algo_effectiveness_crm.RdThis function computes the actual and predicted effectiveness of a given algorithm for different tolerance values.
algo_effectiveness_crm(mod, num = 1)A fitted mirt model using the function irtmodel or R package mirt.
The algorithm number, for which the goodness of the IRT model is computed.
A list with the following components:
effectiveThe x,y coodinates for the actual and predicted effectiveness curves for algorithm num.
predictedEffThe area under the predicted effectiveness curve.
actualEffThe area under the actual effectiveness curve.
# \donttest{
set.seed(1)
x1 <- runif(100)
x2 <- runif(100)
x3 <- runif(100)
X <- cbind.data.frame(x1, x2, x3)
max_item <- rep(1,3)
min_item <- rep(0,3)
mod <- cirtmodel(X, max.item=max_item, min.item=min_item)
#> Warning: NaNs produced
#> Warning: NaNs produced
out <- algo_effectiveness_crm(mod$model, num=1)
out
#> $effective
#> x act_rel pred_rel
#> [1,] 0.00000000 0.00 0.00
#> [2,] 0.01010101 0.01 0.00
#> [3,] 0.02020202 0.01 0.01
#> [4,] 0.03030303 0.01 0.01
#> [5,] 0.04040404 0.02 0.01
#> [6,] 0.05050505 0.02 0.01
#> [7,] 0.06060606 0.03 0.01
#> [8,] 0.07070707 0.04 0.01
#> [9,] 0.08080808 0.04 0.01
#> [10,] 0.09090909 0.05 0.02
#> [11,] 0.10101010 0.06 0.02
#> [12,] 0.11111111 0.08 0.02
#> [13,] 0.12121212 0.08 0.02
#> [14,] 0.13131313 0.10 0.03
#> [15,] 0.14141414 0.13 0.03
#> [16,] 0.15151515 0.13 0.04
#> [17,] 0.16161616 0.13 0.05
#> [18,] 0.17171717 0.14 0.06
#> [19,] 0.18181818 0.16 0.06
#> [20,] 0.19191919 0.17 0.06
#> [21,] 0.20202020 0.17 0.07
#> [22,] 0.21212121 0.19 0.08
#> [23,] 0.22222222 0.21 0.10
#> [24,] 0.23232323 0.24 0.11
#> [25,] 0.24242424 0.26 0.13
#> [26,] 0.25252525 0.27 0.14
#> [27,] 0.26262626 0.27 0.17
#> [28,] 0.27272727 0.28 0.18
#> [29,] 0.28282828 0.29 0.18
#> [30,] 0.29292929 0.32 0.23
#> [31,] 0.30303030 0.32 0.24
#> [32,] 0.31313131 0.33 0.26
#> [33,] 0.32323232 0.34 0.28
#> [34,] 0.33333333 0.34 0.30
#> [35,] 0.34343434 0.37 0.31
#> [36,] 0.35353535 0.39 0.33
#> [37,] 0.36363636 0.41 0.33
#> [38,] 0.37373737 0.41 0.35
#> [39,] 0.38383838 0.42 0.37
#> [40,] 0.39393939 0.42 0.37
#> [41,] 0.40404040 0.43 0.37
#> [42,] 0.41414141 0.44 0.38
#> [43,] 0.42424242 0.44 0.39
#> [44,] 0.43434343 0.45 0.44
#> [45,] 0.44444444 0.45 0.46
#> [46,] 0.45454545 0.46 0.47
#> [47,] 0.46464646 0.46 0.48
#> [48,] 0.47474747 0.46 0.51
#> [49,] 0.48484848 0.47 0.51
#> [50,] 0.49494949 0.48 0.51
#> [51,] 0.50505051 0.48 0.52
#> [52,] 0.51515152 0.50 0.54
#> [53,] 0.52525253 0.51 0.56
#> [54,] 0.53535354 0.55 0.56
#> [55,] 0.54545455 0.55 0.57
#> [56,] 0.55555556 0.57 0.61
#> [57,] 0.56565657 0.57 0.63
#> [58,] 0.57575758 0.59 0.66
#> [59,] 0.58585859 0.59 0.66
#> [60,] 0.59595960 0.61 0.66
#> [61,] 0.60606061 0.62 0.69
#> [62,] 0.61616162 0.63 0.70
#> [63,] 0.62626263 0.68 0.71
#> [64,] 0.63636364 0.69 0.72
#> [65,] 0.64646465 0.69 0.73
#> [66,] 0.65656566 0.69 0.75
#> [67,] 0.66666667 0.71 0.76
#> [68,] 0.67676768 0.74 0.79
#> [69,] 0.68686869 0.75 0.80
#> [70,] 0.69696970 0.76 0.81
#> [71,] 0.70707071 0.76 0.82
#> [72,] 0.71717172 0.77 0.84
#> [73,] 0.72727273 0.77 0.85
#> [74,] 0.73737374 0.77 0.85
#> [75,] 0.74747475 0.79 0.87
#> [76,] 0.75757576 0.80 0.87
#> [77,] 0.76767677 0.82 0.87
#> [78,] 0.77777778 0.83 0.90
#> [79,] 0.78787879 0.83 0.92
#> [80,] 0.79797980 0.84 0.93
#> [81,] 0.80808081 0.87 0.93
#> [82,] 0.81818182 0.87 0.94
#> [83,] 0.82828283 0.88 0.95
#> [84,] 0.83838384 0.89 0.96
#> [85,] 0.84848485 0.89 0.96
#> [86,] 0.85858586 0.89 0.96
#> [87,] 0.86868687 0.90 0.97
#> [88,] 0.87878788 0.90 0.97
#> [89,] 0.88888889 0.92 0.97
#> [90,] 0.89898990 0.92 0.98
#> [91,] 0.90909091 0.93 0.98
#> [92,] 0.91919192 0.94 0.98
#> [93,] 0.92929293 0.95 0.98
#> [94,] 0.93939394 0.96 0.98
#> [95,] 0.94949495 0.97 0.98
#> [96,] 0.95959596 0.98 1.00
#> [97,] 0.96969697 0.98 1.00
#> [98,] 0.97979798 0.98 1.00
#> [99,] 0.98989899 0.99 1.00
#> [100,] 1.00000000 1.00 1.00
#>
#> $predictedEff
#> [1] 0.5118182
#>
#> $actualEff
#> [1] 0.5128283
#>
# }