Práctica 4: Descomposición de series de tiempo
García Herrera Garbriela
Objetivo
Realizar una función para obtener y graficar: tendencia, estacionalidad y componente aleatorio de una serie de tiempo.
Además realizar una función general que descomponga la serie de tiempo y grafique sus componentes
Funciones
Tendencia
Objetivo
Función que obtiene y grafica la tendencia de una serie de tiempo a través de medias móviles
Parámetros
st = Objeto de clase serie de tiempo
mm = número de observaciones a promediar para el cálculo de medias moviles Código de la función
tendencia<-function(st,mm) {
if(class(st)=="ts"){
st<-as.numeric(st)
ma<-SMA(st, mm)
mm<-ma %>% na.omit()
ma<-ma %>% as.numeric()
if(mm[1]<mm[length(mm)])
{tendencia <-"La tendencia de la serie de tiempo es CRECIENTE." }
else if(mm[1]>mm[length(mm)])
{tendencia <- "La tendencia de la serie de tiempo es DECRECIENTE."}
else
{ tendencia<-"La tendencias de la serie es constante"}
t<-c(1:length(st))
t1<-c(1:length(ma))
tend<-ggplot()+
geom_line(aes(x=t, y=ma), color="orange", size=1.3)+
labs(x="Tiempo", y="Tendencia", title = "Tendencia de la serie con Medias Moviles", subtitle = tendencia)+
theme_solarized_2()
return(list(tend, ma))
}else{
stop('El primer parámetro no es de clase Time-Series')
}
}Ejemplos
Ejemplo 1
data("AirPassengers")
st<- AirPassengers
tendencia(st,12)## [[1]]
##
## [[2]]
## [1] NA NA NA NA NA NA NA
## [8] NA NA NA NA 126.6667 126.9167 127.5833
## [15] 128.3333 128.8333 129.1667 130.3333 132.1667 134.0000 135.8333
## [22] 137.0000 137.8333 139.6667 142.1667 144.1667 147.2500 149.5833
## [29] 153.5000 155.9167 158.3333 160.7500 162.9167 165.3333 168.0000
## [36] 170.1667 172.3333 174.8333 176.0833 177.5833 178.5000 181.8333
## [43] 184.4167 188.0000 190.0833 192.5000 194.6667 197.0000 199.0833
## [50] 200.4167 204.0000 208.5000 212.3333 214.4167 217.2500 219.7500
## [57] 222.0833 223.7500 224.4167 225.0000 225.6667 225.0000 224.9167
## [64] 224.2500 224.6667 226.4167 229.5833 231.3333 233.1667 234.6667
## [71] 236.5833 238.9167 242.0833 245.8333 248.5000 252.0000 255.0000
## [78] 259.2500 264.4167 268.9167 273.3333 277.0833 279.9167 284.0000
## [85] 287.5000 291.1667 295.3333 299.0000 303.0000 307.9167 312.0000
## [92] 316.8333 320.4167 323.0833 325.9167 328.2500 330.8333 332.8333
## [99] 336.0833 339.0000 342.0833 346.0833 350.4167 355.5833 359.6667
## [106] 363.0833 365.9167 368.4167 370.5000 371.9167 372.4167 372.4167
## [113] 373.0833 374.1667 376.3333 379.5000 379.5000 380.5000 380.9167
## [120] 381.0000 382.6667 384.6667 388.3333 392.3333 397.0833 400.1667
## [127] 404.9167 409.4167 414.3333 418.3333 422.6667 428.3333 433.0833
## [134] 437.1667 438.2500 443.6667 448.0000 453.2500 459.4167 463.3333
## [141] 467.0833 471.5833 473.9167 476.1667Ejemplo 2
ser1<-c(as.numeric(fecha[100], "%y"),as.numeric(fecha[100], "%m"))
st1<-ts(series[924,-c(1:7)] %>% t() %>% na.omit(),frequency = 12, start=ser1[1])
tendencia(st1,6)## [[1]]
##
## [[2]]
## [1] NA NA NA NA NA 944.6667 946.3333
## [8] 948.3333 950.0000 951.6667 953.3333 955.1667 956.6667 958.1667
## [15] 960.1667 961.8333 963.5000 964.6667 966.3333 968.0000 969.3333
## [22] 971.5000 973.3333 975.1667 976.8333 976.8333 976.8333 976.8333
## [29] 976.8333 977.5000 977.8333 978.5000 979.5000 980.0000 981.1667
## [36] 982.3333 984.0000 986.5000 989.0000 992.0000 994.5000 996.5000
## [43] 998.6667 1000.1667 1001.3333 1002.3333 1003.3333 1004.6667 1005.8333
## [50] 1007.6667 1009.5000 1011.6667 1013.6667 1015.6667 1017.1667 1020.3333
## [57] 1022.0000 1023.0000 1024.1667 1025.5000 1026.8333 1026.5000 1027.1667
## [64] 1027.6667 1028.5000 1029.0000 1029.6667 1030.0000 1030.8333 1032.3333
## [71] 1033.5000 1033.6667 1033.5000 1033.5000 1033.6667 1033.1667 1032.6667
## [78] 1033.5000 1035.0000 1037.1667 1039.0000 1040.8333 1042.5000 1043.8333
## [85] 1045.5000 1047.3333 1049.5000 1052.3333 1055.6667 1058.5000 1061.0000
## [92] 1063.0000 1065.0000 1066.6667 1067.6667 1070.5000 1074.0000 1076.8333
## [99] 1080.0000 1084.0000 1087.8333 1090.3333 1092.0000 1094.1667 1096.0000
## [106] 1097.6667 1099.6667 1102.5000 1104.6667 1105.6667 1105.8333 1105.3333
## [113] 1104.1667 1102.1667 1101.5000 1099.3333 1098.5000 1096.8333 1095.5000
## [120] 1093.8333Ejemplo 3
ser1<-c(as.numeric(fecha[100], "%y"),as.numeric(fecha[100], "%m"))
st1<-ts(series[1000,-c(1:7)] %>% t() %>% na.omit(),frequency = 12, start=ser1[1])
tendencia(st1,12)## [[1]]
##
## [[2]]
## [1] NA NA NA NA NA NA NA
## [8] NA NA NA NA 55.91167 55.87000 55.82000
## [15] 55.79917 55.70500 55.63083 55.45500 55.27083 55.14250 55.02417
## [22] 54.89083 54.81417 54.69833 54.61083 54.53000 54.46000 54.48000
## [29] 54.52250 54.62667 54.72583 54.86917 54.98000 55.13333 55.21917
## [36] 55.29750 55.40833 55.51250 55.67500 55.73167 55.80417 55.80167
## [43] 55.84417 55.81333 55.77750 55.74750 55.71167 55.74750 55.76417
## [50] 55.77833 55.69333 55.64833 55.60167 55.60000 55.53750 55.47417
## [57] 55.48917 55.45083 55.47417 55.46167 55.48333 55.51750 55.59500
## [64] 55.63000 55.61167 55.62500 55.65333 55.67667 55.62500 55.60250
## [71] 55.52167 55.36833 55.18667 55.04083 54.85833 54.71833 54.59000
## [78] 54.42083 54.24667 54.10667 54.01833 53.92917 53.85750 53.82500
## [85] 53.83750 53.80083 53.81667 53.87250 53.95750 54.06250 54.23167
## [92] 54.40667 54.56000 54.71833 54.89000 55.11083 55.29000 55.45250
## [99] 55.59083 55.70417 55.82500 55.93167 55.98167 56.02333 56.06083
## [106] 56.08750 56.08083 56.05167 55.99583 56.00250 56.02750 56.00917
## [113] 55.97000 55.94667 55.94750 55.89250 55.84250 55.79333 55.73000
## [120] 55.67667 55.59333 55.44167 55.26750 55.11333 54.98083 54.82417
## [127] 54.64667 54.53083 54.41417 54.32333 54.25833 54.17833Estacionalidad
Objetivo
Función que obtiene y grafica la estacionalidad de una serie de tiempo
Parámetros
ST Objeto de clase serie de tiempo
tipoST = Tipo de la serie de tiempo, puede ser "multiplicativa" o "aditiva "
mm = número de observaciones a promediar para el cálculo de medias movilesCódigo de la función
estacionalidad<- function(st, tipoST, mm){
if(class(st)=="ts"){
if(tipoST=="aditiva"){
tendencia1<-tendencia(st,mm)
st<-st %>% as.numeric()
sin_tend<-st-tendencia1[[2]]
aux<-matrix(data = sin_tend, nrow = 4) %>% t()
estacional<-colMeans(aux, na.rm=T)
estacional<-rep_len(estacional, length.out = length(st))
df<-data.frame(Tiempo=c(1:length(st)), Serie_T=st, Tendencia=tendencia1[[2]], Estacionalidad=estacional)
estacionalidad1<-ggplot(df)+
geom_line(aes(x=Tiempo, y=Estacionalidad), color="red", size=1.5)+
geom_point(aes(x=Tiempo, y=Estacionalidad), color="black")+
labs(x="Tiempo", y="Estacionalidad", title = "Estacionalidad de la Serie de Tiempo")+
theme_solarized_2()
return(list(estacionalidad1, df$Estacionalidad))
}else if(tipoST=="multiplicativa"){
tendencia1<-tendencia(st,mm)
st<-st %>% as.numeric()
sin_tend<-st/tendencia1[[2]]
aux<-matrix(data = sin_tend, nrow = 4) %>% t()
estacional<-colMeans(aux, na.rm=T)
estacional<-rep_len(estacional, length.out = length(st))
df<-data.frame(Tiempo=c(1:length(st)), Serie_T=st, Tendencia=tendencia1[[2]], Estacionalidad=estacional)
estacionalidad1<-ggplot(df)+geom_line(aes(x=Tiempo, y=Estacionalidad), color="#FF6600", size=1.5)+
geom_point(aes(x=Tiempo, y=Estacionalidad), color="black")+
labs(x="Tiempo", y="Estacionalidad", title = "Estacionalidad de la Serie de Tiempo")+
theme_solarized_2()
return(list(estacionalidad1, df$Estacionalidad))
}else {stop('Parametro incorrecto')}
}else{stop('El objeto no es de clase serie de tiempo')}
}Ejemplos
Ejemplo 1
data("AirPassengers")
st<- AirPassengers
estacionalidad(st,"aditiva", 12)## [[1]]
##
## [[2]]
## [1] 10.161616 6.507576 18.439394 23.671569 10.161616 6.507576 18.439394
## [8] 23.671569 10.161616 6.507576 18.439394 23.671569 10.161616 6.507576
## [15] 18.439394 23.671569 10.161616 6.507576 18.439394 23.671569 10.161616
## [22] 6.507576 18.439394 23.671569 10.161616 6.507576 18.439394 23.671569
## [29] 10.161616 6.507576 18.439394 23.671569 10.161616 6.507576 18.439394
## [36] 23.671569 10.161616 6.507576 18.439394 23.671569 10.161616 6.507576
## [43] 18.439394 23.671569 10.161616 6.507576 18.439394 23.671569 10.161616
## [50] 6.507576 18.439394 23.671569 10.161616 6.507576 18.439394 23.671569
## [57] 10.161616 6.507576 18.439394 23.671569 10.161616 6.507576 18.439394
## [64] 23.671569 10.161616 6.507576 18.439394 23.671569 10.161616 6.507576
## [71] 18.439394 23.671569 10.161616 6.507576 18.439394 23.671569 10.161616
## [78] 6.507576 18.439394 23.671569 10.161616 6.507576 18.439394 23.671569
## [85] 10.161616 6.507576 18.439394 23.671569 10.161616 6.507576 18.439394
## [92] 23.671569 10.161616 6.507576 18.439394 23.671569 10.161616 6.507576
## [99] 18.439394 23.671569 10.161616 6.507576 18.439394 23.671569 10.161616
## [106] 6.507576 18.439394 23.671569 10.161616 6.507576 18.439394 23.671569
## [113] 10.161616 6.507576 18.439394 23.671569 10.161616 6.507576 18.439394
## [120] 23.671569 10.161616 6.507576 18.439394 23.671569 10.161616 6.507576
## [127] 18.439394 23.671569 10.161616 6.507576 18.439394 23.671569 10.161616
## [134] 6.507576 18.439394 23.671569 10.161616 6.507576 18.439394 23.671569
## [141] 10.161616 6.507576 18.439394 23.671569Ejemplo 2
ser1<-c(as.numeric(fecha[100], "%y"),as.numeric(fecha[100], "%m"))
st1<-ts(series[924,-c(1:7)] %>% t() %>% na.omit(),frequency = 12, start=ser1[1])
estacionalidad(st1,"multiplicativa", 6)## [[1]]
##
## [[2]]
## [1] 1.003203 1.002916 1.003053 1.003332 1.003203 1.002916 1.003053
## [8] 1.003332 1.003203 1.002916 1.003053 1.003332 1.003203 1.002916
## [15] 1.003053 1.003332 1.003203 1.002916 1.003053 1.003332 1.003203
## [22] 1.002916 1.003053 1.003332 1.003203 1.002916 1.003053 1.003332
## [29] 1.003203 1.002916 1.003053 1.003332 1.003203 1.002916 1.003053
## [36] 1.003332 1.003203 1.002916 1.003053 1.003332 1.003203 1.002916
## [43] 1.003053 1.003332 1.003203 1.002916 1.003053 1.003332 1.003203
## [50] 1.002916 1.003053 1.003332 1.003203 1.002916 1.003053 1.003332
## [57] 1.003203 1.002916 1.003053 1.003332 1.003203 1.002916 1.003053
## [64] 1.003332 1.003203 1.002916 1.003053 1.003332 1.003203 1.002916
## [71] 1.003053 1.003332 1.003203 1.002916 1.003053 1.003332 1.003203
## [78] 1.002916 1.003053 1.003332 1.003203 1.002916 1.003053 1.003332
## [85] 1.003203 1.002916 1.003053 1.003332 1.003203 1.002916 1.003053
## [92] 1.003332 1.003203 1.002916 1.003053 1.003332 1.003203 1.002916
## [99] 1.003053 1.003332 1.003203 1.002916 1.003053 1.003332 1.003203
## [106] 1.002916 1.003053 1.003332 1.003203 1.002916 1.003053 1.003332
## [113] 1.003203 1.002916 1.003053 1.003332 1.003203 1.002916 1.003053
## [120] 1.003332Ejemplo 3
series<-read_excel("MC1001.xls")## New names:
## * `` -> ...158
## * `` -> ...159
## * `` -> ...160
## * `` -> ...161
## * `` -> ...162
## * ... and 3 more problemsfecha<-series$`Starting date`
ser1<-c(as.numeric(fecha[100], "%y"),as.numeric(fecha[100], "%m"))
st1<-ts(series[1000,-c(1:7)] %>% t() %>% na.omit(),frequency = 12, start=ser1[1])
estacionalidad(st1,"aditiva" , 12)## [[1]]
##
## [[2]]
## [1] -0.08252778 -0.08558333 -0.07455556 -0.07212366 -0.08252778
## [6] -0.08558333 -0.07455556 -0.07212366 -0.08252778 -0.08558333
## [11] -0.07455556 -0.07212366 -0.08252778 -0.08558333 -0.07455556
## [16] -0.07212366 -0.08252778 -0.08558333 -0.07455556 -0.07212366
## [21] -0.08252778 -0.08558333 -0.07455556 -0.07212366 -0.08252778
## [26] -0.08558333 -0.07455556 -0.07212366 -0.08252778 -0.08558333
## [31] -0.07455556 -0.07212366 -0.08252778 -0.08558333 -0.07455556
## [36] -0.07212366 -0.08252778 -0.08558333 -0.07455556 -0.07212366
## [41] -0.08252778 -0.08558333 -0.07455556 -0.07212366 -0.08252778
## [46] -0.08558333 -0.07455556 -0.07212366 -0.08252778 -0.08558333
## [51] -0.07455556 -0.07212366 -0.08252778 -0.08558333 -0.07455556
## [56] -0.07212366 -0.08252778 -0.08558333 -0.07455556 -0.07212366
## [61] -0.08252778 -0.08558333 -0.07455556 -0.07212366 -0.08252778
## [66] -0.08558333 -0.07455556 -0.07212366 -0.08252778 -0.08558333
## [71] -0.07455556 -0.07212366 -0.08252778 -0.08558333 -0.07455556
## [76] -0.07212366 -0.08252778 -0.08558333 -0.07455556 -0.07212366
## [81] -0.08252778 -0.08558333 -0.07455556 -0.07212366 -0.08252778
## [86] -0.08558333 -0.07455556 -0.07212366 -0.08252778 -0.08558333
## [91] -0.07455556 -0.07212366 -0.08252778 -0.08558333 -0.07455556
## [96] -0.07212366 -0.08252778 -0.08558333 -0.07455556 -0.07212366
## [101] -0.08252778 -0.08558333 -0.07455556 -0.07212366 -0.08252778
## [106] -0.08558333 -0.07455556 -0.07212366 -0.08252778 -0.08558333
## [111] -0.07455556 -0.07212366 -0.08252778 -0.08558333 -0.07455556
## [116] -0.07212366 -0.08252778 -0.08558333 -0.07455556 -0.07212366
## [121] -0.08252778 -0.08558333 -0.07455556 -0.07212366 -0.08252778
## [126] -0.08558333 -0.07455556 -0.07212366 -0.08252778 -0.08558333
## [131] -0.07455556 -0.07212366Componente Aleatorio
Objetivo
Función que obtiene y grafica del componente aleatorio de una serie de tiempo
Parámetros
ST Objeto de clase serie de tiempo
tipoST = Tipo de la serie de tiempo, puede ser "multiplicativa" o "aditiva "
mm = número de observaciones a promediar para el cálculo de medias movilesCódigo de la función
componente_aleatorio<-function(st, tipoST, mm){
if(class(st)=="ts"){
if(tipoST=="aditiva"){
tendencia1<-tendencia(st,mm)
estacionalidad1 <- estacionalidad(st, tipoST, mm)
st<-st %>% as.numeric()
c_a<-st-tendencia1[[2]]-estacionalidad1[[2]]
df<-data.frame(Tiempo=c(1:length(st)), ComponenteA=c_a)
aleatorio<-ggplot(df)+
geom_line(aes(x=Tiempo, y=c_a), color="#CC0066", size=1)+
geom_point(aes(x=Tiempo, y=c_a), color="black")+
labs(x="Tiempo", y="Componente Aleatorio", title = "Componente Aleatorio de la Serie de Tiempo")+
theme_solarized_2()
return(list(aleatorio,c_a))
}else if(tipoST=="multiplicativa"){
tendencia1<-tendencia(st,mm)
estacionalidad1 <- estacionalidad(st, tipoST, mm)
st<-st %>% as.numeric()
c_a<-st/(tendencia1[[2]]*estacionalidad1[[2]])
df<-data.frame(Tiempo=c(1:length(st)), ComponenteA=c_a)
aleatorio<-ggplot(df)+
geom_line(aes(x=Tiempo, y=c_a), color="#006699",size=1)+
geom_point(aes(x=Tiempo, y=c_a), color="black")+
labs(x="Tiempo", y="Componente Aleatorio", title = "Componente Aleatorio de la Serie de Tiempo")+
theme_solarized_2()
return(list(aleatorio,c_a))
}else(stop('Parámetro incorrecto'))
} else{stop('El objeto no es de clase "TS')}
}Ejemplos
Ejemplo 1
data("AirPassengers")
st<- AirPassengers
componente_aleatorio(st,"aditiva", 12)## [[1]]
##
## [[2]]
## [1] NA NA NA NA NA
## [6] NA NA NA NA NA
## [11] NA -32.33823529 -22.07828283 -8.09090909 -5.77272727
## [16] -17.50490196 -14.32828283 12.15909091 19.39393939 12.32843137
## [21] 12.00505051 -10.50757576 -42.27272727 -23.33823529 -7.32828283
## [26] -0.67424242 12.31060606 -10.25490196 8.33838384 15.57575758
## [31] 22.22727273 14.57843137 10.92171717 -9.84090909 -40.43939394
## [36] -27.83823529 -11.49494949 -1.34090909 -1.52272727 -20.25490196
## [41] -5.66161616 29.65909091 27.14393939 30.32843137 8.75505051
## [46] -8.00757576 -41.10606061 -26.67156863 -13.24494949 -10.92424242
## [51] 13.56060606 2.82843137 6.50505051 22.07575758 28.31060606
## [56] 28.57843137 4.75505051 -19.25757576 -62.85606061 -47.67156863
## [61] -31.82828283 -43.50757576 -8.35606061 -20.92156863 -0.82828283
## [66] 31.07575758 53.97727273 37.99509804 15.67171717 -12.17424242
## [71] -52.02272727 -33.58823529 -10.24494949 -19.34090909 0.06060606
## [76] -6.67156863 4.83838384 49.24242424 81.14393939 54.41176471
## [81] 28.50505051 -9.59090909 -61.35606061 -29.67156863 -13.66161616
## [86] -20.67424242 3.22727273 -9.67156863 4.83838384 59.57575758
## [91] 82.56060606 64.49509804 24.42171717 -23.59090909 -73.35606061
## [96] -45.92156863 -25.99494949 -38.34090909 1.47727273 -14.67156863
## [101] 2.75505051 69.40909091 96.14393939 87.74509804 34.17171717
## [106] -22.59090909 -79.35606061 -56.08823529 -40.66161616 -60.42424242
## [111] -28.85606061 -48.08823529 -20.24494949 54.32575758 96.22727273
## [116] 101.82843137 14.33838384 -28.00757576 -89.35606061 -67.67156863
## [121] -32.82828283 -49.17424242 -0.77272727 -20.00490196 12.75505051
## [126] 65.32575758 124.64393939 125.91176471 38.50505051 -17.84090909
## [131] -79.10606061 -47.00490196 -26.24494949 -52.67424242 -37.68939394
## [136] -6.33823529 13.83838384 75.24242424 144.14393939 118.99509804
## [141] 30.75505051 -17.09090909 -102.35606061 -67.83823529Ejemplo 2
ser1<-c(as.numeric(fecha[100], "%y"),as.numeric(fecha[100], "%m"))
st1<-ts(series[924,-c(1:7)] %>% t() %>% na.omit(),frequency = 12, start=ser1[1])
componente_aleatorio(st1,"multiplicativa", 6)## [[1]]
##
## [[2]]
## [1] NA NA NA NA NA 1.0006110 1.0008191
## [8] 1.0036859 1.0020540 1.0005851 1.0007908 1.0006793 0.9992389 1.0031630
## [15] 1.0040515 0.9999607 1.0004286 0.9984708 0.9997075 1.0028571 1.0026349
## [22] 1.0037639 1.0017362 0.9985531 0.9990186 0.9942006 0.9950852 0.9978697
## [29] 0.9979982 1.0006628 1.0001849 0.9961700 0.9983341 0.9981101 1.0008513
## [36] 1.0024287 1.0038987 1.0036625 1.0040126 1.0037123 1.0023204 1.0005948
## [43] 1.0022805 0.9985063 0.9984668 0.9997454 0.9996060 0.9999862 1.0019279
## [50] 1.0023700 1.0014004 1.0029188 1.0010689 1.0013468 0.9997333 1.0080755
## [57] 0.9987583 0.9980673 0.9977675 0.9991091 0.9979402 1.0004924 0.9977651
## [64] 0.9960328 0.9982614 0.9990307 0.9992155 0.9986146 0.9989028 1.0006342
## [71] 1.0003326 0.9950723 0.9943964 0.9956455 0.9972778 0.9965185 0.9980947
## [78] 1.0004694 1.0017725 1.0042069 1.0025640 1.0001263 0.9983908 0.9977933
## [85] 1.0010981 1.0053436 1.0050308 1.0048876 1.0046763 1.0003896 1.0007149
## [92] 1.0013674 1.0014875 1.0011434 0.9991351 1.0045932 1.0079452 1.0046546
## [99] 1.0052643 1.0067932 1.0015420 1.0004458 1.0006082 1.0001711 1.0004456
## [106] 1.0037541 1.0017915 1.0043635 1.0007179 0.9955897 0.9925989 0.9936737
## [113] 0.9912406 0.9942279 1.0001241 0.9891241 0.9945391 0.9926989 0.9919511
## [120] 0.9922753Ejemplo 3
series<-read_excel("MC1001.xls")## New names:
## * `` -> ...158
## * `` -> ...159
## * `` -> ...160
## * `` -> ...161
## * `` -> ...162
## * ... and 3 more problemsfecha<-series$`Starting date`
ser1<-c(as.numeric(fecha[100], "%y"),as.numeric(fecha[100], "%m"))
st1<-ts(series[1000,-c(1:7)] %>% t() %>% na.omit(),frequency = 12, start=ser1[1])
componente_aleatorio(st1,"aditiva" , 12)## [[1]]
##
## [[2]]
## [1] NA NA NA NA NA
## [6] NA NA NA NA NA
## [11] NA 0.15045699 -0.37747222 -0.33441667 -0.52461111
## [16] -0.75287634 -0.89830556 -1.12941667 -0.98627778 -0.71037634
## [21] -0.45163889 -0.66525000 0.06038889 -0.02620968 -0.16830556
## [26] -0.01441667 -0.02544444 0.71212366 0.72002778 0.94891667
## [31] 0.74872222 1.28295699 0.92252778 0.93225000 0.68538889
## [36] 0.31462366 0.36419444 0.25308333 0.70955556 0.14045699
## [41] 0.30836111 -0.25608333 0.14038889 -0.03120968 -0.30497222
## [46] -0.04191667 -0.23711111 0.29462366 0.20836111 0.15725000
## [51] -0.32877778 -0.31620968 -0.04913889 -0.07441667 -0.30294444
## [56] -0.45204301 0.16336111 -0.20525000 0.28038889 0.43045699
## [61] 0.74919444 0.82808333 0.69955556 0.12212366 -0.27913889
## [66] 0.06058333 -0.07877778 -0.37454301 -0.59247222 -0.62691667
## [71] -0.73711111 -1.31620968 -1.13413889 -0.44525000 -0.75377778
## [76] -0.64620968 -0.79747222 -0.76525000 -0.76211111 -0.48454301
## [81] -0.04580556 -0.02358333 0.06705556 -0.16287634 0.36502778
## [86] 0.35475000 0.47788889 0.86962366 0.85502778 0.85308333
## [91] 1.28288889 1.31545699 1.25252778 1.08725000 1.09455556
## [96] 1.20129032 1.06252778 0.65308333 0.36372222 0.39795699
## [101] 0.43752778 0.26391667 0.13288889 0.19879032 0.20169444
## [106] 0.03808333 -0.17627778 -0.08954301 -0.31330556 0.18308333
## [111] 0.22705556 -0.12704301 -0.17747222 -0.03108333 0.17705556
## [116] -0.33037634 -0.17997222 -0.25775000 -0.58544444 -0.35454301
## [121] -0.91080556 -1.07608333 -1.10294444 -1.08120968 -0.77830556
## [126] -0.78858333 -0.65211111 -0.35870968 -0.15163889 0.12225000
## [131] 0.10622222 0.18379032Descomposición
Objetivo
Función que descompone y grafica , la serie de tiempo original, la tendencia, estacionalidad y componente aleatorio de una serie de tiempo
Parámetros
ST Objeto de clase serie de tiempo
tipoST = Tipo de la serie de tiempo, puede ser "multiplicativa" o "aditiva "
mm = número de observaciones a promediar para el cálculo de medias movilesCódigo de la función
descomponer_ST<-function (st, tipoST, mm){
if(class(st)=="ts"){
if(tipoST=="aditiva" | tipoST=="multiplicativa"){
tendencia1<-tendencia(st,mm)
estacionalidad1<-estacionalidad(st, tipoST,mm)
componenteA1 <- componente_aleatorio(st, tipoST, mm)
df<-data.frame(Tiempo=c(1:length(st)), Serie=as.numeric(st), Tendencia=tendencia1[[2]], Estacionalidad=estacionalidad1[[2]], Componente_Aleatorio=componenteA1[[2]])
descompon<-ggplot(df)+
geom_line(aes(x=Tiempo, y=Serie), color="#FF9933", size=1.2)+
geom_point(aes(x=Tiempo, y=Serie), color="#CC9966")+
labs(x="Tiempo", y="Serie", title = "Serie de Tiempo")+
theme_solarized_2()
graficas<-grid.arrange(descompon, tendencia1[[1]], estacionalidad1[[1]], componenteA1[[1]], ncol=2)
return(list(df, graficas))
} else{stop('El parametro es incorrecto')}
}else{stop('El objeto no es de clase TS')}
}Ejemplos
Ejemplo 1
data("AirPassengers")
st<- AirPassengers
descomponer_ST(st,"aditiva", 12)
## [[1]]
## Tiempo Serie Tendencia Estacionalidad Componente_Aleatorio
## 1 1 112 NA 10.161616 NA
## 2 2 118 NA 6.507576 NA
## 3 3 132 NA 18.439394 NA
## 4 4 129 NA 23.671569 NA
## 5 5 121 NA 10.161616 NA
## 6 6 135 NA 6.507576 NA
## 7 7 148 NA 18.439394 NA
## 8 8 148 NA 23.671569 NA
## 9 9 136 NA 10.161616 NA
## 10 10 119 NA 6.507576 NA
## 11 11 104 NA 18.439394 NA
## 12 12 118 126.6667 23.671569 -32.33823529
## 13 13 115 126.9167 10.161616 -22.07828283
## 14 14 126 127.5833 6.507576 -8.09090909
## 15 15 141 128.3333 18.439394 -5.77272727
## 16 16 135 128.8333 23.671569 -17.50490196
## 17 17 125 129.1667 10.161616 -14.32828283
## 18 18 149 130.3333 6.507576 12.15909091
## 19 19 170 132.1667 18.439394 19.39393939
## 20 20 170 134.0000 23.671569 12.32843137
## 21 21 158 135.8333 10.161616 12.00505051
## 22 22 133 137.0000 6.507576 -10.50757576
## 23 23 114 137.8333 18.439394 -42.27272727
## 24 24 140 139.6667 23.671569 -23.33823529
## 25 25 145 142.1667 10.161616 -7.32828283
## 26 26 150 144.1667 6.507576 -0.67424242
## 27 27 178 147.2500 18.439394 12.31060606
## 28 28 163 149.5833 23.671569 -10.25490196
## 29 29 172 153.5000 10.161616 8.33838384
## 30 30 178 155.9167 6.507576 15.57575758
## 31 31 199 158.3333 18.439394 22.22727273
## 32 32 199 160.7500 23.671569 14.57843137
## 33 33 184 162.9167 10.161616 10.92171717
## 34 34 162 165.3333 6.507576 -9.84090909
## 35 35 146 168.0000 18.439394 -40.43939394
## 36 36 166 170.1667 23.671569 -27.83823529
## 37 37 171 172.3333 10.161616 -11.49494949
## 38 38 180 174.8333 6.507576 -1.34090909
## 39 39 193 176.0833 18.439394 -1.52272727
## 40 40 181 177.5833 23.671569 -20.25490196
## 41 41 183 178.5000 10.161616 -5.66161616
## 42 42 218 181.8333 6.507576 29.65909091
## 43 43 230 184.4167 18.439394 27.14393939
## 44 44 242 188.0000 23.671569 30.32843137
## 45 45 209 190.0833 10.161616 8.75505051
## 46 46 191 192.5000 6.507576 -8.00757576
## 47 47 172 194.6667 18.439394 -41.10606061
## 48 48 194 197.0000 23.671569 -26.67156863
## 49 49 196 199.0833 10.161616 -13.24494949
## 50 50 196 200.4167 6.507576 -10.92424242
## 51 51 236 204.0000 18.439394 13.56060606
## 52 52 235 208.5000 23.671569 2.82843137
## 53 53 229 212.3333 10.161616 6.50505051
## 54 54 243 214.4167 6.507576 22.07575758
## 55 55 264 217.2500 18.439394 28.31060606
## 56 56 272 219.7500 23.671569 28.57843137
## 57 57 237 222.0833 10.161616 4.75505051
## 58 58 211 223.7500 6.507576 -19.25757576
## 59 59 180 224.4167 18.439394 -62.85606061
## 60 60 201 225.0000 23.671569 -47.67156863
## 61 61 204 225.6667 10.161616 -31.82828283
## 62 62 188 225.0000 6.507576 -43.50757576
## 63 63 235 224.9167 18.439394 -8.35606061
## 64 64 227 224.2500 23.671569 -20.92156863
## 65 65 234 224.6667 10.161616 -0.82828283
## 66 66 264 226.4167 6.507576 31.07575758
## 67 67 302 229.5833 18.439394 53.97727273
## 68 68 293 231.3333 23.671569 37.99509804
## 69 69 259 233.1667 10.161616 15.67171717
## 70 70 229 234.6667 6.507576 -12.17424242
## 71 71 203 236.5833 18.439394 -52.02272727
## 72 72 229 238.9167 23.671569 -33.58823529
## 73 73 242 242.0833 10.161616 -10.24494949
## 74 74 233 245.8333 6.507576 -19.34090909
## 75 75 267 248.5000 18.439394 0.06060606
## 76 76 269 252.0000 23.671569 -6.67156863
## 77 77 270 255.0000 10.161616 4.83838384
## 78 78 315 259.2500 6.507576 49.24242424
## 79 79 364 264.4167 18.439394 81.14393939
## 80 80 347 268.9167 23.671569 54.41176471
## 81 81 312 273.3333 10.161616 28.50505051
## 82 82 274 277.0833 6.507576 -9.59090909
## 83 83 237 279.9167 18.439394 -61.35606061
## 84 84 278 284.0000 23.671569 -29.67156863
## 85 85 284 287.5000 10.161616 -13.66161616
## 86 86 277 291.1667 6.507576 -20.67424242
## 87 87 317 295.3333 18.439394 3.22727273
## 88 88 313 299.0000 23.671569 -9.67156863
## 89 89 318 303.0000 10.161616 4.83838384
## 90 90 374 307.9167 6.507576 59.57575758
## 91 91 413 312.0000 18.439394 82.56060606
## 92 92 405 316.8333 23.671569 64.49509804
## 93 93 355 320.4167 10.161616 24.42171717
## 94 94 306 323.0833 6.507576 -23.59090909
## 95 95 271 325.9167 18.439394 -73.35606061
## 96 96 306 328.2500 23.671569 -45.92156863
## 97 97 315 330.8333 10.161616 -25.99494949
## 98 98 301 332.8333 6.507576 -38.34090909
## 99 99 356 336.0833 18.439394 1.47727273
## 100 100 348 339.0000 23.671569 -14.67156863
## 101 101 355 342.0833 10.161616 2.75505051
## 102 102 422 346.0833 6.507576 69.40909091
## 103 103 465 350.4167 18.439394 96.14393939
## 104 104 467 355.5833 23.671569 87.74509804
## 105 105 404 359.6667 10.161616 34.17171717
## 106 106 347 363.0833 6.507576 -22.59090909
## 107 107 305 365.9167 18.439394 -79.35606061
## 108 108 336 368.4167 23.671569 -56.08823529
## 109 109 340 370.5000 10.161616 -40.66161616
## 110 110 318 371.9167 6.507576 -60.42424242
## 111 111 362 372.4167 18.439394 -28.85606061
## 112 112 348 372.4167 23.671569 -48.08823529
## 113 113 363 373.0833 10.161616 -20.24494949
## 114 114 435 374.1667 6.507576 54.32575758
## 115 115 491 376.3333 18.439394 96.22727273
## 116 116 505 379.5000 23.671569 101.82843137
## 117 117 404 379.5000 10.161616 14.33838384
## 118 118 359 380.5000 6.507576 -28.00757576
## 119 119 310 380.9167 18.439394 -89.35606061
## 120 120 337 381.0000 23.671569 -67.67156863
## 121 121 360 382.6667 10.161616 -32.82828283
## 122 122 342 384.6667 6.507576 -49.17424242
## 123 123 406 388.3333 18.439394 -0.77272727
## 124 124 396 392.3333 23.671569 -20.00490196
## 125 125 420 397.0833 10.161616 12.75505051
## 126 126 472 400.1667 6.507576 65.32575758
## 127 127 548 404.9167 18.439394 124.64393939
## 128 128 559 409.4167 23.671569 125.91176471
## 129 129 463 414.3333 10.161616 38.50505051
## 130 130 407 418.3333 6.507576 -17.84090909
## 131 131 362 422.6667 18.439394 -79.10606061
## 132 132 405 428.3333 23.671569 -47.00490196
## 133 133 417 433.0833 10.161616 -26.24494949
## 134 134 391 437.1667 6.507576 -52.67424242
## 135 135 419 438.2500 18.439394 -37.68939394
## 136 136 461 443.6667 23.671569 -6.33823529
## 137 137 472 448.0000 10.161616 13.83838384
## 138 138 535 453.2500 6.507576 75.24242424
## 139 139 622 459.4167 18.439394 144.14393939
## 140 140 606 463.3333 23.671569 118.99509804
## 141 141 508 467.0833 10.161616 30.75505051
## 142 142 461 471.5833 6.507576 -17.09090909
## 143 143 390 473.9167 18.439394 -102.35606061
## 144 144 432 476.1667 23.671569 -67.83823529
##
## [[2]]
## TableGrob (2 x 2) "arrange": 4 grobs
## z cells name grob
## 1 1 (1-1,1-1) arrange gtable[layout]
## 2 2 (1-1,2-2) arrange gtable[layout]
## 3 3 (2-2,1-1) arrange gtable[layout]
## 4 4 (2-2,2-2) arrange gtable[layout]Ejemplo 2
ser1<-c(as.numeric(fecha[100], "%y"),as.numeric(fecha[100], "%m"))
st1<-ts(series[924,-c(1:7)] %>% t() %>% na.omit(),frequency = 12, start=ser1[1])
descomponer_ST(st1,"multiplicativa", 6)
## [[1]]
## Tiempo Serie Tendencia Estacionalidad Componente_Aleatorio
## 1 1 940 NA 1.003203 NA
## 2 2 943 NA 1.002916 NA
## 3 3 945 NA 1.003053 NA
## 4 4 945 NA 1.003332 NA
## 5 5 947 NA 1.003203 NA
## 6 6 948 944.6667 1.002916 1.0006110
## 7 7 950 946.3333 1.003053 1.0008191
## 8 8 955 948.3333 1.003332 1.0036859
## 9 9 955 950.0000 1.003203 1.0020540
## 10 10 955 951.6667 1.002916 1.0005851
## 11 11 957 953.3333 1.003053 1.0007908
## 12 12 959 955.1667 1.003332 1.0006793
## 13 13 959 956.6667 1.003203 0.9992389
## 14 14 964 958.1667 1.002916 1.0031630
## 15 15 967 960.1667 1.003053 1.0040515
## 16 16 965 961.8333 1.003332 0.9999607
## 17 17 967 963.5000 1.003203 1.0004286
## 18 18 966 964.6667 1.002916 0.9984708
## 19 19 969 966.3333 1.003053 0.9997075
## 20 20 974 968.0000 1.003332 1.0028571
## 21 21 975 969.3333 1.003203 1.0026349
## 22 22 978 971.5000 1.002916 1.0037639
## 23 23 978 973.3333 1.003053 1.0017362
## 24 24 977 975.1667 1.003332 0.9985531
## 25 25 979 976.8333 1.003203 0.9990186
## 26 26 974 976.8333 1.002916 0.9942006
## 27 27 975 976.8333 1.003053 0.9950852
## 28 28 978 976.8333 1.003332 0.9978697
## 29 29 978 976.8333 1.003203 0.9979982
## 30 30 981 977.5000 1.002916 1.0006628
## 31 31 981 977.8333 1.003053 1.0001849
## 32 32 978 978.5000 1.003332 0.9961700
## 33 33 981 979.5000 1.003203 0.9983341
## 34 34 981 980.0000 1.002916 0.9981101
## 35 35 985 981.1667 1.003053 1.0008513
## 36 36 988 982.3333 1.003332 1.0024287
## 37 37 991 984.0000 1.003203 1.0038987
## 38 38 993 986.5000 1.002916 1.0036625
## 39 39 996 989.0000 1.003053 1.0040126
## 40 40 999 992.0000 1.003332 1.0037123
## 41 41 1000 994.5000 1.003203 1.0023204
## 42 42 1000 996.5000 1.002916 1.0005948
## 43 43 1004 998.6667 1.003053 1.0022805
## 44 44 1002 1000.1667 1.003332 0.9985063
## 45 45 1003 1001.3333 1.003203 0.9984668
## 46 46 1005 1002.3333 1.002916 0.9997454
## 47 47 1006 1003.3333 1.003053 0.9996060
## 48 48 1008 1004.6667 1.003332 0.9999862
## 49 49 1011 1005.8333 1.003203 1.0019279
## 50 50 1013 1007.6667 1.002916 1.0023700
## 51 51 1014 1009.5000 1.003053 1.0014004
## 52 52 1018 1011.6667 1.003332 1.0029188
## 53 53 1018 1013.6667 1.003203 1.0010689
## 54 54 1020 1015.6667 1.002916 1.0013468
## 55 55 1020 1017.1667 1.003053 0.9997333
## 56 56 1032 1020.3333 1.003332 1.0080755
## 57 57 1024 1022.0000 1.003203 0.9987583
## 58 58 1024 1023.0000 1.002916 0.9980673
## 59 59 1025 1024.1667 1.003053 0.9977675
## 60 60 1028 1025.5000 1.003332 0.9991091
## 61 61 1028 1026.8333 1.003203 0.9979402
## 62 62 1030 1026.5000 1.002916 1.0004924
## 63 63 1028 1027.1667 1.003053 0.9977651
## 64 64 1027 1027.6667 1.003332 0.9960328
## 65 65 1030 1028.5000 1.003203 0.9982614
## 66 66 1031 1029.0000 1.002916 0.9990307
## 67 67 1032 1029.6667 1.003053 0.9992155
## 68 68 1032 1030.0000 1.003332 0.9986146
## 69 69 1033 1030.8333 1.003203 0.9989028
## 70 70 1036 1032.3333 1.002916 1.0006342
## 71 71 1037 1033.5000 1.003053 1.0003326
## 72 72 1032 1033.6667 1.003332 0.9950723
## 73 73 1031 1033.5000 1.003203 0.9943964
## 74 74 1032 1033.5000 1.002916 0.9956455
## 75 75 1034 1033.6667 1.003053 0.9972778
## 76 76 1033 1033.1667 1.003332 0.9965185
## 77 77 1034 1032.6667 1.003203 0.9980947
## 78 78 1037 1033.5000 1.002916 1.0004694
## 79 79 1040 1035.0000 1.003053 1.0017725
## 80 80 1045 1037.1667 1.003332 1.0042069
## 81 81 1045 1039.0000 1.003203 1.0025640
## 82 82 1044 1040.8333 1.002916 1.0001263
## 83 83 1044 1042.5000 1.003053 0.9983908
## 84 84 1045 1043.8333 1.003332 0.9977933
## 85 85 1050 1045.5000 1.003203 1.0010981
## 86 86 1056 1047.3333 1.002916 1.0053436
## 87 87 1058 1049.5000 1.003053 1.0050308
## 88 88 1061 1052.3333 1.003332 1.0048876
## 89 89 1064 1055.6667 1.003203 1.0046763
## 90 90 1062 1058.5000 1.002916 1.0003896
## 91 91 1065 1061.0000 1.003053 1.0007149
## 92 92 1068 1063.0000 1.003332 1.0013674
## 93 93 1070 1065.0000 1.003203 1.0014875
## 94 94 1071 1066.6667 1.002916 1.0011434
## 95 95 1070 1067.6667 1.003053 0.9991351
## 96 96 1079 1070.5000 1.003332 1.0045932
## 97 97 1086 1074.0000 1.003203 1.0079452
## 98 98 1085 1076.8333 1.002916 1.0046546
## 99 99 1089 1080.0000 1.003053 1.0052643
## 100 100 1095 1084.0000 1.003332 1.0067932
## 101 101 1093 1087.8333 1.003203 1.0015420
## 102 102 1094 1090.3333 1.002916 1.0004458
## 103 103 1096 1092.0000 1.003053 1.0006082
## 104 104 1098 1094.1667 1.003332 1.0001711
## 105 105 1100 1096.0000 1.003203 1.0004456
## 106 106 1105 1097.6667 1.002916 1.0037541
## 107 107 1105 1099.6667 1.003053 1.0017915
## 108 108 1111 1102.5000 1.003332 1.0043635
## 109 109 1109 1104.6667 1.003203 1.0007179
## 110 110 1104 1105.6667 1.002916 0.9955897
## 111 111 1101 1105.8333 1.003053 0.9925989
## 112 112 1102 1105.3333 1.003332 0.9936737
## 113 113 1098 1104.1667 1.003203 0.9912406
## 114 114 1099 1102.1667 1.002916 0.9942279
## 115 115 1105 1101.5000 1.003053 1.0001241
## 116 116 1091 1099.3333 1.003332 0.9891241
## 117 117 1096 1098.5000 1.003203 0.9945391
## 118 118 1092 1096.8333 1.002916 0.9926989
## 119 119 1090 1095.5000 1.003053 0.9919511
## 120 120 1089 1093.8333 1.003332 0.9922753
##
## [[2]]
## TableGrob (2 x 2) "arrange": 4 grobs
## z cells name grob
## 1 1 (1-1,1-1) arrange gtable[layout]
## 2 2 (1-1,2-2) arrange gtable[layout]
## 3 3 (2-2,1-1) arrange gtable[layout]
## 4 4 (2-2,2-2) arrange gtable[layout]Ejemplo 3
ser1<-c(as.numeric(fecha[100], "%y"),as.numeric(fecha[100], "%m"))
st1<-ts(series[1000,-c(1:7)] %>% t() %>% na.omit(),frequency = 12, start=ser1[1])
descomponer_ST(st1,"aditiva" , 12)
## [[1]]
## Tiempo Serie Tendencia Estacionalidad Componente_Aleatorio
## 1 1 55.91 NA -0.08252778 NA
## 2 2 56.00 NA -0.08558333 NA
## 3 3 55.45 NA -0.07455556 NA
## 4 4 56.01 NA -0.07212366 NA
## 5 5 55.54 NA -0.08252778 NA
## 6 6 56.35 NA -0.08558333 NA
## 7 7 56.42 NA -0.07455556 NA
## 8 8 55.90 NA -0.07212366 NA
## 9 9 55.91 NA -0.08252778 NA
## 10 10 55.74 NA -0.08558333 NA
## 11 11 55.72 NA -0.07455556 NA
## 12 12 55.99 55.91167 -0.07212366 0.15045699
## 13 13 55.41 55.87000 -0.08252778 -0.37747222
## 14 14 55.40 55.82000 -0.08558333 -0.33441667
## 15 15 55.20 55.79917 -0.07455556 -0.52461111
## 16 16 54.88 55.70500 -0.07212366 -0.75287634
## 17 17 54.65 55.63083 -0.08252778 -0.89830556
## 18 18 54.24 55.45500 -0.08558333 -1.12941667
## 19 19 54.21 55.27083 -0.07455556 -0.98627778
## 20 20 54.36 55.14250 -0.07212366 -0.71037634
## 21 21 54.49 55.02417 -0.08252778 -0.45163889
## 22 22 54.14 54.89083 -0.08558333 -0.66525000
## 23 23 54.80 54.81417 -0.07455556 0.06038889
## 24 24 54.60 54.69833 -0.07212366 -0.02620968
## 25 25 54.36 54.61083 -0.08252778 -0.16830556
## 26 26 54.43 54.53000 -0.08558333 -0.01441667
## 27 27 54.36 54.46000 -0.07455556 -0.02544444
## 28 28 55.12 54.48000 -0.07212366 0.71212366
## 29 29 55.16 54.52250 -0.08252778 0.72002778
## 30 30 55.49 54.62667 -0.08558333 0.94891667
## 31 31 55.40 54.72583 -0.07455556 0.74872222
## 32 32 56.08 54.86917 -0.07212366 1.28295699
## 33 33 55.82 54.98000 -0.08252778 0.92252778
## 34 34 55.98 55.13333 -0.08558333 0.93225000
## 35 35 55.83 55.21917 -0.07455556 0.68538889
## 36 36 55.54 55.29750 -0.07212366 0.31462366
## 37 37 55.69 55.40833 -0.08252778 0.36419444
## 38 38 55.68 55.51250 -0.08558333 0.25308333
## 39 39 56.31 55.67500 -0.07455556 0.70955556
## 40 40 55.80 55.73167 -0.07212366 0.14045699
## 41 41 56.03 55.80417 -0.08252778 0.30836111
## 42 42 55.46 55.80167 -0.08558333 -0.25608333
## 43 43 55.91 55.84417 -0.07455556 0.14038889
## 44 44 55.71 55.81333 -0.07212366 -0.03120968
## 45 45 55.39 55.77750 -0.08252778 -0.30497222
## 46 46 55.62 55.74750 -0.08558333 -0.04191667
## 47 47 55.40 55.71167 -0.07455556 -0.23711111
## 48 48 55.97 55.74750 -0.07212366 0.29462366
## 49 49 55.89 55.76417 -0.08252778 0.20836111
## 50 50 55.85 55.77833 -0.08558333 0.15725000
## 51 51 55.29 55.69333 -0.07455556 -0.32877778
## 52 52 55.26 55.64833 -0.07212366 -0.31620968
## 53 53 55.47 55.60167 -0.08252778 -0.04913889
## 54 54 55.44 55.60000 -0.08558333 -0.07441667
## 55 55 55.16 55.53750 -0.07455556 -0.30294444
## 56 56 54.95 55.47417 -0.07212366 -0.45204301
## 57 57 55.57 55.48917 -0.08252778 0.16336111
## 58 58 55.16 55.45083 -0.08558333 -0.20525000
## 59 59 55.68 55.47417 -0.07455556 0.28038889
## 60 60 55.82 55.46167 -0.07212366 0.43045699
## 61 61 56.15 55.48333 -0.08252778 0.74919444
## 62 62 56.26 55.51750 -0.08558333 0.82808333
## 63 63 56.22 55.59500 -0.07455556 0.69955556
## 64 64 55.68 55.63000 -0.07212366 0.12212366
## 65 65 55.25 55.61167 -0.08252778 -0.27913889
## 66 66 55.60 55.62500 -0.08558333 0.06058333
## 67 67 55.50 55.65333 -0.07455556 -0.07877778
## 68 68 55.23 55.67667 -0.07212366 -0.37454301
## 69 69 54.95 55.62500 -0.08252778 -0.59247222
## 70 70 54.89 55.60250 -0.08558333 -0.62691667
## 71 71 54.71 55.52167 -0.07455556 -0.73711111
## 72 72 53.98 55.36833 -0.07212366 -1.31620968
## 73 73 53.97 55.18667 -0.08252778 -1.13413889
## 74 74 54.51 55.04083 -0.08558333 -0.44525000
## 75 75 54.03 54.85833 -0.07455556 -0.75377778
## 76 76 54.00 54.71833 -0.07212366 -0.64620968
## 77 77 53.71 54.59000 -0.08252778 -0.79747222
## 78 78 53.57 54.42083 -0.08558333 -0.76525000
## 79 79 53.41 54.24667 -0.07455556 -0.76211111
## 80 80 53.55 54.10667 -0.07212366 -0.48454301
## 81 81 53.89 54.01833 -0.08252778 -0.04580556
## 82 82 53.82 53.92917 -0.08558333 -0.02358333
## 83 83 53.85 53.85750 -0.07455556 0.06705556
## 84 84 53.59 53.82500 -0.07212366 -0.16287634
## 85 85 54.12 53.83750 -0.08252778 0.36502778
## 86 86 54.07 53.80083 -0.08558333 0.35475000
## 87 87 54.22 53.81667 -0.07455556 0.47788889
## 88 88 54.67 53.87250 -0.07212366 0.86962366
## 89 89 54.73 53.95750 -0.08252778 0.85502778
## 90 90 54.83 54.06250 -0.08558333 0.85308333
## 91 91 55.44 54.23167 -0.07455556 1.28288889
## 92 92 55.65 54.40667 -0.07212366 1.31545699
## 93 93 55.73 54.56000 -0.08252778 1.25252778
## 94 94 55.72 54.71833 -0.08558333 1.08725000
## 95 95 55.91 54.89000 -0.07455556 1.09455556
## 96 96 56.24 55.11083 -0.07212366 1.20129032
## 97 97 56.27 55.29000 -0.08252778 1.06252778
## 98 98 56.02 55.45250 -0.08558333 0.65308333
## 99 99 55.88 55.59083 -0.07455556 0.36372222
## 100 100 56.03 55.70417 -0.07212366 0.39795699
## 101 101 56.18 55.82500 -0.08252778 0.43752778
## 102 102 56.11 55.93167 -0.08558333 0.26391667
## 103 103 56.04 55.98167 -0.07455556 0.13288889
## 104 104 56.15 56.02333 -0.07212366 0.19879032
## 105 105 56.18 56.06083 -0.08252778 0.20169444
## 106 106 56.04 56.08750 -0.08558333 0.03808333
## 107 107 55.83 56.08083 -0.07455556 -0.17627778
## 108 108 55.89 56.05167 -0.07212366 -0.08954301
## 109 109 55.60 55.99583 -0.08252778 -0.31330556
## 110 110 56.10 56.00250 -0.08558333 0.18308333
## 111 111 56.18 56.02750 -0.07455556 0.22705556
## 112 112 55.81 56.00917 -0.07212366 -0.12704301
## 113 113 55.71 55.97000 -0.08252778 -0.17747222
## 114 114 55.83 55.94667 -0.08558333 -0.03108333
## 115 115 56.05 55.94750 -0.07455556 0.17705556
## 116 116 55.49 55.89250 -0.07212366 -0.33037634
## 117 117 55.58 55.84250 -0.08252778 -0.17997222
## 118 118 55.45 55.79333 -0.08558333 -0.25775000
## 119 119 55.07 55.73000 -0.07455556 -0.58544444
## 120 120 55.25 55.67667 -0.07212366 -0.35454301
## 121 121 54.60 55.59333 -0.08252778 -0.91080556
## 122 122 54.28 55.44167 -0.08558333 -1.07608333
## 123 123 54.09 55.26750 -0.07455556 -1.10294444
## 124 124 53.96 55.11333 -0.07212366 -1.08120968
## 125 125 54.12 54.98083 -0.08252778 -0.77830556
## 126 126 53.95 54.82417 -0.08558333 -0.78858333
## 127 127 53.92 54.64667 -0.07455556 -0.65211111
## 128 128 54.10 54.53083 -0.07212366 -0.35870968
## 129 129 54.18 54.41417 -0.08252778 -0.15163889
## 130 130 54.36 54.32333 -0.08558333 0.12225000
## 131 131 54.29 54.25833 -0.07455556 0.10622222
## 132 132 54.29 54.17833 -0.07212366 0.18379032
##
## [[2]]
## TableGrob (2 x 2) "arrange": 4 grobs
## z cells name grob
## 1 1 (1-1,1-1) arrange gtable[layout]
## 2 2 (1-1,2-2) arrange gtable[layout]
## 3 3 (2-2,1-1) arrange gtable[layout]
## 4 4 (2-2,2-2) arrange gtable[layout]