CopyOfplots_temp.R

In robertness/bninfo: Queries and information theoritic operations on Bayesian networks.

# Tcells
IG_random <- c(3.928, 2.267, 1.653, 1.103, 0.730)
IG_uninformative <- c(5.718, 1.632, 0.990, 0.142, 0.050)
IG_informative <- c(5.975, 1.560, 1.355)
plot(IG_uninformative, type = "o", pch = "*", ylim = c(0, 6), xlim = c(1, 5))
lines(IG_informative, type = "o", pch = "*", lty = 2)
lines(IG_random, type = "o", pch = "*", lty = 3)

p0_random <- c(0.131, 0.290, 0.438, 0.506, 0.587)
p0_uninformative <- c(0.048, 0.436, 0.532, 0.952, 0.950)
p0_informative <- c(0.10, 0.33, 0.41, 1.00)
plot(p0_uninformative, type = "o", pch = "*", ylim = c(0, 1.2), xlim = c(1, 5))
lines(p0_informative, type = "o", pch = "*", lty = 2)
lines(p0_random, type = "o", pch = "*", lty = 3)

tpr_random <- c(0.475, 0.556, 0.601, 0.653, 0.673, 0.740)
tpr_uninformed <- c(0.475, 0.575, 0.604, 0.648, 0.662 ,0.740)
tpr_informed <- c(0.652, 0.709, 0.737, 0.765)
plot(tpr_uninformed, type = "o", pch = "*", ylim = c(.45, .77))
lines(tpr_informed, type = "o", pch = "*", lty = 2)
lines(tpr_random, type = "o", pch = "*", lty = 3)

L1_random <- c(9.062, 8.410, 7.522, 6.708, 5.985, 5.200)
L1_uninformed <- c(9.046, 7.188, 6.500, 6.083, 5.715, 5.200)
L1_informed <- c(6.367, 4.547, 4.087, 4.004)
plot(L1_uninformed, type = "o", pch = "*", ylim = c(4, 9))
lines(L1_informed, type = "o", pch = "*", lty = 2)
lines(L1_random, type = "o", pch = "*", lty = 3)


###########
# dream
random_IG  <- c(1.776, 1.300, 1.097, 0.881, 0.652, 0.486, 0.445, 0.379, 0.267, 0.186, 0.145, 0.092, 0.044)
dream_IG_uninformed <- c(3.025, 2.085, 0.990, 0.630, 0.435, 0.205, 0.130, 0.120, 0.060, 0.025, 0.015, 0.010, 0.010)
dream_IG_informed <- c(2.210, 0.490, 0.175, 0.065, 0.050, 0.030, 0.030, 0.030, 0.030, 0.030, 0.025)
plot(round(dream_IG_uninformed, 2), type = "o", pch = "*", xlim = c(1, 13))
lines(round(dream_IG_informed, 2), type = "o", pch = "*", lty = 2)
lines(round(random_IG, 2), type = "o", pch = "*", lty = 3)

random_p0 <- c(0.489, 0.515, 0.560, 0.612, 0.652, 0.731, 0.754, 0.801, 0.819, 0.872, 0.891, 0.923, 0.965)
dream_p0_uninformed <- c(0.525, 0.595, 0.675, 0.765, 0.855, 0.910, 0.915, 0.935, 0.955, 0.980, 0.985, 0.990, 0.990)
dream_p0_informed <- c(0.560, 0.685, 0.925, 0.945, 0.955, 0.970, 0.970, 0.970, 0.970, 0.970, 0.975)
plot(dream_p0_uninformed, type = "o", pch = "*", ylim = c(0.485, 1), xlim = c(1, 13))
lines(dream_p0_informed, type = "o", pch = "*", lty = 2)
lines(round(random_p0, 2), type = "o", pch = "*", lty = 3)

#tpr_dream_proposed_uninformed <- c(0.4416667, 0.5404167, 0.5489583, 0.5541667, 0.6156250, 0.6197917, 0.6197750, 0.6198167, 0.6293750, 0.6343750, 0.6541667, 0.6675000, 0.6677083, 0.6791667, 0.6812500, 0.6979167)
tpr_random <- c(0.144, 0.148, 0.152, 0.166, 0.198, 0.241, 0.259, 0.288, 0.311, 0.342, 0.363, 0.379, 0.392, 0.431)
tpr_uninformative <- c(0.137, 0.138, 0.140, 0.159, 0.188, 0.217, 0.271, 0.295, 0.328, 0.370, 0.374, 0.393, 0.416, 0.420)
tpr_informative <- c(0.159, 0.159, 0.172, 0.215, 0.248, 0.291, 0.346, 0.383, 0.430, 0.440, 0.452, 0.456)
plot(0:13, tpr_uninformative, type = "o", pch = "*", xlim = c(0, 13), ylim = c(0.137, 0.48))
lines(0:11,tpr_informative, type = "o", pch = "*", lty = 2)
lines(0:13, tpr_random, type = "o", pch = "*", lty = 3)

#L1_dream_proposed_uninformed <- c(35.49617, 33.86667, 33.85733, 33.56783, 32.85050, 32.78667, 32.67917, 32.53700, 32.53633, 32.45417, 31.64367, 31.63350, 31.31133, 31.20283, 30.11067, 30.10950)
L1_random <- c(62.564, 56.483, 52.141, 50.789, 50.190, 49.712, 49.729, 49.442, 48.990, 48.885, 48.584, 48.299, 47.983, 46.991)
L1_uninformed <- c(62.608, 56.904, 53.397, 51.215, 50.476, 50.136, 49.496, 49.340, 49.251, 48.531, 48.563, 48.285, 47.215, 47.331)
L1_informed <- c(58.032, 52.070, 47.376, 45.201, 44.708, 43.846, 43.092, 42.193, 41.441, 41.429, 40.902, 40.849)
plot(0:13, L1_uninformed,  type = "o", pch = "*", ylim = c(40, 63), xlim = c(0, 13))
lines(0:11, L1_informed,  type = "o", pch = "*", lty = 2)
lines(0:13, L1_random,  type = "o", pch = "*", lty = 3)