# (1) Basics: ----
# A. Using global prob and freq values:
plot_fnet() # default frequency net, same as:
# plot_fnet(by = "cddc", area = "no", scale = "p",
# f_lbl = "num", f_lwd = 0, cex_lbl = .90,
# p_lbl = "mix", arr_c = -2, cex_p_lbl = NA)
# B. Providing values:
plot_fnet(N = 10000, prev = .02, sens = .8, spec = .9) # Binder et al. (2020, Fig. 3)
# C. Rounding and sampling:
plot_fnet(N = 100, prev = 1/3, sens = 2/3, spec = 6/7, area = "sq", round = FALSE)
plot_fnet(N = 100, prev = 1/3, sens = 2/3, spec = 6/7, area = "sq", sample = TRUE, scale = "freq")
# Variants:
plot_fnet(N = 10000, prev = .02, sens = .8, spec = .9, by = "cdac")
plot_fnet(N = 10000, prev = .02, sens = .8, spec = .9, by = "dccd")
# plot_fnet(N = 10000, prev = .02, sens = .8, spec = .9, by = "dcac")
# plot_fnet(N = 10000, prev = .02, sens = .8, spec = .9, by = "accd")
# plot_fnet(N = 10000, prev = .02, sens = .8, spec = .9, by = "acdc")
# Trees (only 1 dimension):
plot_fnet(N = 10000, prev = .02, sens = .8, spec = .9, by = "cd")
# plot_fnet(N = 10000, prev = .02, sens = .8, spec = .9, by = "dc")
# plot_fnet(N = 10000, prev = .02, sens = .8, spec = .9, by = "ac")
# Area and margin notes:
plot_fnet(N = 10, prev = 1/4, sens = 3/5, spec = 2/5, area = "sq", mar_notes = TRUE)
# (2) Use case (highlight horizontal vs. vertical perspectives: ----
# Define scenario:
mammo <- riskyr(N = 10000, prev = .01, sens = .80, fart = .096,
scen_lbl = "Mammography screening", N_lbl = "Women",
cond_lbl = "Breast cancer", dec_lbl = "Test result",
cond_true_lbl = "Cancer (C+)", cond_false_lbl = "no Cancer (C-)",
dec_pos_lbl = "positive (T+)", dec_neg_lbl = "negative (T-)",
hi_lbl = "C+ and T+", mi_lbl = "C+ and T-",
fa_lbl = "C- and T+", cr_lbl = "C- and T-")
# Colors:
my_non <- "grey95"
my_red <- "orange1"
my_blu <- "skyblue1"
# A. Emphasize condition perspective (rows):
my_col_1 <- init_pal(N_col = my_non,
cond_true_col = my_blu, cond_false_col = my_red,
dec_pos_col = my_non, dec_neg_col = my_non,
hi_col = my_blu, mi_col = my_blu,
fa_col = my_red, cr_col = my_red)
plot(mammo, type = "fnet", col_pal = my_col_1,
f_lbl = "namnum", f_lwd = 2, p_lbl = "no", arr_c = 0)
# B. Emphasize decision perspective (columns):
my_col_2 <- init_pal(N_col = my_non,
cond_true_col = my_non, cond_false_col = my_non,
dec_pos_col = my_red, dec_neg_col = my_blu,
hi_col = my_red, mi_col = my_blu,
fa_col = my_red, cr_col = my_blu)
plot(mammo, type = "fnet", col_pal = my_col_2,
f_lbl = "namnum", f_lwd = 2, p_lbl = "no", arr_c = 0)
# (3) Custom color and text settings: ----
plot_fnet(col_pal = pal_bw, f_lwd = .5, p_lwd = .5, lty = 2, # custom fbox color, prob links,
font = 3, cex_p_lbl = .75) # and text labels
plot_fnet(N = 7, prev = 1/2, sens = 3/5, spec = 4/5, round = FALSE,
by = "cdac", lbl_txt = txt_org, f_lbl = "namnum", f_lbl_sep = ":\n",
f_lwd = 1, col_pal = pal_rgb) # custom colors
# plot_fnet(N = 5, prev = 1/2, sens = .8, spec = .5, scale = "p", # Note scale!
# by = "cddc", area = "hr", col_pal = pal_bw, f_lwd = 1) # custom colors
plot_fnet(N = 3, prev = .50, sens = .50, spec = .50, scale = "p", # Note scale!
area = "sq", lbl_txt = txt_org, f_lbl = "namnum", f_lbl_sep = ":\n", # custom text
col_pal = pal_kn, f_lwd = .5) # custom colors
# (4) Other options: ----
plot_fnet(N = 4, prev = .2, sens = .7, spec = .8,
area = "sq", scale = "p") # areas scaled by prob (matters for small N)
# plot_fnet(N = 4, prev = .2, sens = .7, spec = .8,
# area = "sq", scale = "f") # areas scaled by (rounded or non-rounded) freq
## Frequency boxes (f_lbl):
# plot_fnet(f_lbl = NA) # no freq labels
# plot_fnet(f_lbl = "abb") # abbreviated freq names (variable names)
plot_fnet(f_lbl = "nam") # only freq names
plot_fnet(f_lbl = "num") # only numeric freq values (default)
# plot_fnet(f_lbl = "namnum") # names and numeric freq values
plot_fnet(f_lbl = "namnum", cex_lbl = .75) # smaller freq labels
# plot_fnet(f_lbl = "def") # informative default: short name and numeric value (abb = num)
# f_lwd:
# plot_fnet(f_lwd = 1) # basic lines
# plot_fnet(f_lwd = 0) # no lines (default), set to tiny_lwd = .001, lty = 0 (same if NA/NULL)
# plot_fnet(f_lwd = .5) # thinner lines
plot_fnet(f_lwd = 3) # thicker lines
## Probability links (p_lbl, p_lwd, p_scale):
# plot_fnet(p_lbl = NA) # no prob labels (NA/NULL/"none")
plot_fnet(p_lbl = "mix") # abbreviated names with numeric values (abb = num)
# plot_fnet(p_lbl = "min") # minimal names (of key probabilities)
# plot_fnet(p_lbl = "nam") # only prob names
plot_fnet(p_lbl = "num") # only numeric prob values
# plot_fnet(p_lbl = "namnum") # names and numeric prob values
plot_fnet(p_lwd = 6, p_scale = TRUE)
plot_fnet(area = "sq", f_lbl = "num", p_lbl = NA, col_pal = pal_bw, p_lwd = 6, p_scale = TRUE)
# arr_c:
# plot_fnet(arr_c = 0) # acc_c = 0: no arrows
# plot_fnet(arr_c = -3) # arr_c = -1 to -3: points at both ends
# plot_fnet(arr_c = -2) # point at far end
plot_fnet(arr_c = +2) # crr_c = 1-3: V-shape arrows at far end
plot_fnet(by = "cd", joint_p = FALSE) # tree without joint probability links
# plot_fnet(by = "cddc", joint_p = FALSE) # fnet ...
## Plain plot versions:
plot_fnet(area = "no", f_lbl = "def", p_lbl = "num", col_pal = pal_mod, f_lwd = 1,
main = "", mar_notes = FALSE) # remove titles and margin notes
plot_fnet(area = "no", f_lbl = "nam", p_lbl = "min", col_pal = pal_rgb)
plot_fnet(area = "sq", f_lbl = "nam", p_lbl = "num", col_pal = pal_rgb)
# plot_fnet(area = "sq", f_lbl = "def", f_lbl_sep = ":\n", p_lbl = NA, f_lwd = 1, col_pal = pal_kn)
## Suggested combinations:
# plot_fnet(f_lbl = "nam", p_lbl = "mix") # basic plot
plot_fnet(f_lbl = "namnum", p_lbl = "num", cex_lbl = .80, cex_p_lbl = .75)
# plot_fnet(area = "no", f_lbl = "def", p_lbl = "abb", # def/abb labels
# f_lwd = .8, p_lwd = .8, lty = 2, col_pal = pal_bwp) # black-&-white
# plot_fnet(area = "sq", f_lbl = "nam", p_lbl = "abb", lbl_txt = txt_TF, col_pal = pal_bw)
plot_fnet(area = "sq", f_lbl = "num", p_lbl = "num", f_lwd = 1, col_pal = pal_rgb)
plot_fnet(area = "sq", f_lbl = "nam", p_lbl = "num", f_lwd = .5, col_pal = pal_rgb)
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