Projective Mapping
Truong Nguyen Trung Tin
2024-11-23
1. Clean
rm(list = ls())
graphics.off()2. Load Packages and Sources
Note: install all packages if did not install before or required
library(openxlsx)
library(readxl)
library(ggplot2)
library(FactoMineR)
library(SensoMineR)
library(factoextra)## Welcome! Want to learn more? See two factoextra-related books at https://goo.gl/ve3WBa3. Import Data
napping <- read_excel("C:/Users/Admin/OneDrive/Desktop/L02_3_slmt.xlsx", sheet = "dif") #read file
napping2 <- read_excel("C:/Users/Admin/OneDrive/Desktop/L02_3_slmt.xlsx", sheet = "des")## New names:
## • `Độ mỏng` -> `Độ mỏng...17`
## • `Độ mỏng` -> `Độ mỏng...18`napping <- data.frame(napping,row.names = 1) #hiding first column name
head(napping,5) #displaying 5 rows## X1 Y1 X2 Y2 X3 Y3 X4 Y4 X5 Y5 X6
## Fresh Cuts (426,853) - A 9.0 35.4 25.0 22.3 26.2 14.3 11.0 24.9 12.5 28.1 33.6
## O'star (752,104) - B 31.1 35.5 41.8 23.0 11.6 14.2 47.9 14.9 9.0 18.5 46.8
## Lays (913,758) - C 30.3 35.8 22.5 22.0 14.6 14.7 49.8 15.8 9.4 27.9 45.0
## Slide (141,671) - D 5.9 30.4 28.7 22.6 37.9 13.6 9.1 27.9 25.9 21.1 28.5
## Pringles (347,842) - E 19.9 35.1 34.7 23.1 42.0 13.3 28.3 32.9 22.4 20.9 26.2
## Y6 X7 Y7 X8 Y8 X9 Y9 X10 Y10 X11 Y11
## Fresh Cuts (426,853) - A 21.6 13.9 25.5 9.8 31.3 12.1 18.4 14.8 18.3 46.2 21.4
## O'star (752,104) - B 23.5 10.9 24.1 31.8 22.3 14.2 21.5 22.3 19.2 36.8 27.5
## Lays (913,758) - C 24.6 47.3 26.8 14.3 29.9 9.6 17.3 11.8 17.3 48.3 25.8
## Slide (141,671) - D 30.1 35.2 27.7 16.5 20.5 19.1 24.2 27.8 21.2 47.5 28.1
## Pringles (347,842) - E 31.0 31.5 27.9 19.0 19.7 29.3 28.8 23.8 19.9 37.8 25.0
## X12 Y12 X13 Y13 X14 Y14 X15 Y15 X16 Y16 X17
## Fresh Cuts (426,853) - A 21.6 20.8 8.0 24.6 3.1 33.3 32.1 22.8 29.7 26.0 14.2
## O'star (752,104) - B 25.0 29.4 14.9 29.5 24.0 31.3 34.3 22.0 25.5 22.0 14.1
## Lays (913,758) - C 23.9 21.0 6.7 24.0 4.8 33.7 33.2 22.0 32.5 22.0 6.9
## Slide (141,671) - D 24.4 31.3 7.3 30.7 6.3 33.7 19.1 28.0 13.8 26.8 33.3
## Pringles (347,842) - E 26.8 30.5 8.8 36.5 13.3 33.5 20.9 28.0 14.7 31.3 36.1
## Y17 X18 Y18
## Fresh Cuts (426,853) - A 25.0 25.3 17.7
## O'star (752,104) - B 23.6 26.6 17.6
## Lays (913,758) - C 25.7 25.2 17.2
## Slide (141,671) - D 27.5 26.9 29.0
## Pringles (347,842) - E 27.5 33.7 29.04. Show Project Mapping
nS <- ncol(napping)/2 #Calculating subjects because 1 subject has 2 coordinate (x,y)
rectangle<-function(data,i){
plot(data[,((i-1)*2+1):(i*2)],col="blue",xlim=c(0,60),ylim=c(0,40),xlab="",ylab="",
main=paste("Napping: Subject ",i,sep=""),type="n",asp=1)
points(data[,((i-1)*2+1):(i*2)],col="blue",xlim=c(0,60),pch=20)
text(data[,((i-1)*2+1):(i*2)],label=rownames(data),col="blue",pos=3,offset=0.2)
} #Create function for others aims5. Apply MFA and show results
5.1. Individual plot
res.napping <- MFA(napping,
group=rep(2,nS),
type=rep("c",nS),
name.group=paste("S",1:nS,sep=""),
graph=F) #Calculating napping
summary(res.napping) #Show napping##
## Call:
## MFA(base = napping, group = rep(2, nS), type = rep("c", nS),
## name.group = paste("S", 1:nS, sep = ""), graph = F)
##
##
## Eigenvalues
## Dim.1 Dim.2 Dim.3 Dim.4
## Variance 10.569 6.167 1.695 1.031
## % of var. 54.307 31.687 8.710 5.296
## Cumulative % of var. 54.307 85.994 94.704 100.000
##
## Groups
## Dim.1 ctr cos2 Dim.2 ctr cos2 Dim.3
## S1 | 0.251 2.374 0.063 | 0.457 7.408 0.209 | 0.257
## S2 | 0.124 1.172 0.015 | 0.840 13.625 0.706 | 0.036
## S3 | 0.788 7.454 0.621 | 0.174 2.829 0.030 | 0.002
## S4 | 0.344 3.259 0.118 | 0.394 6.385 0.154 | 0.296
## S5 | 0.935 8.848 0.825 | 0.269 4.369 0.068 | 0.003
## S6 | 0.766 7.244 0.582 | 0.214 3.475 0.046 | 0.062
## S7 | 0.009 0.089 0.000 | 0.215 3.480 0.046 | 0.773
## S8 | 0.272 2.575 0.071 | 0.871 14.124 0.729 | 0.010
## S9 | 0.855 8.094 0.732 | 0.028 0.452 0.001 | 0.011
## S10 | 0.774 7.327 0.600 | 0.080 1.291 0.006 | 0.013
## ctr cos2
## S1 15.179 0.066 |
## S2 2.146 0.001 |
## S3 0.121 0.000 |
## S4 17.438 0.087 |
## S5 0.201 0.000 |
## S6 3.656 0.004 |
## S7 45.617 0.598 |
## S8 0.571 0.000 |
## S9 0.659 0.000 |
## S10 0.759 0.000 |
##
## Individuals
## Dim.1 ctr cos2 Dim.2 ctr cos2 Dim.3
## Fresh Cuts (426,853) - A | -2.164 8.863 0.326 | -2.315 17.375 0.373 | -1.996
## O'star (752,104) - B | -1.424 3.836 0.087 | 4.556 67.323 0.888 | -0.594
## Lays (913,758) - C | -4.070 31.348 0.764 | -1.169 4.434 0.063 | 1.936
## Slide (141,671) - D | 3.479 22.907 0.692 | -1.714 9.531 0.168 | 0.030
## Pringles (347,842) - E | 4.179 33.046 0.857 | 0.642 1.337 0.020 | 0.624
## ctr cos2
## Fresh Cuts (426,853) - A 47.016 0.277 |
## O'star (752,104) - B 4.162 0.015 |
## Lays (913,758) - C 44.215 0.173 |
## Slide (141,671) - D 0.011 0.000 |
## Pringles (347,842) - E 4.597 0.019 |
##
## Continuous variables (the 10 first)
## Dim.1 ctr cos2 Dim.2 ctr cos2 Dim.3
## X1 | -5.130 2.240 0.241 | 7.095 7.341 0.461 | 5.349
## Y1 | -1.256 0.134 0.382 | 0.674 0.066 0.110 | 0.058
## X2 | 2.440 1.157 0.123 | 6.391 13.598 0.841 | -1.329
## Y2 | 0.284 0.016 0.468 | 0.285 0.027 0.472 | -0.075
## X3 | 10.750 7.438 0.787 | -5.063 2.828 0.175 | -0.548
## Y3 | -0.498 0.016 0.979 | -0.029 0.000 0.003 | 0.029
## X4 | -8.906 2.276 0.263 | 11.043 5.996 0.404 | 9.822
## Y4 | 5.853 0.983 0.706 | -2.813 0.389 0.163 | -1.013
## X5 | 6.496 7.740 0.866 | -2.331 1.707 0.111 | 0.409
## Y5 | -2.458 1.108 0.388 | -2.910 2.661 0.544 | 0.094
## ctr cos2
## X1 15.177 0.262 |
## Y1 0.002 0.001 |
## X2 2.139 0.036 |
## Y2 0.007 0.033 |
## X3 0.121 0.002 |
## Y3 0.000 0.003 |
## X4 17.255 0.319 |
## Y4 0.183 0.021 |
## X5 0.191 0.003 |
## Y5 0.010 0.001 |plot.MFA(res.napping, choix="ind", habillage="none", graph.type = "classic")
5.2. Group presentation
plot.MFA(res.napping, choix = "group", habillage = "none",
col.hab = c(rep("black",11)),
graph.type = "classic") #Draw plot's MFA group presentation
RV coefficients
res.napping$group$RV #Calculating RV coefficients to understand how much the are relative## S1 S2 S3 S4 S5 S6
## S1 1.00000000 0.122485515 0.500895094 0.97319257 0.4435680 0.56985887
## S2 0.12248551 1.000000000 0.004801930 0.08005653 0.2373547 0.01811628
## S3 0.50089509 0.004801930 1.000000000 0.61998499 0.8156670 0.99307604
## S4 0.97319257 0.080056533 0.619984988 1.00000000 0.4830767 0.68942326
## S5 0.44356798 0.237354698 0.815666981 0.48307665 1.0000000 0.80625053
## S6 0.56985887 0.018116284 0.993076040 0.68942326 0.8062505 1.00000000
## S7 0.01106272 0.319132810 0.046706531 0.02594278 0.1049715 0.06061842
## S8 0.28315977 0.868462790 0.201636385 0.26501132 0.4321125 0.23837763
## S9 0.05422084 0.207454691 0.658633453 0.16369523 0.6495611 0.62487409
## S10 0.15570167 0.344438247 0.360825678 0.17439486 0.7572759 0.35330294
## S11 0.19733880 0.870315334 0.001439442 0.13124582 0.1998114 0.02187758
## S12 0.03920231 0.514712272 0.241703215 0.05344933 0.6526765 0.24287271
## S13 0.10521585 0.658123004 0.507264428 0.15578122 0.6549901 0.48753807
## S14 0.32697337 0.930062491 0.088931933 0.26602739 0.2369563 0.12043396
## S15 0.36738550 0.002796762 0.873572273 0.42594549 0.9495937 0.85104645
## S16 0.25144315 0.133583398 0.773898060 0.33756349 0.9256910 0.75265073
## S17 0.29365457 0.080831068 0.853124252 0.39331002 0.9178585 0.82838416
## S18 0.22830997 0.068491154 0.825774099 0.30787142 0.8859739 0.79459285
## MFA 0.48082592 0.528627801 0.761791996 0.53134696 0.9078579 0.76863638
## S7 S8 S9 S10 S11 S12
## S1 0.011062718 0.2831598 0.054220843 0.15570167 0.197338803 0.03920231
## S2 0.319132810 0.8684628 0.207454691 0.34443825 0.870315334 0.51471227
## S3 0.046706531 0.2016364 0.658633453 0.36082568 0.001439442 0.24170322
## S4 0.025942780 0.2650113 0.163695227 0.17439486 0.131245816 0.05344933
## S5 0.104971511 0.4321125 0.649561091 0.75727593 0.199811428 0.65267648
## S6 0.060618425 0.2383776 0.624874091 0.35330294 0.021877582 0.24287271
## S7 1.000000000 0.1293264 0.007187942 0.01869212 0.285016419 0.01426432
## S8 0.129326371 1.0000000 0.271217883 0.46212405 0.711249871 0.65662274
## S9 0.007187942 0.2712179 1.000000000 0.52943640 0.294510655 0.61976038
## S10 0.018692118 0.4621240 0.529436400 1.00000000 0.229981980 0.89992929
## S11 0.285016419 0.7112499 0.294510655 0.22998198 1.000000000 0.43091009
## S12 0.014264321 0.6566227 0.619760380 0.89992929 0.430910090 1.00000000
## S13 0.198417110 0.6603220 0.863359955 0.59296684 0.654803583 0.75949537
## S14 0.248103467 0.8978929 0.078171360 0.16513599 0.824021971 0.34643224
## S15 0.121441606 0.1999380 0.667438032 0.58194579 0.018687344 0.46930508
## S16 0.012725664 0.3036861 0.832000900 0.81074545 0.127010342 0.73500694
## S17 0.022540615 0.2350166 0.846449705 0.70912423 0.082760458 0.62116620
## S18 0.087750676 0.2351398 0.877329021 0.60392463 0.130487334 0.58695400
## MFA 0.221097732 0.6575141 0.754045941 0.71283284 0.508390739 0.72498858
## S13 S14 S15 S16 S17 S18
## S1 0.1052159 0.326973374 0.367385500 0.25144315 0.293654566 0.22830997
## S2 0.6581230 0.930062491 0.002796762 0.13358340 0.080831068 0.06849115
## S3 0.5072644 0.088931933 0.873572273 0.77389806 0.853124252 0.82577410
## S4 0.1557812 0.266027393 0.425945492 0.33756349 0.393310021 0.30787142
## S5 0.6549901 0.236956257 0.949593661 0.92569099 0.917858495 0.88597388
## S6 0.4875381 0.120433957 0.851046448 0.75265073 0.828384164 0.79459285
## S7 0.1984171 0.248103467 0.121441606 0.01272566 0.022540615 0.08775068
## S8 0.6603220 0.897892937 0.199938044 0.30368610 0.235016572 0.23513978
## S9 0.8633600 0.078171360 0.667438032 0.83200090 0.846449705 0.87732902
## S10 0.5929668 0.165135994 0.581945790 0.81074545 0.709124229 0.60392463
## S11 0.6548036 0.824021971 0.018687344 0.12701034 0.082760458 0.13048733
## S12 0.7594954 0.346432240 0.469305081 0.73500694 0.621166197 0.58695400
## S13 1.0000000 0.535899089 0.558200021 0.72210112 0.703056788 0.73886504
## S14 0.5358991 1.000000000 0.029547945 0.03492822 0.008533544 0.02851934
## S15 0.5582000 0.029547945 1.000000000 0.89598409 0.924321634 0.93619834
## S16 0.7221011 0.034928224 0.895984093 1.00000000 0.984420188 0.93821516
## S17 0.7030568 0.008533544 0.924321634 0.98442019 1.000000000 0.95716890
## S18 0.7388650 0.028519338 0.936198343 0.93821516 0.957168899 1.00000000
## MFA 0.8617731 0.504139575 0.803226692 0.86079261 0.851631856 0.83326288
## MFA
## S1 0.4808259
## S2 0.5286278
## S3 0.7617920
## S4 0.5313470
## S5 0.9078579
## S6 0.7686364
## S7 0.2210977
## S8 0.6575141
## S9 0.7540459
## S10 0.7128328
## S11 0.5083907
## S12 0.7249886
## S13 0.8617731
## S14 0.5041396
## S15 0.8032267
## S16 0.8607926
## S17 0.8516319
## S18 0.8332629
## MFA 1.0000000