Gastos Casa
30 June, 2025
1. Gastos (cálculos antiguos)
Gastos_casa %>%
dplyr::select(-Tiempo,-link) %>%
dplyr::select(fecha, gasto, monto, gastador,obs) %>% tail(30) %>%
knitr::kable(format = "markdown", size=12)| fecha | gasto | monto | gastador | obs |
|---|---|---|---|---|
| 3/3/2025 | Comida | 9370 | Andrés | NA |
| 9/3/2025 | Comida | 61916 | Tami | Supermercado |
| 11/3/2025 | Comida | 27021 | Andrés | NA |
| 11/3/2025 | Enceres | 13190 | Tami | 40 rollos confort |
| 15/3/2025 | Comida | 78061 | Tami | Supermercado |
| 17/3/2025 | Electricidad | 52458 | Andrés | NA |
| 17/3/2025 | VTR | 22000 | Andrés | NA |
| 21/3/2025 | Agua | 19562 | Andrés | NA |
| 22/3/2025 | Comida | 76766 | Tami | Supermercado |
| 21/3/2025 | Diosi | 18500 | Andrés | antiparasitario |
| 27/3/2025 | Gas | 82450 | Andrés | NA |
| 26/3/2025 | Comida | 4000 | Andrés | avena multigrano y chucrut |
| 29/3/2025 | Comida | 70591 | Tami | Supermercado |
| 3/4/2025 | Gas | 83300 | Andrés | NA |
| 4/4/2025 | Agua | 20807 | Andrés | NA |
| 6/4/2025 | Comida | 52655 | Tami | Supermercado |
| 12/4/2025 | Comida | 72108 | Tami | Supermercado |
| 16/4/2025 | VTR | 21990 | Andrés | NA |
| 22/4/2025 | Comida | 107881 | Tami | Supermercado |
| 26/4/2025 | Comida | 55874 | Tami | Supermercado |
| 28/4/2025 | Comida | 13050 | Tami | Cervezas MUT |
| 29/4/2025 | Electricidad | 52507 | Andrés | enel |
| 29/4/2025 | Diosi | 11990 | Andrés | arena 7kg superzoo |
| 3/5/2025 | Agua | 17072 | Andrés | aguas andina |
| 13/5/2025 | VTR | 22000 | Andrés | NA |
| 17/5/2025 | Electricidad | 52404 | Andrés | NA |
| 13/6/2025 | VTR | 22000 | Andrés | NA |
| 22/6/2025 | Electricidad | 52401 | Andrés | NA |
| 31/3/2019 | Comida | 9000 | Andrés | NA |
| 8/9/2019 | Comida | 24588 | Andrés | Super Lider |
#para ver las diferencias depués de la diosi
Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha=strftime(fecha, format = "%Y-W%V")) %>%
dplyr::mutate(gastador=ifelse(gastador=="Andrés",1,0)) %>%
dplyr::group_by(gastador, fecha,.drop = F) %>%
dplyr::summarise(gasto_media=mean(monto,na.rm=T)) %>%
dplyr::mutate(treat=ifelse(fecha>"2019-W26",1,0)) %>%
#dplyr::mutate(fecha_simp=lubridate::week(fecha)) %>%#después de diosi. Junio 24, 2019
dplyr::mutate(gastador_nombre=plyr::revalue(as.character(gastador), c("0" = "Tami", "1"="Andrés"))) %>%
assign("ts_gastos_casa_week_treat", ., envir = .GlobalEnv)
gplots::plotmeans(gasto_media ~ gastador_nombre, main="Promedio de gasto por gastador", data=ts_gastos_casa_week_treat,ylim=c(0,75000), xlab="", ylab="")
par(mfrow=c(1,2))
gplots::plotmeans(gasto_media ~ gastador_nombre, main="Antes de Diosi", data=ts_gastos_casa_week_treat[ts_gastos_casa_week_treat$treat==0,], xlab="", ylab="", ylim=c(0,70000))
gplots::plotmeans(gasto_media ~ gastador_nombre, main="Después de Diosi", data=ts_gastos_casa_week_treat[ts_gastos_casa_week_treat$treat==1,], xlab="", ylab="",ylim=c(0,70000))
library(ggiraph)
library(scales)
#if( requireNamespace("dplyr", quietly = TRUE)){
gg <- Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(gastador=ifelse(gastador=="Andrés",1,0)) %>%
dplyr::mutate(fecha_simp=tsibble::yearweek(fecha)) %>%
dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
dplyr::mutate(treat=ifelse(fecha_week>"2019 W26",1,0)) %>%
dplyr::mutate(gastador_nombre=plyr::revalue(as.character(gastador), c("0" = "Tami", "1"="Andrés"))) %>%
# dplyr::mutate(week=as.Date(as.character(lubridate::floor_date(fecha, "week"))))%>%
#dplyr::mutate(fecha_week= lubridate::parse_date_time(fecha_week, c("%Y-W%V"),exact=T)) %>%
dplyr::group_by(gastador_nombre, fecha_simp) %>%
dplyr::summarise(monto_total=sum(monto)) %>%
dplyr::mutate(tooltip= paste0(substr(gastador_nombre,1,1),"=",round(monto_total/1000,2))) %>%
ggplot(aes(hover_css = "fill:none;")) +#, ) +
#stat_summary(geom = "line", fun.y = median, size = 1, alpha=0.5, aes(color="blue")) +
geom_line(aes(x = fecha_simp, y = monto_total, color=as.factor(gastador_nombre)),size=1,alpha=.5) +
ggiraph::geom_point_interactive(aes(x = fecha_simp, y = monto_total, color=as.factor(gastador_nombre),tooltip=tooltip),size = 1) +
#geom_text(aes(x = fech_ing_qrt, y = perc_dup-0.05, label = paste0(n)), vjust = -1,hjust = 0, angle=45, size=3) +
# guides(color = F)+
theme_custom() +
geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
labs(y="Gastos (en miles)",x="Semanas y Meses", subtitle="Interlineado, incorporación de la Diosi; Azul= Tami; Rojo= Andrés") + ggtitle( "Figura 4. Gastos por Gastador") +
scale_y_continuous(labels = f <- function(x) paste0(x/1000)) +
scale_color_manual(name = "Gastador", values= c("blue", "red"), labels = c("Tami", "Andrés")) +
scale_x_yearweek(date_breaks = "1 month", minor_breaks = "1 week", labels=scales::date_format("%m/%y")) +
theme(axis.text.x = element_text(vjust = 0.5,angle = 35), legend.position='bottom')+
theme(
panel.border = element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
axis.line = element_line(colour = "black")
)
# x <- girafe(ggobj = gg)
# x <- girafe_options(x = x,
# opts_hover(css = "stroke:red;fill:orange") )
# if( interactive() ) print(x)
#}
tooltip_css <- "background-color:gray;color:white;font-style:italic;padding:10px;border-radius:10px 20px 10px 20px;"
#ggiraph(code = {print(gg)}, tooltip_extra_css = tooltip_css, tooltip_opacity = .75 )
x <- girafe(ggobj = gg)
x <- girafe_options(x,
opts_zoom(min = 1, max = 3), opts_hover(css =tooltip_css))xplot<-Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
dplyr::mutate(month=as.Date(as.character(lubridate::floor_date(fecha, "month"))))%>%
dplyr::group_by(month)%>%
dplyr::summarise(gasto_total=sum(monto)/1000) %>%
ggplot2::ggplot(aes(x = month, y = gasto_total)) +
geom_point()+
geom_line(size=1) +
theme_custom() +
geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
geom_vline(xintercept = as.Date("2019-03-23"),linetype = "dashed", color="red") +
labs(y="Gastos (en miles)",x="Meses/Año", subtitle="Interlineado, incorporación de la Diosi") +
ggtitle( "Figura. Suma de Gastos por Mes") +
scale_x_date(breaks = "1 month", minor_breaks = "1 month", labels=scales::date_format("%m/%y")) +
theme(axis.text.x = element_text(vjust = 0.5,angle = 45))
plotly::ggplotly(plot) plot2<-Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))%>%
dplyr::group_by(day)%>%
summarise(gasto_total=sum(monto)/1000) %>%
ggplot2::ggplot(aes(x = day, y = gasto_total)) +
geom_line(size=1) +
theme_custom() +
geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
geom_vline(xintercept = as.Date("2020-03-23"),linetype = "dashed", color="red") +
labs(y="Gastos (en miles)",x="Meses/Año", subtitle="Interlineado, incorporación de la Diosi") +
ggtitle( "Figura. Suma de Gastos por Día") +
scale_x_date(breaks = "1 month", minor_breaks = "1 week", labels=scales::date_format("%m/%y")) +
theme(axis.text.x = element_text(vjust = 0.5,angle = 45))
plotly::ggplotly(plot2) tsData <- Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))%>%
dplyr::group_by(day)%>%
summarise(gasto_total=sum(monto))%>%
dplyr::mutate(covid=case_when(day>as.Date("2019-06-02")~1,TRUE~0))%>%
dplyr::mutate(covid=case_when(day>as.Date("2020-03-10")~covid+1,TRUE~covid))%>%
dplyr::mutate(covid=as.factor(covid))%>%
data.frame()tsData_gastos <-ts(tsData$gasto_total, frequency=7)
mstsData_gastos <- forecast::msts(Gastos_casa$monto, seasonal.periods=c(7,30))
tsData_gastos = decompose(tsData_gastos)
tsdata_gastos_trend<-cbind(tsData,trend=as.vector(tsData_gastos$trend))%>% na.omit()
# Assuming your time series starts on "2019-03-03"
start_date <- as.Date("2019-03-03")
frequency <- 7 # Weekly data
num_periods <- length(tsData_gastos$x) # Total number of periods in your time series
# Generate sequence of dates
dates <- tsData$day# seq.Date(from = start_date, by = "day", length.out = num_periods)
# Create a data frame from the decomposed time series object
tsData_gastos_df <- data.frame(
day = dates,
Actual = as.numeric(tsData_gastos$x),
Seasonal = as.numeric(tsData_gastos$seasonal),
Trend = as.numeric(tsData_gastos$trend),
Random = as.numeric(tsData_gastos$random)
)
tsData_gastos_long <- tsData_gastos_df %>%
pivot_longer(cols = c("Actual", "Seasonal", "Trend", "Random"),
names_to = "Component", values_to = "Value")
# Plotting with facet_wrap
ggplot(tsData_gastos_long, aes(x = day, y = Value)) +
geom_line() +
theme_bw() +
labs(title = "Descomposición de los Gastos Diarios", x = "Date", y = "Value") +
scale_x_date(date_breaks = "3 months", date_labels = "%m %Y") +
facet_wrap(~ Component, scales = "free_y", ncol=1) +
theme(axis.text.x = element_text(angle = 90, hjust = 1))+
theme(strip.text = element_text(size = 12))
#tsData_gastos$trend
#Using the inputted variables, a Type-2 Sum Squares ANCOVA Lagged Dependent Variable model is fitted which estimates the difference in means between interrupted and non-interrupted time periods, while accounting for the lag of the dependent variable and any further specified covariates.
#Typically such analyses use Auto-regressive Integrated Moving Average (ARIMA) models to handle the serial dependence of the residuals of a linear model, which is estimated either as part of the ARIMA process or through a standard linear regression modeling process [9,17]. All such time series methods enable the effect of the event to be separated from general trends and serial dependencies in time, thereby enabling valid statistical inferences to be made about whether an intervention has had an effect on a time series.
#it uses Type-2 Sum Squares ANCOVA Lagged Dependent Variable model
#ITSA model da cuenta de observaciones autocorrelacionadas e impactos dinámicos mediante una regresión de deltas en rezagados. Una vez que se incorporan en el modelo, se controlan.
#residual autocorrelation assumptions
#TSA allows the model to account for baseline levels and trends present in the data therefore allowing us to attribute significant changes to the interruption
#RDestimate(all~agecell,data=metro_region,cutpoint = 21)
tsdata_gastos_trend<-cbind(tsData,trend=as.vector(tsData_gastos$trend))%>% na.omit()
itsa_metro_region_quar2<-
its.analysis::itsa.model(time = "day", depvar = "trend",data=tsdata_gastos_trend,
interrupt_var = "covid",
alpha = 0.05,no.plots = F, bootstrap = TRUE, Reps = 10000, print = F) 
print(itsa_metro_region_quar2)## [[1]]
## [1] "ITSA Model Fit"
##
## $aov.result
## Anova Table (Type II tests)
##
## Response: depvar
## Sum Sq Df F value Pr(>F)
## interrupt_var 9.8113e+08 2 5.0411 0.0067 **
## lag_depvar 2.6346e+11 1 2707.3425 <2e-16 ***
## Residuals 8.1645e+10 839
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## $tukey.result
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: stats::aov(formula = x$depvar ~ x$interrupt_var)
##
## $`x$interrupt_var`
## diff lwr upr p adj
## 1-0 7228.838 -1814.51 16272.19 0.1460574
## 2-0 31315.096 23178.39 39451.80 0.0000000
## 2-1 24086.258 19377.82 28794.69 0.0000000
##
##
## $data
## depvar interrupt_var lag_depvar
## 2 19269.29 0 16010.00
## 3 24139.00 0 19269.29
## 4 23816.14 0 24139.00
## 5 26510.14 0 23816.14
## 6 23456.71 0 26510.14
## 7 24276.71 0 23456.71
## 8 18818.71 0 24276.71
## 9 18517.14 0 18818.71
## 10 15475.29 0 18517.14
## 11 16365.29 0 15475.29
## 12 12621.29 0 16365.29
## 13 12679.86 0 12621.29
## 14 13440.71 0 12679.86
## 15 15382.86 0 13440.71
## 16 13459.71 0 15382.86
## 17 14644.14 0 13459.71
## 18 13927.00 0 14644.14
## 19 22034.57 0 13927.00
## 20 20986.00 0 22034.57
## 21 20390.57 0 20986.00
## 22 22554.14 0 20390.57
## 23 21782.57 0 22554.14
## 24 22529.57 0 21782.57
## 25 24642.71 0 22529.57
## 26 17692.29 0 24642.71
## 27 19668.29 0 17692.29
## 28 28640.00 0 19668.29
## 29 28706.00 0 28640.00
## 30 28331.57 0 28706.00
## 31 25617.86 0 28331.57
## 32 27223.29 0 25617.86
## 33 31622.57 0 27223.29
## 34 32021.43 0 31622.57
## 35 33634.57 0 32021.43
## 36 30784.86 0 33634.57
## 37 34770.57 0 30784.86
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## 776 40161.71 2 36232.14
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## 778 45663.71 2 40629.86
## 779 39252.29 2 45663.71
## 780 39618.57 2 39252.29
## 781 39438.43 2 39618.57
## 782 44650.71 2 39438.43
## 783 38626.71 2 44650.71
## 784 38280.43 2 38626.71
## 785 44134.14 2 38280.43
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## 788 42564.29 2 45598.43
## 789 45699.14 2 42564.29
## 790 49553.86 2 45699.14
## 791 50018.43 2 49553.86
## 792 43772.86 2 50018.43
## 793 39235.43 2 43772.86
## 794 39905.00 2 39235.43
## 795 40374.43 2 39905.00
## 796 34230.57 2 40374.43
## 797 34324.14 2 34230.57
## 798 33491.57 2 34324.14
## 799 33366.43 2 33491.57
## 800 46646.86 2 33366.43
## 801 49770.86 2 46646.86
## 802 57339.86 2 49770.86
## 803 59799.14 2 57339.86
## 804 53577.14 2 59799.14
## 805 61775.29 2 53577.14
## 806 70627.86 2 61775.29
## 807 57888.43 2 70627.86
## 808 49960.71 2 57888.43
## 809 42923.71 2 49960.71
## 810 47284.86 2 42923.71
## 811 52284.86 2 47284.86
## 812 50191.00 2 52284.86
## 813 36465.86 2 50191.00
## 814 34525.14 2 36465.86
## 815 43199.14 2 34525.14
## 816 52757.43 2 43199.14
## 817 43200.86 2 52757.43
## 818 36772.29 2 43200.86
## 819 29568.00 2 36772.29
## 820 42362.00 2 29568.00
## 821 42566.29 2 42362.00
## 822 39596.00 2 42566.29
## 823 32925.00 2 39596.00
## 824 43416.57 2 32925.00
## 825 52624.86 2 43416.57
## 826 57733.71 2 52624.86
## 827 54120.57 2 57733.71
## 828 53353.43 2 54120.57
## 829 56286.86 2 53353.43
## 830 60626.86 2 56286.86
## 831 61375.29 2 60626.86
## 832 53710.86 2 61375.29
## 833 55795.57 2 53710.86
## 834 55130.14 2 55795.57
## 835 57700.14 2 55130.14
## 836 61333.14 2 57700.14
## 837 59230.71 2 61333.14
## 838 49195.00 2 59230.71
## 839 55436.43 2 49195.00
## 840 50353.14 2 55436.43
## 841 43194.86 2 50353.14
## 842 47539.71 2 43194.86
## 843 35271.00 2 47539.71
## 844 34774.86 2 35271.00
##
## $alpha
## [1] 0.05
##
## $itsa.result
## [1] "Significant variation between time periods with chosen alpha"
##
## $group.means
## interrupt_var count mean s.d.
## 1 0 37 22066.04 6308.636
## 2 1 120 29463.10 9187.258
## 3 2 687 53549.36 22054.259
##
## $dependent
## [1] 19269.29 24139.00 23816.14 26510.14 23456.71 24276.71 18818.71
## [8] 18517.14 15475.29 16365.29 12621.29 12679.86 13440.71 15382.86
## [15] 13459.71 14644.14 13927.00 22034.57 20986.00 20390.57 22554.14
## [22] 21782.57 22529.57 24642.71 17692.29 19668.29 28640.00 28706.00
## [29] 28331.57 25617.86 27223.29 31622.57 32021.43 33634.57 30784.86
## [36] 34770.57 38443.00 35073.00 31422.29 30103.29 19319.29 27926.29
## [43] 30715.43 31962.29 39790.14 39211.57 44548.57 49398.00 41039.00
## [50] 34821.29 29123.57 21275.71 28476.14 24561.86 20323.57 25370.00
## [57] 26811.86 27151.86 27623.29 22896.57 41889.29 44000.14 38558.00
## [64] 43373.86 49001.00 61213.29 58939.57 42046.86 39191.71 42646.43
## [71] 36121.57 30915.57 20273.43 23938.29 19274.29 21662.29 15819.00
## [78] 18126.14 17240.71 16127.71 13917.14 15379.86 19510.14 24567.29
## [85] 25700.43 25729.00 26435.00 31157.14 29818.43 30962.43 28746.71
## [92] 27830.71 28252.14 28717.57 21365.43 24816.86 16838.57 15529.14
## [99] 13286.29 13629.43 14404.86 19524.86 18475.71 22495.00 22254.57
## [106] 24173.29 27466.43 24602.43 20531.14 20846.43 23875.71 36312.71
## [113] 34244.00 36347.43 39779.71 42018.71 39372.57 33444.00 29255.86
## [120] 31640.14 29671.14 31023.71 39723.43 39314.14 38239.86 34649.43
## [127] 36688.43 42867.57 42226.86 32155.14 33603.00 37254.43 33145.57
## [134] 31299.43 30252.00 26310.71 27929.86 27666.14 25017.57 27335.00
## [141] 25760.71 18436.86 21906.00 19418.14 22826.14 23444.29 25264.86
## [148] 25473.29 27366.86 28855.86 32326.86 27141.43 26297.71 23499.14
## [155] 30246.29 39931.86 38020.43 35004.00 40750.86 42363.29 46273.57
## [162] 41083.29 35711.29 41921.71 60583.29 63115.57 61300.14 57666.43
## [169] 55834.00 58927.71 57810.57 48987.14 52219.29 56503.57 56545.00
## [176] 64705.57 53833.29 50114.00 39592.43 29907.29 33923.29 45489.00
## [183] 44866.29 51680.57 58257.00 70600.57 76648.00 69430.14 69651.57
## [190] 77745.14 72795.86 67670.71 55357.86 48524.00 50154.43 45111.57
## [197] 36147.00 43501.57 41472.43 41058.00 41605.57 49382.86 59558.57
## [204] 59134.57 61109.00 63004.43 67344.29 78180.86 69117.86 55597.57
## [211] 49426.14 39119.43 35636.86 39201.14 27777.00 47207.00 55587.29
## [218] 56619.71 82679.86 91259.57 93552.71 102242.71 91884.00 85013.86
## [225] 84535.29 80700.43 79740.57 85163.14 86724.86 80355.00 74875.14
## [232] 81347.00 66062.43 56946.43 47732.14 38129.71 42928.29 45392.57
## [239] 37895.43 30660.29 42430.86 35845.14 40350.43 31494.71 30013.29
## [246] 34197.57 37430.14 26932.43 33729.86 38081.43 44028.00 47139.71
## [253] 46558.86 58350.57 78380.00 78168.29 70510.86 72207.14 67881.00
## [260] 69536.43 62390.71 50113.14 45565.57 45805.29 41348.57 51426.86
## [267] 47160.57 51907.43 49751.43 54407.43 54746.29 61634.57 58926.43
## [274] 69999.29 63044.86 63285.29 61395.43 67969.43 60792.57 56859.14
## [281] 44899.43 43064.14 62790.29 69120.71 69589.43 66633.29 65588.57
## [288] 70168.57 74644.71 52891.00 41560.57 34704.86 46520.00 50231.00
## [295] 49216.71 76914.86 83720.71 84485.00 89765.00 87702.86 82013.86
## [302] 85982.43 57248.43 52968.43 52601.86 45493.29 42298.86 46423.71
## [309] 37898.00 36435.14 30209.57 34541.86 33604.71 37990.71 35683.43
## [316] 65201.86 62730.57 64589.14 73744.86 76477.71 105647.43 103790.29
## [323] 76122.29 74746.14 72865.71 63652.57 60358.29 25957.14 30178.43
## [330] 30681.57 33337.29 32582.71 39184.43 40415.71 34975.43 34076.14
## [337] 34221.14 28862.57 35729.86 36489.29 36785.14 37787.71 39832.14
## [344] 41917.86 41633.57 33557.00 22759.57 28877.86 27574.00 27104.71
## [351] 24376.14 29732.29 34030.00 39139.71 37066.57 38509.29 40957.29
## [358] 49423.00 50053.29 50284.14 53103.86 50223.00 49587.14 41167.71
## [365] 37958.71 33582.29 31039.43 26526.57 34869.43 37487.43 46514.43
## [372] 39613.43 38980.57 37306.14 36771.29 26317.00 31580.71 23626.57
## [379] 33035.71 44864.57 48946.14 46969.57 49249.57 56370.14 67228.71
## [386] 59457.29 53124.71 52814.14 61262.00 61861.14 71784.71 59313.29
## [393] 61107.00 60603.43 60012.57 58280.43 56862.71 41704.43 51533.00
## [400] 50388.71 49205.29 56533.29 47996.14 47207.57 45292.00 40343.43
## [407] 39004.86 36788.43 30027.57 39040.14 42390.14 36291.14 30668.29
## [414] 47693.00 52094.43 56592.57 47971.43 43762.43 42246.71 46352.43
## [421] 33094.86 32784.86 26212.43 32611.57 42144.86 50034.86 46332.00
## [428] 42976.29 39456.29 39328.29 35296.14 30875.43 27709.00 29513.29
## [435] 31630.43 29346.14 34916.86 42020.86 38303.00 37966.43 41408.14
## [442] 38988.14 43555.29 38114.00 27847.86 26517.00 39518.29 39153.71
## [449] 45623.14 40627.43 41027.71 42882.86 47139.43 35547.57 41099.00
## [456] 35859.57 44524.57 48554.29 51554.29 47810.29 50490.00 50720.71
## [463] 52720.71 52145.57 55515.57 52457.00 58239.57 50523.57 47788.57
## [470] 46170.00 42305.57 46605.57 55149.57 48769.57 50719.43 44753.71
## [477] 42898.00 46141.14 34022.57 26651.86 28791.86 31879.00 33584.71
## [484] 34690.43 27410.43 41755.00 49379.57 57198.86 51144.57 56677.43
## [491] 65416.43 69779.71 54046.00 43259.57 40998.57 41368.57 42274.29
## [498] 35962.71 38709.00 44778.14 51282.43 52094.86 52221.43 45011.43
## [505] 46545.43 42263.00 45417.43 45034.71 37840.57 39135.43 38191.14
## [512] 39456.86 42479.14 34282.57 28878.43 56227.14 65569.43 69751.29
## [519] 62171.71 63705.14 79257.86 87244.71 58568.00 52695.29 48911.00
## [526] 53924.00 53358.86 42121.14 47835.71 62329.29 56056.86 59946.43
## [533] 64511.57 61137.43 55448.71 47964.43 46425.71 55512.00 55226.29
## [540] 46709.14 49254.71 49056.29 49850.57 39145.71 29799.43 34769.86
## [547] 44061.57 43829.14 45782.00 38924.57 49242.43 50565.00 38864.43
## [554] 49786.71 58787.86 58060.86 62179.43 57333.86 70797.00 89901.71
## [561] 78558.14 65466.00 70525.00 68377.86 69736.29 60085.86 41757.00
## [568] 49780.29 56540.29 57894.29 60270.29 61011.00 57721.43 71741.00
## [575] 59576.00 52390.29 61092.29 62814.00 54908.29 62082.00 57017.71
## [582] 53634.43 69169.00 52488.14 60895.57 59856.57 52670.00 51874.57
## [589] 52190.57 41562.43 44764.14 38612.71 43473.14 53505.00 45870.86
## [596] 52578.00 55300.00 61789.71 57391.71 62902.29 53250.43 55402.57
## [603] 56291.29 58933.57 59590.71 59065.00 52399.57 60483.43 58262.71
## [610] 54939.71 51169.00 43113.29 56289.71 60739.86 50363.14 62270.86
## [617] 67061.57 59609.00 85054.00 68023.29 59242.29 61535.14 56215.86
## [624] 45152.29 57409.57 35151.43 34991.43 45944.71 57944.71 55706.29
## [631] 88593.71 77359.43 79878.71 81753.00 75716.00 67381.43 63528.57
## [638] 49682.86 47815.00 46546.14 44808.71 42959.57 46023.86 51309.57
## [645] 68447.29 84959.29 81666.29 82700.86 89422.14 104812.71 98812.71
## [652] 64779.86 61862.86 58376.43 59503.57 55429.43 44454.57 47184.00
## [659] 52126.71 51202.00 64437.14 64297.14 64628.57 51413.14 52969.43
## [666] 54135.29 48799.43 41907.86 45382.00 42633.29 46624.71 44051.86
## [673] 35852.86 29737.71 29734.86 32881.71 38298.57 40886.14 38601.86
## [680] 38628.86 39142.57 32666.14 39911.57 39336.29 39678.86 41963.14
## [687] 54220.57 63901.86 73116.00 60863.86 56293.86 52725.00 58625.00
## [694] 47513.00 40300.14 33312.43 29556.71 27816.71 34120.29 32132.57
## [701] 32902.57 39694.14 72501.29 79551.14 99637.71 95424.29 98395.14
## [708] 115594.71 114267.57 88353.29 88750.86 78835.71 75519.14 73202.86
## [715] 53433.29 48165.71 52163.14 49306.86 36846.86 43220.57 38952.29
## [722] 41522.29 39090.00 28452.57 32975.00 33690.71 26405.29 47087.43
## [729] 49660.29 47409.71 53881.71 45189.57 45503.86 54640.14 39131.29
## [736] 35024.14 44755.43 41063.29 42783.29 45952.57 44937.43 40838.43
## [743] 48838.43 43139.14 67134.29 73224.29 68770.71 59539.29 82179.86
## [750] 74252.14 73015.00 56116.43 111885.00 131425.14 136678.00 115531.29
## [757] 118310.86 117449.43 115193.57 61025.43 43913.86 46099.29 44524.86
## [764] 42208.71 166486.57 171565.29 200415.71 204498.14 197558.86 195266.57
## [771] 203144.29 85493.71 74721.57 36232.14 40161.71 40629.86 45663.71
## [778] 39252.29 39618.57 39438.43 44650.71 38626.71 38280.43 44134.14
## [785] 47596.43 45598.43 42564.29 45699.14 49553.86 50018.43 43772.86
## [792] 39235.43 39905.00 40374.43 34230.57 34324.14 33491.57 33366.43
## [799] 46646.86 49770.86 57339.86 59799.14 53577.14 61775.29 70627.86
## [806] 57888.43 49960.71 42923.71 47284.86 52284.86 50191.00 36465.86
## [813] 34525.14 43199.14 52757.43 43200.86 36772.29 29568.00 42362.00
## [820] 42566.29 39596.00 32925.00 43416.57 52624.86 57733.71 54120.57
## [827] 53353.43 56286.86 60626.86 61375.29 53710.86 55795.57 55130.14
## [834] 57700.14 61333.14 59230.71 49195.00 55436.43 50353.14 43194.86
## [841] 47539.71 35271.00 34774.86
##
## $interrupt_var
## [1] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1
## [38] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [75] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [112] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [149] 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [186] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [223] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [260] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [297] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [334] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [371] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [408] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [445] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [482] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [519] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [556] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [593] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [630] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [667] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [704] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [741] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [778] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [815] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## Levels: 0 1 2
##
## $residuals
## 2 3 4 5 6
## 2020.793442 4041.189245 -538.848193 2437.398565 -2971.166888
## 7 8 9 10 11
## 518.188065 -5656.668744 -1186.771317 -3964.990006 -415.750978
## 12 13 14 15 16
## -4937.802880 -1606.168169 -896.515084 380.474574 -3240.519905
## 17 18 19 20 21
## -374.849808 -2127.438623 6607.070293 -1529.266721 -1208.017771
## 22 23 24 25 26
## 1476.086616 -1186.912751 234.607044 1694.710833 -7103.060232
## 27 28 29 30 31
## 949.112992 8193.377212 416.164443 -15.962359 -2402.345339
## 32 33 34 35 36
## 1575.454650 4571.249630 1124.182484 2388.638196 -1871.310773
## 37 38 39 40 41
## 4605.668255 4302.424935 -2278.069487 -2982.677132 -1110.165678
## 42 43 44 45 46
## -10741.075275 7293.465974 2558.234987 1366.779877 8104.615030
## 47 48 49 50 51
## 682.808614 6525.604794 6709.344600 -5889.101606 -4799.247522
## 52 53 54 55 56
## -5061.338857 -7928.165172 6132.982699 -4076.030145 -4892.386145
## 57 58 59 60 61
## 3859.218001 889.408460 -31.085577 143.109684 -4995.734823
## 62 63 64 65 66
## 18129.146924 3636.276567 -3651.210576 5922.252860 7339.298178
## 67 68 69 70 71
## 14632.247709 1682.362493 -13222.635248 -1309.903408 4640.821361
## 72 73 74 75 76
## -4904.200974 -4406.068940 -10497.045278 2471.339384 -5396.535976
## 77 78 79 80 81
## 1068.805676 -6862.106939 553.327563 -2349.041332 -2687.985844
## 82 83 84 85 86
## -3925.555288 -530.324929 2321.233108 3767.615678 479.725256
## 87 88 89 90 91
## -482.314502 198.707909 4303.654539 -1163.230794 1151.094146
## 92 93 94 95 96
## -2064.722810 -1043.710771 178.499309 275.508440 -7483.519344
## 97 98 99 100 101
## 2395.267355 -8600.311129 -2934.997706 -4033.131938 -1729.248332
## 102 103 104 105 106
## -1253.800607 3188.307624 -2336.819209 2599.643579 -1154.507346
## 107 108 109 110 111
## 974.393352 2590.166210 -3152.750715 -4720.282893 -845.815613
## 112 113 114 115 116
## 1907.842405 11696.593515 -1244.740440 2667.190474 4260.626075
## 117 118 119 120 121
## 3499.068291 -1104.443340 -4719.715350 -3725.008458 2320.617162
## 122 123 124 125 126
## -1732.762653 1341.139332 8858.414547 843.702713 127.220963
## 127 128 129 130 131
## -2524.050256 2653.758484 7050.375692 1007.758201 -8503.833649
## 132 133 134 135 136
## 1748.873446 4134.562687 -3166.430349 -1420.546111 -854.047759
## 137 138 139 140 141
## -3879.655055 1185.021337 -494.172926 -2912.201206 1720.649879
## 142 143 144 145 146
## -1879.568091 -7827.160072 2044.613100 -3476.022917 2106.900658
## 147 148 149 150 151
## -254.283319 1025.897969 -357.245441 1354.114475 1187.724754
## 152 153 154 155 156
## 3357.017696 -4862.813294 -1173.344925 -3234.328146 5959.369569
## 157 158 159 160 161
## 9746.483320 -3617.735137 -4963.162994 3420.703123 9.139413
## 162 163 164 165 166
## 2509.814884 -6098.903682 -6933.474837 3973.240047 17205.557810
## 167 168 169 170 171
## 3423.606283 -605.586015 -2652.224286 -1308.003070 3387.648896
## 172 173 174 175 176
## -434.067314 -8280.872150 2664.849789 4123.545730 419.584809
## 177 178 179 180 181
## 8543.938733 -9462.445994 -3677.009709 -10947.123470 -11434.144235
## 182 183 184 185 186
## 1048.758935 9103.623290 -1630.019084 5728.653186 6347.926745
## 187 188 189 190 191
## 12942.281591 8198.767210 -4305.846452 2225.545578 10125.540691
## 192 193 194 195 196
## -1899.271590 -2697.671567 -10530.048768 -6599.813866 1004.879366
## 197 198 199 200 201
## -5463.323902 -10019.350845 5172.188955 -3286.435125 -1926.955315
## 202 203 204 205 206
## -1017.083956 6281.506262 9658.195893 338.427517 2683.523512
## 207 208 209 210 211
## 2852.875785 5535.719664 12578.320188 -5958.179824 -11555.449372
## 212 213 214 215 216
## -5907.232982 -10818.788011 -5291.068818 1317.735238 -13222.361866
## 217 218 219 220 221
## 16194.802268 7589.078481 1295.330379 26452.908052 12250.438386
## 222 223 224 225 226
## 7043.060997 13728.359692 -4227.288334 -2041.681357 3485.733414
## 227 228 229 230 231
## 69.250889 2461.885762 8723.579298 5544.796939 -2190.335227
## 232 233 234 235 236
## -2101.563757 9160.870097 -11781.499938 -7535.488846 -8780.424966
## 237 238 239 240 241
## -10327.581036 2865.583442 1134.883058 -8516.576863 -9197.600327
## 242 243 244 245 246
## 8898.046002 -7977.685686 2284.934327 -10509.371104 -4248.995918
## 247 248 249 250 251
## 1230.377794 804.981291 -12518.697431 3455.996946 1865.150140
## 252 253 254 255 256
## 4007.509853 1920.638509 -1380.527868 10918.980805 20639.908558
## 257 258 259 260 261
## 2918.154951 -4554.189685 3836.339697 -1972.722628 3464.687605
## 262 263 264 265 266
## -5128.228196 -11158.904578 -4973.231285 -757.959023 -5424.235281
## 267 268 269 270 271
## 8550.179677 -4526.700835 3949.809920 -2355.966741 4184.842136
## 272 273 274 275 276
## 453.351351 7045.402858 -1684.586967 11755.770963 -4878.722283
## 277 278 279 280 281
## 1441.376375 -658.667180 7567.475454 -5356.475177 -3015.783129
## 282 283 284 285 286
## -11536.832717 -2916.749389 18413.828907 7499.355266 2433.910012
## 287 288 289 290 291
## -931.990196 607.601780 6100.907327 6573.142644 -19093.685646
## 292 293 294 295 296
## -11406.677391 -8357.154035 9451.361339 2833.378898 -1425.120984
## 297 298 299 300 301
## 27159.726288 9751.434294 4565.933384 9177.782875 2499.781543
## 302 303 304 305 306
## -1386.460960 7555.523132 -24647.864001 -3808.152369 -433.080943
## 307 308 309 310 311
## -7221.189903 -4201.194294 2716.282204 -9415.446627 -3424.991168
## 312 313 314 315 316
## -8371.710032 1403.067470 -3321.427229 1883.837695 -4257.757730
## 317 318 319 320 321
## 27277.736053 -999.021004 3019.986999 10550.909110 5279.697810
## 322 323 324 325 326
## 32060.305693 4702.542969 -21341.913736 1469.741188 792.358198
## 327 328 329 330 331
## -6776.884629 -2016.896946 -33538.123774 757.178380 -2429.992672
## 332 333 334 335 336
## -214.133731 -3290.372071 3971.000344 -569.035690 -7085.730609
## 337 338 339 340 341
## -3229.033573 -2297.863955 -7783.196648 3768.635911 -1475.423958
## 342 343 344 345 346
## -1843.471135 -1099.542642 68.422327 366.865221 -1740.784502
## 347 348 349 350 351
## -9568.828918 -13305.592589 2251.973841 -4400.584242 -3730.017676
## 352 353 354 355 356
## -6048.332202 1693.170419 1308.460954 2661.046280 -3879.088621
## 357 358 359 360 361
## -624.000466 562.756170 6888.390617 117.816666 -202.331808
## 362 363 364 365 366
## 2415.563558 -2930.331858 -1047.698687 -8911.251012 -4759.855041
## 367 368 369 370 371
## -6330.925687 -5047.840612 -7337.692321 4950.375028 274.918997
## 372 373 374 375 376
## 7013.222503 -7779.321901 -2379.217049 -3500.392020 -2571.437551
## 377 378 379 380 381
## -12558.142797 1844.871359 -10710.892747 5651.886041 9255.123525
## 382 383 384 385 386
## 2995.723270 -2549.021657 1458.927961 6586.287775 11219.944436
## 387 388 389 390 391
## -6044.216892 -5582.884072 -357.422641 8361.940896 1575.835081
## 392 393 394 395 396
## 10975.626465 -10171.143258 2525.288692 453.624216 302.997082
## 397 398 399 400 401
## -912.609231 -816.057175 -14734.954915 8345.226661 -1391.349721
## 402 403 404 405 406
## -1574.425847 6788.145896 -8155.249027 -1480.516817 -2706.706785
## 407 408 409 410 411
## -5980.655749 -2993.108737 -4039.337257 -8862.557921 6060.460428
## 412 413 414 415 416
## 1531.529706 -7496.092622 -7787.108697 14153.195134 3671.352546
## 417 418 419 420 421
## 4321.697795 -8231.791810 -4904.054060 -2740.194800 2690.580590
## 422 423 424 425 426
## -14156.270399 -2876.294370 -9177.716099 2967.146478 6906.201551
## 427 428 429 430 431
## 6462.054267 -4138.364120 -4256.982852 -4843.365006 -1894.126023
## 432 433 434 435 436
## -5814.369280 -6710.121297 -6011.892388 -1439.465356 -899.657253
## 437 438 439 440 441
## -5034.782324 2532.890215 4766.884770 -5161.400139 -2247.762779
## 442 443 444 445 446
## 1488.187506 -3940.612883 2742.131776 -6691.821564 -12201.107456
## 447 448 449 450 451
## -4557.142153 9607.599664 -2122.898820 4665.243789 -5986.146008
## 452 453 454 455 456
## -1218.528830 286.678003 2921.454568 -12391.563807 3293.647347
## 457 458 459 460 461
## -6798.926797 6446.463502 2901.099438 2378.260264 -3988.386596
## 462 463 464 465 466
## 1964.390971 -147.542829 1650.763138 -672.810959 3199.987910
## 467 468 469 470 471
## -2804.690158 5651.732189 -7119.482079 -3109.034354 -2336.626061
## 472 473 474 475 476
## -4786.074207 2892.269614 7677.142447 -6172.155813 1355.196987
## 477 478 479 480 481
## -6315.112870 -2955.506541 1909.930731 -13043.846842 -9820.316690
## 482 483 484 485 486
## -1236.723129 -20.401699 -1013.515921 -1398.963707 -9645.596407
## 487 488 489 490 491
## 11063.264737 6147.587757 7301.354030 -5588.673395 5236.934889
## 492 493 494 495 496
## 9139.024750 5862.540159 -13685.626654 -10717.396432 -3548.732088
## 497 498 499 500 501
## -1202.130571 -619.876064 -7723.237069 540.722976 4209.019964
## 502 503 504 505 506
## 5407.566191 533.846593 -49.819726 -7370.470446 466.624176
## 507 508 509 510 511
## -5156.851157 1741.343364 -1399.021651 -8258.589702 -674.500499
## 512 513 514 515 516
## -2750.770554 -659.546947 1256.231568 -9582.469240 -7821.041322
## 517 518 519 520 521
## 24252.059063 9685.671555 5700.356598 -5535.059667 2624.548642
## 522 523 524 525 526
## 16836.715718 11227.147095 -24431.802461 -5234.885181 -3885.152333
## 527 528 529 530 531
## 4436.129349 -511.456412 -11255.113984 4283.642809 13781.446631
## 532 533 534 535 536
## -5161.488476 4211.537987 5376.356745 -1988.705307 -4727.691202
## 537 538 539 540 541
## -7238.814028 -2234.648836 8196.804942 -32.282246 -8299.649211
## 542 543 544 545 546
## 1691.741543 -732.065334 235.689737 -11163.544384 -11151.476771
## 547 548 549 550 551
## 1989.620763 6936.108752 -1419.281587 736.768244 -7827.878546
## 552 553 554 555 556
## 8484.849769 787.389315 -12069.394715 9081.713308 8534.423385
## 557 558 559 560 561
## -61.516301 4692.609884 -3753.481021 13945.736068 21280.760570
## 562 563 564 565 566
## -6764.450507 -9939.866032 6564.489756 -5.309924 3230.184472
## 567 568 569 570 571
## -7607.803575 -17500.105319 6546.553611 6292.471914 1736.774321
## 572 573 574 575 576
## 2929.086371 1592.664343 -2344.451084 14550.915071 -9870.213261
## 577 578 579 580 581
## -6421.094526 8562.769145 2677.059103 -6733.804705 7351.208499
## 582 583 584 585 586
## -3984.450299 -2940.458337 15551.834311 -14709.587828 8280.506616
## 587 588 589 590 591
## -108.398767 -6386.660166 -899.475738 111.900343 -10792.494650
## 592 593 594 595 596
## 1700.508136 -7249.909076 2988.194440 8770.989004 -7633.160067
## 597 598 599 600 601
## 5747.869723 2606.380670 6716.480037 -3354.929562 6000.442164
## 602 603 604 605 606
## -8468.842598 2121.104538 1128.381921 2993.739725 1340.955137
## 607 608 609 610 611
## 240.756301 -5965.084630 7945.794286 -1341.954186 -2723.571069
## 612 613 614 615 616
## -3589.266849 -8348.563807 11870.296026 4801.399206 -9465.699477
## 617 618 619 620 621
## 11513.500524 5894.304979 -5746.383710 26213.770644 -13061.360099
## 622 623 624 625 626
## -6953.843646 3015.500858 -4308.236386 -10721.605154 11207.627519
## 627 628 629 630 631
## -21766.025972 -2467.609808 8625.550405 11050.016941 -1678.999074
## 632 633 634 635 636
## 33165.298720 -6819.690762 5520.783006 5192.669797 -2482.860051
## 637 638 639 640 641
## -5539.791780 -2106.436392 -12583.922781 -2347.640221 -1983.587474
## 642 643 644 645 646
## -2611.761311 -2942.016972 1738.818312 4345.686162 16862.546455
## 647 648 649 650 651
## 18392.488931 664.440609 4577.804075 10394.651286 19909.369755
## 652 653 654 655 656
## 454.691808 -28332.871614 -1497.816297 -2434.157905 1740.875269
## 657 658 659 660 661
## -3318.633481 -10731.811299 1592.008817 4148.614011 -1097.098309
## 662 663 664 665 666
## 12946.444221 1236.075599 1689.894357 -11815.274249 1294.145551
## 667 668 669 670 671
## 1099.473413 -5255.594255 -7482.476022 2016.386226 -3769.478012
## 672 673 674 675 676
## 2624.919523 -3437.306824 -9387.074921 -8334.523908 -2991.427645
## 677 678 679 680 681
## 157.927257 2823.752731 675.823033 -3870.558043 -1846.599791
## 682 683 684 685 686
## -1356.489327 -8282.014951 4625.208641 -2284.143906 -1438.648720
## 687 688 689 690 691
## 546.155701 10806.626020 9772.276213 10522.887874 -9784.402596
## 692 693 694 695 696
## -3643.387929 -3217.079687 5802.870974 -10467.001186 -7965.574356
## 697 698 699 700 701
## -8647.696261 -6294.641568 -4751.337475 3073.369133 -4425.025759
## 702 703 704 705 706
## -1917.334882 4201.090519 31070.935548 9440.275949 23363.752143
## 707 708 709 710 711
## 1590.329071 8244.631286 22847.032995 6483.756133 -18270.320565
## 712 713 714 715 716
## 4781.924223 -5480.781788 -129.380453 453.732381 -17290.905895
## 717 718 719 720 721
## -5275.609175 3326.812620 -3024.087593 -12987.078005 4279.362909
## 722 723 724 725 726
## -5560.923393 740.478650 -3938.541208 -12449.627615 1372.207172
## 727 728 729 730 731
## -1865.656240 -9776.773420 17274.404894 1766.609691 -2733.193640
## 732 733 734 735 736
## 5706.291057 -8643.775294 -730.682521 8130.849713 -15365.091113
## 737 738 739 740 741
## -5914.148803 7407.665351 -4791.719482 156.009476 1821.644323
## 742 743 744 745 746
## -1964.137611 -5175.683868 6407.725959 -6285.284717 22692.262736
## 747 748 749 750 751
## 7805.334040 -1972.210516 -7310.257379 23400.573104 -4319.882376
## 752 753 754 755 756
## 1373.506429 -14443.535390 56098.031142 26884.417737 15054.976772
## 757 758 759 760 761
## -10683.867279 10582.492095 7291.118763 5788.335933 -46407.701372
## 762 763 764 765 766
## -16164.636195 979.995406 -2504.968959 -3444.721766 122857.943641
## 767 768 769 770 771
## 19291.013961 43701.551173 22562.484436 12054.275896 15828.422147
## 772 773 774 775 776
## 25710.088410 -98827.303899 -6747.479482 -35819.732504 1757.898596
## 777 778 779 780 781
## -1209.251270 3415.348074 -7396.757041 -1425.500322 -1925.855872
## 782 783 784 785 786
## 3443.913542 -7136.748047 -2216.758865 3939.683802 2284.561051
## 787 788 789 790 791
## -2740.223202 -4027.683250 1759.668972 2873.842177 -31.437835
## 792 793 794 795 796
## -6683.144863 -5760.597334 -1124.334973 -1240.256203 -7794.495136
## 797 798 799 800 801
## -2329.867824 -3244.240857 -2641.536767 10748.293645 2262.335545
## 802 803 804 805 806
## 7100.285947 2942.633631 -5429.312355 8208.200092 9893.826978
## 807 808 809 810 811
## -10584.657816 -7375.364652 -7481.832990 3031.165187 4218.585979
## 812 813 814 815 816
## -2246.349265 -14141.009502 -4082.989511 6287.613232 8262.952656
## 817 818 819 820 821
## -9649.621447 -7723.688857 -9308.017012 9784.082092 -1196.346839
## 822 823 824 825 826
## -4345.222316 -8419.552148 7903.911683 7940.301774 4999.131710
## 827 828 829 830 831
## -3080.253864 -688.730797 2915.346043 4690.896965 1645.229745
## 832 833 834 835 836
## -6673.486774 2111.590689 -376.327675 2775.400376 4161.666232
## 837 838 839 840 841
## -1116.787688 -9314.526076 5700.280685 -4839.359379 -7553.757320
## 842 843 844
## 3048.985008 -13018.071259 -2788.712447
##
## $fitted.values
## 2 3 4 5 6 7 8 9
## 17248.49 20097.81 24354.99 24072.74 26427.88 23758.53 24475.38 19703.91
## 10 11 12 13 14 15 16 17
## 19440.28 16781.04 17559.09 14286.03 14337.23 15002.38 16700.23 15018.99
## 18 19 20 21 22 23 24 25
## 16054.44 15427.50 22515.27 21598.59 21078.06 22969.48 22294.96 22948.00
## 26 27 28 29 30 31 32 33
## 24795.35 18719.17 20446.62 28289.84 28347.53 28020.20 25647.83 27051.32
## 34 35 36 37 38 39 40 41
## 30897.25 31245.93 32656.17 30164.90 34140.58 37351.07 34404.96 31213.45
## 42 43 44 45 46 47 48 49
## 30060.36 20632.82 28157.19 30595.51 31685.53 38528.76 38022.97 42688.66
## 50 51 52 53 54 55 56 57
## 46928.10 39620.53 34184.91 29203.88 22343.16 28637.89 25215.96 21510.78
## 58 59 60 61 62 63 64 65
## 25922.45 27182.94 27480.18 27892.31 23760.14 40363.87 42209.21 37451.60
## 66 67 68 69 70 71 72 73
## 41661.70 46581.04 57257.21 55269.49 40501.62 38005.61 41025.77 35321.64
## 74 75 76 77 78 79 80 81
## 30770.47 21466.95 24670.82 20593.48 22681.11 17572.82 19589.76 18815.70
## 82 83 84 85 86 87 88 89
## 17842.70 15910.18 17188.91 20799.67 25220.70 26211.31 26236.29 26853.49
## 90 91 92 93 94 95 96 97
## 30981.66 29811.33 30811.44 28874.43 28073.64 28442.06 28848.95 22421.59
## 98 99 100 101 102 103 104 105
## 25438.88 18464.14 17319.42 15358.68 15658.66 16336.55 20812.53 19895.36
## 106 107 108 109 110 111 112 113
## 23409.08 23198.89 24876.26 27755.18 25251.43 21692.24 21967.87 24616.12
## 114 115 116 117 118 119 120 121
## 35488.74 33680.24 35519.09 38519.65 40477.01 38163.72 32980.87 29319.53
## 122 123 124 125 126 127 128 129
## 31403.91 29682.57 30865.01 38470.44 38112.64 37173.48 34034.67 35817.20
## 130 131 132 133 134 135 136 137
## 41219.10 40658.98 31854.13 33119.87 36312.00 32719.97 31106.05 30190.37
## 138 139 140 141 142 143 144 145
## 26744.84 28160.32 27929.77 25614.35 27640.28 26264.02 19861.39 22894.17
## 146 147 148 149 150 151 152 153
## 20719.24 23698.57 24238.96 25830.53 26012.74 27668.13 28969.84 32004.24
## 154 155 156 157 158 159 160 161
## 27471.06 26733.47 24286.92 30185.37 41638.16 39967.16 37330.15 42354.15
## 162 163 164 165 166 167 168 169
## 43763.76 47182.19 42644.76 37948.47 43377.73 59691.97 61905.73 60318.65
## 170 171 172 173 174 175 176 177
## 57142.00 55540.07 58244.64 57268.02 49554.44 52380.03 56125.42 56161.63
## 178 179 180 181 182 183 184 185
## 63295.73 53791.01 50539.55 41341.43 32874.53 36385.38 46496.30 45951.92
## 186 187 188 189 190 191 192 193
## 51909.07 57658.29 68449.23 73735.99 67426.03 67619.60 74695.13 70368.39
## 194 195 196 197 198 199 200 201
## 65887.91 55123.81 49149.55 50574.90 46166.35 38329.38 44758.86 42984.96
## 202 203 204 205 206 207 208 209
## 42622.66 43101.35 49900.38 58796.14 58425.48 60151.55 61808.57 65602.54
## 210 211 212 213 214 215 216 217
## 75076.04 67153.02 55333.38 49938.22 40927.93 37883.41 40999.36 31012.20
## 218 219 220 221 222 223 224 225
## 47998.21 55324.38 56226.95 79009.13 86509.65 88514.35 96111.29 87055.54
## 226 227 228 229 230 231 232 233
## 81049.55 80631.18 77278.69 76439.56 81180.06 82545.34 76976.71 72186.13
## 234 235 236 237 238 239 240 241
## 77843.93 64481.92 56512.57 48457.30 40062.70 44257.69 46412.01 39857.89
## 242 243 244 245 246 247 248 249
## 33532.81 43822.83 38065.49 42004.09 34262.28 32967.19 36625.16 39451.13
## 250 251 252 253 254 255 256 257
## 30273.86 36216.28 40020.49 45219.08 47939.39 47431.59 57740.09 75250.13
## 258 259 260 261 262 263 264 265
## 75065.05 68370.80 69853.72 66071.74 67518.94 61272.05 50538.80 46563.24
## 266 267 268 269 270 271 272 273
## 46772.81 42876.68 51687.27 47957.62 52107.40 50222.59 54292.93 54589.17
## 274 275 276 277 278 279 280 281
## 60611.02 58243.51 67923.58 61843.91 62054.10 60401.95 66149.05 59874.93
## 282 283 284 285 286 287 288 289
## 56436.26 45980.89 44376.46 61621.36 67155.52 67565.28 64980.97 64067.66
## 290 291 292 293 294 295 296 297
## 68071.57 71984.69 52967.25 43062.01 37068.64 47397.62 50641.84 49755.13
## 298 299 300 301 302 303 304 305
## 73969.28 79919.07 80587.22 85203.08 83400.32 78426.91 81896.29 56776.58
## 306 307 308 309 310 311 312 313
## 53034.94 52714.48 46500.05 43707.43 47313.45 39860.13 38581.28 33138.79
## 314 315 316 317 318 319 320 321
## 36926.14 36106.88 39941.19 37924.12 63729.59 61569.16 63193.95 71198.02
## 322 323 324 325 326 327 328 329
## 73587.12 99087.74 97464.20 73276.40 72073.36 70429.46 62375.18 59495.27
## 330 331 332 333 334 335 336 337
## 29421.25 33111.56 33551.42 35873.09 35213.43 40984.75 42061.16 37305.18
## 338 339 340 341 342 343 344 345
## 36519.01 36645.77 31961.22 37964.71 38628.61 38887.26 39763.72 41550.99
## 346 347 348 349 350 351 352 353
## 43374.36 43125.83 36065.16 26625.88 31974.58 30834.73 30424.48 28039.12
## 354 355 356 357 358 359 360 361
## 32721.54 36478.67 40945.66 39133.29 40394.53 42534.61 49935.47 50486.47
## 362 363 364 365 366 367 368 369
## 50688.29 53153.33 50634.84 50078.97 42718.57 39913.21 36087.27 33864.26
## 370 371 372 373 374 375 376 377
## 29919.05 37212.51 39501.21 47392.75 41359.79 40806.53 39342.72 38875.14
## 378 379 380 381 382 383 384 385
## 29735.84 34337.46 27383.83 35609.45 45950.42 49518.59 47790.64 49783.86
## 386 387 388 389 390 391 392 393
## 56008.77 65501.50 58707.60 53171.57 52900.06 60285.31 60809.09 69484.43
## 394 395 396 397 398 399 400 401
## 58581.71 60149.80 59709.57 59193.04 57678.77 56439.38 43187.77 51780.06
## 402 403 404 405 406 407 408 409
## 50779.71 49745.14 56151.39 48688.09 47998.71 46324.08 41997.97 40827.77
## 410 411 412 413 414 415 416 417
## 38890.13 32979.68 40858.61 43787.24 38455.39 33539.80 48423.08 52270.87
## 418 419 420 421 422 423 424 425
## 56203.22 48666.48 44986.91 43661.85 47251.13 35661.15 35390.14 29644.42
## 426 427 428 429 430 431 432 433
## 35238.66 43572.80 50470.36 47233.27 44299.65 41222.41 41110.51 37585.55
## 434 435 436 437 438 439 440 441
## 33720.89 30952.75 32530.09 34380.93 32383.97 37253.97 43464.40 40214.19
## 442 443 444 445 446 447 448 449
## 39919.96 42928.76 40813.15 44805.82 40048.96 31074.14 29910.69 41276.61
## 450 451 452 453 454 455 456 457
## 40957.90 46613.57 42246.24 42596.18 44217.97 47939.14 37805.35 42658.50
## 458 459 460 461 462 463 464 465
## 38078.11 45653.19 49176.03 51798.67 48525.61 50868.26 51069.95 52818.38
## 466 467 468 469 470 471 472 473
## 52315.58 55261.69 52587.84 57643.05 50897.61 48506.63 47091.65 43713.30
## 474 475 476 477 478 479 480 481
## 47472.43 54941.73 49364.23 51068.83 45853.51 44231.21 47066.42 36472.17
## 482 483 484 485 486 487 488 489
## 30028.58 31899.40 34598.23 36089.39 37056.02 30691.74 43231.98 49897.50
## 490 491 492 493 494 495 496 497
## 56733.24 51440.49 56277.40 63917.17 67731.63 53976.97 44547.30 42570.70
## 498 499 500 501 502 503 504 505
## 42894.16 43685.95 38168.28 40569.12 45874.86 51561.01 52271.25 52381.90
## 506 507 508 509 510 511 512 513
## 46078.80 47419.85 43676.09 46433.74 46099.16 39809.93 40941.91 40116.40
## 514 515 516 517 518 519 520 521
## 41222.91 43865.04 36699.47 31975.08 55883.76 64050.93 67706.77 61080.59
## 522 523 524 525 526 527 528 529
## 62421.14 76017.57 82999.80 57930.17 52796.15 49487.87 53870.31 53376.26
## 530 531 532 533 534 535 536 537
## 43552.07 48547.84 61218.35 55734.89 59135.21 63126.13 60176.41 55203.24
## 538 539 540 541 542 543 544 545
## 48660.36 47315.20 55258.57 55008.79 47562.97 49788.35 49614.88 50309.26
## 546 547 548 549 550 551 552 553
## 40950.91 32780.24 37125.46 45248.42 45045.23 46752.45 40757.58 49777.61
## 554 555 556 557 558 559 560 561
## 50933.82 40705.00 50253.43 58122.37 57486.82 61087.34 56851.26 68620.95
## 562 563 564 565 566 567 568 569
## 85322.59 75405.87 63960.51 68383.17 66506.10 67693.66 59257.11 43233.73
## 570 571 572 573 574 575 576 577
## 50247.81 56157.51 57341.20 59418.34 60065.88 57190.08 69446.21 58811.38
## 578 579 580 581 582 583 584 585
## 52529.52 60136.94 61642.09 54730.79 61002.16 56574.89 53617.17 67197.73
## 586 587 588 589 590 591 592 593
## 52615.06 59964.97 59056.66 52774.05 52078.67 52354.92 43063.63 45862.62
## 594 595 596 597 598 599 600 601
## 40484.95 44734.01 53504.02 46830.13 52693.62 55073.23 60746.64 56901.84
## 602 603 604 605 606 607 608 609
## 61719.27 53281.47 55162.90 55939.83 58249.76 58824.24 58364.66 52537.63
## 610 611 612 613 614 615 616 617
## 59604.67 57663.29 54758.27 51461.85 44419.42 55938.46 59828.84 50757.36
## 618 619 620 621 622 623 624 625
## 61167.27 65355.38 58840.23 81084.65 66196.13 58519.64 60524.09 55873.89
## 626 627 628 629 630 631 632 633
## 46201.94 56917.45 37459.04 37319.16 46894.70 57385.28 55428.42 84179.12
## 634 635 636 637 638 639 640 641
## 74357.93 76560.33 78198.86 72921.22 65635.01 62266.78 50162.64 48529.73
## 642 643 644 645 646 647 648 649
## 47420.48 45901.59 44285.04 46963.89 51584.74 66566.80 81001.85 78123.05
## 650 651 652 653 654 655 656 657
## 79027.49 84903.34 98358.02 93112.73 63360.67 60810.59 57762.70 58748.06
## 658 659 660 661 662 663 664 665
## 55186.38 45591.99 47978.10 52299.10 51490.70 63061.07 62938.68 63228.42
## 666 667 668 669 670 671 672 673
## 51675.28 53035.81 54055.02 49390.33 43365.61 46402.76 43999.79 47489.16
## 674 675 676 677 678 679 680 681
## 45239.93 38072.24 32726.28 32723.79 35474.82 40210.32 42472.42 40475.46
## 682 683 684 685 686 687 688 689
## 40499.06 40948.16 35286.36 41620.43 41117.51 41416.99 43413.95 54129.58
## 690 691 692 693 694 695 696 697
## 62593.11 70648.26 59937.25 55942.08 52822.13 57980.00 48265.72 41960.12
## 698 699 700 701 702 703 704 705
## 35851.36 32568.05 31046.92 36557.60 34819.91 35493.05 41430.35 70110.87
## 706 707 708 709 710 711 712 713
## 76273.96 93833.96 90150.51 92747.68 107783.82 106623.61 83968.93 84316.50
## 714 715 716 717 718 719 720 721
## 75648.52 72749.12 70724.19 53441.32 48836.33 52330.94 49833.94 38941.21
## 722 723 724 725 726 727 728 729
## 44513.21 40781.81 43028.54 40902.20 31602.79 35556.37 36182.06 29813.02
## 730 731 732 733 734 735 736 737
## 47893.68 50142.91 48175.42 53833.35 46234.54 46509.29 54496.38 40938.29
## 738 739 740 741 742 743 744 745
## 37347.76 45855.01 42627.28 44130.93 46901.57 46014.11 42430.70 49424.43
## 746 747 748 749 750 751 752 753
## 44442.02 65418.95 70742.92 66849.54 58779.28 78572.03 71641.49 70559.96
## 754 755 756 757 758 759 760 761
## 55786.97 104540.73 121623.02 126215.15 107728.37 110158.31 109405.24 107433.13
## 762 763 764 765 766 767 768 769
## 60078.49 45119.29 47029.83 45653.44 43628.63 152274.27 156714.16 181935.66
## 770 771 772 773 774 775 776 777
## 185504.58 179438.15 177434.20 184321.02 81469.05 72051.88 38403.82 41839.11
## 778 779 780 781 782 783 784 785
## 42248.37 46649.04 41044.07 41364.28 41206.80 45763.46 40497.19 40194.46
## 786 787 788 789 790 791 792 793
## 45311.87 48338.65 46591.97 43939.47 46680.01 50049.87 50456.00 44996.03
## 794 795 796 797 798 799 800 801
## 41029.33 41614.68 42025.07 36654.01 36735.81 36007.97 35898.56 47508.52
## 802 803 804 805 806 807 808 809
## 50239.57 56856.51 59006.46 53567.09 60734.03 68473.09 57336.08 50405.55
## 810 811 812 813 814 815 816 817
## 44253.69 48066.27 52437.35 50606.87 38608.13 36911.53 44494.48 52850.48
## 818 819 820 821 822 823 824 825
## 44495.97 38876.02 32577.92 43762.63 43941.22 41344.55 35512.66 44684.56
## 826 827 828 829 830 831 832 833
## 52734.58 57200.83 54042.16 53371.51 55935.96 59730.06 60384.34 53683.98
## 834 835 836 837 838 839 840 841
## 55506.47 54924.74 57171.48 60347.50 58509.53 49736.15 55192.50 50748.61
## 842 843 844
## 44490.73 48289.07 37563.57
##
## $shapiro.test
## [1] 0
##
## $levenes.test
## [1] 0
##
## $autcorr
## [1] "No autocorrelation evidence"
##
## $post_sums
## [1] "Post-Est Warning"
##
## $adjr_sq
## [1] 0.8098
##
## $fstat.bootstrap
##
## ORDINARY NONPARAMETRIC BOOTSTRAP
##
##
## Call:
## boot::boot(data = x, statistic = f.stat, R = Reps, formula = depvar ~
## ., parallel = parr)
##
##
## Bootstrap Statistics :
## original bias std. error
## t1* 5.041115 0.7690207 3.874757
## t2* 2707.342465 167.0931757 886.620239
## WARNING: All values of t3* are NA
##
## $itsa.plot
##
## $booted.ints
## Parameter Lower CI Median F-value Upper CI
## 1 interrupt_var 1.122777 5.014462 13.21351
## 2 lag_depvar 1644.388785 2752.006490 4507.58176Ahora con las tendencias descompuestas
require(zoo)
require(scales)
Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha2=strftime(fecha, format = "%Y-W%V")) %>%
dplyr::mutate(gastador=ifelse(gastador=="Andrés",1,0)) %>%
dplyr::mutate(treat=ifelse(fecha2>"2019-W26",1,0)) %>%
dplyr::mutate(gasto= dplyr::case_when(gasto=="Gas"~"Gas/Bencina",
gasto=="aspiradora"~"Electrodomésticos/mantención casa",
gasto=="Plata fiestas patrias basureros"~"Donaciones/regalos",
gasto=="Tina"~"Electrodomésticos/mantención casa",
gasto=="Nexium"~"Farmacia",
gasto=="donaciones"~"Donaciones/regalos",
gasto=="Regalo chocolates"~"Donaciones/regalos",
gasto=="filtro piscina msp"~"Electrodomésticos/mantención casa",
gasto=="Chromecast"~"Electrodomésticos/mantención casa",
gasto=="Muebles ratan"~"Electrodomésticos/mantención casa",
gasto=="Vacuna Influenza"~"Farmacia",
gasto=="Easy"~"Electrodomésticos/mantención casa",
gasto=="Sopapo"~"Electrodomésticos/mantención casa",
gasto=="filtro agua"~"Electrodomésticos/mantención casa",
gasto=="ropa tami"~"Donaciones/regalos",
gasto=="yaz"~"Farmacia",
gasto=="Yaz"~"Farmacia",
gasto=="Remedio"~"Farmacia",
gasto=="Entel"~"VTR",
gasto=="Kerosen"~"Gas/Bencina",
gasto=="Parafina"~"Gas/Bencina",
gasto=="Plata basurero"~"Donaciones/regalos",
gasto=="Matri Andrés Kogan"~"Donaciones/regalos",
gasto=="Wild Protein"~"Comida",
gasto=="Granola Wild Foods"~"Comida",
gasto=="uber"~"Transporte",
gasto=="Uber Reñaca"~"Transporte",
gasto=="filtro piscina mspa"~"Electrodomésticos/mantención casa",
gasto=="Limpieza Alfombra"~"Electrodomésticos/mantención casa",
gasto=="Aspiradora"~"Electrodomésticos/mantención casa",
gasto=="Limpieza alfombras"~"Electrodomésticos/mantención casa",
gasto=="Pila estufa"~"Electrodomésticos/mantención casa",
gasto=="Reloj"~"Electrodomésticos/mantención casa",
gasto=="Arreglo"~"Electrodomésticos/mantención casa",
gasto=="Pan Pepperino"~"Comida",
gasto=="Cookidoo"~"Comida",
gasto=="remedios"~"Farmacia",
gasto=="Bendina Reñaca"~"Gas/Bencina",
gasto=="Bencina Reñaca"~"Gas/Bencina",
gasto=="Vacunas Influenza"~"Farmacia",
gasto=="Remedios"~"Farmacia",
gasto=="Plata fiestas patrias basureros"~"Donaciones/regalos",
#2024
gasto=="cartero"~"Correo",
gasto=="correo"~"Correo",
gasto=="Gaviscón y Paracetamol"~"Farmacia",
gasto=="Regalo Matri Cony"~"Donaciones/regalos",
gasto=="Regalo Matri Chepa"~"Donaciones/regalos",
gasto=="Aporte Basureros"~"Donaciones/regalos",
gasto=="donación"~"Donaciones/regalos",
gasto=="Plata Reciclaje y Basurero"~"Donaciones/regalos",
gasto=="basureros"~"Donaciones/regalos",
gasto=="Microondas regalo"~"Donaciones/regalos",
gasto=="Cruz Verde"~"Farmacia",
gasto=="Remedios Covid"~"Farmacia",
gasto=="nacho"~"Electrodomésticos/mantención casa",
gasto=="Jardinero"~"Electrodomésticos/mantención casa",
gasto=="mantencion toyotomi"~"Electrodomésticos/mantención casa",
gasto=="Cámaras Seguridad M.Barrios"~"Electrodomésticos/mantención casa",
gasto=="Uber cumple papá"~"Transporte",
gasto=="Uber"~"Transporte",
gasto=="Uber Matri Cony"~"Transporte",
gasto=="Bencina + tag"~"Gas/Bencina",
gasto=="Bencina + Tag cumple Delox"~"Gas/Bencina",
gasto=="Bencina + peajes Maite"~"Gas/Bencina",
gasto=="Crunchyroll"~"Netflix",
gasto=="Crunchyroll"~"Netflix",
gasto=="Incoludido"~"Enceres",
gasto=="Cortina baño"~"Electrodomésticos/mantención casa",
gasto=="Forro cortina ducha"~"Electrodomésticos/mantención casa",
gasto=="Brussels"~"Comida",
gasto=="Tres toques"~"Enceres",
gasto=="Transferencia"~"Otros",
gasto=="prestamo"~"Otros",
gasto=="Préstamo Andrés"~"Otros",
gasto=="mouse"~"Otros",
gasto=="lamina"~"Otros",
T~gasto)) %>%
dplyr::group_by(gastador, fecha,gasto, .drop=F) %>%
#dplyr::mutate(fecha_simp=week(parse_date(fecha))) %>%
# dplyr::mutate(fecha_simp=tsibble::yearweek(fecha)) %>%#después de diosi. Junio 24, 2019
dplyr::summarise(monto=sum(monto)) %>%
dplyr::mutate(gastador_nombre=plyr::revalue(as.character(gastador), c("0" = "Tami", "1"="Andrés"))) %>%
ggplot2::ggplot(aes(x = fecha, y = monto, color=as.factor(gastador_nombre))) +
#stat_summary(geom = "line", fun.y = median, size = 1, alpha=0.5, aes(color="blue")) +
geom_line(size=1) +
facet_grid(gasto~.)+
geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
labs(y="Gastos (en miles)",x="Semanas y Meses", subtitle="Interlineado, incorporación de la Diosi; Azul= Tami; Rojo= Andrés") +
ggtitle( "Figura 6. Gastos Semanales por Gastador e ítem (media)") +
scale_y_continuous(labels = f <- function(x) paste0(x/1000)) +
scale_color_manual(name = "Gastador", values= c("blue", "red"), labels = c("Tami", "Andrés")) +
scale_x_yearweek(breaks = "1 month", minor_breaks = "1 week", labels=date_format("%m/%y")) +
guides(color = F)+
theme_custom() +
theme(axis.text.x = element_text(vjust = 0.5,angle = 35)) +
theme(
panel.border = element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
axis.line = element_line(colour = "black")
)
# Apply MSTL decomposition
mstl_data_autplt <- forecast::mstl(Gastos_casa$monto, lambda = "auto",iterate=5000000,start =
lubridate::decimal_date(as.Date("2019-03-03")))
# Convert the decomposed time series to a data frame
mstl_df <- data.frame(
Date = as.Date(Gastos_casa$fecha, format="%d/%m/%Y"),
Data = as.numeric(mstl_data_autplt[, "Data"]),
Trend = as.numeric(mstl_data_autplt[, "Trend"]),
Remainder = as.numeric(mstl_data_autplt[, "Remainder"])
)
# Reshape the data frame for ggplot2
mstl_long <- mstl_df %>%
pivot_longer(cols = -Date, names_to = "Component", values_to = "Value")
# Plotting with ggplot2
ggplot(mstl_long, aes(x = Date, y = Value)) +
geom_line() +
theme_bw() +
labs(title = "Descomposición MSTL", x = "Fecha", y = "Valor") +
scale_x_date(date_breaks = "3 months", date_labels = "%m-%Y") +
facet_wrap(~ Component, scales = "free_y", ncol = 1) +
theme(strip.text = element_text(size = 12),
axis.text.x = element_text(angle = 90, hjust = 1))
library(bsts)
library(CausalImpact)
ts_week_covid<-
Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))%>%
dplyr::group_by(fecha_week)%>%
dplyr::summarise(gasto_total=sum(monto,na.rm=T)/1000,min_day=min(day))%>%
dplyr::ungroup() %>%
dplyr::mutate(covid=dplyr::case_when(min_day>=as.Date("2020-03-17")~1,TRUE~0))%>%
dplyr::mutate(covid=as.factor(covid))%>%
data.frame()
ts_week_covid$gasto_total_na<-ts_week_covid$gasto_total
post_resp<-ts_week_covid$gasto_total[which(ts_week_covid$covid==1)]
ts_week_covid$gasto_total_na[which(ts_week_covid$covid==1)]<-NA
ts_week_covid$gasto_total[which(ts_week_covid$covid==0)]## [1] 98.357 4.780 56.784 50.506 64.483 67.248 49.299 35.786 58.503
## [10] 64.083 20.148 73.476 127.004 81.551 69.599 134.446 58.936 26.145
## [19] 129.927 104.989 130.860 81.893 95.697 64.579 303.471 151.106 49.275
## [28] 76.293 33.940 83.071 119.512 20.942 58.055 71.728 44.090 33.740
## [37] 59.264 77.410 60.831 63.376 48.754 235.284 29.604 115.143 72.419
## [46] 5.980 80.063 149.178 69.918 107.601 72.724 63.203 99.681 130.309
## [55] 195.898 112.066# Model 1
ssd <- list()
# Local trend, weekly-seasonal #https://qastack.mx/stats/209426/predictions-from-bsts-model-in-r-are-failing-completely - PUSE UN GENERALIZED LOCAL TREND
ssd <- AddLocalLevel(ssd, ts_week_covid$gasto_total_na) #AddSemilocalLinearTrend #AddLocalLevel
# Add weekly seasonal
ssd <- AddSeasonal(ssd, ts_week_covid$gasto_total_na,nseasons=5, season.duration = 52) #weeks OJO, ESTOS NO SON WEEKS VERDADEROS. PORQUE TENGO MAS DE EUN AÑO
ssd <- AddSeasonal(ssd, ts_week_covid$gasto_total_na, nseasons = 12, season.duration =4) #years
# For example, to add a day-of-week component to data with daily granularity, use model.args = list(nseasons = 7, season.duration = 1). To add a day-of-week component to data with hourly granularity, set model.args = list(nseasons = 7, season.duration = 24).
model1d1 <- bsts(ts_week_covid$gasto_total_na,
state.specification = ssd, #A list with elements created by AddLocalLinearTrend, AddSeasonal, and similar functions for adding components of state. See the help page for state.specification.
family ="student", #A Bayesian Analysis of Time-Series Event Count Data. POISSON NO SE PUEDE OCUPAR
niter = 20000,
#burn = 200, #http://finzi.psych.upenn.edu/library/bsts/html/SuggestBurn.html Suggest the size of an MCMC burn in sample as a proportion of the total run.
seed= 2125)## =-=-=-=-= Iteration 0 Mon Jun 30 01:02:26 2025
## =-=-=-=-=
## =-=-=-=-= Iteration 2000 Mon Jun 30 01:02:36 2025
## =-=-=-=-=
## =-=-=-=-= Iteration 4000 Mon Jun 30 01:02:45 2025
## =-=-=-=-=
## =-=-=-=-= Iteration 6000 Mon Jun 30 01:02:54 2025
## =-=-=-=-=
## =-=-=-=-= Iteration 8000 Mon Jun 30 01:03:04 2025
## =-=-=-=-=
## =-=-=-=-= Iteration 10000 Mon Jun 30 01:03:13 2025
## =-=-=-=-=
## =-=-=-=-= Iteration 12000 Mon Jun 30 01:03:23 2025
## =-=-=-=-=
## =-=-=-=-= Iteration 14000 Mon Jun 30 01:03:32 2025
## =-=-=-=-=
## =-=-=-=-= Iteration 16000 Mon Jun 30 01:03:42 2025
## =-=-=-=-=
## =-=-=-=-= Iteration 18000 Mon Jun 30 01:03:51 2025
## =-=-=-=-=#,
# dynamic.regression=T)
#plot(model1d1, main = "Model 1")
#plot(model1d1, "components")
impact2d1 <- CausalImpact(bsts.model = model1d1,
post.period.response = post_resp)
plot(impact2d1)+
xlab("Date")+
ylab("Monto Semanal (En miles)")
burn1d1 <- SuggestBurn(0.1, model1d1)corpus <- Corpus(VectorSource(Gastos_casa$obs)) # formato de texto
d <- tm_map(corpus, tolower)
d <- tm_map(d, stripWhitespace)
d <- tm_map(d, removePunctuation)
d <- tm_map(d, removeNumbers)
d <- tm_map(d, removeWords, stopwords("spanish"))
d <- tm_map(d, removeWords, "menos")
tdm <- TermDocumentMatrix(d)
m <- as.matrix(tdm) #lo vuelve una matriz
v <- sort(rowSums(m),decreasing=TRUE) #lo ordena y suma
df <- data.frame(word = names(v),freq=v) # lo nombra y le da formato de data.frame
#findFreqTerms(tdm)
#require(devtools)
#install_github("lchiffon/wordcloud2")
#wordcloud2::wordcloud2(v, size=1.2)
wordcloud(words = df$word, freq = df$freq,
max.words=100, random.order=FALSE, rot.per=0.35,
colors=brewer.pal(8, "Dark2"), main="Figura 7. Nube de Palabras, Observaciones")
fit_month_gasto <- Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha_month=strftime(fecha, format = "%Y-%m")) %>%
dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))%>%
dplyr::mutate(gasto2= dplyr::case_when(gasto=="Gas"~"Gas/Bencina",
gasto=="aspiradora"~"Electrodomésticos/mantención casa",
gasto=="Plata fiestas patrias basureros"~"Donaciones/regalos",
gasto=="Tina"~"Electrodomésticos/mantención casa",
gasto=="Nexium"~"Farmacia",
gasto=="donaciones"~"Donaciones/regalos",
gasto=="Regalo chocolates"~"Donaciones/regalos",
gasto=="filtro piscina msp"~"Electrodomésticos/mantención casa",
gasto=="Chromecast"~"Electrodomésticos/mantención casa",
gasto=="Muebles ratan"~"Electrodomésticos/mantención casa",
gasto=="Vacuna Influenza"~"Farmacia",
gasto=="Easy"~"Electrodomésticos/mantención casa",
gasto=="Sopapo"~"Electrodomésticos/mantención casa",
gasto=="filtro agua"~"Electrodomésticos/mantención casa",
gasto=="ropa tami"~"Donaciones/regalos",
gasto=="yaz"~"Farmacia",
gasto=="Yaz"~"Farmacia",
gasto=="Remedio"~"Farmacia",
gasto=="Entel"~"VTR",
gasto=="Kerosen"~"Gas/Bencina",
gasto=="Parafina"~"Gas/Bencina",
gasto=="Plata basurero"~"Donaciones/regalos",
gasto=="Matri Andrés Kogan"~"Donaciones/regalos",
gasto=="Wild Protein"~"Comida",
gasto=="Granola Wild Foods"~"Comida",
gasto=="uber"~"Transporte",
gasto=="Uber Reñaca"~"Transporte",
gasto=="filtro piscina mspa"~"Electrodomésticos/mantención casa",
gasto=="Limpieza Alfombra"~"Electrodomésticos/mantención casa",
gasto=="Aspiradora"~"Electrodomésticos/mantención casa",
gasto=="Limpieza alfombras"~"Electrodomésticos/mantención casa",
gasto=="Pila estufa"~"Electrodomésticos/mantención casa",
gasto=="Reloj"~"Electrodomésticos/mantención casa",
gasto=="Arreglo"~"Electrodomésticos/mantención casa",
gasto=="Pan Pepperino"~"Comida",
gasto=="Cookidoo"~"Comida",
gasto=="remedios"~"Farmacia",
gasto=="Bendina Reñaca"~"Gas/Bencina",
gasto=="Bencina Reñaca"~"Gas/Bencina",
gasto=="Vacunas Influenza"~"Farmacia",
gasto=="Remedios"~"Farmacia",
gasto=="Plata fiestas patrias basureros"~"Donaciones/regalos",
#2024
gasto=="cartero"~"Correo",
gasto=="correo"~"Correo",
gasto=="Gaviscón y Paracetamol"~"Farmacia",
gasto=="Regalo Matri Cony"~"Donaciones/regalos",
gasto=="Regalo Matri Chepa"~"Donaciones/regalos",
gasto=="Aporte Basureros"~"Donaciones/regalos",
gasto=="donación"~"Donaciones/regalos",
gasto=="Plata Reciclaje y Basurero"~"Donaciones/regalos",
gasto=="basureros"~"Donaciones/regalos",
gasto=="Microondas regalo"~"Donaciones/regalos",
gasto=="Cruz Verde"~"Farmacia",
gasto=="Remedios Covid"~"Farmacia",
gasto=="nacho"~"Electrodomésticos/mantención casa",
gasto=="Jardinero"~"Electrodomésticos/mantención casa",
gasto=="mantencion toyotomi"~"Electrodomésticos/mantención casa",
gasto=="Cámaras Seguridad M.Barrios"~"Electrodomésticos/mantención casa",
gasto=="Uber cumple papá"~"Transporte",
gasto=="Uber"~"Transporte",
gasto=="Uber Matri Cony"~"Transporte",
gasto=="Bencina + tag"~"Gas/Bencina",
gasto=="Bencina + Tag cumple Delox"~"Gas/Bencina",
gasto=="Bencina + peajes Maite"~"Gas/Bencina",
gasto=="Crunchyroll"~"Netflix",
gasto=="Crunchyroll"~"Netflix",
gasto=="Incoludido"~"Enceres",
gasto=="Cortina baño"~"Electrodomésticos/mantención casa",
gasto=="Forro cortina ducha"~"Electrodomésticos/mantención casa",
gasto=="Brussels"~"Comida",
gasto=="Tres toques"~"Enceres",
gasto=="Transferencia"~"Otros",
gasto=="prestamo"~"Otros",
gasto=="Préstamo Andrés"~"Otros",
gasto=="mouse"~"Otros",
gasto=="lamina"~"Otros",
T~gasto)) %>%
dplyr::mutate(fecha_month=factor(fecha_month, levels=format(seq(from = as.Date("2019-03-03"), to = as.Date(substr(Sys.time(),1,10)), by = "1 month"),"%Y-%m")))%>%
dplyr::mutate(gasto2=factor(gasto2, levels=c("Agua", "Comida", "Comunicaciones","Electricidad", "Enceres", "Farmacia", "Gas/Bencina", "Diosi", "donaciones/regalos", "Electrodomésticos/ Mantención casa", "VTR", "Netflix", "Otros")))%>%
dplyr::group_by(fecha_month, gasto2, .drop=F)%>%
dplyr::summarise(gasto_total=sum(monto, na.rm = T)/1000)%>%
data.frame() %>% na.omit()
fit_month_gasto_25<-
fit_month_gasto %>%
#dplyr::filter()
dplyr::filter(grepl("2025",fecha_month)) %>%
#sacar el ultimo mes
dplyr::filter(as.character(format(as.Date(substr(Sys.time(),1,10)),"%Y-%m"))!=fecha_month) %>%
dplyr::group_by(gasto2) %>%
dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>%
data.frame()%>% ungroup()
fit_month_gasto_24<-
fit_month_gasto %>%
#dplyr::filter()
dplyr::filter(grepl("2024",fecha_month)) %>%
#sacar el ultimo mes
dplyr::filter(as.character(format(as.Date(substr(Sys.time(),1,10)),"%Y-%m"))!=fecha_month) %>%
dplyr::group_by(gasto2) %>%
dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>%
data.frame()%>% ungroup()
fit_month_gasto_23<-
fit_month_gasto %>%
#dplyr::filter()
dplyr::filter(grepl("2023",fecha_month)) %>%
dplyr::group_by(gasto2) %>%
dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>%
data.frame()%>% ungroup()
fit_month_gasto_22<-
fit_month_gasto %>%
#dplyr::filter()
dplyr::filter(grepl("2022",fecha_month)) %>%
dplyr::group_by(gasto2) %>%
dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>%
data.frame()%>% ungroup()
fit_month_gasto_21<-
fit_month_gasto %>%
#dplyr::filter()
dplyr::filter(grepl("2021",fecha_month)) %>%
dplyr::group_by(gasto2) %>%
dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>%
data.frame()%>% ungroup()
fit_month_gasto_20<-
fit_month_gasto %>%
#dplyr::filter()
dplyr::filter(grepl("2020",fecha_month)) %>%
dplyr::group_by(gasto2) %>%
dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>%
data.frame() %>% ungroup()
fit_month_gasto_25 %>%
dplyr::right_join(fit_month_gasto_24,by="gasto2") %>%
dplyr::right_join(fit_month_gasto_23,by="gasto2") %>%
dplyr::right_join(fit_month_gasto_22,by="gasto2") %>%
dplyr::right_join(fit_month_gasto_21,by="gasto2") %>%
dplyr::right_join(fit_month_gasto_20,by="gasto2") %>%
janitor::adorn_totals() %>%
#dplyr::select(-3)%>%
knitr::kable(format = "markdown", size=12, col.names= c("Item","2025","2024","2023","2022","2021","2020"))| Item | 2025 | 2024 | 2023 | 2022 | 2021 | 2020 |
|---|---|---|---|---|---|---|
| Agua | 11.4882 | 6.993667 | 5.195333 | 5.410333 | 5.849167 | 9.93775 |
| Comida | 250.2842 | 326.890000 | 366.009167 | 312.386750 | 317.896583 | 392.93367 |
| Comunicaciones | 0.0000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.00000 |
| Electricidad | 55.4514 | 83.582750 | 38.104750 | 47.072333 | 29.523000 | 20.60458 |
| Enceres | 2.6380 | 23.989000 | 18.259750 | 24.219750 | 14.801167 | 39.01200 |
| Farmacia | 0.0000 | 0.000000 | 10.704083 | 2.835000 | 13.996083 | 14.03675 |
| Gas/Bencina | 33.1500 | 44.292667 | 42.636000 | 45.575000 | 13.583667 | 17.25833 |
| Diosi | 20.8978 | 33.319583 | 55.804250 | 31.180667 | 52.687833 | 37.12133 |
| donaciones/regalos | 0.0000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.00000 |
| Electrodomésticos/ Mantención casa | 0.0000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.00000 |
| VTR | 17.5960 | 18.326667 | 12.829167 | 25.156667 | 19.086917 | 19.11375 |
| Netflix | 0.0000 | 1.391417 | 8.713833 | 7.151583 | 7.028750 | 8.24725 |
| Otros | 0.0000 | 76.164000 | 5.481667 | 5.000000 | 0.000000 | 0.00000 |
| Total | 391.5056 | 614.949750 | 563.738000 | 505.988083 | 474.453167 | 558.26542 |
## Joining with `by = join_by(word)`
2. UF Proyectada
Saqué la UF proyectada
#options(max.print=5000)
uf18 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2018.htm")%>% rvest::html_nodes("table")
uf19 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2019.htm")%>% rvest::html_nodes("table")
uf20 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2020.htm")%>% rvest::html_nodes("table")
uf21 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2021.htm")%>% rvest::html_nodes("table")
uf22 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2022.htm")%>% rvest::html_nodes("table")
uf23 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2023.htm")%>% rvest::html_nodes("table")
uf24 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2024.htm")%>% rvest::html_nodes("table")
tryCatch(uf25 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2025.htm")%>% rvest::html_nodes("table"),
error = function(c) {
uf24b <<- cbind.data.frame(Día=NA, variable=NA, value=NA)
}
)
tryCatch(uf25 <-uf25[[length(uf25)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1),
error = function(c) {
uf25 <<- cbind.data.frame(Día=NA, variable=NA, value=NA)
}
)
uf_serie<-
bind_rows(
cbind.data.frame(anio= 2018, uf18[[length(uf18)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),
cbind.data.frame(anio= 2019, uf19[[length(uf19)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),
cbind.data.frame(anio= 2020, uf20[[length(uf20)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),
cbind.data.frame(anio= 2021, uf21[[length(uf21)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),
cbind.data.frame(anio= 2022, uf22[[length(uf22)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),
cbind.data.frame(anio= 2023, uf23[[length(uf23)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),
cbind.data.frame(anio= 2024, uf23[[length(uf24)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),
cbind.data.frame(anio= 2025, uf25)
)
uf_serie_corrected<-
uf_serie %>%
dplyr::mutate(month=plyr::revalue(tolower(.[[3]]),c("ene" = 1, "feb"=2, "mar"=3, "abr"=4, "may"=5, "jun"=6, "jul"=7, "ago"=8, "sep"=9, "oct"=10, "nov"=11, "dic"=12))) %>%
dplyr::mutate(value=stringr::str_trim(value), value= sub("\\.","",value),value= as.numeric(sub("\\,",".",value))) %>%
dplyr::mutate(date=paste0(sprintf("%02d", .[[2]])," ",sprintf("%02d",as.numeric(month)),", ",.[[1]]), date3=lubridate::parse_date_time(date,c("%d %m, %Y"),exact=T),date2=date3) %>%
na.omit()#%>% dplyr::filter(is.na(date3))## Warning: There was 1 warning in `dplyr::mutate()`.
## i In argument: `date3 = lubridate::parse_date_time(date, c("%d %m, %Y"), exact
## = T)`.
## Caused by warning:
## ! 54 failed to parse.#Day of the month as decimal number (1–31), with a leading space for a single-digit number.
#Abbreviated month name in the current locale on this platform. (Also matches full name on input: in some locales there are no abbreviations of names.)
warning(paste0("number of observations:",nrow(uf_serie_corrected),", min uf: ",min(uf_serie_corrected$value),", min date: ",min(uf_serie_corrected $date3 )))## Warning: number of observations:2746, min uf: 26799.01, min date: 2018-01-01#
# uf_proyectado <- readxl::read_excel("uf_proyectado.xlsx") %>% dplyr::arrange(Período) %>%
# dplyr::mutate(Período= as.Date(lubridate::parse_date_time(Período, c("%Y-%m-%d"),exact=T)))
ts_uf_proy<-
ts(data = uf_serie_corrected$value,
start = as.numeric(as.Date("2018-01-01")),
end = as.numeric(as.Date(uf_serie_corrected$date3[length(uf_serie_corrected$date3)])), frequency = 1,
deltat = 1, ts.eps = getOption("ts.eps"))
fit_tbats <- forecast::tbats(ts_uf_proy)## Warning in bats(as.numeric(y), use.box.cox = use.box.cox, use.trend =
## use.trend, : optim() did not converge.fr_fit_tbats<-forecast::forecast(fit_tbats, h=298)# Configurar API Key
nixtlar::nixtla_set_api_key(Sys.getenv("API_NIXTLA"))## API key has been set for the current session.try(nixtlar::nixtla_set_api_key(Sys.getenv("NIXTLA")))## API key has been set for the current session.# Preparar datos en formato requerido por TimeGPT
uf_timegpt <- uf_serie_corrected %>%
dplyr::rename(ds = date3, y = value) %>%
dplyr::mutate(ds = format(ds, "%Y-%m-%d")) %>%
dplyr::mutate(unique_id = "serie_1")%>%
dplyr::select(unique_id, ds, y)
# Realizar pronóstico con TimeGPT
timegpt_fcst <- nixtlar::nixtla_client_forecast(
uf_timegpt,
h = 298, # 298 días a pronosticar
freq = "D", # Frecuencia diaria
add_history = TRUE, # Incluir datos históricos en el output
level = c(80,95),
model= "timegpt-1-long-horizon",
clean_ex_first = TRUE
)## The specified horizon h exceeds the model horizon. This may lead to less accurate forecasts. Please consider using a smaller horizon.# 1. Convertir 'ds' a fecha en ambas tablas
uf_timegpt <- uf_timegpt %>%
mutate(ds = as.Date(ds))
timegpt_fcst <- timegpt_fcst %>%
mutate(ds = as.Date(ds))
# 2. Combinar los datos históricos y el pronóstico
full_data <- bind_rows(
uf_timegpt %>% mutate(type = "Histórico"),
timegpt_fcst %>% mutate(type = "Pronóstico")
)
# Visualizar resultados
ggplot(full_data, aes(x = ds, y = TimeGPT)) +
# Intervalo de confianza del 95%
geom_ribbon(aes(ymin = `TimeGPT-lo-95`, ymax = `TimeGPT-hi-95`),
fill = "#4B9CD3", alpha = 0.2) +
# Intervalo de confianza del 80%
geom_ribbon(aes(ymin = `TimeGPT-lo-80`, ymax = `TimeGPT-hi-80`),
fill = "#4B9CD3", alpha = 0.3) +
# Línea histórica
geom_line(data = filter(full_data, type == "Histórico"),
aes(color = "Histórico"), size = 1) +
# Línea de pronóstico
geom_line(data = filter(full_data, type == "Pronóstico"),
aes(color = "Pronóstico"), size = 1) +
# Línea vertical separadora
geom_vline(xintercept = max(filter(full_data, type == "Histórico")$ds),
linetype = "dashed", color = "red", size = 0.8) +
# Configuración del eje x
scale_x_date(
date_breaks = "3 months", # Reduce la frecuencia de las etiquetas
date_labels = "%b %Y", # Formato de etiquetas (mes y año)
) +
# Configuración del eje y
scale_y_continuous(labels = function(x) format(x, scientific = FALSE)) +
# Configuración de colores
scale_color_manual(
name = "Leyenda",
values = c("Histórico" = "black", "Pronóstico" = "#4B9CD3")
) +
# Títulos y subtítulos
labs(
title = "Pronóstico de Serie Temporal con TimeGPT",
subtitle = "Intervalos de confianza al 80% (más oscuro) y 95% (más claro)",
x = "Fecha",
y = "Valor",
color = "Leyenda"
) +
# Tema y estilos
theme_minimal() +
theme(
axis.text.x = element_text(angle = 45, hjust = 1, size = 8),
axis.title.x = element_text(size = 10),
axis.title.y = element_text(size = 10),
legend.position = "bottom",
panel.grid.major = element_blank(),
panel.grid.minor = element_blank()
)## Warning: Removed 2746 rows containing missing values or values outside the scale range
## (`geom_line()`).
library(prophet)## Warning: package 'prophet' was built under R version 4.4.3## Loading required package: Rcpp## Warning: package 'Rcpp' was built under R version 4.4.3## Loading required package: rlang## Warning: package 'rlang' was built under R version 4.4.3##
## Attaching package: 'rlang'## The following objects are masked from 'package:purrr':
##
## %@%, flatten, flatten_chr, flatten_dbl, flatten_int, flatten_lgl,
## flatten_raw, invoke, splice## The following object is masked from 'package:sparklyr':
##
## invoke## The following object is masked from 'package:data.table':
##
## := model <- prophet(
cbind.data.frame(ds= as.Date(uf_timegpt$ds), y=uf_timegpt$y),
# Trend flexibility
growth = "linear",
changepoint.prior.scale = 0.05, # Reduced for smoother trend
n.changepoints = 50, # Increased from default 25
# Seasonality
yearly.seasonality = TRUE,
weekly.seasonality = TRUE,
daily.seasonality = FALSE, # Disabled for daily data
seasonality.mode = "additive",
seasonality.prior.scale = 15, # Increased to capture stronger seasonality
# Holidays (if applicable)
# holidays = generated_holidays # Create with add_country_holidays()
# Uncertainty intervals
interval.width = 0.95,
uncertainty.samples = 1000
)
future <- make_future_dataframe(model, periods = 298, include_history = T)
forecast <- predict(model, future)
forecast <- forecast[, c("ds", "yhat", "yhat_lower", "yhat_upper")]
forecast$pred <- ifelse(forecast$ds > max(uf_timegpt$ds), 1,0)## Warning in check_tzones(e1, e2): 'tzone' attributes are inconsistentforecast$ds <- as.Date(forecast$ds)
ggplot(forecast, aes(x = ds, y = yhat)) +
geom_ribbon(aes(ymin = yhat_lower, ymax = yhat_upper),
fill = "#9ecae1", alpha = 0.4) +
geom_line(color = "#08519c", linewidth = 0.8) +
geom_vline(xintercept = max(uf_timegpt$ds), color = "red", linetype = "dashed", linewidth=1) +
scale_x_date(date_breaks = "6 months", date_labels = "%y %b") +
scale_y_continuous(labels = scales::comma) +
labs(title = "Valores predichos (95%IC)",
# subtitle = "March 10, 2025 - May 7, 2025",
x = "Fecha",
y = "Valor",
# caption = "Source: Prophet Forecast Model"
) +
theme_minimal() +
theme(
plot.title = element_text(face = "bold", size = 14),
plot.subtitle = element_text(color = "gray50"),
axis.text.x = element_text(angle = 45, hjust = 1),
panel.grid.minor = element_blank(),
panel.border = element_blank(),
plot.caption = element_text(color = "gray30")
)
La proyección de la UF a 298 días más 2025-07-09 00:04:58 sería de: 26.580 pesos// Percentil 95% más alto proyectado: 35.072,1
Según TimeGPT: La proyección de la UF a 298 días más 2026-05-03 sería de: 40.188,82 pesos// Percentil 80% más alto proyectado: 40.557,46 pesos// Percentil 95% más alto proyectado: 41.617,64
Según prophet: La proyección de la UF a 298 días más 2026-05-03 sería de: 41.974 pesos// Percentil 95% más alto proyectado: 50.885
Ahora con un modelo ARIMA automático
arima_optimal_uf = forecast::auto.arima(ts_uf_proy)
autoplotly::autoplotly(forecast::forecast(arima_optimal_uf, h=298), ts.colour = "darkred",
predict.colour = "blue", predict.linetype = "dashed")%>%
plotly::layout(showlegend = F,
yaxis = list(title = "Gastos"),
xaxis = list(
title="Fecha",
ticktext = as.list(seq(from = as.Date("2018-01-01"),
to = as.Date("2018-01-01")+length(fit_tbats$fitted.values)+298, by = 90)),
tickvals = as.list(seq(from = as.numeric(as.Date("2018-01-01")),
to = as.numeric(as.Date("2018-01-01"))+length(fit_tbats$fitted.values)+298, by = 90)),
tickmode = "array",
tickangle = 90
))fr_fit_tbats_uf<-forecast::forecast(arima_optimal_uf, h=298)dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats)),variable) %>% dplyr::summarise(max=max(value)) %>%
dplyr::right_join(dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats_uf)),variable) %>% dplyr::summarise(max=max(value)),by="variable") %>%
dplyr::mutate(variable=factor(variable,levels=c("Lo.95","Lo.80","Point.Forecast","Hi.80","Hi.95"))) %>%
dplyr::arrange(variable) %>%
knitr::kable(format="markdown", caption="Tabla. Estimación UF (de aquí a 298 días) según cálculos de gastos mensuales",
col.names= c("Item","UF Proyectada (TBATS)","UF Proyectada (ARIMA)"))## No id variables; using all as measure variables
## No id variables; using all as measure variables| Item | UF Proyectada (TBATS) | UF Proyectada (ARIMA) |
|---|---|---|
| Lo.95 | 26197.33 | 26323.55 |
| Lo.80 | 26329.23 | 26488.13 |
| Point.Forecast | 26580.21 | 26799.01 |
| Hi.80 | 31419.69 | 32165.71 |
| Hi.95 | 34328.67 | 35006.68 |
3. Gastos proyectados
Lo haré en base a 2 cálculos: el gasto semanal y el gasto mensual en base a mis gastos desde marzo de 2019. La primera proyección la hice añadiendo el precio del arriendo mensual y partiendo en 2 (porque es con yo y Tami). No se incluye el último mes.
Gastos_casa_nvo <- readr::read_csv(as.character(path_sec),
col_names = c("Tiempo", "gasto", "fecha", "obs", "monto", "gastador",
"link"),skip=1) %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha_month=strftime(fecha, format = "%Y-%m")) %>%
dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))
Gastos_casa_m <-
Gastos_casa_nvo %>% dplyr::group_by(fecha_month)%>%
dplyr::summarise(gasto_total=(sum(monto)+500000)/1000,fecha=first(fecha))%>%
data.frame()
uf_serie_corrected_m <-
uf_serie_corrected %>% dplyr::mutate(ano_m=paste0(anio,"-",sprintf("%02d",as.numeric(month)))) %>% dplyr::group_by(ano_m)%>%
dplyr::summarise(uf=(mean(value))/1000,fecha=first(date3))%>%
data.frame() %>%
dplyr::filter(fecha>="2019-02-28")
#Error: Error in standardise_path(file) : object 'enlace_gastos' not found
ts_uf_serie_corrected_m<-
ts(data = uf_serie_corrected_m$uf[-length(uf_serie_corrected_m$uf)],
start = 1,
end = nrow(uf_serie_corrected_m),
frequency = 1,
deltat = 1, ts.eps = getOption("ts.eps"))
ts_gastos_casa_m<-
ts(data = Gastos_casa_m$gasto_total[-length(Gastos_casa_m$gasto_total)],
start = 1,
end = nrow(Gastos_casa_m),
frequency = 1,
deltat = 1, ts.eps = getOption("ts.eps"))
fit_tbats_m <- forecast::tbats(ts_gastos_casa_m)
seq_dates<-format(seq(as.Date("2019/03/01"), by = "month", length = dim(Gastos_casa_m)[1]+12), "%m\n'%y")
autplo2t<-
autoplotly::autoplotly(forecast::forecast(fit_tbats_m, h=12), ts.colour = "darkred",
predict.colour = "blue", predict.linetype = "dashed")%>%
plotly::layout(showlegend = F,
yaxis = list(title = "Gastos (en miles)"),
xaxis = list(
title="Fecha",
ticktext = as.list(seq_dates[seq(from = 1, to = (dim(Gastos_casa_m)[1]+12), by = 3)]),
tickvals = as.list(seq(from = 1, to = (dim(Gastos_casa_m)[1]+12), by = 3)),
tickmode = "array"#"array"
))
autplo2tAhora asumiendo un modelo ARIMA, e incluimos como regresor al precio de la UF.
paste0("Optimo pero sin regresor")## [1] "Optimo pero sin regresor"arima_optimal = forecast::auto.arima(ts_gastos_casa_m)
arima_optimal## Series: ts_gastos_casa_m
## ARIMA(1,0,0) with non-zero mean
##
## Coefficients:
## ar1 mean
## 0.4299 1039.5096
## s.e. 0.1057 38.3598
##
## sigma^2 = 37907: log likelihood = -507.56
## AIC=1021.12 AICc=1021.45 BIC=1028.11paste0("Optimo pero con regresor")## [1] "Optimo pero con regresor"arima_optimal2 = forecast::auto.arima(ts_gastos_casa_m, xreg=as.numeric(ts_uf_serie_corrected_m[1:(length(Gastos_casa_m$gasto_total))]))
arima_optimal2## Series: ts_gastos_casa_m
## Regression with ARIMA(1,0,0) errors
##
## Coefficients:
## ar1 intercept xreg
## 0.4100 701.0985 10.4065
## s.e. 0.1073 320.5008 9.7774
##
## sigma^2 = 37890: log likelihood = -507.01
## AIC=1022.03 AICc=1022.59 BIC=1031.35forecast_uf<-
cbind.data.frame(fecha=as.Date(seq(as.numeric(as.Date(uf_serie_corrected$date3[length(uf_serie_corrected$date3)])),(as.numeric(as.Date(uf_serie_corrected$date3[length(uf_serie_corrected$date3)]))+299),by=1), origin = "1970-01-01"),forecast::forecast(fit_tbats, h=300)) %>%
dplyr::mutate(ano_m=stringr::str_extract(fecha,".{7}")) %>%
dplyr::group_by(ano_m)%>%
dplyr::summarise(uf=(mean(`Hi 95`,na.rm=T))/1000,fecha=first(fecha))%>%
data.frame()
autplo2t2<-
autoplotly::autoplotly(forecast::forecast(arima_optimal2,xreg=c(forecast_uf$uf[1],forecast_uf$uf), h=12), ts.colour = "darkred",
predict.colour = "blue", predict.linetype = "dashed")%>%
plotly::layout(showlegend = F,
yaxis = list(title = "Gastos (en miles)"),
xaxis = list(
title="Fecha",
ticktext = as.list(seq_dates[seq(from = 1, to = (dim(Gastos_casa_m)[1]+12), by = 3)]),
tickvals = as.list(seq(from = 1, to = (dim(Gastos_casa_m)[1]+12), by = 3)),
tickmode = "array"#"array"
))
autplo2t2fr_fit_tbats_m<-forecast::forecast(fit_tbats_m, h=12)
fr_fit_tbats_m2<-forecast::forecast(arima_optimal, h=12)
fr_fit_tbats_m3<-forecast::forecast(arima_optimal2, h=12,xreg=c(forecast_uf$uf[1],forecast_uf$uf))
dplyr::right_join(dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats_m3)),variable) %>% dplyr::summarise(max=max(value)), dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats_m2)),variable) %>% dplyr::summarise(max=max(value)),by="variable") %>%
dplyr::right_join(dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats_m)),variable) %>% dplyr::summarise(max=max(value)),by="variable") %>%
dplyr::mutate(variable=factor(variable,levels=c("Lo.95","Lo.80","Point.Forecast","Hi.80","Hi.95"))) %>%
dplyr::arrange(variable) %>%
knitr::kable(format="markdown", caption="Estimación en miles de la plata a gastar en el futuro (de aquí a 12 meses) según cálculos de gastos mensuales",
col.names= c("Item","Modelo ARIMA con regresor (UF)","Modelo ARIMA sin regresor","Modelo TBATS")) ## No id variables; using all as measure variables
## No id variables; using all as measure variables
## No id variables; using all as measure variables| Item | Modelo ARIMA con regresor (UF) | Modelo ARIMA sin regresor | Modelo TBATS |
|---|---|---|---|
| Lo.95 | 640.1200 | 616.8435 | 607.9829 |
| Lo.80 | 784.9037 | 763.1390 | 701.1458 |
| Point.Forecast | 1058.4066 | 1039.4978 | 917.6171 |
| Hi.80 | 1331.9094 | 1315.8566 | 1200.5224 |
| Hi.95 | 1476.6931 | 1462.1521 | 1383.8652 |
4. Gastos mensuales (resumen manual)
path_sec2<- paste0("https://docs.google.com/spreadsheets/d/",Sys.getenv("SUPERSECRET"),"/export?format=csv&id=",Sys.getenv("SUPERSECRET"),"&gid=847461368")
Gastos_casa_mensual_2022 <- readr::read_csv(as.character(path_sec2),
#col_names = c("Tiempo", "gasto", "fecha", "obs", "monto", "gastador","link"),
skip=0)## Rows: 80 Columns: 4
## -- Column specification --------------------------------------------------------
## Delimiter: ","
## chr (1): mes_ano
## dbl (3): n, Tami, Andrés
##
## i Use `spec()` to retrieve the full column specification for this data.
## i Specify the column types or set `show_col_types = FALSE` to quiet this message.head(Gastos_casa_mensual_2022,5) %>%
knitr::kable("markdown",caption="Resumen mensual, primeras 5 observaciones")| n | mes_ano | Tami | Andrés |
|---|---|---|---|
| 1 | marzo_2019 | 175533 | 68268 |
| 2 | abril_2019 | 152640 | 55031 |
| 3 | mayo_2019 | 152985 | 192219 |
| 4 | junio_2019 | 291067 | 84961 |
| 5 | julio_2019 | 241389 | 205893 |
(
Gastos_casa_mensual_2022 %>%
reshape2::melt(id.var=c("n","mes_ano")) %>%
dplyr::mutate(gastador=as.factor(variable)) %>%
dplyr::select(-variable) %>%
ggplot2::ggplot(aes(x = n, y = value, color=gastador)) +
scale_color_manual(name="Gastador", values=c("red", "blue"))+
geom_line(size=1) +
#geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
labs(y="Gastos (en miles)",x="Meses", subtitle="Azul= Tami; Rojo= Andrés") +
ggtitle( "Gastos Mensuales (total manual)") +
scale_y_continuous(labels = f <- function(x) paste0(x/1000)) +
# scale_color_manual(name = "Gastador", values= c("blue", "red"), labels = c("Tami", "Andrés")) +
# scale_x_yearweek(breaks = "1 month", minor_breaks = "1 week", labels=date_format("%m/%y")) +
# guides(color = F)+
theme_custom() +
theme(axis.text.x = element_text(vjust = 0.5,angle = 35)) +
theme(
panel.border = element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
axis.line = element_line(colour = "black")
)
) %>% ggplotly()Gastos_casa_mensual_2022$mes_ano <- gsub("marzo", "Mar", Gastos_casa_mensual_2022$mes_ano)
Gastos_casa_mensual_2022$mes_ano <- gsub("abril", "Apr", Gastos_casa_mensual_2022$mes_ano)
Gastos_casa_mensual_2022$mes_ano <- gsub("mayo", "May", Gastos_casa_mensual_2022$mes_ano)
Gastos_casa_mensual_2022$mes_ano <- gsub("junio", "Jun", Gastos_casa_mensual_2022$mes_ano)
Gastos_casa_mensual_2022$mes_ano <- gsub("julio", "Jul", Gastos_casa_mensual_2022$mes_ano)
Gastos_casa_mensual_2022$mes_ano <- gsub("agosto", "Aug", Gastos_casa_mensual_2022$mes_ano)
Gastos_casa_mensual_2022$mes_ano <- gsub("septiembre", "Sep", Gastos_casa_mensual_2022$mes_ano)
Gastos_casa_mensual_2022$mes_ano <- gsub("octubre", "Oct", Gastos_casa_mensual_2022$mes_ano)
Gastos_casa_mensual_2022$mes_ano <- gsub("noviembre", "Nov", Gastos_casa_mensual_2022$mes_ano)
Gastos_casa_mensual_2022$mes_ano <- gsub("diciembre", "Dec", Gastos_casa_mensual_2022$mes_ano)
Gastos_casa_mensual_2022$mes_ano <- gsub("enero", "Jan", Gastos_casa_mensual_2022$mes_ano)
Gastos_casa_mensual_2022$mes_ano <- gsub("febrero", "Feb", Gastos_casa_mensual_2022$mes_ano)
Gastos_casa_mensual_2022<- dplyr::filter(Gastos_casa_mensual_2022, !is.na(Tami))
Gastos_casa_mensual_2022$mes_ano <- parse_date_time(Gastos_casa_mensual_2022$mes_ano, "%b_%Y")
Gastos_casa_mensual_2022$mes_ano <- as.Date(as.character(Gastos_casa_mensual_2022$mes_ano))
Gastos_casa_mensual_2022_timegpt <- Gastos_casa_mensual_2022 %>%
mutate(value = Tami + Andrés) %>%
rename(ds = mes_ano, y = value) %>%
mutate(#ds= format(ds, "%Y-%m"),
unique_id = "1") %>% #it is only one series
select(unique_id, ds, y)
#Convertir la base de UF a mensual
uf_timegpt_my <- uf_serie_corrected %>%
dplyr::rename(ds = date3, y = value) %>%
dplyr::mutate(ds = format(ds, "%Y-%m-%d")) %>%
dplyr::mutate(unique_id = "serie_1")%>%
dplyr::select(unique_id, ds, y) %>%
mutate(ds = ymd(ds)) %>% # Convert 'ds' to Date
mutate(month = month(ds), year = year(ds)) %>% # Extract month and year
group_by(month, year) %>% # Group by month and year
summarise(average_y = mean(y))%>% # Calculate average y
mutate(ds = as.Date(paste0(year,"-",month, "-01")))%>%
ungroup()%>%
select(ds, uf=average_y)
Gastos_casa_mensual_2022_timegpt_ex<-
Gastos_casa_mensual_2022_timegpt |>
dplyr::left_join(uf_timegpt_my, by=c("ds"="ds"))
#Historical Exogenous Variables: These should be included in the input data immediately following the id_col, ds, and y columns
gastos_timegpt_fcst <- nixtlar::nixtla_client_forecast(
Gastos_casa_mensual_2022_timegpt_ex,
h = 12,
freq = "M", # Monthly frequency
add_history = TRUE,
level = c(80, 95),
model = "timegpt-1",#"timegpt-1-long-horizon",
clean_ex_first = TRUE
)
# Convert 'ds' to Date format in both tables
Gastos_casa_mensual_2022_timegpt_corr <- Gastos_casa_mensual_2022_timegpt %>%
mutate(ds = as.Date(paste0(ds, "-01"))) # Add day to make it a complete date
gastos_timegpt_fcst <- gastos_timegpt_fcst %>%
mutate(ds = as.Date(paste0(ds, "-01"))) # Add day to make it a complete date
# Combine historical and forecast data
full_data_gastos <- bind_rows(
Gastos_casa_mensual_2022_timegpt_corr %>% mutate(type = "Histórico"),
gastos_timegpt_fcst %>% mutate(type = "Pronóstico")
)
full_data_gastos |>
dplyr::mutate(y= ifelse(is.na(y),TimeGPT, y)) |>
# Visualize results
ggplot(aes(x = ds, y = y)) +
geom_ribbon(aes(ymin = `TimeGPT-lo-95`, ymax = `TimeGPT-hi-95`),
fill = "#4B9CD3", alpha = 0.2) +
geom_ribbon(aes(ymin = `TimeGPT-lo-80`, ymax = `TimeGPT-hi-80`),
fill = "#4B9CD3", alpha = 0.3) +
geom_line(aes(color = type), linewidth = 1.5) +
geom_vline(xintercept = max(filter(full_data_gastos, type == "Histórico")$ds),
linetype = "dashed", color = "red", linewidth = 0.8) +
scale_x_date(
date_breaks = "3 months",
date_labels = "%b %Y"
) +
scale_y_continuous(
name = "Gastos Totales",
labels = scales::comma,
breaks = pretty(full_data_gastos$y, n = 10),
expand = expansion(mult = c(0.05, 0.05))
) +
scale_color_manual(
name = "Leyenda",
values = c("Histórico" = "black", "Pronóstico" = "#4B9CD3")
) +
labs(
title = "Pronóstico de Gastos Mensuales (TimeGPT, ajustando por UF promedio mensual)",
subtitle = "Intervalos de confianza al 80% (más oscuro) y 95% (más claro)",
x = "Fecha",
y = "Gastos Totales",
color = "Leyenda"
) +
theme_minimal() +
theme(
axis.text.x = element_text(angle = 45, hjust = 1),
axis.title.x = element_text(size = 10),
axis.title.y = element_text(size = 10),
legend.position = "bottom",
panel.grid.major = element_blank(),
panel.grid.minor = element_blank()
)
Session Info
Sys.getenv("R_LIBS_USER")## [1] "D:\\a\\_temp\\Library"sessionInfo()## R version 4.4.0 (2024-04-24 ucrt)
## Platform: x86_64-w64-mingw32/x64
## Running under: Windows Server 2022 x64 (build 20348)
##
## Matrix products: default
##
##
## locale:
## [1] LC_COLLATE=Spanish_Chile.1252 LC_CTYPE=Spanish_Chile.1252
## [3] LC_MONETARY=Spanish_Chile.1252 LC_NUMERIC=C
## [5] LC_TIME=Spanish_Chile.1252
## system code page: 65001
##
## time zone: UTC
## tzcode source: internal
##
## attached base packages:
## [1] grid stats graphics grDevices utils datasets methods
## [8] base
##
## other attached packages:
## [1] prophet_1.0 rlang_1.1.6 Rcpp_1.0.14
## [4] CausalImpact_1.3.0 bsts_0.9.10 BoomSpikeSlab_1.2.6
## [7] Boom_0.9.15 scales_1.4.0 ggiraph_0.8.13
## [10] tidytext_0.4.2 DT_0.33 janitor_2.2.1
## [13] autoplotly_0.1.4 rvest_1.0.4 plotly_4.11.0
## [16] xts_0.14.1 forecast_8.24.0 wordcloud_2.6
## [19] RColorBrewer_1.1-3 SnowballC_0.7.1 tm_0.7-16
## [22] NLP_0.3-2 tsibble_1.1.6 lubridate_1.9.4
## [25] forcats_1.0.0 dplyr_1.1.4 purrr_1.0.4
## [28] tidyr_1.3.1 tibble_3.3.0 tidyverse_2.0.0
## [31] gsynth_1.2.1 sjPlot_2.8.17 lattice_0.22-6
## [34] GGally_2.2.1 ggplot2_3.5.2 gridExtra_2.3
## [37] plotrix_3.8-4 sparklyr_1.9.0 httr_1.4.7
## [40] readxl_1.4.5 zoo_1.8-14 stringr_1.5.1
## [43] stringi_1.8.7 DataExplorer_0.8.3 data.table_1.17.6
## [46] reshape2_1.4.4 fUnitRoots_4040.81 plyr_1.8.9
## [49] readr_2.1.5
##
## loaded via a namespace (and not attached):
## [1] bitops_1.0-9 cellranger_1.1.0 datawizard_1.1.0
## [4] httr2_1.1.2 lifecycle_1.0.4 StanHeaders_2.32.10
## [7] doParallel_1.0.17 globals_0.18.0 vroom_1.6.5
## [10] MASS_7.3-60.2 insight_1.3.0 crosstalk_1.2.1
## [13] magrittr_2.0.3 sass_0.4.10 rmarkdown_2.29
## [16] jquerylib_0.1.4 yaml_2.3.10 fracdiff_1.5-3
## [19] doRNG_1.8.6.2 askpass_1.2.1 pkgbuild_1.4.8
## [22] DBI_1.2.3 abind_1.4-8 quadprog_1.5-8
## [25] nnet_7.3-19 rappdirs_0.3.3 sandwich_3.1-1
## [28] inline_0.3.21 data.tree_1.1.0 tokenizers_0.3.0
## [31] listenv_0.9.1 anytime_0.3.11 performance_0.14.0
## [34] spatial_7.3-17 parallelly_1.45.0 codetools_0.2-20
## [37] xml2_1.3.8 tidyselect_1.2.1 ggeffects_2.3.0
## [40] farver_2.1.2 urca_1.3-4 its.analysis_1.6.0
## [43] matrixStats_1.5.0 stats4_4.4.0 jsonlite_2.0.0
## [46] ellipsis_0.3.2 Formula_1.2-5 iterators_1.0.14
## [49] systemfonts_1.2.3 foreach_1.5.2 tools_4.4.0
## [52] glue_1.8.0 xfun_0.52 TTR_0.24.4
## [55] ggfortify_0.4.17 loo_2.8.0 withr_3.0.2
## [58] timeSeries_4041.111 fastmap_1.2.0 boot_1.3-30
## [61] openssl_2.3.3 caTools_1.18.3 digest_0.6.37
## [64] timechange_0.3.0 R6_2.6.1 lfe_3.1.1
## [67] colorspace_2.1-1 networkD3_0.4.1 gtools_3.9.5
## [70] generics_0.1.4 htmlwidgets_1.6.4 ggstats_0.9.0
## [73] pkgconfig_2.0.3 gtable_0.3.6 timeDate_4041.110
## [76] lmtest_0.9-40 selectr_0.4-2 janeaustenr_1.0.0
## [79] htmltools_0.5.8.1 carData_3.0-5 tseries_0.10-58
## [82] snakecase_0.11.1 knitr_1.50 rstudioapi_0.17.1
## [85] tzdb_0.5.0 uuid_1.2-1 nlme_3.1-164
## [88] curl_6.4.0 cachem_1.1.0 sjlabelled_1.2.0
## [91] KernSmooth_2.23-22 parallel_4.4.0 fBasics_4041.97
## [94] pillar_1.10.2 vctrs_0.6.5 gplots_3.2.0
## [97] slam_0.1-55 car_3.1-3 dbplyr_2.5.0
## [100] xtable_1.8-4 evaluate_1.0.4 mvtnorm_1.3-3
## [103] cli_3.6.5 compiler_4.4.0 crayon_1.5.3
## [106] rngtools_1.5.2 future.apply_1.20.0 labeling_0.4.3
## [109] sjmisc_2.8.10 rstan_2.32.7 QuickJSR_1.8.0
## [112] viridisLite_0.4.2 assertthat_0.2.1 lazyeval_0.2.2
## [115] Matrix_1.7-0 sjstats_0.19.1 hms_1.1.3
## [118] bit64_4.6.0-1 future_1.58.0 nixtlar_0.6.2
## [121] extraDistr_1.10.0 igraph_2.1.4 RcppParallel_5.1.10
## [124] bslib_0.9.0 quantmod_0.4.28 bit_4.6.0#save.image("__analisis.RData")
sesion_info <- devtools::session_info()
dplyr::select(
tibble::as_tibble(sesion_info$packages),
c(package, loadedversion, source)
) %>%
DT::datatable(filter = 'top', colnames = c('Row number' =1,'Variable' = 2, 'Percentage'= 3),
caption = htmltools::tags$caption(
style = 'caption-side: top; text-align: left;',
'', htmltools::em('Packages')),
options=list(
initComplete = htmlwidgets::JS(
"function(settings, json) {",
"$(this.api().tables().body()).css({
'font-family': 'Helvetica Neue',
'font-size': '50%',
'code-inline-font-size': '15%',
'white-space': 'nowrap',
'line-height': '0.75em',
'min-height': '0.5em'
});",#;
"}")))