 'C38292', 'A30619', 'D46627', 'E29198', 'A41418', 'D51456', 'C88669', 'E03673', 'A63155', 'B66033'), date = as.Date(c(rep("2014-12-01",3), rep("2015-09-21",5), rep("2016-05-19",2), "2016-11-12", rep("2017-03-27",4))), pval = c(2.90e-14, 0.06743, 0.01514, 0.08174, 0.00171, 3.61e-05, 0.79149, 0.27201, 0.28295, 7.59e-08, 0.69274, 0.30443, 0.000487, 0.72342, 0.54757)) # Assuming a bound of 20 hypotheses bound <- setBound("LOND", alpha = 0.04, 20) set.seed(1) LOND_results <- LOND(sample.df, alpha = 0.04, betai = bound)