Análisis de componentes principales
Valores propios
PcaEv(M=D,s=T) #s=T: Estandalizar (Necesario con unidades diferentes)
D=Ip('s4.txt') #Ejemplo sencillo´Puntaje de 4 personas## File: s4.txt / Class: data.frame / Rows: 4 / Columns: 3
##
## v1.English v2.Physics v3.Latin
## i1.Ana 9 14 18
## i2.Juan 17 7 11
## i3.Mary 15 13 14
## i4.Ken 5 18 8PcaEv() #Valor propio (varianza), Porcentaj, Porcentaje acumulado## Ev V% V%(cum)
## Dim.1 1.9570 65.2 65.2
## Dim.2 0.9492 31.6 96.9
## Dim.3 0.0938 3.1 100.0PcaEv(s=F) #s=F: Sin estandarilizar (Unidades iguales)## Ev V% V%(cum)
## Dim.1 49.4511 71.4 71.4
## Dim.2 17.4946 25.3 96.7
## Dim.3 2.3043 3.3 100.0Biplot
gPcaBp(M=D,lx=’‘,ly=’‘,s=0,c=’‘,a=T,w=F,f=12,m=1)
M:Martiz num:erica,lx,ly:R:otulos,s=[0]:Variable+Individuo/s=1:Variable/s=2:Individuo,
c:Asignar colores(ex:’V=>blue,..’),a:Flecha,w:Blanco y negro,f:Tamaño de letra,m:Multiplicador de variable
Colores: http://www.sthda.com/english/wiki/colors-in-r\ [b]lue(#039), [c]harcoal(#c30), [o]range(#f60), [p]urple(#coc), [r]ed (#f30)
D=Ip('s4.txt') #Ejemplo sencillo: Puntaje de 4 personas## File: s4.txt / Class: data.frame / Rows: 4 / Columns: 3
##
## v1.English v2.Physics v3.Latin
## i1.Ana 9 14 18
## i2.Juan 17 7 11
## i3.Mary 15 13 14
## i4.Ken 5 18 8gPcaBp() #Bilot
gPcaBp(s=1) #s=1:Dispersión de variables
gPcaBp(s=2) #s=2:Dispersión de individuos
gPcaBp(c='V=>#f60,V:v2=>orange,I:i3=>red') #c:Asignar colores
gPcaBp(c='V=>b',a=F) #a:Flecha=F
gPcaBp(c='V=>r',st=T) #st:Estandarizar=T
gPcaBp(c='V=>p',f=12) #f:Tamaño de letras=10
gPcaBp(lx='Eje-1 (65.2%)',ly='Eje-2 (31.6%)',w=T) #w=T:Blanco y negro
gChart(a=90)+gPcaBp()#Comparación:Distribución de frecuencia+Biplot de PCA
D=USArrests; Dt() #Datos:USArrests## Class: data.frame / Rows: 50 / Columns: 4
##
## Murder Assault UrbanPop Rape
## Alabama 13.2 236 58 21.2
## Alaska 10.0 263 48 44.5
## Arizona 8.1 294 80 31.0
## Arkansas 8.8 190 50 19.5
## California 9.0 276 91 40.6
## Colorado 7.9 204 78 38.7
## Connecticut 3.3 110 77 11.1
## Delaware 5.9 238 72 15.8
## Florida 15.4 335 80 31.9
## Georgia 17.4 211 60 25.8PcaEv() #Valores propios (varianza), Porcentaje, Porcentaje acumulado## Ev V% V%(cum)
## Dim.1 2.4802 62.0 62.0
## Dim.2 0.9898 24.7 86.8
## Dim.3 0.3566 8.9 95.7
## Dim.4 0.1734 4.3 100.0gPcaBp(c='V=>blue') #c:Color de rótulos=blue
gPcaBp(c='V=>blue',m=1.8,f=10) #Id.+m:Multiplicador de variable=1.8,f:Tamaño de letra=10
D=decathlon2; Dt(n=T) #Datos: decathlon2(+Rank,Points,Competition)## Class: data.frame / Rows: 27 / Columns: 13
##
## X100m Long.jump Shot.put High.jump X400m X110m.hurdle Discus
## SEBRLE 11.04 7.58 14.83 2.07 49.81 14.69 43.75
## CLAY 10.76 7.40 14.26 1.86 49.37 14.05 50.72
## BERNARD 11.02 7.23 14.25 1.92 48.93 14.99 40.87
## YURKOV 11.34 7.09 15.19 2.10 50.42 15.31 46.26
## ZSIVOCZKY 11.13 7.30 13.48 2.01 48.62 14.17 45.67
## McMULLEN 10.83 7.31 13.76 2.13 49.91 14.38 44.41
## MARTINEAU 11.64 6.81 14.57 1.95 50.14 14.93 47.60
## HERNU 11.37 7.56 14.41 1.86 51.10 15.06 44.99
## BARRAS 11.33 6.97 14.09 1.95 49.48 14.48 42.10
## NOOL 11.33 7.27 12.68 1.98 49.20 15.29 37.92
## Pole.vault Javeline X1500m Rank Points Competition
## SEBRLE 5.02 63.19 291.7 1 8217 Decastar
## CLAY 4.92 60.15 301.5 2 8122 Decastar
## BERNARD 5.32 62.77 280.1 4 8067 Decastar
## YURKOV 4.72 63.44 276.4 5 8036 Decastar
## ZSIVOCZKY 4.42 55.37 268.0 7 8004 Decastar
## McMULLEN 4.42 56.37 285.1 8 7995 Decastar
## MARTINEAU 4.92 52.33 262.1 9 7802 Decastar
## HERNU 4.82 57.19 285.1 10 7733 Decastar
## BARRAS 4.72 55.40 282.0 11 7708 Decastar
## NOOL 4.62 57.44 266.6 12 7651 Decastar
##
## Row names: 1.SEBRLE 2.CLAY 3.BERNARD 4.YURKOV 5.ZSIVOCZKY 6.McMULLEN 7.MARTINEAU 8.HERNU 9.BARRAS 10.NOOL
##
## Column names: 1.X100m 2.Long.jump 3.Shot.put 4.High.jump 5.X400m 6.X110m.hurdle 7.Discus 8.Pole.vault 9.Javeline 10.X1500m 11.Rank 12.Points 13.CompetitionD=D[1:15,1:10]; Dt() #Filas:1-15, Columnas:1-10## Class: data.frame / Rows: 15 / Columns: 10
##
## X100m Long.jump Shot.put High.jump X400m X110m.hurdle Discus
## SEBRLE 11.04 7.58 14.83 2.07 49.81 14.69 43.75
## CLAY 10.76 7.40 14.26 1.86 49.37 14.05 50.72
## BERNARD 11.02 7.23 14.25 1.92 48.93 14.99 40.87
## YURKOV 11.34 7.09 15.19 2.10 50.42 15.31 46.26
## ZSIVOCZKY 11.13 7.30 13.48 2.01 48.62 14.17 45.67
## McMULLEN 10.83 7.31 13.76 2.13 49.91 14.38 44.41
## MARTINEAU 11.64 6.81 14.57 1.95 50.14 14.93 47.60
## HERNU 11.37 7.56 14.41 1.86 51.10 15.06 44.99
## BARRAS 11.33 6.97 14.09 1.95 49.48 14.48 42.10
## NOOL 11.33 7.27 12.68 1.98 49.20 15.29 37.92
## Pole.vault Javeline X1500m
## SEBRLE 5.02 63.19 291.7
## CLAY 4.92 60.15 301.5
## BERNARD 5.32 62.77 280.1
## YURKOV 4.72 63.44 276.4
## ZSIVOCZKY 4.42 55.37 268.0
## McMULLEN 4.42 56.37 285.1
## MARTINEAU 4.92 52.33 262.1
## HERNU 4.82 57.19 285.1
## BARRAS 4.72 55.40 282.0
## NOOL 4.62 57.44 266.6PcaEv() #Valores propios (varianza), Porcentaje, Porcentaje acumulado## Ev V% V%(cum)
## Dim.1 4.7291 47.3 47.3
## Dim.2 1.9975 20.0 67.3
## Dim.3 1.0856 10.9 78.1
## Dim.4 0.7539 7.5 85.7
## Dim.5 0.6559 6.6 92.2
## Dim.6 0.3021 3.0 95.2
## Dim.7 0.2253 2.3 97.5
## Dim.8 0.1417 1.4 98.9
## Dim.9 0.0714 0.7 99.6
## Dim.10 0.0374 0.4 100.0gPcaBp() #Bilpot
gPcaBp(c='V=>b') #c:Color de rótulos=blue
gPcaBp(c='V=>b',m=3.5) #Id.+m:Multiplicador de variable=3.5
Grp=decathlon2[1:15,13]; G #Grupos:Competition
gPcaGp(D,Grp,lg='Grupo') #ACP-Grupo
gPcaGp(D,Grp,lg='Grupo',c='forestgreen,red') #Id.c:Color de drupo
—–
(Hiroto Ueda, Universidad de Tokio, 2022)