plot_1d {adaptMT} | R Documentation |
Plotting the outputs of adapt
when x
is 1-dimensional, including threshold curves and level curves of local FDR.
plot_1d_thresh(obj, x, pvals, alpha, title, xlab = "x", xlim = NULL, disp_ymax = 0.2, num_yticks = 3, rand_seed_perturb = NA, ...) plot_1d_lfdr(obj, x, pvals, alpha, title, xlab = "x", xlim = NULL, disp_ymax = 0.2, num_yticks = 3, legend_pos = "topright", ...)
obj |
an 'adapt' object |
x |
covariates (i.e. side-information). Should be compatible to |
pvals |
a vector of values in [0, 1]. P-values |
alpha |
a positive scalar in (0, 1). Target FDR level |
title |
a string. Title of the figure |
xlab |
a string. Label of the x-axis |
xlim |
a vector of length 2. Limits of x-axis |
disp_ymax |
a positive scalar in (0, 1]. Maximum value displayed in the y-axis |
num_yticks |
a positive integer. Number of ticks in the y-axis |
rand_seed_perturb |
random seed if jitter is added. NA if no jittering is needed |
... |
other arguments passed to |
legend_pos |
a string. Position of the legend |
# Load estrogen data data(estrogen) pvals <- as.numeric(estrogen$pvals) x <- data.frame(x = as.numeric(estrogen$ord_high)) dist <- beta_family() # Subsample the data for convenience inds <- (x$x <= 5000) pvals <- pvals[inds] x <- x[inds,,drop = FALSE] # Run adapt_glm library("splines") formulas <- paste0("ns(x, df = ", 6:10, ")") res <- adapt_glm(x = x, pvals = pvals, pi_formulas = formulas, mu_formulas = formulas, dist = dist, nfits = 10) # Plots par(mfrow = c(2, 1)) plot_1d_thresh(res, x, pvals, 0.1, "P-value Thresholds (alpha = 0.1)", disp_ymax = 0.5) plot_1d_lfdr(res, x, pvals, 0.1, "Level Curves of lfdr (alpha = 0.1)", disp_ymax = 0.5)