This function takes exametrika output as input and generates Class Membership Profile (CMP) plots using ggplot2. CMP shows each student's membership probability across all latent classes.
Usage
plotCMP_gg(
data,
title = TRUE,
colors = NULL,
linetype = "dashed",
show_legend = FALSE,
legend_position = "right"
)Arguments
- data
An object of class
c("exametrika", "LCA")orc("exametrika", "BINET"). If LRA, Biclustering, LDLRA, or LDB output is provided, RMP will be plotted instead with a warning.- title
Logical or character. If
TRUE(default), display an auto-generated title. IfFALSE, no title. If a character string, use it as a custom title (only for single-student plots).- colors
Character vector. Color(s) for points and lines. If
NULL(default), a colorblind-friendly palette is used.- linetype
Character or numeric specifying the line type. Default is
"dashed".- show_legend
Logical. If
TRUE, display the legend. Default isFALSE.- legend_position
Character. Position of the legend. One of
"right"(default),"top","bottom","left","none".
Value
A list of ggplot objects, one for each student. Each plot shows the membership probability across all latent classes.
Details
The Class Membership Profile visualizes how strongly each student belongs to each latent class. Students with high membership probability in a single class are well-classified, while students with similar probabilities across classes may be ambiguous cases.
Examples
library(exametrika)
# \donttest{
result <- LCA(J15S500, ncls = 5)
#>
iter 1 log_lik -3920.06
#>
iter 2 log_lik -3868.38
#>
iter 3 log_lik -3856.57
#>
iter 4 log_lik -3845.74
#>
iter 5 log_lik -3833.37
#>
iter 6 log_lik -3820.23
#>
iter 7 log_lik -3806.72
#>
iter 8 log_lik -3792.97
#>
iter 9 log_lik -3779.19
#>
iter 10 log_lik -3765.86
#>
iter 11 log_lik -3753.64
#>
iter 12 log_lik -3743.12
#>
iter 13 log_lik -3734.56
#>
iter 14 log_lik -3727.88
#>
iter 15 log_lik -3722.77
#>
iter 16 log_lik -3718.86
#>
iter 17 log_lik -3715.8
#>
iter 18 log_lik -3713.3
#>
iter 19 log_lik -3711.18
#>
iter 20 log_lik -3709.29
#>
iter 21 log_lik -3707.55
#>
iter 22 log_lik -3705.91
#>
iter 23 log_lik -3704.34
#>
iter 24 log_lik -3702.83
#>
iter 25 log_lik -3701.39
#>
iter 26 log_lik -3699.99
#>
iter 27 log_lik -3698.66
#>
iter 28 log_lik -3697.39
#>
iter 29 log_lik -3696.18
#>
iter 30 log_lik -3695.02
#>
iter 31 log_lik -3693.92
#>
iter 32 log_lik -3692.86
#>
iter 33 log_lik -3691.86
#>
iter 34 log_lik -3690.89
#>
iter 35 log_lik -3689.96
#>
iter 36 log_lik -3689.06
#>
iter 37 log_lik -3688.17
#>
iter 38 log_lik -3687.31
#>
iter 39 log_lik -3686.45
#>
iter 40 log_lik -3685.61
#>
iter 41 log_lik -3684.76
#>
iter 42 log_lik -3683.91
#>
iter 43 log_lik -3683.07
#>
iter 44 log_lik -3682.22
#>
iter 45 log_lik -3681.37
#>
iter 46 log_lik -3680.53
#>
iter 47 log_lik -3679.69
#>
iter 48 log_lik -3678.86
#>
iter 49 log_lik -3678.05
#>
iter 50 log_lik -3677.25
#>
iter 51 log_lik -3676.47
#>
iter 52 log_lik -3675.71
#>
iter 53 log_lik -3674.97
#>
iter 54 log_lik -3674.26
#>
iter 55 log_lik -3673.57
#>
iter 56 log_lik -3672.9
#>
iter 57 log_lik -3672.24
#>
iter 58 log_lik -3671.61
#>
iter 59 log_lik -3670.99
#>
iter 60 log_lik -3670.38
#>
iter 61 log_lik -3669.79
#>
iter 62 log_lik -3669.21
#>
iter 63 log_lik -3668.64
#>
iter 64 log_lik -3668.09
#>
iter 65 log_lik -3667.55
#>
iter 66 log_lik -3667.03
#>
iter 67 log_lik -3666.53
#>
iter 68 log_lik -3666.05
#>
iter 69 log_lik -3665.59
#>
iter 70 log_lik -3665.15
#>
iter 71 log_lik -3664.74
#>
iter 72 log_lik -3664.35
#>
iter 73 log_lik -3663.99
plots <- plotCMP_gg(result)
plots[[1]] # Show CMP for the first student
# }