Gastos Casa
27 October, 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 |
|---|---|---|---|---|
| 16/8/2025 | VTR | 22000 | Andrés | NA |
| 17/8/2025 | Comida | 72586 | Tami | Supermercado |
| 20/8/2025 | Electricidad | 65242 | Andrés | 49393- 42306 -47872= 1521 |
| 25/8/2025 | Comida | 35500 | Andrés | ida al super |
| 25/8/2025 | Comida | 35000 | Andrés | piwen |
| 27/8/2025 | Comida | 37314 | Tami | Supermercado |
| 30/8/2025 | Comida | 67203 | Tami | Supermercado |
| 31/8/2025 | Diosi | 28000 | Andrés | american litter |
| 1/9/2025 | Comida | 7800 | Andrés | piwen |
| 7/9/2025 | Comida | 93538 | Tami | Supermercado |
| 10/9/2025 | Diosi | 49990 | Andrés | braloy 7.5 kg |
| 13/9/2025 | Comida | 60499 | Tami | Supermercado |
| 23/9/2025 | Comida | 76691 | Tami | Supermercado |
| 28/9/2025 | Comida | 87200 | Tami | Supermercado |
| 30/9/2025 | Gas | 82000 | Andrés | 79500+propina |
| 24/9/2025 | Electrodomésticos/mantención casa | 40000 | Andrés | mantención calefont 50 mil |
| 23/9/2025 | Gas | 80000 | Andrés | gas 79 mil + propina |
| 30/9/2025 | VTR | 22000 | Andrés | NA |
| 4/10/2025 | Comida | 67440 | Tami | Supermercado |
| 5/10/2025 | Comida | 7190 | Andrés | NA |
| 6/10/2025 | Electricidad | 75365 | Andrés | del mes pasado |
| 6/10/2025 | Comida | 7660 | Andrés | NA |
| 10/10/2025 | Enceres | 16990 | Andrés | casa nativa |
| 12/10/2025 | Comida | 99947 | Tami | Supermercado |
| 18/10/2025 | Comida | 114896 | Tami | Supermercado |
| 22/10/2025 | Otros | 18275 | Andrés | alfred anual |
| 25/10/2025 | Comida | 87042 | Tami | Supermercado |
| 25/10/2025 | Comida | 37026 | Andrés | la burguesia |
| 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 1.0615e+09 2 5.5217 0.0041 **
## lag_depvar 2.6601e+11 1 2767.5267 <2e-16 ***
## Residuals 8.3525e+10 869
## ---
## 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 -1713.50 16171.18 0.1398792
## 2-0 31591.276 23553.84 39628.71 0.0000000
## 2-1 24362.438 19721.10 29003.78 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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## 848 51313.14 2 51727.43
## 849 56125.29 2 51313.14
## 850 68503.71 2 56125.29
## 851 62945.00 2 68503.71
## 852 50209.71 2 62945.00
## 853 49436.29 2 50209.71
## 854 55308.00 2 49436.29
## 855 62650.86 2 55308.00
## 856 55683.00 2 62650.86
## 857 49762.14 2 55683.00
## 858 51835.00 2 49762.14
## 859 49806.43 2 51835.00
## 860 52799.57 2 49806.43
## 861 50620.71 2 52799.57
## 862 49192.00 2 50620.71
## 863 66245.86 2 49192.00
## 864 62359.71 2 66245.86
## 865 70816.86 2 62359.71
## 866 84559.71 2 70816.86
## 867 80831.43 2 84559.71
## 868 74717.14 2 80831.43
## 869 77935.14 2 74717.14
## 870 57977.86 2 77935.14
## 871 66541.71 2 57977.86
## 872 70498.29 2 66541.71
## 873 58251.86 2 70498.29
## 874 66341.57 2 58251.86
##
## $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 2 717 53825.54 21730.052
##
## $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 48788.71 50717.71 51727.43 51313.14
## [848] 56125.29 68503.71 62945.00 50209.71 49436.29 55308.00 62650.86
## [855] 55683.00 49762.14 51835.00 49806.43 52799.57 50620.71 49192.00
## [862] 66245.86 62359.71 70816.86 84559.71 80831.43 74717.14 77935.14
## [869] 57977.86 66541.71 70498.29 58251.86 66341.57
##
## $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 2 2 2 2 2 2 2 2
## [852] 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
## 2011.530111 4037.219817 -534.907975 2440.814380 -2963.375336
## 7 8 9 10 11
## 521.020078 -5652.504844 -1191.472587 -3970.181104 -425.882819
## 12 13 14 15 16
## -4946.489136 -1620.935627 -911.187407 367.038075 -3250.801874
## 17 18 19 20 21
## -388.255446 -2138.920451 6594.423644 -1528.744627 -1209.198821
## 22 23 24 25 26
## 1473.938439 -1185.546742 234.719826 1696.036932 -7098.301856
## 27 28 29 30 31
## 942.582118 8190.055861 427.415421 -4.604181 -2391.595327
## 32 33 34 35 36
## 1581.796905 4580.199506 1140.277910 2405.381467 -1851.947351
## 37 38 39 40 41
## 4620.403021 4311.115468 -2263.414002 -2973.495378 -1106.913606
## 42 43 44 45 46
## -10739.965592 7277.059719 2555.808673 1368.883835 8108.744198
## 47 48 49 50 51
## 699.652198 6541.508632 6733.917074 -5856.652437 -4780.375479
## 52 53 54 55 56
## -5052.565951 -7928.646792 6119.754179 -4077.563352 -4900.277141
## 57 58 59 60 61
## 3844.442960 882.830093 -35.322004 139.425502 -4998.653285
## 62 63 64 65 66
## 18118.551084 3656.529690 -3627.528889 5937.095133 7361.962619
## 67 68 69 70 71
## 14664.052050 1734.002669 -13174.688158 -1289.394350 4656.692947
## 72 73 74 75 76
## -4882.718060 -4395.184041 -10494.616237 2456.482898 -5405.439809
## 77 78 79 80 81
## 1052.326331 -6874.707570 531.235961 -2367.385552 -2707.768224
## 82 83 84 85 86
## -3947.145460 -555.505627 2298.428228 3751.519424 471.843078
## 87 88 89 90 91
## -488.356169 192.712649 4298.806000 -1160.409380 1151.741149
## 92 93 94 95 96
## -2062.217661 -1044.804502 175.917762 273.611400 -7484.660411
## 97 98 99 100 101
## 2382.184553 -8607.787939 -2955.433266 -4055.694341 -1755.453702
## 102 103 104 105 106
## -1279.448625 3163.919097 -2352.891564 2581.867151 -1165.755439
## 107 108 109 110 111
## 962.754743 2581.644077 -3155.923954 -4728.107990 -860.253506
## 112 113 114 115 116
## 1893.916616 11687.588051 -1233.545077 2675.025723 4271.877823
## 117 118 119 120 121
## 3515.894937 -1083.979993 -4703.550006 -3718.472611 2320.350407
## 122 123 124 125 126
## -1729.156725 1341.547106 8861.019239 860.437936 143.291403
## 127 128 129 130 131
## -2509.724727 2662.252251 7062.181309 1029.600306 -8483.032225
## 132 133 134 135 136
## 1753.315863 4141.356790 -3153.705405 -1414.494987 -850.995238
## 137 138 139 140 141
## -3878.303822 1179.970927 -496.593439 -2915.050058 1713.499079
## 142 143 144 145 146
## -1882.954802 -7833.103820 2026.773559 -3488.227695 2090.654972
## 147 148 149 150 151
## -264.993552 1016.191755 -363.994586 1347.703870 1184.389787
## 152 153 154 155 156
## 3356.101240 -4858.091969 -1177.046045 -3239.399671 5949.752457
## 157 158 159 160 161
## 9747.825271 -3713.744174 -5062.276674 3316.690002 -85.539362
## 162 163 164 165 166
## 2417.755100 -6184.612174 -7027.613664 3870.375736 17112.780802
## 167 168 169 170 171
## 3361.140378 -663.938849 -2713.525834 -1375.206687 3317.468953
## 172 173 174 175 176
## -499.222285 -8347.841642 2583.548821 4047.494578 350.492418
## 177 178 179 180 181
## 8474.913633 -9518.216267 -3750.439318 -11026.594138 -11530.704590
## 182 183 184 185 186
## 936.467463 8997.854816 -1717.001918 5640.658906 6271.000585
## 187 188 189 190 191
## 12876.037211 8152.571906 -4342.219206 2177.449202 10077.803971
## 192 193 194 195 196
## -1933.862307 -2740.301173 -10581.002900 -6670.767186 922.826137
## 197 198 199 200 201
## -5542.728903 -10106.946719 5070.032354 -3376.646046 -2020.462076
## 202 203 204 205 206
## -1111.263853 6188.215759 9577.537665 274.297218 2618.704529
## 207 208 209 210 211
## 2791.263773 5477.186306 12526.835854 -5992.062832 -11604.052979
## 212 213 214 215 216
## -5977.796945 -10899.375932 -5388.397444 1214.750036 -13319.557768
## 217 218 219 220 221
## 16079.050675 7504.886112 1224.749709 26384.004306 12223.862890
## 222 223 224 225 226
## 7030.421123 13719.444461 -4222.088812 -2053.307001 3462.948923
## 227 228 229 230 231
## 45.689078 2432.095175 8692.229661 5522.254926 -2210.340621
## 232 233 234 235 236
## -2131.915407 9121.617776 -11810.240330 -7589.055240 -8848.798045
## 237 238 239 240 241
## -10410.920441 2766.647269 1043.740977 -8603.716322 -9296.917039
## 242 243 244 245 246
## 8786.977592 -8069.635717 2182.287434 -10604.700275 -4358.709005
## 247 248 249 250 251
## 1118.258493 699.658326 -12618.769884 3338.873555 1759.067489
## 252 253 254 255 256
## 3908.495253 1831.282641 -1464.829526 10833.735688 20573.816162
## 257 258 259 260 261
## 2884.595402 -4588.093112 3789.998674 -2016.308455 3414.075030
## 262 263 264 265 266
## -5176.151937 -11218.434763 -5052.703345 -844.817486 -5510.704388
## 267 268 269 270 271
## 8456.471741 -4604.039091 3865.542139 -2432.524428 4104.782561
## 272 273 274 275 276
## 380.854295 6973.456192 -1745.345319 11690.613904 -4925.894228
## 277 278 279 280 281
## 1382.908684 -716.744355 7506.328674 -5406.944121 -3077.909101
## 282 283 284 285 286
## -11605.347570 -3004.689836 18322.907493 7440.474086 2385.311046
## 287 288 289 290 291
## -979.827851 554.962602 6046.571270 6526.245662 -19133.312242
## 292 293 294 295 296
## -11481.637509 -8450.517629 9346.862334 2748.070667 -1504.401613
## 297 298 299 300 301
## 27078.798203 9715.494983 4541.048504 9154.139387 2484.714107
## 302 303 304 305 306
## -1404.877836 7527.865884 -24669.075287 -3876.034924 -507.915298
## 307 308 309 310 311
## -7296.619662 -4288.170167 2624.117772 -9500.911250 -3524.303702
## 312 313 314 315 316
## -8473.398616 1291.266987 -3426.190987 1777.551780 -4356.919673
## 317 318 319 320 321
## 27174.826495 -1053.985183 2961.008828 10494.949728 5238.609619
## 322 323 324 325 326
## 32023.656352 4713.272607 -21334.200567 1432.514541 752.896348
## 327 328 329 330 331
## -6819.400770 -2074.377556 -33600.955136 638.470880 -2541.843739
## 332 333 334 335 336
## -325.167567 -3397.092357 3863.054444 -666.258740 -7180.953740
## 337 338 339 340 341
## -3333.093102 -2403.384150 -7888.481327 3654.647560 -1578.258104
## 342 343 344 345 346
## -1945.071777 -1200.662737 -31.069340 270.694222 -1833.567774
## 347 348 349 350 351
## -9662.073942 -13411.956003 2128.072678 -4514.547766 -3846.098992
## 352 353 354 355 356
## -6165.175757 1571.894975 1195.885239 2555.451137 -3976.384298
## 357 358 359 360 361
## -724.663451 464.436517 6794.047135 37.223640 -281.901090
## 362 363 364 365 366
## 2336.369245 -3004.946243 -1126.992311 -8991.577428 -4853.856735
## 367 368 369 370 371
## -6430.139607 -5154.162956 -7448.144907 4832.592424 170.687297
## 372 373 374 375 376
## 6913.243096 -7864.639181 -2475.743294 -3597.946186 -2671.711411
## 377 378 379 380 381
## -12659.285400 1726.748357 -10820.466148 5529.393105 9147.913411
## 382 383 384 385 386
## 2907.726206 -2630.389219 1374.349948 6505.413058 11150.635324
## 387 388 389 390 391
## -6095.888944 -5647.178885 -432.003149 8286.855942 1514.471579
## 392 393 394 395 396
## 10915.236123 -10215.415215 2460.759986 392.008956 240.563894
## 397 398 399 400 401
## -976.002120 -882.263501 -14803.463967 8252.096727 -1468.515574
## 402 403 404 405 406
## -1653.450309 6707.199247 -8224.293154 -1563.427419 -2790.898226
## 407 408 409 410 411
## -6067.958562 -3088.449279 -4136.851976 -8963.672680 5948.364330
## 412 413 414 415 416
## 1434.072300 -7588.108783 -7889.031173 14042.139718 3587.949564
## 417 418 419 420 421
## 4245.443843 -8300.739642 -4987.004804 -2829.982022 2598.331465
## 422 423 424 425 426
## -14241.850808 -2983.408421 -9285.333668 2848.853626 6798.302522
## 427 428 429 430 431
## 6369.639700 -4217.963335 -4342.596442 -4934.429123 -1990.907508
## 432 433 434 435 436
## -5911.358670 -6813.659905 -6122.611351 -1555.327398 -1012.588680
## 437 438 439 440 441
## -5144.274976 2419.687305 4662.730107 -5254.016113 -2346.417492
## 442 443 444 445 446
## 1388.986117 -4034.224059 2644.589908 -6781.945239 -12300.069152
## 447 448 449 450 451
## -4672.778657 9489.801512 -2219.579601 4567.970852 -6072.910961
## 452 453 454 455 456
## -1313.408084 192.448914 2830.238699 -12475.865929 3190.517123
## 457 458 459 460 461
## -6893.040099 6343.840045 2812.550128 2296.256227 -4065.517876
## 462 463 464 465 466
## 1881.178491 -226.402778 1572.277928 -748.047665 3123.817028
## 467 468 469 470 471
## -2875.387310 5576.067145 -7185.754768 -3187.839774 -2419.873811
## 472 473 474 475 476
## -4871.950926 2800.116087 7591.973206 -6243.447441 1273.542629
## 477 478 479 480 481
## -6393.600169 -3043.683666 1818.739458 -13129.770432 -9925.923898
## 482 483 484 485 486
## -1354.302238 -134.504908 -1122.604831 -1505.282107 -9750.118848
## 487 488 489 490 491
## 10946.917739 6054.539963 7220.690466 -5656.636466 5159.138130
## 492 493 494 495 496
## 9070.214748 5807.924498 -13733.155237 -10790.480539 -3639.336078
## 497 498 499 500 501
## -1296.406995 -713.551515 -7815.441411 438.267049 4111.024698
## 502 503 504 505 506
## 5319.428745 456.273748 -126.072981 -7446.518117 378.865645
## 507 508 509 510 511
## -5242.118085 1649.120691 -1486.120736 -8346.310411 -773.906312
## 512 513 514 515 516
## -2848.073192 -758.383343 1159.451011 -9674.340843 -7926.226226
## 517 518 519 520 521
## 24138.096468 9616.130175 5645.989447 -5582.634426 2564.662746
## 522 523 524 525 526
## 16779.320498 11195.013408 -24450.963477 -5300.624419 -3960.430341
## 527 528 529 530 531
## 4354.704706 -584.738678 -11329.314185 4191.189724 13698.275453
## 532 533 534 535 536
## -5221.118436 4141.720019 5312.856423 -2044.790684 -4789.257039
## 537 538 539 540 541
## -7309.619773 -2317.610950 8111.343567 -102.985198 -8370.816236
## 542 543 544 545 546
## 1606.740528 -812.931698 154.501075 -11243.442926 -11248.762703
## 547 548 549 550 551
## 1877.154104 6831.715324 -1508.582926 647.089382 -7914.385475
## 552 553 554 555 556
## 8387.204645 706.502996 -12148.132844 8983.970497 8454.421123
## 557 558 559 560 561
## -126.898435 4626.046918 -3813.354387 13877.992271 21234.884316
## 562 563 564 565 566
## -6779.295884 -9973.136232 6509.954612 -51.627975 3180.378919
## 567 568 569 570 571
## -7655.402698 -17563.379173 6453.509066 6212.459210 1667.741564
## 572 573 574 575 576
## 2862.252852 1529.690047 -2406.222273 14483.800788 -9914.556221
## 577 578 579 580 581
## -6485.196517 8486.995740 2615.419943 -6792.647366 7279.524962
## 582 583 584 585 586
## -4044.481914 -3008.715629 15478.081708 -14758.108366 8204.892156
## 587 588 589 590 591
## -170.357441 -6450.306438 -974.794817 35.289289 -10868.592441
## 592 593 594 595 596
## 1607.147558 -7338.069261 2890.042782 8680.731909 -7707.122895
## 597 598 599 600 601
## 5661.507121 2530.912161 6645.432743 -3415.435922 5932.792342
## 602 603 604 605 606
## -8527.541861 2046.728222 1057.501228 2924.302529 1275.809680
## 607 608 609 610 611
## 176.678210 -6030.016614 7870.035963 -1404.582284 -2789.806168
## 612 613 614 615 616
## -3660.899338 -8426.320888 11779.454431 4731.959458 -9527.911071
## 617 618 619 620 621
## 11434.434527 5834.580117 -5798.327244 26149.722253 -13084.079387
## 622 623 624 625 626
## -7004.225113 2950.856831 -4369.156234 -10791.164865 11120.097775
## 627 628 629 630 631
## -21833.646790 -2571.383468 8521.516865 10963.774301 -1745.750685
## 632 633 634 635 636
## 33094.911336 -6836.660660 5485.565792 5161.544539 -2510.940996
## 637 638 639 640 641
## -5577.678337 -2157.860397 -12641.604798 -2427.811174 -2066.792297
## 642 643 644 645 646
## -2697.027079 -3030.104762 1647.727047 4259.572070 16785.017697
## 647 648 649 650 651
## 18342.796147 641.567481 4549.582284 10368.109900 19893.745434
## 652 653 654 655 656
## 464.065659 -28333.243277 -1553.465911 -2494.545462 1674.824871
## 657 658 659 660 661
## -3382.853114 -10802.648368 1503.345809 4064.384284 -1173.299821
## 662 663 664 665 666
## 12868.740740 1179.869331 1633.460693 -11871.169588 1216.785020
## 667 668 669 670 671
## 1024.640682 -5328.533340 -7564.081885 1923.586711 -3856.634642
## 672 673 674 675 676
## 2533.298288 -3522.444972 -9476.392038 -8437.158271 -3103.994543
## 677 678 679 680 681
## 45.355718 2716.292482 577.161127 -3965.017080 -1944.769084
## 682 683 684 685 686
## -1454.614765 -8379.305988 4517.398250 -2380.185892 -1535.625115
## 687 688 689 690 691
## 449.735727 10713.916303 9699.475654 10465.812167 -9826.512209
## 692 693 694 695 696
## -3705.398115 -3286.512706 5727.641230 -10532.647841 -8049.269704
## 697 698 699 700 701
## -8743.107109 -6401.402228 -4864.198363 2957.682046 -4530.474255
## 702 703 704 705 706
## -2026.011927 4093.664148 30974.540402 9397.167884 23332.094826
## 707 708 709 710 711
## 1591.297416 8238.755959 22845.983090 6510.642673 -18245.589640
## 712 713 714 715 716
## 4764.563807 -5497.496447 -162.199807 415.526078 -17332.874431
## 717 718 719 720 721
## -5349.688485 3244.177445 -3100.229935 -13067.859676 4178.343053
## 722 723 724 725 726
## -5651.590728 642.878541 -4031.966988 -12547.004041 1257.552878
## 727 728 729 730 731
## -1972.964970 -9882.919649 17156.425291 1682.223108 -2813.401254
## 732 733 734 735 736
## 5622.427947 -8717.126243 -818.151703 8043.891010 -15437.210182
## 737 738 739 740 741
## -6011.458171 7303.684947 -4879.893822 61.838164 1730.266725
## 742 743 744 745 746
## -2050.367488 -5263.562594 6313.189423 -6366.827234 22601.463140
## 747 748 749 750 751
## 7753.508613 -2014.144246 -7359.424833 23336.411480 -4347.269997
## 752 753 754 755 756
## 1333.242198 -14485.809053 56028.309933 26905.278765 15107.575924
## 757 758 759 760 761
## -10622.736161 10609.275612 7322.417005 5818.234998 -46381.466389
## 762 763 764 765 766
## -16226.383948 890.454142 -2590.960536 -3533.270613 122765.632794
## 767 768 769 770 771
## 19400.561723 43819.348049 22727.141690 12225.564044 15988.439143
## 772 773 774 775 776
## 25866.382156 -98658.214757 -6769.484563 -35859.234264 1655.880289
## 777 778 779 780 781
## -1304.886962 3320.472765 -7483.456096 -1522.613155 -2022.373764
## 782 783 784 785 786
## 3347.103052 -7225.092469 -2314.887783 3840.992427 2195.377586
## 787 788 789 790 791
## -2823.783041 -4114.488345 1667.935664 2787.200667 -111.818316
## 792 793 794 795 796
## -6762.770763 -5850.367618 -1221.475186 -1336.308863 -7889.785325
## 797 798 799 800 801
## -2435.137189 -3349.358238 -2748.006454 10641.620695 2177.233362
## 802 803 804 805 806
## 7020.257929 2874.899580 -5493.051905 8134.354443 9833.297182
## 807 808 809 810 811
## -10630.808802 -7442.207685 -7561.552632 2940.015680 4134.520069
## 812 813 814 815 816
## -2322.293913 -14220.355101 -4184.628208 6182.822326 8172.250515
## 817 818 819 820 821
## -9724.798519 -7814.388213 -9409.157991 9671.239535 -1288.408711
## 822 823 824 825 826
## -4436.952376 -8516.106702 7796.521741 7849.952792 4923.739308
## 827 828 829 830 831
## -3147.348192 -761.693781 2841.137026 4621.452576 1582.834611
## 832 833 834 835 836
## -6734.666271 2037.962225 -446.570036 2704.077191 4094.517376
## 837 838 839 840 841
## -1178.035635 -9379.188898 5619.317329 -4910.185079 -7632.839559
## 842 843 844 845 846
## 2958.275907 -13101.723216 -2892.291893 11554.495224 1255.124451
## 847 848 849 850 851
## 581.609988 -713.743696 4459.901299 12639.295553 -3720.728652
## 852 853 854 855 856
## -11605.528264 -1266.256454 5280.344927 7499.594869 -5875.576032
## 857 858 859 860 861
## -5716.341179 1523.004797 -2314.323882 2448.932893 -2341.714870
## 862 863 864 865 866
## -1869.177221 16431.363675 -2335.827713 9512.329855 15875.557144
## 867 868 869 870 871
## 155.372917 -2705.642866 5847.630069 -16917.654667 9060.757716
## 872 873 874
## 5544.581326 -10154.317320 8621.524824
##
## $fitted.values
## 2 3 4 5 6 7 8 9
## 17257.76 20101.78 24351.05 24069.33 26420.09 23755.69 24471.22 19708.62
## 10 11 12 13 14 15 16 17
## 19445.47 16791.17 17567.77 14300.79 14351.90 15015.82 16710.52 15032.40
## 18 19 20 21 22 23 24 25
## 16065.92 15440.15 22514.74 21599.77 21080.20 22968.12 22294.85 22946.68
## 26 27 28 29 30 31 32 33
## 24790.59 18725.70 20449.94 28278.58 28336.18 28009.45 25641.49 27042.37
## 34 35 36 37 38 39 40 41
## 30881.15 31229.19 32636.80 30150.17 34131.88 37336.41 34395.78 31210.20
## 42 43 44 45 46 47 48 49
## 30059.25 20649.23 28159.62 30593.40 31681.40 38511.92 38007.06 42664.08
## 50 51 52 53 54 55 56 57
## 46895.65 39601.66 34176.14 29204.36 22356.39 28639.42 25223.85 21525.56
## 58 59 60 61 62 63 64 65
## 25929.03 27187.18 27483.86 27895.22 23770.73 40343.61 42185.53 37436.76
## 66 67 68 69 70 71 72 73
## 41639.04 46549.23 57205.57 55221.55 40481.11 37989.74 41004.29 35310.76
## 74 75 76 77 78 79 80 81
## 30768.04 21481.80 24679.73 20609.96 22693.71 17594.91 19608.10 18835.48
## 82 83 84 85 86 87 88 89
## 17864.29 15935.36 17211.71 20815.77 25228.59 26217.36 26242.29 26858.34
## 90 91 92 93 94 95 96 97
## 30978.84 29810.69 30808.93 28875.52 28076.23 28443.96 28850.09 22434.67
## 98 99 100 101 102 103 104 105
## 25446.36 18484.58 17341.98 15384.88 15684.31 16360.94 20828.61 19913.13
## 106 107 108 109 110 111 112 113
## 23420.33 23210.53 24884.78 27758.35 25259.25 21706.68 21981.80 24625.13
## 114 115 116 117 118 119 120 121
## 35477.55 33672.40 35507.84 38502.82 40456.55 38147.55 32974.33 29319.79
## 122 123 124 125 126 127 128 129
## 31400.30 29682.17 30862.41 38453.70 38096.57 37159.15 34026.18 35805.39
## 130 131 132 133 134 135 136 137
## 41197.26 40638.18 31849.68 33113.07 36299.28 32713.92 31103.00 30189.02
## 138 139 140 141 142 143 144 145
## 26749.89 28162.74 27932.62 25621.50 27643.67 26269.96 19879.23 22906.37
## 146 147 148 149 150 151 152 153
## 20735.49 23709.28 24248.67 25837.28 26019.15 27671.47 28970.76 31999.52
## 154 155 156 157 158 159 160 161
## 27474.76 26738.54 24296.53 30184.03 41734.17 40066.28 37434.17 42448.83
## 162 163 164 165 166 167 168 169
## 43855.82 47267.90 42738.90 38051.34 43470.50 59754.43 61964.08 60379.95
## 170 171 172 173 174 175 176 177
## 57209.21 55610.25 58309.79 57334.98 49635.74 52456.08 56194.51 56230.66
## 178 179 180 181 182 183 184 185
## 63351.50 53864.44 50619.02 41437.99 32986.82 36491.15 46583.29 46039.91
## 186 187 188 189 190 191 192 193
## 51986.00 57724.53 68495.43 73772.36 67474.12 67667.34 74729.72 70411.02
## 194 195 196 197 198 199 200 201
## 65938.86 55194.77 49231.60 50654.30 46253.95 38431.54 44849.07 43078.46
## 202 203 204 205 206 207 208 209
## 42716.84 43194.64 49981.03 58860.27 58490.30 60213.16 61867.10 65654.02
## 210 211 212 213 214 215 216 217
## 75109.92 67201.62 55403.94 50018.80 41025.25 37986.39 41096.56 31127.95
## 218 219 220 221 222 223 224 225
## 48082.40 55394.96 56295.85 79035.71 86522.29 88523.27 96106.09 87067.16
## 226 227 228 229 230 231 232 233
## 81072.34 80654.74 77308.48 76470.91 81202.60 82565.34 77007.06 72225.38
## 234 235 236 237 238 239 240 241
## 77872.67 64535.48 56580.94 48540.63 40161.64 44348.83 46499.14 39957.20
## 242 243 244 245 246 247 248 249
## 33643.88 43914.78 38168.14 42099.41 34371.99 33079.31 36730.48 39551.20
## 250 251 252 253 254 255 256 257
## 30390.98 36322.36 40119.50 45308.43 48023.69 47516.84 57806.18 75283.69
## 258 259 260 261 262 263 264 265
## 75098.95 68417.14 69897.31 66122.35 67566.87 61331.58 50618.27 46650.10
## 266 267 268 269 270 271 272 273
## 46859.28 42970.39 51764.61 48041.89 52183.95 50302.65 54365.43 54661.12
## 274 275 276 277 278 279 280 281
## 60671.77 58308.67 67970.75 61902.38 62112.17 60463.10 66199.52 59937.05
## 282 283 284 285 286 287 288 289
## 56504.78 46068.83 44467.38 61680.24 67204.12 67613.11 65033.61 64122.00
## 290 291 292 293 294 295 296 297
## 68118.47 72024.31 53042.21 43155.37 37173.14 47482.93 50721.12 49836.06
## 298 299 300 301 302 303 304 305
## 74005.22 79943.95 80610.86 85218.14 83418.73 78454.56 81917.50 56844.46
## 306 307 308 309 310 311 312 313
## 53109.77 52789.91 46587.03 43799.60 47398.91 39959.45 38682.97 33250.59
## 314 315 316 317 318 319 320 321
## 37030.91 36213.16 40040.35 38027.03 63784.56 61628.13 63249.91 71239.10
## 322 323 324 325 326 327 328 329
## 73623.77 99077.01 97456.49 73313.63 72112.82 70471.97 62432.66 59558.10
## 330 331 332 333 334 335 336 337
## 29539.96 33223.42 33662.45 35979.81 35321.37 41081.97 42156.38 37409.24
## 338 339 340 341 342 343 344 345
## 36624.53 36751.05 32075.21 38067.54 38730.21 38988.38 39863.21 41647.16
## 346 347 348 349 350 351 352 353
## 43467.14 43219.07 36171.53 26749.78 32088.55 30950.81 30541.32 28160.39
## 354 355 356 357 358 359 360 361
## 32834.11 36584.26 41042.96 39233.95 40492.85 42628.95 50016.06 50566.04
## 362 363 364 365 366 367 368 369
## 50767.49 53227.95 50714.14 50159.29 42812.57 40012.43 36193.59 33974.72
## 370 371 372 373 374 375 376 377
## 30036.84 37316.74 39601.19 47478.07 41456.31 40904.09 39443.00 38976.29
## 378 379 380 381 382 383 384 385
## 29853.97 34447.04 27506.32 35716.66 46038.42 49599.96 47875.22 49864.73
## 386 387 388 389 390 391 392 393
## 56078.08 65553.17 58771.89 53246.15 52975.14 60346.67 60869.48 69528.70
## 394 395 396 397 398 399 400 401
## 58646.24 60211.42 59772.01 59256.43 57744.98 56507.89 43280.90 51857.23
## 402 403 404 405 406 407 408 409
## 50858.74 49826.09 56220.44 48771.00 48082.90 46411.39 42093.31 40925.28
## 410 411 412 413 414 415 416 417
## 38991.24 33091.78 40956.07 43879.25 38557.32 33650.86 48506.48 52347.13
## 418 419 420 421 422 423 424 425
## 56272.17 48749.43 45076.70 43754.10 47336.71 35768.27 35497.76 29762.72
## 426 427 428 429 430 431 432 433
## 35346.55 43665.22 50549.96 47318.88 44390.71 41319.19 41207.50 37689.09
## 434 435 436 437 438 439 440 441
## 33831.61 31068.61 32643.02 34490.42 32497.17 37358.13 43557.02 40312.85
## 442 443 444 445 446 447 448 449
## 40019.16 43022.37 40910.70 44895.95 40147.93 31189.78 30028.48 41373.29
## 450 451 452 453 454 455 456 457
## 41055.17 46700.34 42341.12 42690.41 44309.19 48023.44 37908.48 42752.61
## 458 459 460 461 462 463 464 465
## 38180.73 45741.74 49258.03 51875.80 48608.82 50947.12 51148.44 52893.62
## 466 467 468 469 470 471 472 473
## 52391.75 55332.39 52663.50 57709.33 50976.41 48589.87 47177.52 43805.46
## 474 475 476 477 478 479 480 481
## 47557.60 55013.02 49445.89 51147.31 45941.68 44322.40 47152.34 36577.78
## 482 483 484 485 486 487 488 489
## 30146.16 32013.50 34707.32 36195.71 37160.55 30808.08 43325.03 49978.17
## 490 491 492 493 494 495 496 497
## 56801.21 51518.29 56346.21 63971.79 67779.16 54050.05 44637.91 42664.98
## 498 499 500 501 502 503 504 505
## 42987.84 43778.16 38270.73 40667.12 45963.00 51638.58 52347.50 52457.95
## 506 507 508 509 510 511 512 513
## 46166.56 47505.12 43768.31 46520.84 46186.88 39909.33 41039.22 40215.24
## 514 515 516 517 518 519 520 521
## 41319.69 43956.91 36804.65 32089.05 55953.30 64105.30 67754.35 61140.48
## 522 523 524 525 526 527 528 529
## 62478.54 76049.70 83018.96 57995.91 52871.43 49569.30 53943.60 53450.46
## 530 531 532 533 534 535 536 537
## 43644.52 48631.01 61277.98 55804.71 59198.72 63182.22 60237.97 55274.05
## 538 539 540 541 542 543 544 545
## 48743.33 47400.66 55329.27 55079.96 47647.97 49869.22 49696.07 50389.16
## 546 547 548 549 550 551 552 553
## 41048.19 32892.70 37229.86 45337.73 45134.91 46838.96 40855.22 49858.50
## 554 555 556 557 558 559 560 561
## 51012.56 40802.74 50333.44 58187.76 57553.38 61147.21 56919.01 68666.83
## 562 563 564 565 566 567 568 569
## 85337.44 75439.14 64015.05 68429.49 66555.91 67741.26 59320.38 43326.78
## 570 571 572 573 574 575 576 577
## 50327.83 56226.54 57408.03 59481.31 60127.65 57257.20 69490.56 58875.48
## 578 579 580 581 582 583 584 585
## 52605.29 60198.58 61700.93 54802.48 61062.20 56643.14 53690.92 67246.25
## 586 587 588 589 590 591 592 593
## 52690.68 60026.93 59120.31 52849.37 52155.28 52431.02 43157.00 45950.78
## 594 595 596 597 598 599 600 601
## 40583.10 44824.27 53577.98 46916.49 52769.09 55144.28 60807.15 56969.49
## 602 603 604 605 606 607 608 609
## 61777.97 53355.84 55233.78 56009.27 58314.90 58888.32 58429.59 52613.39
## 610 611 612 613 614 615 616 617
## 59667.30 57729.52 54829.90 51539.61 44510.26 56007.90 59891.05 50836.42
## 618 619 620 621 622 623 624 625
## 61226.99 65407.33 58904.28 81107.37 66246.51 58584.29 60585.01 55943.45
## 626 627 628 629 630 631 632 633
## 46289.47 56985.08 37562.81 37423.20 46980.94 57452.04 55498.80 84196.09
## 634 635 636 637 638 639 640 641
## 74393.15 76591.46 78226.94 72959.11 65686.43 62324.46 50242.81 48612.94
## 642 643 644 645 646 647 648 649
## 47505.74 45989.68 44376.13 47050.00 51662.27 66616.49 81024.72 78151.27
## 650 651 652 653 654 655 656 657
## 79054.03 84918.97 98348.65 93113.10 63416.32 60870.97 57828.75 58812.28
## 658 659 660 661 662 663 664 665
## 55257.22 45680.65 48062.33 52375.30 51568.40 63117.27 62995.11 63284.31
## 666 667 668 669 670 671 672 673
## 51752.64 53110.65 54127.96 49471.94 43458.41 46489.92 44091.42 47574.30
## 674 675 676 677 678 679 680 681
## 45329.25 38174.87 32838.85 32836.36 35582.28 40308.98 42566.87 40573.63
## 682 683 684 685 686 687 688 689
## 40597.19 41045.45 35394.17 41716.47 41214.48 41513.41 43506.66 54202.38
## 690 691 692 693 694 695 696 697
## 62650.19 70690.37 59999.26 56011.51 52897.36 58045.65 48349.41 42055.54
## 698 699 700 701 702 703 704 705
## 35958.12 32680.91 31162.60 36663.05 34928.58 35600.48 41526.75 70153.97
## 706 707 708 709 710 711 712 713
## 76305.62 93832.99 90156.39 92748.73 107756.93 106598.88 83986.29 84333.21
## 714 715 716 717 718 719 720 721
## 75681.34 72787.33 70766.16 53515.40 48918.97 52407.09 49914.72 39042.23
## 722 723 724 725 726 727 728 729
## 44603.88 40879.41 43121.97 40999.58 31717.45 35663.68 36288.21 29931.00
## 730 731 732 733 734 735 736 737
## 47978.06 50223.12 48259.29 53906.70 46322.01 46596.25 54568.50 41035.60
## 738 739 740 741 742 743 744 745
## 37451.74 45943.18 42721.45 44222.30 46987.80 46101.99 42525.24 49505.97
## 746 747 748 749 750 751 752 753
## 44532.82 65470.78 70784.86 66898.71 58843.45 78599.41 71681.76 70602.24
## 754 755 756 757 758 759 760 761
## 55856.69 104519.86 121570.42 126154.02 107701.58 110127.01 109375.34 107406.89
## 762 763 764 765 766 767 768 769
## 60140.24 45208.83 47115.82 45741.98 43720.94 152164.72 156596.37 181771.00
## 770 771 772 773 774 775 776 777
## 185333.29 179278.13 177277.90 184151.93 81491.06 72091.38 38505.83 41934.74
## 778 779 780 781 782 783 784 785
## 42343.24 46735.74 41141.18 41460.80 41303.61 45851.81 40595.32 40293.15
## 786 787 788 789 790 791 792 793
## 45401.05 48422.21 46678.77 44031.21 46766.66 50130.25 50535.63 45085.80
## 794 795 796 797 798 799 800 801
## 41126.48 41710.74 42120.36 36759.28 36840.93 36114.44 36005.24 47593.62
## 802 803 804 805 806 807 808 809
## 50319.60 56924.24 59070.19 53640.93 60794.56 68519.24 57402.92 50485.27
## 810 811 812 813 814 815 816 817
## 44344.84 48150.34 52513.29 50686.21 38709.77 37016.32 44585.18 52925.66
## 818 819 820 821 822 823 824 825
## 44586.67 38977.16 32690.76 43854.69 44032.95 41441.11 35620.05 44774.90
## 826 827 828 829 830 831 832 833
## 52809.97 57267.92 54115.12 53445.72 56005.40 59792.45 60445.52 53757.61
## 834 835 836 837 838 839 840 841
## 55576.71 54996.07 57238.63 60408.75 58574.19 49817.11 55263.33 50827.70
## 842 843 844 845 846 847 848 849
## 44581.44 48372.72 37667.15 37234.22 49462.59 51145.82 52026.89 51665.38
## 850 851 852 853 854 855 856 857
## 55864.42 66665.73 61815.24 50702.54 50027.66 55151.26 61558.58 55478.48
## 858 859 860 861 862 863 864 865
## 50312.00 52120.75 50350.64 52962.43 51061.18 49814.49 64695.54 61304.53
## 866 867 868 869 870 871 872 873
## 68684.16 80676.06 77422.79 72087.51 74895.51 57480.96 64953.70 68406.17
## 874
## 57720.05
##
## $shapiro.test
## [1] 0
##
## $levenes.test
## [1] 0
##
## $autcorr
## [1] "No autocorrelation evidence"
##
## $post_sums
## [1] "Post-Est Warning"
##
## $adjr_sq
## [1] 0.8086
##
## $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* 5.521714 0.8041206 4.131249
## t2* 2767.526694 164.2502787 894.962471
## WARNING: All values of t3* are NA
##
## $itsa.plot
##
## $booted.ints
## Parameter Lower CI Median F-value Upper CI
## 1 interrupt_var 1.28572 5.514956 14.23204
## 2 lag_depvar 1693.49828 2809.617667 4578.98936Ahora 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$fecha, format="%d/%m/%Y"),
Data = as.numeric(mstl_data_autplt[, "Data"]),
Trend = as.numeric(mstl_data_autplt[, "Trend"]),
Remainder = as.numeric(mstl_data_autplt[, "Remainder"])
)
# Reshape the data frame for ggplot2
mstl_long <- mstl_df %>%
pivot_longer(cols = -Date, names_to = "Component", values_to = "Value")
# Plotting with ggplot2
ggplot(mstl_long, aes(x = Date, y = Value)) +
geom_line() +
theme_bw() +
labs(title = "Descomposición MSTL", x = "Fecha", y = "Valor") +
scale_x_date(date_breaks = "3 months", date_labels = "%m-%Y") +
facet_wrap(~ Component, scales = "free_y", ncol = 1) +
theme(strip.text = element_text(size = 12),
axis.text.x = element_text(angle = 90, hjust = 1))
ts_week_covid<-
Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))%>%
dplyr::group_by(fecha_week)%>%
dplyr::summarise(gasto_total=sum(monto,na.rm=T)/1000,min_day=min(day))%>%
dplyr::ungroup() %>%
dplyr::mutate(covid=dplyr::case_when(min_day>=as.Date("2020-03-17")~1,TRUE~0))%>%
dplyr::mutate(covid=as.factor(covid))%>%
data.frame()
ts_week_covid$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$covid==0)]## [1] 98.357 4.780 56.784 50.506 64.483 67.248 49.299 35.786 58.503
## [10] 64.083 20.148 73.476 127.004 81.551 69.599 134.446 58.936 26.145
## [19] 129.927 104.989 130.860 81.893 95.697 64.579 303.471 151.106 49.275
## [28] 76.293 33.940 83.071 119.512 20.942 58.055 71.728 44.090 33.740
## [37] 59.264 77.410 60.831 63.376 48.754 235.284 29.604 115.143 72.419
## [46] 5.980 80.063 149.178 69.918 107.601 72.724 63.203 99.681 130.309
## [55] 195.898 112.066corpus <- Corpus(VectorSource(Gastos_casa$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 | 6.382333 | 6.993667 | 5.195333 | 5.410333 | 5.849167 | 9.93775 |
| Comida | 234.753000 | 326.890000 | 366.009167 | 312.386750 | 317.896583 | 392.93367 |
| Comunicaciones | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.00000 |
| Electricidad | 49.655556 | 83.582750 | 38.104750 | 47.072333 | 29.523000 | 20.60458 |
| Enceres | 2.964444 | 23.989000 | 18.259750 | 24.219750 | 14.801167 | 39.01200 |
| Farmacia | 0.000000 | 0.000000 | 10.704083 | 2.835000 | 13.996083 | 14.03675 |
| Gas/Bencina | 38.538889 | 44.292667 | 42.636000 | 45.575000 | 13.583667 | 17.25833 |
| Diosi | 20.275444 | 33.319583 | 55.804250 | 31.180667 | 52.687833 | 37.12133 |
| donaciones/regalos | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.00000 |
| Electrodomésticos/ Mantención casa | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.00000 |
| VTR | 17.108889 | 18.326667 | 12.829167 | 25.156667 | 19.086917 | 19.11375 |
| Netflix | 0.000000 | 1.391417 | 8.713833 | 7.151583 | 7.028750 | 8.24725 |
| Otros | 0.000000 | 76.164000 | 5.481667 | 5.000000 | 0.000000 | 0.00000 |
| Total | 369.678556 | 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 $date3 )))## Warning: number of observations:2869, min uf: 26799.01, min date: 2018-01-01#
# uf_proyectado <- readxl::read_excel("uf_proyectado.xlsx") %>% dplyr::arrange(Período) %>%
# dplyr::mutate(Período= as.Date(lubridate::parse_date_time(Período, c("%Y-%m-%d"),exact=T)))
ts_uf_proy<-
ts(data = uf_serie_corrected$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)
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")$ds),
linetype = "dashed", color = "red", size = 0.8) +
# Configuración del eje x
scale_x_date(
date_breaks = "3 months", # Reduce la frecuencia de las etiquetas
date_labels = "%b %Y", # Formato de etiquetas (mes y año)
) +
# Configuración del eje y
scale_y_continuous(labels = function(x) format(x, scientific = FALSE)) +
# Configuración de colores
scale_color_manual(
name = "Leyenda",
values = c("Histórico" = "black", "Pronóstico" = "#4B9CD3")
) +
# Títulos y subtítulos
labs(
title = "Pronóstico de Serie Temporal con TimeGPT",
subtitle = "Intervalos de confianza al 80% (más oscuro) y 95% (más claro)",
x = "Fecha",
y = "Valor",
color = "Leyenda"
) +
# Tema y estilos
theme_minimal() +
theme(
axis.text.x = element_text(angle = 45, hjust = 1, size = 8),
axis.title.x = element_text(size = 10),
axis.title.y = element_text(size = 10),
legend.position = "bottom",
panel.grid.major = element_blank(),
panel.grid.minor = element_blank()
)## Warning: Removed 2869 rows containing missing values or values outside the scale range
## (`geom_ribbon()`).
## Removed 2869 rows containing missing values or values outside the scale range
## (`geom_ribbon()`).## Warning: Removed 2869 rows containing missing values or values outside the scale range
## (`geom_line()`).
library(prophet)## Warning: package 'prophet' was built under R version 4.4.3## Loading required package: Rcpp## Warning: package 'Rcpp' was built under R version 4.4.3## Loading required package: rlang## Warning: package 'rlang' was built under R version 4.4.3##
## Attaching package: 'rlang'## The following objects are masked from 'package:purrr':
##
## %@%, flatten, flatten_chr, flatten_dbl, flatten_int, flatten_lgl,
## flatten_raw, invoke, splice## The following object is masked from 'package:sparklyr':
##
## invoke## The following object is masked from 'package:data.table':
##
## := model <- prophet(
cbind.data.frame(ds= as.Date(uf_timegpt$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$ds), color = "red", linetype = "dashed", linewidth=1) +
scale_x_date(date_breaks = "6 months", date_labels = "%y %b") +
scale_y_continuous(labels = scales::comma) +
labs(title = "Valores predichos (95%IC)",
# subtitle = "March 10, 2025 - May 7, 2025",
x = "Fecha",
y = "Valor",
# caption = "Source: Prophet Forecast Model"
) +
theme_minimal() +
theme(
plot.title = element_text(face = "bold", size = 14),
plot.subtitle = element_text(color = "gray50"),
axis.text.x = element_text(angle = 45, hjust = 1),
panel.grid.minor = element_blank(),
panel.border = element_blank(),
plot.caption = element_text(color = "gray30")
)
La proyección de la UF a 298 días más 2025-11-09 00:04:58 sería de: 26.702 pesos// Percentil 95% más alto proyectado: 35.122,54
Según TimeGPT: La proyección de la UF a 298 días más 2026-09-03 sería de: 40.200,95 pesos// Percentil 80% más alto proyectado: 42.731,44 pesos// Percentil 95% más alto proyectado: 42.804,31
Según prophet: La proyección de la UF a 298 días más 2026-09-03 sería de: 42.605 pesos// Percentil 95% más alto proyectado: 49.970
Ahora con un modelo ARIMA automático
arima_optimal_uf = forecast::auto.arima(ts_uf_proy)
autoplotly::autoplotly(forecast::forecast(arima_optimal_uf, h=298), ts.colour = "darkred",
predict.colour = "blue", predict.linetype = "dashed")%>%
plotly::layout(showlegend = F,
yaxis = list(title = "Gastos"),
xaxis = list(
title="Fecha",
ticktext = as.list(seq(from = as.Date("2018-01-01"),
to = as.Date("2018-01-01")+length(fit_tbats$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
))## Warning: `aes_string()` was deprecated in ggplot2 3.0.0.
## i Please use tidy evaluation idioms with `aes()`.
## i See also `vignette("ggplot2-in-packages")` for more information.
## i The deprecated feature was likely used in the ggfortify package.
## Please report the issue at <https://github.com/sinhrks/ggfortify/issues>.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.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 | 26316.69 | 26321.15 |
| Lo.80 | 26449.27 | 26486.55 |
| Point.Forecast | 26701.54 | 26799.01 |
| Hi.80 | 31503.83 | 32192.83 |
| Hi.95 | 34386.37 | 35048.15 |
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$gasto_total)],
start = 1,
end = nrow(Gastos_casa_m),
frequency = 1,
deltat = 1, ts.eps = getOption("ts.eps"))
fit_tbats_m <- forecast::tbats(ts_gastos_casa_m)
seq_dates<-format(seq(as.Date("2019/03/01"), by = "month", length = dim(Gastos_casa_m)[1]+12), "%m\n'%y")
autplo2t<-
autoplotly::autoplotly(forecast::forecast(fit_tbats_m, h=12), ts.colour = "darkred",
predict.colour = "blue", predict.linetype = "dashed")%>%
plotly::layout(showlegend = F,
yaxis = list(title = "Gastos (en miles)"),
xaxis = list(
title="Fecha",
ticktext = as.list(seq_dates[seq(from = 1, to = (dim(Gastos_casa_m)[1]+12), by = 3)]),
tickvals = as.list(seq(from = 1, to = (dim(Gastos_casa_m)[1]+12), by = 3)),
tickmode = "array"#"array"
))
autplo2tAhora asumiendo un modelo ARIMA, e incluimos como regresor al precio de la UF.
paste0("Optimo pero sin regresor")## [1] "Optimo pero sin regresor"arima_optimal = forecast::auto.arima(ts_gastos_casa_m)
arima_optimal## Series: ts_gastos_casa_m
## ARIMA(1,0,0) with non-zero mean
##
## Coefficients:
## ar1 mean
## 0.4461 1030.7989
## s.e. 0.1017 38.8318
##
## sigma^2 = 38578: log likelihood = -535.03
## AIC=1076.06 AICc=1076.38 BIC=1083.21paste0("Optimo pero con regresor")## [1] "Optimo pero con regresor"arima_optimal2 = forecast::auto.arima(ts_gastos_casa_m, xreg=as.numeric(ts_uf_serie_corrected_m[1:(length(Gastos_casa_m$gasto_total))]))
arima_optimal2## Series: ts_gastos_casa_m
## Regression with ARIMA(1,0,0) errors
##
## Coefficients:
## ar1 intercept xreg
## 0.4415 851.7119 5.4465
## s.e. 0.1021 320.8872 9.6817
##
## sigma^2 = 38929: log likelihood = -534.87
## AIC=1077.75 AICc=1078.28 BIC=1087.28forecast_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 | 608.0395 | 600.6350 | 590.2443 |
| Lo.80 | 757.2162 | 749.5237 | 680.5277 |
| Point.Forecast | 1039.0176 | 1030.7811 | 890.4046 |
| Hi.80 | 1320.8189 | 1312.0385 | 1213.2736 |
| Hi.95 | 1469.9956 | 1460.9272 | 1429.1281 |
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 26100)
##
## 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_1.1.0 scales_1.4.0
## [5] ggiraph_0.9.2 tidytext_0.4.3 DT_0.34.0 janitor_2.2.1
## [9] autoplotly_0.1.4 rvest_1.0.5 plotly_4.11.0 xts_0.14.1
## [13] forecast_8.24.0 wordcloud_2.6 RColorBrewer_1.1-3 SnowballC_0.7.1
## [17] tm_0.7-16 NLP_0.3-2 tsibble_1.1.6 lubridate_1.9.4
## [21] forcats_1.0.1 dplyr_1.1.4 purrr_1.1.0 tidyr_1.3.1
## [25] tibble_3.3.0 tidyverse_2.0.0 gsynth_1.2.1 sjPlot_2.9.0
## [29] lattice_0.22-6 GGally_2.4.0 ggplot2_4.0.0 gridExtra_2.3
## [33] plotrix_3.8-4 sparklyr_1.9.2 httr_1.4.7 readxl_1.4.5
## [37] zoo_1.8-14 stringr_1.5.2 stringi_1.8.7 DataExplorer_0.8.4
## [41] data.table_1.17.8 reshape2_1.4.4 fUnitRoots_4040.81 plyr_1.8.9
## [45] readr_2.1.5
##
## loaded via a namespace (and not attached):
## [1] bitops_1.0-9 cellranger_1.1.0 httr2_1.2.1
## [4] lifecycle_1.0.4 StanHeaders_2.32.10 doParallel_1.0.17
## [7] globals_0.18.0 vroom_1.6.6 MASS_7.3-60.2
## [10] crosstalk_1.2.2 magrittr_2.0.4 sass_0.4.10
## [13] rmarkdown_2.30 jquerylib_0.1.4 yaml_2.3.10
## [16] fracdiff_1.5-3 doRNG_1.8.6.2 askpass_1.2.1
## [19] pkgbuild_1.4.8 DBI_1.2.3 abind_1.4-8
## [22] quadprog_1.5-8 nnet_7.3-19 rappdirs_0.3.3
## [25] sandwich_3.1-1 gdtools_0.4.4 inline_0.3.21
## [28] data.tree_1.2.0 tokenizers_0.3.0 listenv_0.9.1
## [31] anytime_0.3.12 spatial_7.3-17 parallelly_1.45.1
## [34] codetools_0.2-20 xml2_1.4.0 tidyselect_1.2.1
## [37] farver_2.1.2 urca_1.3-4 its.analysis_1.6.0
## [40] matrixStats_1.5.0 stats4_4.4.0 jsonlite_2.0.0
## [43] ellipsis_0.3.2 Formula_1.2-5 iterators_1.0.14
## [46] systemfonts_1.3.1 foreach_1.5.2 tools_4.4.0
## [49] glue_1.8.0 xfun_0.53 TTR_0.24.4
## [52] ggfortify_0.4.19 loo_2.8.0 withr_3.0.2
## [55] timeSeries_4041.111 fastmap_1.2.0 boot_1.3-30
## [58] openssl_2.3.4 caTools_1.18.3 digest_0.6.37
## [61] timechange_0.3.0 R6_2.6.1 lfe_3.1.1
## [64] colorspace_2.1-2 networkD3_0.4.1 gtools_3.9.5
## [67] generics_0.1.4 fontLiberation_0.1.0 htmlwidgets_1.6.4
## [70] ggstats_0.11.0 pkgconfig_2.0.3 gtable_0.3.6
## [73] timeDate_4051.111 lmtest_0.9-40 S7_0.2.0
## [76] selectr_0.4-2 janeaustenr_1.0.0 htmltools_0.5.8.1
## [79] fontBitstreamVera_0.1.1 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 nlme_3.1-164 curl_7.0.0
## [88] cachem_1.1.0 KernSmooth_2.23-22 parallel_4.4.0
## [91] fBasics_4041.97 pillar_1.11.1 vctrs_0.6.5
## [94] gplots_3.2.0 slam_0.1-55 car_3.1-3
## [97] dbplyr_2.5.1 xtable_1.8-4 evaluate_1.0.5
## [100] mvtnorm_1.3-3 cli_3.6.5 compiler_4.4.0
## [103] crayon_1.5.3 rngtools_1.5.2 future.apply_1.20.0
## [106] labeling_0.4.3 rstan_2.32.7 QuickJSR_1.8.1
## [109] viridisLite_0.4.2 lazyeval_0.2.2 fontquiver_0.2.1
## [112] Matrix_1.7-0 hms_1.1.4 bit64_4.6.0-1
## [115] future_1.67.0 nixtlar_0.6.2 extraDistr_1.10.0
## [118] igraph_2.2.0 RcppParallel_5.1.11-1 bslib_0.9.0
## [121] 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'
});",#;
"}")))