inven_forecast_GLM
GLM(Generalized Linear Model)를 통한 inven 예측 R code = 1.Btv_Preroll_인벤예측_요약본_20190712-GLM테스트판(중요).R 일반화 선형모형 - gaussian, Gamma, inverse.gaussian
if(!require(ggplot2)){install.packages("ggplot2"); library(ggplot2)}## Loading required package: ggplot2if(!require(dplyr)){install.packages("dplyr"); library(dplyr)}## Loading required package: dplyr##
## Attaching package: 'dplyr'## The following objects are masked from 'package:stats':
##
## filter, lag## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, union### ★★★★★★ 모든 변수명 지우기
rm(list = ls())
gc()## used (Mb) gc trigger (Mb) max used (Mb)
## Ncells 588765 31.5 1212858 64.8 1212858 64.8
## Vcells 1069292 8.2 8388608 64.0 1753441 13.4#getwd()
setwd("/home/mjs0428/inven_forcast")
btv_pre_roll <- read.csv("./Btv_Preroll-Req_Inv_Res_Imp-data_20190702.csv", header = TRUE, sep = ",", stringsAsFactors = FALSE, fileEncoding = "euc-kr")
glimpse(btv_pre_roll)## Observations: 548
## Variables: 7
## $ Date <chr> "2018-01-01", "2018-01-02", "2018-01-03", "2018-…
## $ VOD_시청시간_초 <chr> "8,421,536,616", "7,878,348,464", "7,498,034,394", "7…
## $ VOD_시청건수 <chr> "4,496,473", "4,658,646", "4,535,338", "4,500,479", …
## $ REQ <chr> "4,567,997", "4,630,415", "4,533,702", "4,490,70…
## $ INV <chr> "7,377,096", "7,110,607", "6,932,244", "6,839,95…
## $ Res <chr> "4,787,165", "4,391,051", "4,252,549", "4,101,29…
## $ Imp <chr> "4,498,714", "4,141,349", "4,011,362", "3,872,03…# 숫자형 데이터 변환
btv_pre_roll$REQ <- as.numeric(gsub(",", "", btv_pre_roll$REQ))
btv_pre_roll$INV <- as.numeric(gsub(",", "", btv_pre_roll$INV))
btv_pre_roll$Res <- as.numeric(gsub(",", "", btv_pre_roll$Res))
btv_pre_roll$Imp <- as.numeric(gsub(",", "", btv_pre_roll$Imp))
btv_pre_roll$VOD_시청시간_초 <- round(as.numeric(gsub(",", "", btv_pre_roll$VOD_시청시간_초))/3600,0)
# 변수명 바꾸기
btv_pre_roll <- rename(btv_pre_roll, VOD_시청시간 = VOD_시청시간_초)
btv_pre_roll$VOD_시청건수 <- as.numeric(gsub(",", "", btv_pre_roll$VOD_시청건수))
# 날짜형 타입으로 변환
btv_pre_roll$Date <- as.Date(btv_pre_roll$Date)
### VOD 시청건수와 큰 차이 데이터 대체처리, 260만 미만 8개
btv_pre_roll$REQ_chg_flag = FALSE
arrange(btv_pre_roll %>% filter(REQ < 2650000), REQ)## Date VOD_시청시간 VOD_시청건수 REQ INV Res Imp
## 1 2018-09-30 2135526 3896865 221664 297700 209140 554894
## 2 2018-07-02 1775243 3532602 736092 1189102 705927 709272
## 3 2018-10-01 1755646 3356570 2324932 2846820 2099119 1355917
## 4 2018-12-07 1807782 3567662 2466112 2872066 2032149 2852099
## 5 2018-02-16 1448428 2751726 2492996 4050293 3108664 2858587
## 6 2019-06-11 1328498 2600687 2577232 4380563 2897582 2809274
## 7 2018-12-06 1725200 3412828 2589182 2954937 2111337 2770486
## 8 2018-07-10 1709360 3476201 2590101 4002227 2311666 2309964
## 9 2019-05-31 1369062 2656316 2618025 4496329 2779988 2697892
## 10 2019-06-12 1340062 2657927 2628286 4491931 2957502 2867923
## 11 2019-05-30 1397194 2680261 2642520 4534099 2751991 2674374
## REQ_chg_flag
## 1 FALSE
## 2 FALSE
## 3 FALSE
## 4 FALSE
## 5 FALSE
## 6 FALSE
## 7 FALSE
## 8 FALSE
## 9 FALSE
## 10 FALSE
## 11 FALSEbtv_pre_roll$REQ_chg_flag [btv_pre_roll$REQ < 2600000] <- TRUE
#btv_pre_roll$REQ [REQ < 2600000] <- round(btv_pre_roll$VOD_시청건수 [REQ < 2600000] * 0.98, 0)
btv_pre_roll$REQ [btv_pre_roll$REQ < 2600000] <- btv_pre_roll$VOD_시청건수 [btv_pre_roll$REQ < 2600000]
filter(btv_pre_roll, REQ_chg_flag == TRUE)## Date VOD_시청시간 VOD_시청건수 REQ INV Res Imp
## 1 2018-02-16 1448428 2751726 2751726 4050293 3108664 2858587
## 2 2018-07-02 1775243 3532602 3532602 1189102 705927 709272
## 3 2018-07-10 1709360 3476201 3476201 4002227 2311666 2309964
## 4 2018-09-30 2135526 3896865 3896865 297700 209140 554894
## 5 2018-10-01 1755646 3356570 3356570 2846820 2099119 1355917
## 6 2018-12-06 1725200 3412828 3412828 2954937 2111337 2770486
## 7 2018-12-07 1807782 3567662 3567662 2872066 2032149 2852099
## 8 2019-06-11 1328498 2600687 2600687 4380563 2897582 2809274
## REQ_chg_flag
## 1 TRUE
## 2 TRUE
## 3 TRUE
## 4 TRUE
## 5 TRUE
## 6 TRUE
## 7 TRUE
## 8 TRUE###2018-05-15 ~ 2018-06-12까지 이상 데이터 대체 처리 >> 29개
btv_pre_roll$REQ_chg_flag [btv_pre_roll$Date >= '2018-05-15' & btv_pre_roll$Date <= '2018-06-12'] <- TRUE
btv_pre_roll$REQ [btv_pre_roll$Date >= '2018-05-15' & btv_pre_roll$Date <= '2018-06-12'] <-
btv_pre_roll$VOD_시청건수 [btv_pre_roll$Date >= '2018-05-15' & btv_pre_roll$Date <= '2018-06-12']
## btv_pre_roll$REQ/btv_pre_roll$VOD_시청건수 ## >>> VOD_시청건수를 가지고 예측 후에 비교분석하여 나중에 검토할 것...
## boxplot(btv_pre_roll$REQ/btv_pre_roll$VOD_시청건수)
## summary(btv_pre_roll$REQ/btv_pre_roll$VOD_시청건수)
## filter(btv_pre_roll, btv_pre_roll$REQ/btv_pre_roll$VOD_시청건수 < 0.9 | btv_pre_roll$REQ/btv_pre_roll$VOD_시청건수 > 1.2)
ggplot(btv_pre_roll, aes(x=Date, y=REQ)) +
geom_point() + geom_smooth(method = 'lm', color = 'red', linetype =2) + # 직선 추세선 추가
geom_smooth() + # 곡선 추세선 추가
ggtitle("Raw Data - after correction")## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
# 요일 구하기
btv_pre_roll$weekday = factor(weekdays(btv_pre_roll$Date),
levels = c("Monday", "Tuesday", "Wednesday", "Thursday", "Friday", "Saturday", "Sunday"))
summary(btv_pre_roll$weekday)## Monday Tuesday Wednesday Thursday Friday Saturday Sunday
## 79 79 78 78 78 78 78# 월(month) 구하기
btv_pre_roll$month <-factor(substr(btv_pre_roll$Date,6,7),
levels = c('01', '02', '03', '04', '05', '06', '07', '08', '09', '10', '11', '12'))
summary(btv_pre_roll$month)## 01 02 03 04 05 06 07 08 09 10 11 12
## 62 56 62 60 62 60 33 31 30 31 30 31length(btv_pre_roll$month)## [1] 548# 특일 구하기
# 참고 페이지 : https://m.blog.naver.com/hancury/221057426711
# 사이트 : https://www.data.go.kr/
# 메뉴명 : 마이페이지 > OPEN API > 개발계정 상세보기
#인증키 "SD5uTucKQxefBuN79R8TKiBa15fAbw5j1id%2FdBH0SsWFDK4boXqDpgMPtc8QJ8UNyaSO8HpGJf%2FAbh65oBzg7g%3D%3D"
if(!require(glue)){install.packages("glue"); library(glue)}## Loading required package: glue##
## Attaching package: 'glue'## The following object is masked from 'package:dplyr':
##
## collapseif(!require(XML)){install.packages("XML"); library(XML)}## Loading required package: XMLif(!require(stringr)){install.packages("stringr"); library(stringr)}## Loading required package: stringrapi.key <- "SD5uTucKQxefBuN79R8TKiBa15fAbw5j1id%2FdBH0SsWFDK4boXqDpgMPtc8QJ8UNyaSO8HpGJf%2FAbh65oBzg7g%3D%3D"
url.format <-
'http://apis.data.go.kr/B090041/openapi/service/SpcdeInfoService/getRestDeInfo?ServiceKey={key}&solYear={year}&solMonth={month}'
holiday.request <- function(key, year, month) glue(url.format)
df_holiday <- data.frame(dateName=NULL, Date=NULL)
for(m in 1:12){
holiday_2018 <- xmlToList(holiday.request(api.key, 2018, str_pad(m, 2, pad=0)))
holiday_2019 <- xmlToList(holiday.request(api.key, 2019, str_pad(m, 2, pad=0)))
items_2018 <- holiday_2018$body$items
items_2019 <- holiday_2019$body$items
items_test <- holiday_2018$body$items
items_test <- holiday_2019$body$items
for(item_2018 in items_2018){
if(item_2018$isHoliday == 'Y') {
#print(paste(item_2018$dateName, item_2018$locdate, sep=' : '))
df_holiday <- rbind(df_holiday,
data.frame(dateName = item_2018$dateName,
Date = (paste(substr(item_2018$locdate,1,4),
substr(item_2018$locdate,5,6),
substr(item_2018$locdate,7,8), sep = '-')),
stringsAsFactors = FALSE))
}
}
for(item_2019 in items_2019){
if(item_2019$isHoliday == 'Y') {
#print(paste(item_2019$dateName, item_2019$locdate, sep=' : '))
df_holiday <- rbind(df_holiday,
data.frame(dateName = item_2019$dateName,
Date = (paste(substr(item_2019$locdate,1,4),
substr(item_2019$locdate,5,6),
substr(item_2019$locdate,7,8), sep = '-')),
stringsAsFactors = FALSE))
}
}
}
# 날짜 데이터의 데이터 타입 변환
df_holiday$Date <- as.Date(df_holiday$Date)
### Left 조인하기 : 광고 데이터 + 특일 데이터..
btv_pre_roll <- left_join(btv_pre_roll, df_holiday)## Joining, by = "Date"# 특일 데이터 범주화
btv_pre_roll <- mutate(btv_pre_roll, isHolyday = as.numeric(!is.na(dateName)))
# 필요한 데이터 잘라내기
#head(btv_pre_roll); tail(btv_pre_roll)
btv_pre_roll2<- filter(btv_pre_roll, Date <= '2019-04-30')
btv_pre_roll3 <- filter(btv_pre_roll, Date <= '2019-05-31')
#tail(btv_pre_roll2); tail(btv_pre_roll3)
test_btv_pre_roll2 <- select(btv_pre_roll2, Date, REQ, weekday, month, isHolyday)
test_btv_pre_roll3 <- select(btv_pre_roll3, Date, REQ, weekday, month, isHolyday)
#tail(test_btv_pre_roll2); tail(test_btv_pre_roll3)
# 예측 Dataset 생성
# 예측할 날짜로 된 수열 생성하기
s_date2 <- as.Date("2019-05-01")
e_date2 <- as.Date("2019-05-31")
s_date3 <- as.Date("2019-06-01")
e_date3 <- as.Date("2019-06-30")
add_df2 <- data.frame(Date = seq(from = s_date2, to=e_date2, by=1))
add_df2$Date <- as.Date(add_df2$Date); add_df2$REQ <- NA; add_df2$weekday <- NA; add_df2$month <- NA; add_df2$isHolyday <- NA;
add_df3 <- data.frame(Date = seq(from = s_date3, to=e_date3, by=1))
add_df3$Date <- as.Date(add_df3$Date); add_df3$REQ <- NA; add_df3$weekday <- NA; add_df3$month <- NA; add_df3$isHolyday <- NA;
# 요일 구하기
#Sys.setlocale("LC_TIME", "English")
#as.character.Date(weekdays(as.Date(add_df2$Date)))
#as.character(weekdays(as.Date('2019-10-01')))
#localeToCharset()
#as.character(weekdays(as.Date(add_df2$Date)))
add_df2$weekday = factor(weekdays(add_df2$Date),
levels = c("Monday", "Tuesday", "Wednesday", "Thursday", "Friday", "Saturday", "Sunday"))
summary(add_df2$weekday)## Monday Tuesday Wednesday Thursday Friday Saturday Sunday
## 4 4 5 5 5 4 4add_df3$weekday = factor(weekdays(add_df3$Date),
levels = c("Monday", "Tuesday", "Wednesday", "Thursday", "Friday", "Saturday", "Sunday"))
summary(add_df3$weekday)## Monday Tuesday Wednesday Thursday Friday Saturday Sunday
## 4 4 4 4 4 5 5# 월(month) 구하기
add_df2$month <-factor(substr(add_df2$Date,6,7),
levels = c('01', '02', '03', '04', '05', '06', '07', '08', '09', '10', '11', '12'))
summary(add_df2$month)## 01 02 03 04 05 06 07 08 09 10 11 12
## 0 0 0 0 31 0 0 0 0 0 0 0add_df3$month <-factor(substr(add_df3$Date,6,7),
levels = c('01', '02', '03', '04', '05', '06', '07', '08', '09', '10', '11', '12'))
summary(add_df3$month)## 01 02 03 04 05 06 07 08 09 10 11 12
## 0 0 0 0 0 30 0 0 0 0 0 0# 특일 데이터 범주화 추가
add_df2$isHolyday <- as.numeric(!is.na(left_join(add_df2, df_holiday)$dateName))## Joining, by = "Date"add_df3$isHolyday <- as.numeric(!is.na(left_join(add_df3, df_holiday)$dateName))## Joining, by = "Date"### 숙제 :: 특일 데이터가 몇개인지 세어볼 것???
# 기존 데이터와 예측치 결합
test_btv_pre_roll2 <- rbind(test_btv_pre_roll2, add_df2)
test_btv_pre_roll3 <- rbind(test_btv_pre_roll3, add_df3)
#View(filter(test_btv_pre_roll2, Date >= '2019-04-01'))
#View(filter(test_btv_pre_roll3, Date >= '2019-05-01'))
### 모델 평가하기as.numeric()
## 원래는 training dataset와 test dataset를 나눠서 테스트 해야 하지만...
## 표본이 작은 관계로.. 관측치 데이터 전체를 사용하여 평가함...
# several models
model1 = lm(formula = REQ ~ I(as.numeric(Date)^3) + I(as.numeric(Date)^2) +
weekday:month + weekday:isHolyday + weekday:month:isHolyday,
data = btv_pre_roll2)
model2 = glm(formula = REQ ~ I(as.numeric(Date)^3) + I(as.numeric(Date)^2) +
weekday:month + weekday:isHolyday + weekday:month:isHolyday,
family = gaussian(link = 'identity'), data = btv_pre_roll2)
model3 = glm(formula = REQ ~ I(as.numeric(Date)^3) + I(as.numeric(Date)^2) +
weekday:month + weekday:isHolyday + weekday:month:isHolyday,
family = Gamma(link = 'inverse'), data = btv_pre_roll2)
model4 = glm(formula = REQ ~ I(as.numeric(Date)^3) + I(as.numeric(Date)^2) +
weekday:month + weekday:isHolyday + weekday:month:isHolyday,
family = inverse.gaussian(link = '1/mu^2'), data = btv_pre_roll2)
#test_btv_pre_roll3
model1_2 = lm(formula = REQ ~ I(as.numeric(Date)^3) + I(as.numeric(Date)^2) +
weekday:month + weekday:isHolyday + weekday:month:isHolyday,
data = btv_pre_roll3)
model2_2 = glm(formula = REQ ~ I(as.numeric(Date)^3) + I(as.numeric(Date)^2) +
weekday:month + weekday:isHolyday + weekday:month:isHolyday,
family = gaussian(link = 'identity'), data = btv_pre_roll3)
model3_2 = glm(formula = REQ ~ I(as.numeric(Date)^3) + I(as.numeric(Date)^2) +
weekday:month + weekday:isHolyday + weekday:month:isHolyday,
family = Gamma(link = 'identity'), data = btv_pre_roll3)
model4_2 = glm(formula = REQ ~ I(as.numeric(Date)^3) + I(as.numeric(Date)^2) +
weekday:month + weekday:isHolyday + weekday:month:isHolyday,
family = inverse.gaussian(link = '1/mu^2'), data = btv_pre_roll3)
# 점/구간 추정 예측
m1_c_interval <- predict(model1, newdata=test_btv_pre_roll2, type='response', interval="confidence", level = 0.99)## Warning in predict.lm(model1, newdata = test_btv_pre_roll2, type =
## "response", : prediction from a rank-deficient fit may be misleadingm1_p_interval <- predict(model1, newdata=test_btv_pre_roll2, type='response', interval="prediction")## Warning in predict.lm(model1, newdata = test_btv_pre_roll2, type =
## "response", : prediction from a rank-deficient fit may be misleadingm2_c_interval <- predict(model2, newdata=test_btv_pre_roll2, type='response', interval="confidence")## Warning in predict.lm(object, newdata, se.fit, scale = 1, type = if (type
## == : prediction from a rank-deficient fit may be misleadingm2_p_interval <- predict(model2, newdata=test_btv_pre_roll2, type='response', interval="prediction")## Warning in predict.lm(object, newdata, se.fit, scale = 1, type = if (type
## == : prediction from a rank-deficient fit may be misleadingm3_c_interval <- predict(model3, newdata=test_btv_pre_roll2, type='response', interval="confidence")## Warning in predict.lm(object, newdata, se.fit, scale = 1, type = if (type
## == : prediction from a rank-deficient fit may be misleadingm3_p_interval <- predict(model3, newdata=test_btv_pre_roll2, type='response', interval="prediction")## Warning in predict.lm(object, newdata, se.fit, scale = 1, type = if (type
## == : prediction from a rank-deficient fit may be misleadingm4_c_interval <- predict(model4, newdata=test_btv_pre_roll2, type='response', interval="confidence")## Warning in predict.lm(object, newdata, se.fit, scale = 1, type = if (type
## == : prediction from a rank-deficient fit may be misleadingm4_p_interval <- predict(model4, newdata=test_btv_pre_roll2, type='response', interval="prediction")## Warning in predict.lm(object, newdata, se.fit, scale = 1, type = if (type
## == : prediction from a rank-deficient fit may be misleadingtest_btv_pre_roll2$m1_fit <- m1_c_interval[,1] # fit
test_btv_pre_roll2$m1_cnf_lwr <- m1_c_interval[,2] # confience lower
test_btv_pre_roll2$m1_cnf_upr <- m1_c_interval[,3] # confience upper
test_btv_pre_roll2$m1_prd_lwr <- m1_p_interval[,2] # prediction lower
test_btv_pre_roll2$m1_prd_upr <- m1_p_interval[,3] # prediction upper
test_btv_pre_roll2$m2_fit <- m2_c_interval # fit
test_btv_pre_roll2$m3_fit <- m3_c_interval # fit
test_btv_pre_roll2$m4_fit <- m4_c_interval # fit
#test_btv_pre_roll2 %>% filter(Date >= '2019-05-01' & Date <= '2019-05-31') %>%
# summarise(sum(m1_cnf_lwr), sum(m1_fit), sum(m1_cnf_upr), sum(m2_cnf_lwr), sum(m2_fit), sum(m2_cnf_upr), sum(m3_cnf_lwr), sum(m3_fit), sum(m3_cnf_upr), sum(m4_cnf_lwr), sum(m4_fit), sum(m4_cnf_upr))
test_btv_pre_roll2 %>% filter(Date >= '2019-05-01' & Date <= '2019-05-31') %>%
summarise(sum(m1_cnf_lwr), sum(m1_fit), sum(m1_cnf_upr), sum(m2_fit), sum(m3_fit), sum(m4_fit)) ## sum(m1_cnf_lwr) sum(m1_fit) sum(m1_cnf_upr) sum(m2_fit) sum(m3_fit)
## 1 94388014 105831672 117275329 105831672 105543903
## sum(m4_fit)
## 1 105504004pp <- ggplot(test_btv_pre_roll2, aes(x=Date))
pp2 <- pp + geom_point(aes(y = REQ), color = "black", alpha = 0.3)
pp3 <- pp2 + geom_point(aes(y = m1_fit), color = "red", alpha = 0.3)
pp4 <- pp2 + geom_point(aes(y = m2_c_interval), color = "red", alpha = 0.3)
pp5 <- pp2 + geom_point(aes(y = m3_c_interval), color = "red", alpha = 0.3)
pp6 <- pp2 + geom_point(aes(y = m4_c_interval), color = "red", alpha = 0.3)
#test_btv_pre_roll3
# 점/구간 추정 예측
m1_c_interval2 <- predict(model1_2, newdata=test_btv_pre_roll3, type='response', interval="confidence", level = 0.99)## Warning in predict.lm(model1_2, newdata = test_btv_pre_roll3, type =
## "response", : prediction from a rank-deficient fit may be misleadingm1_p_interval2 <- predict(model1_2, newdata=test_btv_pre_roll3, type='response', interval="prediction")## Warning in predict.lm(model1_2, newdata = test_btv_pre_roll3, type =
## "response", : prediction from a rank-deficient fit may be misleadingm2_c_interval2 <- predict(model2_2, newdata=test_btv_pre_roll3, type='response', interval="confidence")## Warning in predict.lm(object, newdata, se.fit, scale = 1, type = if (type
## == : prediction from a rank-deficient fit may be misleadingm2_p_interval2 <- predict(model2_2, newdata=test_btv_pre_roll3, type='response', interval="prediction")## Warning in predict.lm(object, newdata, se.fit, scale = 1, type = if (type
## == : prediction from a rank-deficient fit may be misleadingm3_c_interval2 <- predict(model3_2, newdata=test_btv_pre_roll3, type='response', interval="confidence")## Warning in predict.lm(object, newdata, se.fit, scale = 1, type = if (type
## == : prediction from a rank-deficient fit may be misleadingm3_p_interval2 <- predict(model3_2, newdata=test_btv_pre_roll3, type='response', interval="prediction")## Warning in predict.lm(object, newdata, se.fit, scale = 1, type = if (type
## == : prediction from a rank-deficient fit may be misleadingm4_c_interval2 <- predict(model4_2, newdata=test_btv_pre_roll3, type='response', interval="confidence")## Warning in predict.lm(object, newdata, se.fit, scale = 1, type = if (type
## == : prediction from a rank-deficient fit may be misleadingm4_p_interval2 <- predict(model4_2, newdata=test_btv_pre_roll3, type='response', interval="prediction")## Warning in predict.lm(object, newdata, se.fit, scale = 1, type = if (type
## == : prediction from a rank-deficient fit may be misleadingtest_btv_pre_roll3$m1_fit <- m1_c_interval2[,1] # fit
test_btv_pre_roll3$m1_cnf_lwr <- m1_c_interval2[,2] # confience lower
test_btv_pre_roll3$m1_cnf_upr <- m1_c_interval2[,3] # confience upper
test_btv_pre_roll3$m1_prd_lwr <- m1_p_interval2[,2] # prediction lower
test_btv_pre_roll3$m1_prd_upr <- m1_p_interval2[,3] # prediction upper
test_btv_pre_roll3$m2_fit <- m2_c_interval2 # fit
test_btv_pre_roll3$m3_fit <- m3_c_interval2 # fit
test_btv_pre_roll3$m4_fit <- m4_c_interval2 # fit
#test_btv_pre_roll3 %>% filter(Date >= '2019-06-01' & Date <= '2019-06-30') %>%
# summarise(sum(m1_cnf_lwr), sum(m1_fit), sum(m1_cnf_upr), sum(m2_cnf_lwr), sum(m2_fit), sum(m2_cnf_upr), sum(m3_cnf_lwr), sum(m3_fit), sum(m3_cnf_upr), sum(m4_cnf_lwr), sum(m4_fit), sum(m4_cnf_upr))
test_btv_pre_roll3 %>% filter(Date >= '2019-06-01' & Date <= '2019-06-30') %>%
summarise(sum(m1_prd_lwr), sum(m1_fit), sum(m1_prd_upr), sum(m2_fit), sum(m3_fit), sum(m4_fit)) ## sum(m1_prd_lwr) sum(m1_fit) sum(m1_prd_upr) sum(m2_fit) sum(m3_fit)
## 1 73443787 88901594 104359400 88901594 86832119
## sum(m4_fit)
## 1 90640824pp <- ggplot(test_btv_pre_roll3, aes(x=Date))
pp2 <- pp + geom_point(aes(y = REQ), color = "black", alpha = 0.3)
pp3 <- pp2 + geom_point(aes(y = m1_fit), color = "blue", alpha = 0.3)
pp4 <- pp2 + geom_point(aes(y = m2_c_interval2), color = "blue", alpha = 0.3)
pp5 <- pp2 + geom_point(aes(y = m3_c_interval2), color = "blue", alpha = 0.3)
pp6 <- pp2 + geom_point(aes(y = m4_c_interval2), color = "blue", alpha = 0.3)
filter(test_btv_pre_roll2, Date >= '2019-04-01')## Date REQ weekday month isHolyday m1_fit m1_cnf_lwr
## 1 2019-04-01 3282152 Monday 04 0 3289837 3104535
## 2 2019-04-02 3184513 Tuesday 04 0 3211098 3017734
## 3 2019-04-03 3160823 Wednesday 04 0 3203076 2996555
## 4 2019-04-04 3087324 Thursday 04 0 3209590 3002911
## 5 2019-04-05 3107286 Friday 04 0 3239256 3032415
## 6 2019-04-06 3981096 Saturday 04 0 3908532 3701523
## 7 2019-04-07 3946311 Sunday 04 0 3914475 3716812
## 8 2019-04-08 3210087 Monday 04 0 3284184 3097018
## 9 2019-04-09 3292649 Tuesday 04 0 3205430 3010556
## 10 2019-04-10 3232678 Wednesday 04 0 3197391 2989044
## 11 2019-04-11 3185824 Thursday 04 0 3203888 2995332
## 12 2019-04-12 3153292 Friday 04 0 3233538 3024769
## 13 2019-04-13 3815088 Saturday 04 0 3902798 3693809
## 14 2019-04-14 4119517 Sunday 04 0 3908725 3708720
## 15 2019-04-15 3175054 Monday 04 0 3278418 3088899
## 16 2019-04-16 3068774 Tuesday 04 0 3199647 3002791
## 17 2019-04-17 3041754 Wednesday 04 0 3191592 2980966
## 18 2019-04-18 3101583 Thursday 04 0 3198073 2987184
## 19 2019-04-19 3117815 Friday 04 0 3227707 3016549
## 20 2019-04-20 3761864 Saturday 04 0 3896950 3685518
## 21 2019-04-21 3846860 Sunday 04 0 3902861 3700029
## 22 2019-04-22 3138914 Monday 04 0 3272538 3080151
## 23 2019-04-23 3122087 Tuesday 04 0 3193750 2994413
## 24 2019-04-24 3025289 Wednesday 04 0 3185679 2972299
## 25 2019-04-25 3144534 Thursday 04 0 3192144 2978442
## 26 2019-04-26 3217561 Friday 04 0 3221762 3007733
## 27 2019-04-27 3636698 Saturday 04 0 3890988 3676627
## 28 2019-04-28 3714765 Sunday 04 0 3896883 3690716
## 29 2019-04-29 3187055 Monday 04 0 3266544 3070756
## 30 2019-04-30 3024436 Tuesday 04 0 3187740 2985397
## 31 2019-05-01 NA Wednesday 05 0 3290099 2992034
## 32 2019-05-02 NA Thursday 05 0 3214975 2915944
## 33 2019-05-03 NA Friday 05 0 3150689 2825612
## 34 2019-05-04 NA Saturday 05 0 3987377 3621653
## 35 2019-05-05 NA Sunday 05 1 3497715 2783210
## 36 2019-05-06 NA Monday 05 1 3942262 3352082
## 37 2019-05-07 NA Tuesday 05 0 3395649 3068187
## 38 2019-05-08 NA Wednesday 05 0 3283958 2980684
## 39 2019-05-09 NA Thursday 05 0 3208818 2904503
## 40 2019-05-10 NA Friday 05 0 3144516 2814550
## 41 2019-05-11 NA Saturday 05 0 3981187 3610974
## 42 2019-05-12 NA Sunday 05 1 3491509 2774700
## 43 2019-05-13 NA Monday 05 0 3297765 2925762
## 44 2019-05-14 NA Tuesday 05 0 3389411 3056860
## 45 2019-05-15 NA Wednesday 05 0 3277703 2968809
## 46 2019-05-16 NA Thursday 05 0 3202547 2892538
## 47 2019-05-17 NA Friday 05 0 3138228 2802981
## 48 2019-05-18 NA Saturday 05 0 3974883 3599814
## 49 2019-05-19 NA Sunday 05 0 3742575 3405213
## 50 2019-05-20 NA Monday 05 0 3291428 2914434
## 51 2019-05-21 NA Tuesday 05 0 3383058 3045018
## 52 2019-05-22 NA Wednesday 05 0 3271334 2956404
## 53 2019-05-23 NA Thursday 05 0 3196161 2880040
## 54 2019-05-24 NA Friday 05 0 3131827 2790893
## 55 2019-05-25 NA Saturday 05 0 3968465 3588160
## 56 2019-05-26 NA Sunday 05 0 3736141 3392946
## 57 2019-05-27 NA Monday 05 0 3284977 2902611
## 58 2019-05-28 NA Tuesday 05 0 3376591 3032653
## 59 2019-05-29 NA Wednesday 05 0 3264851 2943460
## 60 2019-05-30 NA Thursday 05 0 3189662 2867004
## 61 2019-05-31 NA Friday 05 0 3125311 2778279
## m1_cnf_upr m1_prd_lwr m1_prd_upr m2_fit m3_fit m4_fit
## 1 3475138 2835668 3744006 3289837 3297556 3301685
## 2 3404463 2754995 3667202 3211098 3220428 3224994
## 3 3409597 2743658 3662494 3203076 3213765 3219095
## 4 3416268 2750130 3669049 3209590 3219968 3225213
## 5 3446097 2779755 3698757 3239256 3248511 3253367
## 6 4115540 3448987 4368076 3908532 3891653 3882855
## 7 4112138 3457310 4371640 3914475 3895171 3885206
## 8 3471350 2829575 3738794 3284184 3291730 3296027
## 9 3400303 2748956 3661903 3205430 3214852 3219702
## 10 3405738 2737497 3657284 3197391 3208194 3213814
## 11 3412444 2743940 3663836 3203888 3214357 3219883
## 12 3442308 2773534 3693542 3233538 3242782 3247879
## 13 4111786 3442736 4362859 3902798 3883406 3873502
## 14 4108729 3450973 4366477 3908725 3886882 3875804
## 15 3467937 2823246 3733589 3278418 3285788 3290262
## 16 3396503 2742683 3656611 3199647 3209166 3214309
## 17 3402217 2731100 3652084 3191592 3202513 3208433
## 18 3408962 2737512 3658634 3198073 3208636 3214453
## 19 3438864 2767074 3688339 3227707 3236940 3242287
## 20 4108382 3436245 4357655 3896950 3875005 3863996
## 21 4105693 3444391 4361330 3902861 3878438 3866249
## 22 3464924 2816673 3728403 3272538 3279733 3284391
## 23 3393088 2736166 3651335 3193750 3203372 3208816
## 24 3399060 2724456 3646902 3185679 3196724 3202952
## 25 3405846 2730835 3653453 3192144 3202806 3208923
## 26 3435791 2760365 3683158 3221762 3230988 3236594
## 27 4105350 3429503 4352474 3890988 3866452 3854339
## 28 4103050 3437556 4356209 3896883 3869843 3856544
## 29 3462332 2809844 3723243 3266544 3273566 3278415
## 30 3390083 2729396 3646084 3187740 3197469 3203225
## 31 3588164 2802544 3777654 3290099 3297857 3304662
## 32 3514005 2727079 3702871 3214975 3229092 3238443
## 33 3475766 2653277 3648102 3150689 3170566 3182036
## 34 4353101 3473941 4500812 3987377 3925994 3896607
## 35 4212219 2804214 4191216 3497715 3452166 3436462
## 36 4532441 3319864 4564659 3942262 3886326 3860226
## 37 3723111 2897335 3893963 3395649 3393262 3395940
## 38 3587233 2794553 3773363 3283958 3291446 3298398
## 39 3513132 2719041 3698595 3208818 3222926 3232529
## 40 3474481 2645250 3643782 3144516 3164603 3176408
## 41 4351399 3465899 4496475 3981187 3916828 3886253
## 42 4208318 2796638 4186380 3491509 3445055 3429332
## 43 3669767 2781734 3813795 3297765 3302745 3308953
## 44 3721962 2889156 3889665 3389411 3386351 3389015
## 45 3586597 2786276 3769131 3277703 3284923 3292032
## 46 3512555 2710714 3694379 3202547 3216653 3226519
## 47 3473476 2636937 3639520 3138228 3158537 3170686
## 48 4349952 3457573 4492193 3974883 3907512 3875757
## 49 4079938 3240466 4244685 3742575 3701131 3684844
## 50 3668422 2773312 3809544 3291428 3296080 3302427
## 51 3721098 2880686 3885430 3383058 3379324 3381984
## 52 3586265 2777701 3764967 3271334 3278291 3285567
## 53 3512282 2702090 3690233 3196161 3210275 3220413
## 54 3472761 2628328 3635326 3131827 3152369 3164874
## 55 4348770 3448955 4487975 3968465 3898049 3865123
## 56 4079336 3231757 4240525 3736141 3692615 3675675
## 57 3667344 2764595 3805360 3284977 3289305 3295802
## 58 3720529 2871915 3881267 3376591 3372182 3374848
## 59 3586242 2768822 3760880 3264851 3271550 3279004
## 60 3512319 2693159 3686165 3189662 3203792 3214213
## 61 3472342 2619415 3631207 3125311 3146100 3158972filter(test_btv_pre_roll3, Date >= '2019-05-01')## Date REQ weekday month isHolyday m1_fit m1_cnf_lwr
## 1 2019-05-01 3616125 Wednesday 05 0 3006488 2818096
## 2 2019-05-02 2955011 Thursday 05 0 2901835 2713319
## 3 2019-05-03 2907704 Friday 05 0 2870009 2673210
## 4 2019-05-04 3236108 Saturday 05 0 3570458 3348141
## 5 2019-05-05 3036148 Sunday 05 1 3269726 2860513
## 6 2019-05-06 3660025 Monday 05 1 3690200 3277719
## 7 2019-05-07 2868780 Tuesday 05 0 2982781 2772528
## 8 2019-05-08 2703155 Wednesday 05 0 2986413 2796487
## 9 2019-05-09 2747889 Thursday 05 0 2881690 2691607
## 10 2019-05-10 2807466 Friday 05 0 2849793 2651702
## 11 2019-05-11 3451599 Saturday 05 0 3550172 3326644
## 12 2019-05-12 3482948 Sunday 05 1 3249370 2840156
## 13 2019-05-13 2890072 Monday 05 0 2999994 2757674
## 14 2019-05-14 2775220 Tuesday 05 0 2962283 2750477
## 15 2019-05-15 2806413 Wednesday 05 0 2965844 2774077
## 16 2019-05-16 2800079 Thursday 05 0 2861050 2669092
## 17 2019-05-17 2819470 Friday 05 0 2829083 2629403
## 18 2019-05-18 3544076 Saturday 05 0 3529391 3304385
## 19 2019-05-19 3722838 Sunday 05 0 3508866 3260035
## 20 2019-05-20 2884655 Monday 05 0 2979072 2735158
## 21 2019-05-21 2742861 Tuesday 05 0 2941290 2727641
## 22 2019-05-22 2691009 Wednesday 05 0 2944781 2750852
## 23 2019-05-23 2695802 Thursday 05 0 2839916 2645759
## 24 2019-05-24 2752395 Friday 05 0 2807879 2606296
## 25 2019-05-25 3354832 Saturday 05 0 3508116 3281347
## 26 2019-05-26 3359779 Sunday 05 0 3487520 3236409
## 27 2019-05-27 3006505 Monday 05 0 2957656 2711878
## 28 2019-05-28 2680322 Tuesday 05 0 2919803 2704003
## 29 2019-05-29 2682910 Wednesday 05 0 2923222 2726796
## 30 2019-05-30 2642520 Thursday 05 0 2818287 2621594
## 31 2019-05-31 2618025 Friday 05 0 2786179 2582366
## 32 2019-06-01 NA Saturday 06 0 3441481 3146587
## 33 2019-06-02 NA Sunday 06 0 3366607 3044361
## 34 2019-06-03 NA Monday 06 0 2782393 2459545
## 35 2019-06-04 NA Tuesday 06 0 2787854 2464398
## 36 2019-06-05 NA Wednesday 06 0 2665811 2230725
## 37 2019-06-06 NA Thursday 06 1 3783893 3087435
## 38 2019-06-07 NA Friday 06 0 2779416 2481444
## 39 2019-06-08 NA Saturday 06 0 3419215 3120553
## 40 2019-06-09 NA Sunday 06 0 3344271 3018552
## 41 2019-06-10 NA Monday 06 0 2759985 2433621
## 42 2019-06-11 NA Tuesday 06 0 2765376 2438359
## 43 2019-06-12 NA Wednesday 06 0 2643261 2205437
## 44 2019-06-13 NA Thursday 06 0 2696619 2368279
## 45 2019-06-14 NA Friday 06 0 2756725 2454761
## 46 2019-06-15 NA Saturday 06 0 3396453 3093752
## 47 2019-06-16 NA Sunday 06 0 3321438 2991990
## 48 2019-06-17 NA Monday 06 0 2737082 2406943
## 49 2019-06-18 NA Tuesday 06 0 2742401 2411565
## 50 2019-06-19 NA Wednesday 06 0 2620216 2179443
## 51 2019-06-20 NA Thursday 06 0 2673502 2341252
## 52 2019-06-21 NA Friday 06 0 2733538 2427305
## 53 2019-06-22 NA Saturday 06 0 3373195 3066177
## 54 2019-06-23 NA Sunday 06 0 3298109 2964668
## 55 2019-06-24 NA Monday 06 0 2713682 2379503
## 56 2019-06-25 NA Tuesday 06 0 2718930 2384008
## 57 2019-06-26 NA Wednesday 06 0 2596674 2152735
## 58 2019-06-27 NA Thursday 06 0 2649890 2313460
## 59 2019-06-28 NA Friday 06 0 2709854 2399070
## 60 2019-06-29 NA Saturday 06 0 3349440 3037822
## 61 2019-06-30 NA Sunday 06 0 3274283 2936577
## m1_cnf_upr m1_prd_lwr m1_prd_upr m2_fit m3_fit m4_fit
## 1 3194879 2544207 3468768 3006488 2980956 3037399
## 2 3090351 2439525 3364145 2901835 2873765 2946387
## 3 3066809 2405712 3334306 2870009 2850409 2912311
## 4 3792776 3099563 4041354 3570458 3539154 3516246
## 5 3678940 2731354 3808099 3269726 3273133 3268598
## 6 4102682 3150391 4230010 3690200 3669246 3603377
## 7 3193034 2515094 3450469 2982781 2940377 3018634
## 8 3176339 2523770 3449056 2986413 2957552 3023061
## 9 3071772 2419010 3344369 2881690 2850276 2933248
## 10 3047885 2385179 3314408 2849793 2826835 2899575
## 11 3773700 3078946 4021398 3550172 3515495 3493819
## 12 3658583 2710997 3787742 3249370 3249389 3250498
## 13 3242315 2523439 3476550 2999994 2974950 3034724
## 14 3174089 2494191 3430375 2962283 2916463 3004259
## 15 3157611 2502764 3428925 2965844 2933553 3008573
## 16 3053008 2397924 3324176 2861050 2826192 2919961
## 17 3028764 2364077 3294090 2829083 2802666 2886695
## 18 3754398 3057759 4001023 3529391 3491241 3471276
## 19 3757698 3030379 3987354 3508866 3483664 3433415
## 20 3222987 2502048 3456097 2979072 2950526 3019814
## 21 3154940 2472715 3409865 2941290 2891954 2989742
## 22 3138709 2481181 3408380 2944781 2908958 2993944
## 23 3034073 2376261 3303571 2839916 2801513 2906536
## 24 3009461 2342399 3273358 2807879 2777901 2873678
## 25 3734885 3035997 3980235 3508116 3466391 3448633
## 26 3738631 3008346 3966695 3487520 3458729 3411427
## 27 3203433 2480079 3435232 2957656 2925505 3004771
## 28 3135602 2450660 3388946 2919803 2866848 2975093
## 29 3119649 2459017 3387428 2923222 2883768 2979185
## 30 3014980 2354016 3282558 2818287 2776237 2892982
## 31 2989992 2320139 3252219 2786179 2752540 2860532
## 32 3736375 2948124 3934838 3441481 3380332 3345242
## 33 3688854 2863476 3869739 3366607 3305518 3298108
## 34 3105241 2279039 3285747 2782393 2720565 2910698
## 35 3111310 2284275 3291433 2787854 2725462 2914704
## 36 3100896 2115857 3215764 2665811 2600948 2829033
## 37 4480351 3096073 4471712 3783893 3725935 3402525
## 38 3077389 2284993 3273840 2779416 2715224 2909411
## 39 3717877 2924552 3913879 3419215 3354289 3324012
## 40 3669989 2839851 3848690 3344271 3279389 3277693
## 41 3086350 2255325 3264646 2759985 2694351 2896591
## 42 3092392 2260472 3270280 2765376 2699163 2900493
## 43 3081085 2092055 3194467 2643261 2574564 2815992
## 44 3024959 2191219 3202018 2696619 2628620 2853523
## 45 3058690 2260906 3252544 2756725 2688669 2895143
## 46 3699155 2900375 3892531 3396453 3327649 3302714
## 47 3650885 2815623 3827253 3321438 3252664 3257203
## 48 3067220 2231007 3243157 2737082 2667541 2882377
## 49 3073237 2236063 3248739 2742401 2672267 2886177
## 50 3060989 2067656 3172776 2620216 2547583 2802845
## 51 3005753 2166631 3180374 2673502 2601554 2839803
## 52 3039771 2236211 3230865 2733538 2661517 2880773
## 53 3680213 2875589 3870801 3373195 3300412 3281362
## 54 3631550 2790786 3805432 3298109 3225341 3236654
## 55 3047860 2206079 3221284 2713682 2640132 2868063
## 56 3053853 2211045 3226816 2718930 2644774 2871763
## 57 3040613 2042653 3150695 2596674 2520004 2789598
## 58 2986319 2141430 3158349 2649890 2573889 2825986
## 59 3020638 2210901 3208808 2709854 2633767 2866309
## 60 3661059 2850187 3848694 3349440 3272576 3259970
## 61 3611989 2765335 3783231 3274283 3197420 3216057###
# 모델 신뢰구간
confint(model1)## 2.5 % 97.5 %
## (Intercept) -3.469885e+08 1.236784e+08
## I(as.numeric(Date)^3) -1.259679e-04 4.164062e-05
## I(as.numeric(Date)^2) -1.119159e+00 3.349907e+00
## weekdayMonday:month01 1.268842e+05 6.326868e+05
## weekdayTuesday:month01 1.519181e+05 6.505763e+05
## weekdayWednesday:month01 1.029470e+05 5.901752e+05
## weekdayThursday:month01 1.308425e+05 6.248463e+05
## weekdayFriday:month01 7.996563e+04 5.861026e+05
## weekdaySaturday:month01 3.381051e+05 8.441283e+05
## weekdaySunday:month01 3.166464e+05 8.225581e+05
## weekdayMonday:month02 -2.478518e+05 2.705986e+05
## weekdayTuesday:month02 -3.032691e+05 2.150849e+05
## weekdayWednesday:month02 -1.470887e+05 3.711711e+05
## weekdayThursday:month02 -7.998359e+04 4.367486e+05
## weekdayFriday:month02 -1.205115e+05 3.951449e+05
## weekdaySaturday:month02 1.435873e+05 6.592396e+05
## weekdaySunday:month02 -1.184281e+05 3.852537e+05
## weekdayMonday:month03 -7.367679e+05 -2.337347e+05
## weekdayTuesday:month03 -8.120594e+05 -3.090192e+05
## weekdayWednesday:month03 -7.863651e+05 -2.833160e+05
## weekdayThursday:month03 -7.898586e+05 -2.867986e+05
## weekdayFriday:month03 -7.509385e+05 -2.578262e+05
## weekdaySaturday:month03 -4.860050e+04 4.355895e+05
## weekdaySunday:month03 -9.839968e+04 3.950084e+05
## weekdayMonday:month04 -7.914522e+05 -3.059993e+05
## weekdayTuesday:month04 -8.747030e+05 -3.786245e+05
## weekdayWednesday:month04 -8.859579e+05 -3.818090e+05
## weekdayThursday:month04 -8.786747e+05 -3.744543e+05
## weekdayFriday:month04 -8.482379e+05 -3.439434e+05
## weekdaySaturday:month04 -1.781907e+05 3.261803e+05
## weekdaySunday:month04 -1.658081e+05 3.273086e+05
## weekdayMonday:month05 -8.222092e+05 -1.881397e+05
## weekdayTuesday:month05 -7.042797e+05 -1.209807e+05
## weekdayWednesday:month05 -7.985764e+05 -2.482976e+05
## weekdayThursday:month05 -8.728090e+05 -3.225722e+05
## weekdayFriday:month05 -9.527247e+05 -3.694828e+05
## weekdaySaturday:month05 -1.406067e+05 4.935237e+05
## weekdaySunday:month05 -3.465192e+05 2.366405e+05
## weekdayMonday:month06 -9.878358e+05 -4.052308e+05
## weekdayTuesday:month06 -9.787287e+05 -3.961276e+05
## weekdayWednesday:month06 -1.165181e+06 -4.400006e+05
## weekdayThursday:month06 -1.040045e+06 -4.574499e+05
## weekdayFriday:month06 -9.614963e+05 -4.118360e+05
## weekdaySaturday:month06 -3.180148e+05 2.316415e+05
## weekdaySunday:month06 -4.072650e+05 1.753447e+05
## weekdayMonday:month07 -5.870947e+05 -3.737968e+04
## weekdayTuesday:month07 -6.753232e+05 -1.256047e+05
## weekdayWednesday:month07 -7.375985e+05 -1.549443e+05
## weekdayThursday:month07 -7.574637e+05 -1.748059e+05
## weekdayFriday:month07 -7.463423e+05 -1.636811e+05
## weekdaySaturday:month07 -4.248109e+05 1.578537e+05
## weekdaySunday:month07 -3.814277e+05 1.682837e+05
## weekdayMonday:month08 -5.359704e+05 4.664868e+04
## weekdayTuesday:month08 -7.327251e+05 -1.501144e+05
## weekdayWednesday:month08 -6.325750e+05 -4.996685e+04
## weekdayThursday:month08 -6.745376e+05 -1.248638e+05
## weekdayFriday:month08 -5.178712e+05 3.179386e+04
## weekdaySaturday:month08 -4.168458e+05 1.657886e+05
## weekdaySunday:month08 -3.807110e+05 2.019160e+05
## weekdayMonday:month09 -1.112167e+06 -4.787825e+05
## weekdayTuesday:month09 -1.037004e+06 -4.036401e+05
## weekdayWednesday:month09 -1.116428e+06 -4.830858e+05
## weekdayThursday:month09 -1.017346e+06 -4.352596e+05
## weekdayFriday:month09 -9.887969e+05 -4.067373e+05
## weekdaySaturday:month09 -3.946104e+05 1.545351e+05
## weekdaySunday:month09 -5.819094e+05 2.171002e+02
## weekdayMonday:month10 -8.918877e+05 -3.437776e+05
## weekdayTuesday:month10 -9.097817e+05 -3.287553e+05
## weekdayWednesday:month10 -9.203727e+05 -3.394388e+05
## weekdayThursday:month10 -9.287902e+05 -3.476249e+05
## weekdayFriday:month10 -7.653711e+05 -1.842439e+05
## weekdaySaturday:month10 -1.829419e+05 3.981470e+05
## weekdaySunday:month10 -2.648691e+05 3.161813e+05
## weekdayMonday:month11 -7.919543e+05 -2.120056e+05
## weekdayTuesday:month11 -9.087612e+05 -3.288461e+05
## weekdayWednesday:month11 -8.089352e+05 -2.290531e+05
## weekdayThursday:month11 -7.420895e+05 -1.952264e+05
## weekdayFriday:month11 -7.545440e+05 -2.077165e+05
## weekdaySaturday:month11 -1.510222e+05 4.289954e+05
## weekdaySunday:month11 -2.031984e+05 3.767845e+05
## weekdayMonday:month12 -4.162313e+05 1.299751e+05
## weekdayTuesday:month12 -6.458482e+05 -1.512331e+04
## weekdayWednesday:month12 -6.138438e+05 -3.450497e+04
## weekdayThursday:month12 -5.491809e+05 3.015759e+04
## weekdayFriday:month12 -4.636162e+05 1.157238e+05
## weekdaySaturday:month12 -3.117020e+05 2.345044e+05
## weekdaySunday:month12 NA NA
## weekdayMonday:isHolyday -2.400913e+05 6.842176e+05
## weekdayTuesday:isHolyday -2.431502e+05 6.746868e+05
## weekdayWednesday:isHolyday 3.534231e+05 1.319025e+06
## weekdayThursday:isHolyday 6.275424e+05 1.545671e+06
## weekdayFriday:isHolyday 5.455527e+05 1.457763e+06
## weekdaySaturday:isHolyday -8.863037e+05 1.110326e+05
## weekdaySunday:isHolyday -7.401707e+05 2.253976e+05
## weekdayMonday:month02:isHolyday -1.107694e+06 2.076888e+05
## weekdayTuesday:month02:isHolyday -1.408049e+06 -1.102273e+05
## weekdayWednesday:month02:isHolyday -1.353262e+06 -1.516952e+04
## weekdayThursday:month02:isHolyday -2.783733e+06 -1.482989e+06
## weekdayFriday:month02:isHolyday -3.012963e+06 -1.710304e+06
## weekdaySaturday:month02:isHolyday -1.777767e+06 -4.174307e+05
## weekdaySunday:month02:isHolyday NA NA
## weekdayMonday:month03:isHolyday NA NA
## weekdayTuesday:month03:isHolyday NA NA
## weekdayWednesday:month03:isHolyday NA NA
## weekdayThursday:month03:isHolyday NA NA
## weekdayFriday:month03:isHolyday NA NA
## weekdaySaturday:month03:isHolyday NA NA
## weekdaySunday:month03:isHolyday NA NA
## weekdayMonday:month04:isHolyday NA NA
## weekdayTuesday:month04:isHolyday NA NA
## weekdayWednesday:month04:isHolyday NA NA
## weekdayThursday:month04:isHolyday NA NA
## weekdayFriday:month04:isHolyday NA NA
## weekdaySaturday:month04:isHolyday NA NA
## weekdaySunday:month04:isHolyday NA NA
## weekdayMonday:month05:isHolyday -2.630468e+05 1.095470e+06
## weekdayTuesday:month05:isHolyday 7.705845e+04 1.408493e+06
## weekdayWednesday:month05:isHolyday NA NA
## weekdayThursday:month05:isHolyday NA NA
## weekdayFriday:month05:isHolyday NA NA
## weekdaySaturday:month05:isHolyday NA NA
## weekdaySunday:month05:isHolyday NA NA
## weekdayMonday:month06:isHolyday NA NA
## weekdayTuesday:month06:isHolyday NA NA
## weekdayWednesday:month06:isHolyday -2.992962e+05 9.962453e+05
## weekdayThursday:month06:isHolyday NA NA
## weekdayFriday:month06:isHolyday NA NA
## weekdaySaturday:month06:isHolyday NA NA
## weekdaySunday:month06:isHolyday NA NA
## weekdayMonday:month07:isHolyday NA NA
## weekdayTuesday:month07:isHolyday NA NA
## weekdayWednesday:month07:isHolyday NA NA
## weekdayThursday:month07:isHolyday NA NA
## weekdayFriday:month07:isHolyday NA NA
## weekdaySaturday:month07:isHolyday NA NA
## weekdaySunday:month07:isHolyday NA NA
## weekdayMonday:month08:isHolyday NA NA
## weekdayTuesday:month08:isHolyday NA NA
## weekdayWednesday:month08:isHolyday -8.701177e+05 4.954202e+05
## weekdayThursday:month08:isHolyday NA NA
## weekdayFriday:month08:isHolyday NA NA
## weekdaySaturday:month08:isHolyday NA NA
## weekdaySunday:month08:isHolyday NA NA
## weekdayMonday:month09:isHolyday -1.319498e+06 4.028265e+04
## weekdayTuesday:month09:isHolyday -9.826464e+05 3.726364e+05
## weekdayWednesday:month09:isHolyday -9.054644e+05 4.826826e+05
## weekdayThursday:month09:isHolyday NA NA
## weekdayFriday:month09:isHolyday NA NA
## weekdaySaturday:month09:isHolyday NA NA
## weekdaySunday:month09:isHolyday NA NA
## weekdayMonday:month10:isHolyday NA NA
## weekdayTuesday:month10:isHolyday 1.634807e+05 1.495516e+06
## weekdayWednesday:month10:isHolyday NA NA
## weekdayThursday:month10:isHolyday NA NA
## weekdayFriday:month10:isHolyday NA NA
## weekdaySaturday:month10:isHolyday NA NA
## weekdaySunday:month10:isHolyday NA NA
## weekdayMonday:month11:isHolyday NA NA
## weekdayTuesday:month11:isHolyday NA NA
## weekdayWednesday:month11:isHolyday NA NA
## weekdayThursday:month11:isHolyday NA NA
## weekdayFriday:month11:isHolyday NA NA
## weekdaySaturday:month11:isHolyday NA NA
## weekdaySunday:month11:isHolyday NA NA
## weekdayMonday:month12:isHolyday NA NA
## weekdayTuesday:month12:isHolyday -7.998234e+04 1.276224e+06
## weekdayWednesday:month12:isHolyday NA NA
## weekdayThursday:month12:isHolyday NA NA
## weekdayFriday:month12:isHolyday NA NA
## weekdaySaturday:month12:isHolyday NA NA
## weekdaySunday:month12:isHolyday NA NAconfint(model2, level = 0.99)## Waiting for profiling to be done...## 0.5 % 99.5 %
## (Intercept) -4.199427e+08 1.966327e+08
## I(as.numeric(Date)^3) -1.519475e-04 6.762024e-05
## I(as.numeric(Date)^2) -1.811872e+00 4.042621e+00
## weekdayMonday:month01 4.848389e+04 7.110872e+05
## weekdayTuesday:month01 7.462519e+04 7.278692e+05
## weekdayWednesday:month01 2.742568e+04 6.656965e+05
## weekdayThursday:month01 5.427100e+04 7.014178e+05
## weekdayFriday:month01 1.513458e+03 6.645548e+05
## weekdaySaturday:month01 2.596706e+05 9.225628e+05
## weekdaySunday:month01 2.382291e+05 9.009754e+05
## weekdayMonday:month02 -3.282126e+05 3.509594e+05
## weekdayTuesday:month02 -3.836150e+05 2.954307e+05
## weekdayWednesday:month02 -2.274200e+05 4.515023e+05
## weekdayThursday:month02 -1.600780e+05 5.168431e+05
## weekdayFriday:month02 -2.004392e+05 4.750726e+05
## weekdaySaturday:month02 6.366026e+04 7.391666e+05
## weekdaySunday:month02 -1.964997e+05 4.633253e+05
## weekdayMonday:month03 -8.147390e+05 -1.557636e+05
## weekdayTuesday:month03 -8.900315e+05 -2.310470e+05
## weekdayWednesday:month03 -8.643386e+05 -2.053424e+05
## weekdayThursday:month03 -8.678338e+05 -2.088234e+05
## weekdayFriday:month03 -8.273719e+05 -1.813929e+05
## weekdaySaturday:month03 -1.236509e+05 5.106399e+05
## weekdaySunday:month03 -1.748788e+05 4.714875e+05
## weekdayMonday:month04 -8.666983e+05 -2.307532e+05
## weekdayTuesday:month04 -9.515960e+05 -3.017314e+05
## weekdayWednesday:month04 -9.641018e+05 -3.036650e+05
## weekdayThursday:month04 -9.568298e+05 -2.962992e+05
## weekdayFriday:month04 -9.264045e+05 -2.657768e+05
## weekdaySaturday:month04 -2.563691e+05 4.043588e+05
## weekdaySunday:month04 -2.422421e+05 4.037425e+05
## weekdayMonday:month05 -9.204912e+05 -8.985775e+04
## weekdayTuesday:month05 -7.946921e+05 -3.056833e+04
## weekdayWednesday:month05 -8.838706e+05 -1.630034e+05
## weekdayThursday:month05 -9.580968e+05 -2.372844e+05
## weekdayFriday:month05 -1.043128e+06 -2.790792e+05
## weekdaySaturday:month05 -2.388981e+05 5.918151e+05
## weekdaySunday:month05 -4.369100e+05 3.270314e+05
## weekdayMonday:month06 -1.078141e+06 -3.149259e+05
## weekdayTuesday:month06 -1.069033e+06 -3.058234e+05
## weekdayWednesday:month06 -1.277585e+06 -3.275963e+05
## weekdayThursday:month06 -1.130348e+06 -3.671466e+05
## weekdayFriday:month06 -1.046695e+06 -3.266376e+05
## weekdaySaturday:month06 -4.032125e+05 3.168392e+05
## weekdaySunday:month06 -4.975705e+05 2.656503e+05
## weekdayMonday:month07 -6.723016e+05 4.782716e+04
## weekdayTuesday:month07 -7.605306e+05 -4.039733e+04
## weekdayWednesday:month07 -8.279110e+05 -6.463180e+04
## weekdayThursday:month07 -8.477767e+05 -8.449286e+04
## weekdayFriday:month07 -8.366559e+05 -7.336749e+04
## weekdaySaturday:month07 -5.151250e+05 2.481678e+05
## weekdaySunday:month07 -4.666340e+05 2.534900e+05
## weekdayMonday:month08 -6.262775e+05 1.369557e+05
## weekdayTuesday:month08 -8.230308e+05 -5.980871e+04
## weekdayWednesday:month08 -7.228803e+05 4.033848e+04
## weekdayThursday:month08 -7.597380e+05 -3.966336e+04
## weekdayFriday:month08 -6.030703e+05 1.169930e+05
## weekdaySaturday:month08 -5.071552e+05 2.560981e+05
## weekdaySunday:month08 -4.710193e+05 2.922243e+05
## weekdayMonday:month09 -1.210343e+06 -3.806067e+05
## weekdayTuesday:month09 -1.135176e+06 -3.054676e+05
## weekdayWednesday:month09 -1.214597e+06 -3.849166e+05
## weekdayThursday:month09 -1.107570e+06 -3.450352e+05
## weekdayFriday:month09 -1.079017e+06 -3.165170e+05
## weekdaySaturday:month09 -4.797290e+05 2.396536e+05
## weekdaySunday:month09 -6.721401e+05 9.044778e+04
## weekdayMonday:month10 -9.768458e+05 -2.588195e+05
## weekdayTuesday:month10 -9.998419e+05 -2.386952e+05
## weekdayWednesday:month10 -1.010419e+06 -2.493930e+05
## weekdayThursday:month10 -1.018872e+06 -2.575432e+05
## weekdayFriday:month10 -8.554469e+05 -9.416817e+04
## weekdaySaturday:month10 -2.730117e+05 4.882168e+05
## weekdaySunday:month10 -3.549329e+05 4.062452e+05
## weekdayMonday:month11 -8.818474e+05 -1.221125e+05
## weekdayTuesday:month11 -9.986492e+05 -2.389582e+05
## weekdayWednesday:month11 -8.988180e+05 -1.391703e+05
## weekdayThursday:month11 -8.268543e+05 -1.104616e+05
## weekdayFriday:month11 -8.393032e+05 -1.229572e+05
## weekdaySaturday:month11 -2.409259e+05 5.188991e+05
## weekdaySunday:month11 -2.930968e+05 4.666829e+05
## weekdayMonday:month12 -5.008943e+05 2.146381e+05
## weekdayTuesday:month12 -7.436117e+05 8.264021e+04
## weekdayWednesday:month12 -7.036423e+05 5.529361e+04
## weekdayThursday:month12 -6.389795e+05 1.199561e+05
## weekdayFriday:month12 -5.534149e+05 2.055226e+05
## weekdaySaturday:month12 -3.963650e+05 3.191674e+05
## weekdaySunday:month12 NA NA
## weekdayMonday:isHolyday -3.833608e+05 8.274872e+05
## weekdayTuesday:isHolyday -3.854167e+05 8.169532e+05
## weekdayWednesday:isHolyday 2.037531e+05 1.468695e+06
## weekdayThursday:isHolyday 4.852307e+05 1.687983e+06
## weekdayFriday:isHolyday 4.041585e+05 1.599157e+06
## weekdaySaturday:isHolyday -1.040893e+06 2.656216e+05
## weekdaySunday:isHolyday -8.898355e+05 3.750624e+05
## weekdayMonday:month02:isHolyday -1.311581e+06 4.115756e+05
## weekdayTuesday:month02:isHolyday -1.609214e+06 9.093742e+04
## weekdayWednesday:month02:isHolyday -1.560669e+06 1.922373e+05
## weekdayThursday:month02:isHolyday -2.985351e+06 -1.281371e+06
## weekdayFriday:month02:isHolyday -3.214877e+06 -1.508390e+06
## weekdaySaturday:month02:isHolyday -1.988622e+06 -2.065761e+05
## weekdaySunday:month02:isHolyday NA NA
## weekdayMonday:month03:isHolyday NA NA
## weekdayTuesday:month03:isHolyday NA NA
## weekdayWednesday:month03:isHolyday NA NA
## weekdayThursday:month03:isHolyday NA NA
## weekdayFriday:month03:isHolyday NA NA
## weekdaySaturday:month03:isHolyday NA NA
## weekdaySunday:month03:isHolyday NA NA
## weekdayMonday:month04:isHolyday NA NA
## weekdayTuesday:month04:isHolyday NA NA
## weekdayWednesday:month04:isHolyday NA NA
## weekdayThursday:month04:isHolyday NA NA
## weekdayFriday:month04:isHolyday NA NA
## weekdaySaturday:month04:isHolyday NA NA
## weekdaySunday:month04:isHolyday NA NA
## weekdayMonday:month05:isHolyday -4.736194e+05 1.306043e+06
## weekdayTuesday:month05:isHolyday -1.293164e+05 1.614868e+06
## weekdayWednesday:month05:isHolyday NA NA
## weekdayThursday:month05:isHolyday NA NA
## weekdayFriday:month05:isHolyday NA NA
## weekdaySaturday:month05:isHolyday NA NA
## weekdaySunday:month05:isHolyday NA NA
## weekdayMonday:month06:isHolyday NA NA
## weekdayTuesday:month06:isHolyday NA NA
## weekdayWednesday:month06:isHolyday -5.001075e+05 1.197057e+06
## weekdayThursday:month06:isHolyday NA NA
## weekdayFriday:month06:isHolyday NA NA
## weekdaySaturday:month06:isHolyday NA NA
## weekdaySunday:month06:isHolyday NA NA
## weekdayMonday:month07:isHolyday NA NA
## weekdayTuesday:month07:isHolyday NA NA
## weekdayWednesday:month07:isHolyday NA NA
## weekdayThursday:month07:isHolyday NA NA
## weekdayFriday:month07:isHolyday NA NA
## weekdaySaturday:month07:isHolyday NA NA
## weekdaySunday:month07:isHolyday NA NA
## weekdayMonday:month08:isHolyday NA NA
## weekdayTuesday:month08:isHolyday NA NA
## weekdayWednesday:month08:isHolyday -1.081779e+06 7.070811e+05
## weekdayThursday:month08:isHolyday NA NA
## weekdayFriday:month08:isHolyday NA NA
## weekdaySaturday:month08:isHolyday NA NA
## weekdaySunday:month08:isHolyday NA NA
## weekdayMonday:month09:isHolyday -1.530267e+06 2.510512e+05
## weekdayTuesday:month09:isHolyday -1.192718e+06 5.827077e+05
## weekdayWednesday:month09:isHolyday -1.120630e+06 6.978479e+05
## weekdayThursday:month09:isHolyday NA NA
## weekdayFriday:month09:isHolyday NA NA
## weekdaySaturday:month09:isHolyday NA NA
## weekdaySunday:month09:isHolyday NA NA
## weekdayMonday:month10:isHolyday NA NA
## weekdayTuesday:month10:isHolyday -4.298717e+04 1.701984e+06
## weekdayWednesday:month10:isHolyday NA NA
## weekdayThursday:month10:isHolyday NA NA
## weekdayFriday:month10:isHolyday NA NA
## weekdaySaturday:month10:isHolyday NA NA
## weekdaySunday:month10:isHolyday NA NA
## weekdayMonday:month11:isHolyday NA NA
## weekdayTuesday:month11:isHolyday NA NA
## weekdayWednesday:month11:isHolyday NA NA
## weekdayThursday:month11:isHolyday NA NA
## weekdayFriday:month11:isHolyday NA NA
## weekdaySaturday:month11:isHolyday NA NA
## weekdaySunday:month11:isHolyday NA NA
## weekdayMonday:month12:isHolyday NA NA
## weekdayTuesday:month12:isHolyday -2.901967e+05 1.486438e+06
## weekdayWednesday:month12:isHolyday NA NA
## weekdayThursday:month12:isHolyday NA NA
## weekdayFriday:month12:isHolyday NA NA
## weekdaySaturday:month12:isHolyday NA NA
## weekdaySunday:month12:isHolyday NA NAconfint(model3, level = 0.99)## Waiting for profiling to be done...## 0.5 % 99.5 %
## (Intercept) -7.739625e-06 3.409380e-05
## I(as.numeric(Date)^3) -2.783310e-18 1.212907e-17
## I(as.numeric(Date)^2) -3.227123e-13 7.477420e-14
## weekdayMonday:month01 -4.514521e-08 -1.900320e-09
## weekdayTuesday:month01 -4.606463e-08 -3.415087e-09
## weekdayWednesday:month01 -4.272606e-08 -7.944815e-10
## weekdayThursday:month01 -4.465730e-08 -2.287857e-09
## weekdayFriday:month01 -4.268991e-08 7.322840e-10
## weekdaySaturday:month01 -5.569067e-08 -1.306906e-08
## weekdaySunday:month01 -5.465876e-08 -1.198406e-08
## weekdayMonday:month02 -2.459174e-08 2.110411e-08
## weekdayTuesday:month02 -2.108656e-08 2.486077e-08
## weekdayWednesday:month02 -3.068629e-08 1.455030e-08
## weekdayThursday:month02 -3.467662e-08 1.024752e-08
## weekdayFriday:month02 -3.215288e-08 1.283921e-08
## weekdaySaturday:month02 -4.674538e-08 -2.789223e-09
## weekdaySunday:month02 -3.139404e-08 1.256937e-08
## weekdayMonday:month03 1.171152e-08 5.861035e-08
## weekdayTuesday:month03 1.804108e-08 6.540106e-08
## weekdayWednesday:month03 1.585530e-08 6.305830e-08
## weekdayThursday:month03 1.615543e-08 6.338265e-08
## weekdayFriday:month03 1.375380e-08 5.960855e-08
## weekdaySaturday:month03 -3.420783e-08 7.962398e-09
## weekdaySunday:month03 -3.207969e-08 1.104244e-08
## weekdayMonday:month04 1.814930e-08 6.344358e-08
## weekdayTuesday:month04 2.453929e-08 7.150050e-08
## weekdayWednesday:month04 2.462831e-08 7.260752e-08
## weekdayThursday:month04 2.397222e-08 7.191020e-08
## weekdayFriday:month04 2.125915e-08 6.900443e-08
## weekdaySaturday:month04 -2.804521e-08 1.626241e-08
## weekdaySunday:month04 -2.786231e-08 1.540735e-08
## weekdayMonday:month05 7.045353e-09 6.775721e-08
## weekdayTuesday:month05 2.383206e-09 5.689443e-08
## weekdayWednesday:month05 1.255543e-08 6.461342e-08
## weekdayThursday:month05 1.862528e-08 7.131906e-08
## weekdayFriday:month05 2.224812e-08 7.921520e-08
## weekdaySaturday:month05 -3.716409e-08 1.705280e-08
## weekdaySunday:month05 -2.178910e-08 2.982547e-08
## weekdayMonday:month06 2.564568e-08 8.301687e-08
## weekdayTuesday:month06 2.485757e-08 8.213006e-08
## weekdayWednesday:month06 2.761804e-08 1.028952e-07
## weekdayThursday:month06 3.033214e-08 8.829934e-08
## weekdayFriday:month06 2.654273e-08 8.006059e-08
## weekdaySaturday:month06 -2.092596e-08 2.771424e-08
## weekdaySunday:month06 -1.774857e-08 3.431929e-08
## weekdayMonday:month07 -2.918269e-09 4.756178e-08
## weekdayTuesday:month07 3.534576e-09 5.467692e-08
## weekdayWednesday:month07 5.404654e-09 6.029793e-08
## weekdayThursday:month07 6.930947e-09 6.201325e-08
## weekdayFriday:month07 6.082388e-09 6.106221e-08
## weekdaySaturday:month07 -1.648375e-08 3.578027e-08
## weekdaySunday:month07 -1.682117e-08 3.226152e-08
## weekdayMonday:month08 -9.043453e-09 4.414906e-08
## weekdayTuesday:month08 5.182114e-09 6.010020e-08
## weekdayWednesday:month08 -2.256168e-09 5.175079e-08
## weekdayThursday:month08 3.597823e-09 5.478368e-08
## weekdayFriday:month08 -7.683347e-09 4.235205e-08
## weekdaySaturday:month08 -1.695459e-08 3.529508e-08
## weekdaySunday:month08 -1.925812e-08 3.272138e-08
## weekdayMonday:month09 3.253657e-08 9.733042e-08
## weekdayTuesday:month09 2.569296e-08 8.941911e-08
## weekdayWednesday:month09 3.295237e-08 9.781473e-08
## weekdayThursday:month09 2.901698e-08 8.693816e-08
## weekdayFriday:month09 2.645911e-08 8.405613e-08
## weekdaySaturday:month09 -1.592548e-08 3.328601e-08
## weekdaySunday:month09 -5.812939e-09 4.778610e-08
## weekdayMonday:month10 2.136373e-08 7.440834e-08
## weekdayTuesday:month10 1.976253e-08 7.652399e-08
## weekdayWednesday:month10 2.068754e-08 7.756741e-08
## weekdayThursday:month10 2.137877e-08 7.834338e-08
## weekdayFriday:month10 7.906901e-09 6.318761e-08
## weekdaySaturday:month10 -3.138178e-08 1.919243e-08
## weekdaySunday:month10 -2.654904e-08 2.458548e-08
## weekdayMonday:month11 1.008272e-08 6.565813e-08
## weekdayTuesday:month11 1.981725e-08 7.661665e-08
## weekdayWednesday:month11 1.145886e-08 6.720976e-08
## weekdayThursday:month11 9.068201e-09 6.080682e-08
## weekdayFriday:month11 1.006462e-08 6.190891e-08
## weekdaySaturday:month11 -3.344682e-08 1.689068e-08
## weekdaySunday:month11 -3.042628e-08 2.026199e-08
## weekdayMonday:month12 -1.494330e-08 3.440097e-08
## weekdayTuesday:month12 -5.606823e-09 5.355211e-08
## weekdayWednesday:month12 -3.708578e-09 5.026848e-08
## weekdayThursday:month12 -8.384990e-09 4.502763e-08
## weekdayFriday:month12 -1.432852e-08 3.837166e-08
## weekdaySaturday:month12 -2.175835e-08 2.689232e-08
## weekdaySunday:month12 NA NA
## weekdayMonday:isHolyday -4.565266e-08 2.507963e-08
## weekdayTuesday:isHolyday -4.466077e-08 2.618509e-08
## weekdayWednesday:isHolyday -1.023150e-07 -1.710748e-08
## weekdayThursday:isHolyday -1.063742e-07 -3.291248e-08
## weekdayFriday:isHolyday -1.035362e-07 -2.868522e-08
## weekdaySaturday:isHolyday -1.882764e-08 7.096829e-08
## weekdaySunday:isHolyday -2.615505e-08 7.096688e-08
## weekdayMonday:month02:isHolyday -2.801644e-08 8.464698e-08
## weekdayTuesday:month02:isHolyday -4.644580e-09 1.141200e-07
## weekdayWednesday:month02:isHolyday -6.050017e-09 1.093755e-07
## weekdayThursday:month02:isHolyday 9.133726e-08 2.156658e-07
## weekdayFriday:month02:isHolyday 1.215976e-07 2.570493e-07
## weekdaySaturday:month02:isHolyday 2.442878e-08 1.642049e-07
## weekdaySunday:month02:isHolyday NA NA
## weekdayMonday:month03:isHolyday NA NA
## weekdayTuesday:month03:isHolyday NA NA
## weekdayWednesday:month03:isHolyday NA NA
## weekdayThursday:month03:isHolyday NA NA
## weekdayFriday:month03:isHolyday NA NA
## weekdaySaturday:month03:isHolyday NA NA
## weekdaySunday:month03:isHolyday NA NA
## weekdayMonday:month04:isHolyday NA NA
## weekdayTuesday:month04:isHolyday NA NA
## weekdayWednesday:month04:isHolyday NA NA
## weekdayThursday:month04:isHolyday NA NA
## weekdayFriday:month04:isHolyday NA NA
## weekdaySaturday:month04:isHolyday NA NA
## weekdaySunday:month04:isHolyday NA NA
## weekdayMonday:month05:isHolyday -8.966103e-08 2.340397e-08
## weekdayTuesday:month05:isHolyday -1.016376e-07 4.050339e-09
## weekdayWednesday:month05:isHolyday NA NA
## weekdayThursday:month05:isHolyday NA NA
## weekdayFriday:month05:isHolyday NA NA
## weekdaySaturday:month05:isHolyday NA NA
## weekdaySunday:month05:isHolyday NA NA
## weekdayMonday:month06:isHolyday NA NA
## weekdayTuesday:month06:isHolyday NA NA
## weekdayWednesday:month06:isHolyday -8.516214e-08 3.345773e-08
## weekdayThursday:month06:isHolyday NA NA
## weekdayFriday:month06:isHolyday NA NA
## weekdaySaturday:month06:isHolyday NA NA
## weekdaySunday:month06:isHolyday NA NA
## weekdayMonday:month07:isHolyday NA NA
## weekdayTuesday:month07:isHolyday NA NA
## weekdayWednesday:month07:isHolyday NA NA
## weekdayThursday:month07:isHolyday NA NA
## weekdayFriday:month07:isHolyday NA NA
## weekdaySaturday:month07:isHolyday NA NA
## weekdaySunday:month07:isHolyday NA NA
## weekdayMonday:month08:isHolyday NA NA
## weekdayTuesday:month08:isHolyday NA NA
## weekdayWednesday:month08:isHolyday -4.012300e-08 7.776659e-08
## weekdayThursday:month08:isHolyday NA NA
## weekdayFriday:month08:isHolyday NA NA
## weekdaySaturday:month08:isHolyday NA NA
## weekdaySunday:month08:isHolyday NA NA
## weekdayMonday:month09:isHolyday -9.363671e-09 1.321042e-07
## weekdayTuesday:month09:isHolyday -4.491088e-08 8.550365e-08
## weekdayWednesday:month09:isHolyday -5.589533e-08 7.284226e-08
## weekdayThursday:month09:isHolyday NA NA
## weekdayFriday:month09:isHolyday NA NA
## weekdaySaturday:month09:isHolyday NA NA
## weekdaySunday:month09:isHolyday NA NA
## weekdayMonday:month10:isHolyday NA NA
## weekdayTuesday:month10:isHolyday -1.156194e-07 -7.157231e-09
## weekdayWednesday:month10:isHolyday NA NA
## weekdayThursday:month10:isHolyday NA NA
## weekdayFriday:month10:isHolyday NA NA
## weekdaySaturday:month10:isHolyday NA NA
## weekdaySunday:month10:isHolyday NA NA
## weekdayMonday:month11:isHolyday NA NA
## weekdayTuesday:month11:isHolyday NA NA
## weekdayWednesday:month11:isHolyday NA NA
## weekdayThursday:month11:isHolyday NA NA
## weekdayFriday:month11:isHolyday NA NA
## weekdaySaturday:month11:isHolyday NA NA
## weekdaySunday:month11:isHolyday NA NA
## weekdayMonday:month12:isHolyday NA NA
## weekdayTuesday:month12:isHolyday -9.622219e-08 1.349108e-08
## weekdayWednesday:month12:isHolyday NA NA
## weekdayThursday:month12:isHolyday NA NA
## weekdayFriday:month12:isHolyday NA NA
## weekdaySaturday:month12:isHolyday NA NA
## weekdaySunday:month12:isHolyday NA NAconfint(model4, level = 0.99)## Waiting for profiling to be done...## 0.5 % 99.5 %
## (Intercept) -2.987200e-12 1.893455e-11
## I(as.numeric(Date)^3) -1.054917e-24 6.763298e-24
## I(as.numeric(Date)^2) -1.799500e-19 2.840999e-20
## weekdayMonday:month01 -2.288675e-14 -6.171788e-16
## weekdayTuesday:month01 -2.331982e-14 -1.352046e-15
## weekdayWednesday:month01 -2.177027e-14 -1.144361e-16
## weekdayThursday:month01 -2.266942e-14 -8.190397e-16
## weekdayFriday:month01 -2.176036e-14 6.315071e-16
## weekdaySaturday:month01 -2.759229e-14 -5.768391e-15
## weekdaySunday:month01 -2.714148e-14 -5.278767e-15
## weekdayMonday:month02 -1.306333e-14 1.079520e-14
## weekdayTuesday:month02 -1.130362e-14 1.275463e-14
## weekdayWednesday:month02 -1.606035e-14 7.447242e-15
## weekdayThursday:month02 -1.803600e-14 5.266633e-15
## weekdayFriday:month02 -1.679552e-14 6.581699e-15
## weekdaySaturday:month02 -2.364873e-14 -1.041822e-15
## weekdaySunday:month02 -1.640758e-14 6.438667e-15
## weekdayMonday:month03 6.413790e-15 3.167925e-14
## weekdayTuesday:month03 1.008592e-14 3.575575e-14
## weekdayWednesday:month03 8.810472e-15 3.434162e-14
## weekdayThursday:month03 8.986722e-15 3.453888e-14
## weekdayFriday:month03 7.569190e-15 3.224404e-14
## weekdaySaturday:month03 -1.776649e-14 4.122569e-15
## weekdaySunday:month03 -1.678330e-14 5.635231e-15
## weekdayMonday:month04 1.019137e-14 3.462755e-14
## weekdayTuesday:month04 1.403148e-14 3.959533e-14
## weekdayWednesday:month04 1.405234e-14 4.024223e-14
## weekdayThursday:month04 1.366041e-14 3.981098e-14
## weekdayFriday:month04 1.204182e-14 3.801721e-14
## weekdaySaturday:month04 -1.479046e-14 8.294448e-15
## weekdaySunday:month04 -1.466212e-14 7.870864e-15
## weekdayMonday:month05 3.841020e-15 3.695707e-14
## weekdayTuesday:month05 1.239267e-15 3.059379e-14
## weekdayWednesday:month05 6.885376e-15 3.512653e-14
## weekdayThursday:month05 1.035878e-14 3.915001e-14
## weekdayFriday:month05 1.249188e-14 4.395259e-14
## weekdaySaturday:month05 -1.873333e-14 9.173594e-15
## weekdaySunday:month05 -1.133620e-14 1.567414e-14
## weekdayMonday:month06 1.454917e-14 4.637991e-14
## weekdayTuesday:month06 1.408322e-14 4.582690e-14
## weekdayWednesday:month06 1.594411e-14 5.882432e-14
## weekdayThursday:month06 1.736175e-14 4.972135e-14
## weekdayFriday:month06 1.504902e-14 4.457652e-14
## weekdaySaturday:month06 -1.084611e-14 1.460408e-14
## weekdaySunday:month06 -9.268216e-15 1.811120e-14
## weekdayMonday:month07 -1.532118e-15 2.539338e-14
## weekdayTuesday:month07 1.958496e-15 2.943421e-14
## weekdayWednesday:month07 2.993026e-15 3.268143e-14
## weekdayThursday:month07 3.839490e-15 3.368741e-14
## weekdayFriday:month07 3.370111e-15 3.313130e-14
## weekdaySaturday:month07 -8.594343e-15 1.893886e-14
## weekdaySunday:month07 -8.760217e-15 1.703637e-14
## weekdayMonday:month08 -4.750498e-15 2.353049e-14
## weekdayTuesday:month08 2.902356e-15 3.261306e-14
## weekdayWednesday:month08 -1.153213e-15 2.779619e-14
## weekdayThursday:month08 2.019529e-15 2.953313e-14
## weekdayFriday:month08 -4.036308e-15 2.252643e-14
## weekdaySaturday:month08 -8.824368e-15 1.869811e-14
## weekdaySunday:month08 -9.986258e-15 1.732230e-14
## weekdayMonday:month09 1.892608e-14 5.562327e-14
## weekdayTuesday:month09 1.477723e-14 5.052351e-14
## weekdayWednesday:month09 1.918506e-14 5.594525e-14
## weekdayThursday:month09 1.671767e-14 4.905279e-14
## weekdayFriday:month09 1.518101e-14 4.722876e-14
## weekdaySaturday:month09 -8.307221e-15 1.760544e-14
## weekdaySunday:month09 -3.048233e-15 2.557840e-14
## weekdayMonday:month10 1.215597e-14 4.131663e-14
## weekdayTuesday:month10 1.124761e-14 4.259326e-14
## weekdayWednesday:month10 1.179358e-14 4.324606e-14
## weekdayThursday:month10 1.219505e-14 4.371439e-14
## weekdayFriday:month10 4.443034e-15 3.450614e-14
## weekdaySaturday:month10 -1.601329e-14 1.023732e-14
## weekdaySunday:month10 -1.367121e-14 1.301213e-14
## weekdayMonday:month11 5.672803e-15 3.602383e-14
## weekdayTuesday:month11 1.130404e-14 4.271889e-14
## weekdayWednesday:month11 6.456893e-15 3.696090e-14
## weekdayThursday:month11 5.080580e-15 3.314734e-14
## weekdayFriday:month11 5.644447e-15 3.380289e-14
## weekdaySaturday:month11 -1.708443e-14 9.008004e-15
## weekdaySunday:month11 -1.563497e-14 1.072696e-14
## weekdayMonday:month12 -7.966194e-15 1.812687e-14
## weekdayTuesday:month12 -3.015437e-15 2.889053e-14
## weekdayWednesday:month12 -2.005495e-15 2.701134e-14
## weekdayThursday:month12 -4.520161e-15 2.402930e-14
## weekdayFriday:month12 -7.651338e-15 2.031624e-14
## weekdaySaturday:month12 -1.144411e-14 1.409498e-14
## weekdaySunday:month12 NA NA
## weekdayMonday:isHolyday -2.150176e-14 1.291901e-14
## weekdayTuesday:isHolyday -2.087589e-14 1.373926e-14
## weekdayWednesday:isHolyday -5.466689e-14 -9.787137e-15
## weekdayThursday:isHolyday -5.414451e-14 -1.725126e-14
## weekdayFriday:isHolyday -5.318918e-14 -1.526751e-14
## weekdaySaturday:isHolyday -1.010426e-14 3.733962e-14
## weekdaySunday:isHolyday -1.395287e-14 4.022964e-14
## weekdayMonday:month02:isHolyday -1.474357e-14 4.374884e-14
## weekdayTuesday:month02:isHolyday -2.115780e-15 6.213530e-14
## weekdayWednesday:month02:isHolyday -1.436635e-15 5.851031e-14
## weekdayThursday:month02:isHolyday 4.911411e-14 1.182160e-13
## weekdayFriday:month02:isHolyday 7.011860e-14 1.494469e-13
## weekdaySaturday:month02:isHolyday 1.631876e-14 9.648223e-14
## weekdaySunday:month02:isHolyday NA NA
## weekdayMonday:month03:isHolyday NA NA
## weekdayTuesday:month03:isHolyday NA NA
## weekdayWednesday:month03:isHolyday NA NA
## weekdayThursday:month03:isHolyday NA NA
## weekdayFriday:month03:isHolyday NA NA
## weekdaySaturday:month03:isHolyday NA NA
## weekdaySunday:month03:isHolyday NA NA
## weekdayMonday:month04:isHolyday NA NA
## weekdayTuesday:month04:isHolyday NA NA
## weekdayWednesday:month04:isHolyday NA NA
## weekdayThursday:month04:isHolyday NA NA
## weekdayFriday:month04:isHolyday NA NA
## weekdaySaturday:month04:isHolyday NA NA
## weekdaySunday:month04:isHolyday NA NA
## weekdayMonday:month05:isHolyday -4.730555e-14 1.050214e-14
## weekdayTuesday:month05:isHolyday -5.149029e-14 1.211132e-15
## weekdayWednesday:month05:isHolyday NA NA
## weekdayThursday:month05:isHolyday NA NA
## weekdayFriday:month05:isHolyday NA NA
## weekdaySaturday:month05:isHolyday NA NA
## weekdaySunday:month05:isHolyday NA NA
## weekdayMonday:month06:isHolyday NA NA
## weekdayTuesday:month06:isHolyday NA NA
## weekdayWednesday:month06:isHolyday -4.643820e-14 1.756649e-14
## weekdayThursday:month06:isHolyday NA NA
## weekdayFriday:month06:isHolyday NA NA
## weekdaySaturday:month06:isHolyday NA NA
## weekdaySunday:month06:isHolyday NA NA
## weekdayMonday:month07:isHolyday NA NA
## weekdayTuesday:month07:isHolyday NA NA
## weekdayWednesday:month07:isHolyday NA NA
## weekdayThursday:month07:isHolyday NA NA
## weekdayFriday:month07:isHolyday NA NA
## weekdaySaturday:month07:isHolyday NA NA
## weekdaySunday:month07:isHolyday NA NA
## weekdayMonday:month08:isHolyday NA NA
## weekdayTuesday:month08:isHolyday NA NA
## weekdayWednesday:month08:isHolyday -1.908904e-14 4.224377e-14
## weekdayThursday:month08:isHolyday NA NA
## weekdayFriday:month08:isHolyday NA NA
## weekdaySaturday:month08:isHolyday NA NA
## weekdaySunday:month08:isHolyday NA NA
## weekdayMonday:month09:isHolyday -1.813809e-15 8.131655e-14
## weekdayTuesday:month09:isHolyday -2.521855e-14 4.738789e-14
## weekdayWednesday:month09:isHolyday -3.214018e-14 3.736768e-14
## weekdayThursday:month09:isHolyday NA NA
## weekdayFriday:month09:isHolyday NA NA
## weekdaySaturday:month09:isHolyday NA NA
## weekdaySunday:month09:isHolyday NA NA
## weekdayMonday:month10:isHolyday NA NA
## weekdayTuesday:month10:isHolyday -6.097182e-14 -6.021432e-15
## weekdayWednesday:month10:isHolyday NA NA
## weekdayThursday:month10:isHolyday NA NA
## weekdayFriday:month10:isHolyday NA NA
## weekdaySaturday:month10:isHolyday NA NA
## weekdaySunday:month10:isHolyday NA NA
## weekdayMonday:month11:isHolyday NA NA
## weekdayTuesday:month11:isHolyday NA NA
## weekdayWednesday:month11:isHolyday NA NA
## weekdayThursday:month11:isHolyday NA NA
## weekdayFriday:month11:isHolyday NA NA
## weekdaySaturday:month11:isHolyday NA NA
## weekdaySunday:month11:isHolyday NA NA
## weekdayMonday:month12:isHolyday NA NA
## weekdayTuesday:month12:isHolyday -4.936094e-14 5.863299e-15
## weekdayWednesday:month12:isHolyday NA NA
## weekdayThursday:month12:isHolyday NA NA
## weekdayFriday:month12:isHolyday NA NA
## weekdaySaturday:month12:isHolyday NA NA
## weekdaySunday:month12:isHolyday NA NAconfint(model1_2, level = 0.99)## 0.5 % 99.5 %
## (Intercept) -7.348625e+08 -2.802958e+08
## I(as.numeric(Date)^3) -2.642670e-04 -1.028087e-04
## I(as.numeric(Date)^2) 2.727230e+00 7.035960e+00
## weekdayMonday:month01 1.151060e+05 7.883310e+05
## weekdayTuesday:month01 1.489019e+05 8.115215e+05
## weekdayWednesday:month01 9.489390e+04 7.429890e+05
## weekdayThursday:month01 1.193388e+05 7.768555e+05
## weekdayFriday:month01 6.880570e+04 7.424000e+05
## weekdaySaturday:month01 3.267314e+05 1.000201e+06
## weekdaySunday:month01 3.050666e+05 9.784126e+05
## weekdayMonday:month02 -2.625198e+05 4.278485e+05
## weekdayTuesday:month02 -3.181015e+05 3.721579e+05
## weekdayWednesday:month02 -1.620778e+05 5.280746e+05
## weekdayThursday:month02 -9.067253e+04 5.969186e+05
## weekdayFriday:month02 -1.366849e+05 5.501356e+05
## weekdaySaturday:month02 1.276092e+05 8.144012e+05
## weekdaySunday:month02 -1.326815e+05 5.380161e+05
## weekdayMonday:month03 -7.474567e+05 -7.805909e+04
## weekdayTuesday:month03 -8.225095e+05 -1.531325e+05
## weekdayWednesday:month03 -7.965687e+05 -1.272108e+05
## weekdayThursday:month03 -7.998080e+05 -1.304676e+05
## weekdayFriday:month03 -7.616666e+05 -1.054344e+05
## weekdaySaturday:month03 -5.547942e+04 5.884133e+05
## weekdaySunday:month03 -9.979415e+04 5.556015e+05
## weekdayMonday:month04 -7.874028e+05 -1.433464e+05
## weekdayTuesday:month04 -8.605123e+05 -2.039118e+05
## weekdayWednesday:month04 -8.860925e+05 -2.166123e+05
## weekdayThursday:month04 -8.783358e+05 -2.088272e+05
## weekdayFriday:month04 -8.474176e+05 -1.778787e+05
## weekdaySaturday:month04 -1.768809e+05 4.926900e+05
## weekdaySunday:month04 -1.702070e+05 4.852115e+05
## weekdayMonday:month05 -9.395202e+05 -2.274662e+05
## weekdayTuesday:month05 -9.542739e+05 -2.822182e+05
## weekdayWednesday:month05 -9.346546e+05 -2.887780e+05
## weekdayThursday:month05 -1.036516e+06 -3.905466e+05
## weekdayFriday:month05 -1.072419e+06 -4.126002e+05
## weekdaySaturday:month05 -3.846288e+05 3.062235e+05
## weekdaySunday:month05 -4.108700e+05 2.974332e+05
## weekdayMonday:month06 -1.126576e+06 -3.471110e+05
## weekdayTuesday:month06 -1.117927e+06 -3.384969e+05
## weekdayWednesday:month06 -1.332361e+06 -3.617885e+05
## weekdayThursday:month06 -1.180136e+06 -4.007742e+05
## weekdayFriday:month06 -1.094691e+06 -3.594621e+05
## weekdaySaturday:month06 -4.516628e+05 2.835293e+05
## weekdaySunday:month06 -5.455408e+05 2.339611e+05
## weekdayMonday:month07 -7.309493e+05 3.509269e+03
## weekdayTuesday:month07 -8.194055e+05 -8.496249e+04
## weekdayWednesday:month07 -8.868958e+05 -1.081485e+05
## weekdayThursday:month07 -9.070002e+05 -1.282680e+05
## weekdayFriday:month07 -8.961107e+05 -1.173932e+05
## weekdaySaturday:month07 -5.748039e+05 2.038992e+05
## weekdaySunday:month07 -5.250473e+05 2.094272e+05
## weekdayMonday:month08 -6.893213e+05 8.903185e+04
## weekdayTuesday:month08 -8.860750e+05 -1.077330e+05
## weekdayWednesday:month08 -7.856891e+05 -7.329767e+03
## weekdayThursday:month08 -8.220770e+05 -8.800931e+04
## weekdayFriday:month08 -6.654132e+05 6.864269e+04
## weekdaySaturday:month08 -5.701767e+05 2.081985e+05
## weekdaySunday:month08 -5.340555e+05 2.443087e+05
## weekdayMonday:month09 -1.272281e+06 -4.255302e+05
## weekdayTuesday:month09 -1.196937e+06 -3.501998e+05
## weekdayWednesday:month09 -1.276173e+06 -4.294497e+05
## weekdayThursday:month09 -1.167263e+06 -3.893211e+05
## weekdayFriday:month09 -1.138485e+06 -3.605601e+05
## weekdaySaturday:month09 -5.390492e+05 1.946008e+05
## weekdaySunday:month09 -7.321438e+05 4.582473e+04
## weekdayMonday:month10 -1.026588e+06 -2.935358e+05
## weekdayTuesday:month10 -1.048876e+06 -2.715418e+05
## weekdayWednesday:month10 -1.058351e+06 -2.810676e+05
## weekdayThursday:month10 -1.069504e+06 -2.920920e+05
## weekdayFriday:month10 -9.056477e+05 -1.282571e+05
## weekdaySaturday:month10 -3.227739e+05 4.545954e+05
## weekdaySunday:month10 -4.042490e+05 3.730990e+05
## weekdayMonday:month11 -9.149183e+05 -1.381796e+05
## weekdayTuesday:month11 -1.031044e+06 -2.543241e+05
## weekdayWednesday:month11 -9.305292e+05 -1.538275e+05
## weekdayThursday:month11 -8.596096e+05 -1.272476e+05
## weekdayFriday:month11 -8.713871e+05 -1.390450e+05
## weekdaySaturday:month11 -2.753251e+05 5.014517e+05
## weekdaySunday:month11 -3.268357e+05 4.499219e+05
## weekdayMonday:month12 -5.082049e+05 2.237909e+05
## weekdayTuesday:month12 -7.578495e+05 8.739776e+04
## weekdayWednesday:month12 -7.129716e+05 6.342691e+04
## weekdayThursday:month12 -6.473915e+05 1.290068e+05
## weekdayFriday:month12 -5.609016e+05 2.154975e+05
## weekdaySaturday:month12 -4.055101e+05 3.264857e+05
## weekdaySunday:month12 NA NA
## weekdayMonday:isHolyday -3.296636e+05 9.068931e+05
## weekdayTuesday:isHolyday -4.619423e+05 7.661083e+05
## weekdayWednesday:isHolyday 1.816168e+05 1.475637e+06
## weekdayThursday:isHolyday 4.495687e+05 1.679739e+06
## weekdayFriday:isHolyday 4.096274e+05 1.631925e+06
## weekdaySaturday:isHolyday -8.087916e+05 4.346494e+05
## weekdaySunday:isHolyday -9.026416e+05 3.913597e+05
## weekdayMonday:month02:isHolyday -1.413037e+06 3.474427e+05
## weekdayTuesday:month02:isHolyday -1.579436e+06 1.589942e+05
## weekdayWednesday:month02:isHolyday -1.586526e+06 2.067012e+05
## weekdayThursday:month02:isHolyday -2.988074e+06 -1.244984e+06
## weekdayFriday:month02:isHolyday -3.254691e+06 -1.509113e+06
## weekdaySaturday:month02:isHolyday -2.178665e+06 -4.230358e+05
## weekdaySunday:month02:isHolyday NA NA
## weekdayMonday:month03:isHolyday NA NA
## weekdayTuesday:month03:isHolyday NA NA
## weekdayWednesday:month03:isHolyday NA NA
## weekdayThursday:month03:isHolyday NA NA
## weekdayFriday:month03:isHolyday NA NA
## weekdaySaturday:month03:isHolyday NA NA
## weekdaySunday:month03:isHolyday NA NA
## weekdayMonday:month04:isHolyday NA NA
## weekdayTuesday:month04:isHolyday NA NA
## weekdayWednesday:month04:isHolyday NA NA
## weekdayThursday:month04:isHolyday NA NA
## weekdayFriday:month04:isHolyday NA NA
## weekdaySaturday:month04:isHolyday NA NA
## weekdaySunday:month04:isHolyday NA NA
## weekdayMonday:month05:isHolyday -3.971751e+05 1.159502e+06
## weekdayTuesday:month05:isHolyday 1.167514e+05 1.852869e+06
## weekdayWednesday:month05:isHolyday NA NA
## weekdayThursday:month05:isHolyday NA NA
## weekdayFriday:month05:isHolyday NA NA
## weekdaySaturday:month05:isHolyday NA NA
## weekdaySunday:month05:isHolyday -8.286980e+05 7.792834e+05
## weekdayMonday:month06:isHolyday NA NA
## weekdayTuesday:month06:isHolyday NA NA
## weekdayWednesday:month06:isHolyday -5.055325e+05 1.230613e+06
## weekdayThursday:month06:isHolyday NA NA
## weekdayFriday:month06:isHolyday NA NA
## weekdaySaturday:month06:isHolyday NA NA
## weekdaySunday:month06:isHolyday NA NA
## weekdayMonday:month07:isHolyday NA NA
## weekdayTuesday:month07:isHolyday NA NA
## weekdayWednesday:month07:isHolyday NA NA
## weekdayThursday:month07:isHolyday NA NA
## weekdayFriday:month07:isHolyday NA NA
## weekdaySaturday:month07:isHolyday NA NA
## weekdaySunday:month07:isHolyday NA NA
## weekdayMonday:month08:isHolyday NA NA
## weekdayTuesday:month08:isHolyday NA NA
## weekdayWednesday:month08:isHolyday -1.095214e+06 7.347889e+05
## weekdayThursday:month08:isHolyday NA NA
## weekdayFriday:month08:isHolyday NA NA
## weekdaySaturday:month08:isHolyday NA NA
## weekdaySunday:month08:isHolyday NA NA
## weekdayMonday:month09:isHolyday -1.613503e+06 2.074763e+05
## weekdayTuesday:month09:isHolyday -1.145463e+06 6.693271e+05
## weekdayWednesday:month09:isHolyday -1.130576e+06 7.297037e+05
## weekdayThursday:month09:isHolyday NA NA
## weekdayFriday:month09:isHolyday NA NA
## weekdaySaturday:month09:isHolyday NA NA
## weekdaySunday:month09:isHolyday NA NA
## weekdayMonday:month10:isHolyday NA NA
## weekdayTuesday:month10:isHolyday -2.978267e+03 1.780952e+06
## weekdayWednesday:month10:isHolyday NA NA
## weekdayThursday:month10:isHolyday NA NA
## weekdayFriday:month10:isHolyday NA NA
## weekdaySaturday:month10:isHolyday NA NA
## weekdaySunday:month10:isHolyday NA NA
## weekdayMonday:month11:isHolyday NA NA
## weekdayTuesday:month11:isHolyday NA NA
## weekdayWednesday:month11:isHolyday NA NA
## weekdayThursday:month11:isHolyday NA NA
## weekdayFriday:month11:isHolyday NA NA
## weekdaySaturday:month11:isHolyday NA NA
## weekdaySunday:month11:isHolyday NA NA
## weekdayMonday:month12:isHolyday NA NA
## weekdayTuesday:month12:isHolyday -2.330462e+05 1.582519e+06
## weekdayWednesday:month12:isHolyday NA NA
## weekdayThursday:month12:isHolyday NA NA
## weekdayFriday:month12:isHolyday NA NA
## weekdaySaturday:month12:isHolyday NA NA
## weekdaySunday:month12:isHolyday NA NAconfint(model2_2, level = 0.99)## Waiting for profiling to be done...## 0.5 % 99.5 %
## (Intercept) -7.337969e+08 -2.813614e+08
## I(as.numeric(Date)^3) -2.638885e-04 -1.031872e-04
## I(as.numeric(Date)^2) 2.737330e+00 7.025860e+00
## weekdayMonday:month01 1.166842e+05 7.867528e+05
## weekdayTuesday:month01 1.504552e+05 8.099682e+05
## weekdayWednesday:month01 9.641317e+04 7.414697e+05
## weekdayThursday:month01 1.208802e+05 7.753142e+05
## weekdayFriday:month01 7.038475e+04 7.408210e+05
## weekdaySaturday:month01 3.283102e+05 9.986218e+05
## weekdaySunday:month01 3.066451e+05 9.768341e+05
## weekdayMonday:month02 -2.609014e+05 4.262301e+05
## weekdayTuesday:month02 -3.164834e+05 3.705398e+05
## weekdayWednesday:month02 -1.604600e+05 5.264567e+05
## weekdayThursday:month02 -8.906067e+04 5.953067e+05
## weekdayFriday:month02 -1.350749e+05 5.485255e+05
## weekdaySaturday:month02 1.292192e+05 8.127912e+05
## weekdaySunday:month02 -1.311092e+05 5.364438e+05
## weekdayMonday:month03 -7.458875e+05 -7.962831e+04
## weekdayTuesday:month03 -8.209403e+05 -1.547017e+05
## weekdayWednesday:month03 -7.949996e+05 -1.287799e+05
## weekdayThursday:month03 -7.982390e+05 -1.320367e+05
## weekdayFriday:month03 -7.601283e+05 -1.069727e+05
## weekdaySaturday:month03 -5.397000e+04 5.869039e+05
## weekdaySunday:month03 -9.825776e+04 5.540651e+05
## weekdayMonday:month04 -7.858930e+05 -1.448562e+05
## weekdayTuesday:month04 -8.589730e+05 -2.054510e+05
## weekdayWednesday:month04 -8.845231e+05 -2.181817e+05
## weekdayThursday:month04 -8.767664e+05 -2.103967e+05
## weekdayFriday:month04 -8.458480e+05 -1.794482e+05
## weekdaySaturday:month04 -1.753113e+05 4.911203e+05
## weekdaySunday:month04 -1.686705e+05 4.836750e+05
## weekdayMonday:month05 -9.378510e+05 -2.291354e+05
## weekdayTuesday:month05 -9.526985e+05 -2.837936e+05
## weekdayWednesday:month05 -9.331405e+05 -2.902920e+05
## weekdayThursday:month05 -1.035002e+06 -3.920608e+05
## weekdayFriday:month05 -1.070872e+06 -4.141470e+05
## weekdaySaturday:month05 -3.830093e+05 3.046040e+05
## weekdaySunday:month05 -4.092096e+05 2.957727e+05
## weekdayMonday:month06 -1.124749e+06 -3.489383e+05
## weekdayTuesday:month06 -1.116100e+06 -3.403241e+05
## weekdayWednesday:month06 -1.330085e+06 -3.640637e+05
## weekdayThursday:month06 -1.178309e+06 -4.026012e+05
## weekdayFriday:month06 -1.092968e+06 -3.611856e+05
## weekdaySaturday:month06 -4.499393e+05 2.818059e+05
## weekdaySunday:month06 -5.437135e+05 2.321337e+05
## weekdayMonday:month07 -7.292276e+05 1.787540e+03
## weekdayTuesday:month07 -8.176838e+05 -8.668418e+04
## weekdayWednesday:month07 -8.850703e+05 -1.099741e+05
## weekdayThursday:month07 -9.051747e+05 -1.300935e+05
## weekdayFriday:month07 -8.942852e+05 -1.192187e+05
## weekdaySaturday:month07 -5.729784e+05 2.020738e+05
## weekdaySunday:month07 -5.233255e+05 2.077054e+05
## weekdayMonday:month08 -6.874967e+05 8.720722e+04
## weekdayTuesday:month08 -8.842504e+05 -1.095576e+05
## weekdayWednesday:month08 -7.838645e+05 -9.154409e+03
## weekdayThursday:month08 -8.203562e+05 -8.973012e+04
## weekdayFriday:month08 -6.636924e+05 6.692190e+04
## weekdaySaturday:month08 -5.683520e+05 2.063739e+05
## weekdaySunday:month08 -5.322309e+05 2.424840e+05
## weekdayMonday:month09 -1.270296e+06 -4.275152e+05
## weekdayTuesday:month09 -1.194952e+06 -3.521847e+05
## weekdayWednesday:month09 -1.274188e+06 -4.314346e+05
## weekdayThursday:month09 -1.165439e+06 -3.911448e+05
## weekdayFriday:month09 -1.136662e+06 -3.623838e+05
## weekdaySaturday:month09 -5.373293e+05 1.928810e+05
## weekdaySunday:month09 -7.303201e+05 4.400100e+04
## weekdayMonday:month10 -1.024870e+06 -2.952543e+05
## weekdayTuesday:month10 -1.047054e+06 -2.733641e+05
## weekdayWednesday:month10 -1.056529e+06 -2.828897e+05
## weekdayThursday:month10 -1.067681e+06 -2.939144e+05
## weekdayFriday:month10 -9.038253e+05 -1.300794e+05
## weekdaySaturday:month10 -3.209516e+05 4.527731e+05
## weekdaySunday:month10 -4.024267e+05 3.712767e+05
## weekdayMonday:month11 -9.130975e+05 -1.400005e+05
## weekdayTuesday:month11 -1.029223e+06 -2.561449e+05
## weekdayWednesday:month11 -9.287085e+05 -1.556483e+05
## weekdayThursday:month11 -8.578927e+05 -1.289644e+05
## weekdayFriday:month11 -8.696704e+05 -1.407617e+05
## weekdaySaturday:month11 -2.735042e+05 4.996308e+05
## weekdaySunday:month11 -3.250148e+05 4.481010e+05
## weekdayMonday:month12 -5.064890e+05 2.220749e+05
## weekdayTuesday:month12 -7.558681e+05 8.541632e+04
## weekdayWednesday:month12 -7.111515e+05 6.160687e+04
## weekdayThursday:month12 -6.455715e+05 1.271867e+05
## weekdayFriday:month12 -5.590815e+05 2.136775e+05
## weekdaySaturday:month12 -4.037942e+05 3.247697e+05
## weekdaySunday:month12 NA NA
## weekdayMonday:isHolyday -3.267649e+05 9.039944e+05
## weekdayTuesday:isHolyday -4.590635e+05 7.632295e+05
## weekdayWednesday:isHolyday 1.846502e+05 1.472603e+06
## weekdayThursday:isHolyday 4.524525e+05 1.676855e+06
## weekdayFriday:isHolyday 4.124927e+05 1.629060e+06
## weekdaySaturday:isHolyday -8.058767e+05 4.317345e+05
## weekdaySunday:isHolyday -8.996082e+05 3.883263e+05
## weekdayMonday:month02:isHolyday -1.408910e+06 3.433158e+05
## weekdayTuesday:month02:isHolyday -1.575361e+06 1.549189e+05
## weekdayWednesday:month02:isHolyday -1.582322e+06 2.024975e+05
## weekdayThursday:month02:isHolyday -2.983988e+06 -1.249070e+06
## weekdayFriday:month02:isHolyday -3.250599e+06 -1.513205e+06
## weekdaySaturday:month02:isHolyday -2.174549e+06 -4.271514e+05
## weekdaySunday:month02:isHolyday NA NA
## weekdayMonday:month03:isHolyday NA NA
## weekdayTuesday:month03:isHolyday NA NA
## weekdayWednesday:month03:isHolyday NA NA
## weekdayThursday:month03:isHolyday NA NA
## weekdayFriday:month03:isHolyday NA NA
## weekdaySaturday:month03:isHolyday NA NA
## weekdaySunday:month03:isHolyday NA NA
## weekdayMonday:month04:isHolyday NA NA
## weekdayTuesday:month04:isHolyday NA NA
## weekdayWednesday:month04:isHolyday NA NA
## weekdayThursday:month04:isHolyday NA NA
## weekdayFriday:month04:isHolyday NA NA
## weekdaySaturday:month04:isHolyday NA NA
## weekdaySunday:month04:isHolyday NA NA
## weekdayMonday:month05:isHolyday -3.935259e+05 1.155853e+06
## weekdayTuesday:month05:isHolyday 1.208213e+05 1.848800e+06
## weekdayWednesday:month05:isHolyday NA NA
## weekdayThursday:month05:isHolyday NA NA
## weekdayFriday:month05:isHolyday NA NA
## weekdaySaturday:month05:isHolyday NA NA
## weekdaySunday:month05:isHolyday -8.249286e+05 7.755140e+05
## weekdayMonday:month06:isHolyday NA NA
## weekdayTuesday:month06:isHolyday NA NA
## weekdayWednesday:month06:isHolyday -5.014626e+05 1.226543e+06
## weekdayThursday:month06:isHolyday NA NA
## weekdayFriday:month06:isHolyday NA NA
## weekdaySaturday:month06:isHolyday NA NA
## weekdaySunday:month06:isHolyday NA NA
## weekdayMonday:month07:isHolyday NA NA
## weekdayTuesday:month07:isHolyday NA NA
## weekdayWednesday:month07:isHolyday NA NA
## weekdayThursday:month07:isHolyday NA NA
## weekdayFriday:month07:isHolyday NA NA
## weekdaySaturday:month07:isHolyday NA NA
## weekdaySunday:month07:isHolyday NA NA
## weekdayMonday:month08:isHolyday NA NA
## weekdayTuesday:month08:isHolyday NA NA
## weekdayWednesday:month08:isHolyday -1.090925e+06 7.304989e+05
## weekdayThursday:month08:isHolyday NA NA
## weekdayFriday:month08:isHolyday NA NA
## weekdaySaturday:month08:isHolyday NA NA
## weekdaySunday:month08:isHolyday NA NA
## weekdayMonday:month09:isHolyday -1.609235e+06 2.032075e+05
## weekdayTuesday:month09:isHolyday -1.141209e+06 6.650728e+05
## weekdayWednesday:month09:isHolyday -1.126215e+06 7.253428e+05
## weekdayThursday:month09:isHolyday NA NA
## weekdayFriday:month09:isHolyday NA NA
## weekdaySaturday:month09:isHolyday NA NA
## weekdaySunday:month09:isHolyday NA NA
## weekdayMonday:month10:isHolyday NA NA
## weekdayTuesday:month10:isHolyday 1.203650e+03 1.776770e+06
## weekdayWednesday:month10:isHolyday NA NA
## weekdayThursday:month10:isHolyday NA NA
## weekdayFriday:month10:isHolyday NA NA
## weekdaySaturday:month10:isHolyday NA NA
## weekdaySunday:month10:isHolyday NA NA
## weekdayMonday:month11:isHolyday NA NA
## weekdayTuesday:month11:isHolyday NA NA
## weekdayWednesday:month11:isHolyday NA NA
## weekdayThursday:month11:isHolyday NA NA
## weekdayFriday:month11:isHolyday NA NA
## weekdaySaturday:month11:isHolyday NA NA
## weekdaySunday:month11:isHolyday NA NA
## weekdayMonday:month12:isHolyday NA NA
## weekdayTuesday:month12:isHolyday -2.287901e+05 1.578263e+06
## weekdayWednesday:month12:isHolyday NA NA
## weekdayThursday:month12:isHolyday NA NA
## weekdayFriday:month12:isHolyday NA NA
## weekdaySaturday:month12:isHolyday NA NA
## weekdaySunday:month12:isHolyday NA NAconfint(model3_2, level = 0.99)## Waiting for profiling to be done...## 0.5 % 99.5 %
## (Intercept) -8.400104e+08 -3.869234e+08
## I(as.numeric(Date)^3) -3.015263e-04 -1.408301e-04
## I(as.numeric(Date)^2) 3.740589e+00 8.031071e+00
## weekdayMonday:month01 1.113733e+05 8.382384e+05
## weekdayTuesday:month01 1.481356e+05 8.643837e+05
## weekdayWednesday:month01 9.357838e+04 7.884040e+05
## weekdayThursday:month01 1.151334e+05 8.229120e+05
## weekdayFriday:month01 6.703842e+04 7.922342e+05
## weekdaySaturday:month01 3.159186e+05 1.056581e+06
## weekdaySunday:month01 2.946563e+05 1.033203e+06
## weekdayMonday:month02 -2.498932e+05 4.660874e+05
## weekdayTuesday:month02 -3.011490e+05 4.100738e+05
## weekdayWednesday:month02 -1.554529e+05 5.680143e+05
## weekdayThursday:month02 -8.728276e+04 6.376418e+05
## weekdayFriday:month02 -1.328936e+05 5.881497e+05
## weekdaySaturday:month02 1.220810e+05 8.633671e+05
## weekdaySunday:month02 -1.236700e+05 5.790056e+05
## weekdayMonday:month03 -7.265998e+05 -6.491921e+04
## weekdayTuesday:month03 -7.994100e+05 -1.423896e+05
## weekdayWednesday:month03 -7.739485e+05 -1.153607e+05
## weekdayThursday:month03 -7.804679e+05 -1.219802e+05
## weekdayFriday:month03 -7.433277e+05 -9.277015e+04
## weekdaySaturday:month03 -5.030940e+04 6.266122e+05
## weekdaySunday:month03 -9.123388e+04 5.951490e+05
## weekdayMonday:month04 -7.674014e+05 -1.304138e+05
## weekdayTuesday:month04 -8.339835e+05 -1.915318e+05
## weekdayWednesday:month04 -8.604304e+05 -2.081974e+05
## weekdayThursday:month04 -8.510472e+05 -1.981990e+05
## weekdayFriday:month04 -8.215339e+05 -1.668895e+05
## weekdaySaturday:month04 -1.728233e+05 5.239100e+05
## weekdaySunday:month04 -1.593448e+05 5.238237e+05
## weekdayMonday:month05 -9.060091e+05 -2.253617e+05
## weekdayTuesday:month05 -9.463679e+05 -2.989376e+05
## weekdayWednesday:month05 -9.181458e+05 -2.879967e+05
## weekdayThursday:month05 -1.019922e+06 -3.944362e+05
## weekdayFriday:month05 -1.043530e+06 -4.101399e+05
## weekdaySaturday:month05 -3.808527e+05 3.181445e+05
## weekdaySunday:month05 -3.957322e+05 3.280824e+05
## weekdayMonday:month06 -1.109843e+06 -3.717067e+05
## weekdayTuesday:month06 -1.101643e+06 -3.625949e+05
## weekdayWednesday:month06 -1.279624e+06 -4.037949e+05
## weekdayThursday:month06 -1.161626e+06 -4.291381e+05
## weekdayFriday:month06 -1.085404e+06 -3.817452e+05
## weekdaySaturday:month06 -4.693461e+05 2.937652e+05
## weekdaySunday:month06 -5.570102e+05 2.444620e+05
## weekdayMonday:month07 -7.414083e+05 -5.396758e+03
## weekdayTuesday:month07 -8.263734e+05 -9.860055e+04
## weekdayWednesday:month07 -8.865681e+05 -1.233851e+05
## weekdayThursday:month07 -9.058034e+05 -1.448154e+05
## weekdayFriday:month07 -8.955695e+05 -1.334202e+05
## weekdaySaturday:month07 -5.903275e+05 2.073938e+05
## weekdaySunday:month07 -5.440555e+05 2.115065e+05
## weekdayMonday:month08 -7.015212e+05 8.250664e+04
## weekdayTuesday:month08 -8.887963e+05 -1.264549e+05
## weekdayWednesday:month08 -7.931704e+05 -1.979275e+04
## weekdayThursday:month08 -8.313780e+05 -1.046080e+05
## weekdayFriday:month08 -6.813113e+05 6.001908e+04
## weekdaySaturday:month08 -5.880503e+05 2.094322e+05
## weekdaySunday:month08 -5.538803e+05 2.476528e+05
## weekdayMonday:month09 -1.246283e+06 -4.697895e+05
## weekdayTuesday:month09 -1.175370e+06 -3.892843e+05
## weekdayWednesday:month09 -1.249971e+06 -4.741367e+05
## weekdayThursday:month09 -1.155993e+06 -4.252557e+05
## weekdayFriday:month09 -1.128601e+06 -3.949449e+05
## weekdaySaturday:month09 -5.599869e+05 1.918438e+05
## weekdaySunday:month09 -7.414740e+05 3.612871e+04
## weekdayMonday:month10 -1.026715e+06 -3.224141e+05
## weekdayTuesday:month10 -1.041230e+06 -3.010689e+05
## weekdayWednesday:month10 -1.049980e+06 -3.110968e+05
## weekdayThursday:month10 -1.061295e+06 -3.229134e+05
## weekdayFriday:month10 -9.050799e+05 -1.493496e+05
## weekdaySaturday:month10 -3.518962e+05 4.689769e+05
## weekdaySunday:month10 -4.283897e+05 3.829953e+05
## weekdayMonday:month11 -9.089806e+05 -1.582095e+05
## weekdayTuesday:month11 -1.020324e+06 -2.820811e+05
## weekdayWednesday:month11 -9.240003e+05 -1.752114e+05
## weekdayThursday:month11 -8.610810e+05 -1.451944e+05
## weekdayFriday:month11 -8.721535e+05 -1.574583e+05
## weekdaySaturday:month11 -3.018152e+05 5.208226e+05
## weekdaySunday:month11 -3.507467e+05 4.657623e+05
## weekdayMonday:month12 -5.158921e+05 2.284569e+05
## weekdayTuesday:month12 -7.487520e+05 8.234255e+04
## weekdayWednesday:month12 -7.095141e+05 5.858383e+04
## weekdayThursday:month12 -6.464482e+05 1.287434e+05
## weekdayFriday:month12 -5.640801e+05 2.205712e+05
## weekdaySaturday:month12 -4.160886e+05 3.384809e+05
## weekdaySunday:month12 NA NA
## weekdayMonday:isHolyday -3.682866e+05 1.124025e+06
## weekdayTuesday:isHolyday -5.507146e+05 9.213353e+05
## weekdayWednesday:isHolyday 1.927769e+05 1.571245e+06
## weekdayThursday:isHolyday 4.140721e+05 1.858945e+06
## weekdayFriday:isHolyday 3.837275e+05 1.774691e+06
## weekdaySaturday:isHolyday -7.653746e+05 4.785598e+05
## weekdaySunday:isHolyday -8.139893e+05 3.777076e+05
## weekdayMonday:month02:isHolyday -1.567559e+06 3.737232e+05
## weekdayTuesday:month02:isHolyday -1.648227e+06 1.939710e+05
## weekdayWednesday:month02:isHolyday -1.677185e+06 2.667502e+05
## weekdayThursday:month02:isHolyday -3.044770e+06 -1.245151e+06
## weekdayFriday:month02:isHolyday -3.255098e+06 -1.547964e+06
## weekdaySaturday:month02:isHolyday -2.111424e+06 -4.963138e+05
## weekdaySunday:month02:isHolyday NA NA
## weekdayMonday:month03:isHolyday NA NA
## weekdayTuesday:month03:isHolyday NA NA
## weekdayWednesday:month03:isHolyday NA NA
## weekdayThursday:month03:isHolyday NA NA
## weekdayFriday:month03:isHolyday NA NA
## weekdaySaturday:month03:isHolyday NA NA
## weekdaySunday:month03:isHolyday NA NA
## weekdayMonday:month04:isHolyday NA NA
## weekdayTuesday:month04:isHolyday NA NA
## weekdayWednesday:month04:isHolyday NA NA
## weekdayThursday:month04:isHolyday NA NA
## weekdayFriday:month04:isHolyday NA NA
## weekdaySaturday:month04:isHolyday NA NA
## weekdaySunday:month04:isHolyday NA NA
## weekdayMonday:month05:isHolyday -5.634754e+05 1.196960e+06
## weekdayTuesday:month05:isHolyday -1.780456e+04 2.044360e+06
## weekdayWednesday:month05:isHolyday NA NA
## weekdayThursday:month05:isHolyday NA NA
## weekdayFriday:month05:isHolyday NA NA
## weekdaySaturday:month05:isHolyday NA NA
## weekdaySunday:month05:isHolyday -7.561916e+05 7.108993e+05
## weekdayMonday:month06:isHolyday NA NA
## weekdayTuesday:month06:isHolyday NA NA
## weekdayWednesday:month06:isHolyday -5.633877e+05 1.244116e+06
## weekdayThursday:month06:isHolyday NA NA
## weekdayFriday:month06:isHolyday NA NA
## weekdaySaturday:month06:isHolyday NA NA
## weekdaySunday:month06:isHolyday NA NA
## weekdayMonday:month07:isHolyday NA NA
## weekdayTuesday:month07:isHolyday NA NA
## weekdayWednesday:month07:isHolyday NA NA
## weekdayThursday:month07:isHolyday NA NA
## weekdayFriday:month07:isHolyday NA NA
## weekdaySaturday:month07:isHolyday NA NA
## weekdaySunday:month07:isHolyday NA NA
## weekdayMonday:month08:isHolyday NA NA
## weekdayTuesday:month08:isHolyday NA NA
## weekdayWednesday:month08:isHolyday -1.171451e+06 8.178206e+05
## weekdayThursday:month08:isHolyday NA NA
## weekdayFriday:month08:isHolyday NA NA
## weekdaySaturday:month08:isHolyday NA NA
## weekdaySunday:month08:isHolyday NA NA
## weekdayMonday:month09:isHolyday -1.666991e+06 1.379792e+05
## weekdayTuesday:month09:isHolyday -1.163401e+06 6.913278e+05
## weekdayWednesday:month09:isHolyday -1.152862e+06 7.385143e+05
## weekdayThursday:month09:isHolyday NA NA
## weekdayFriday:month09:isHolyday NA NA
## weekdaySaturday:month09:isHolyday NA NA
## weekdaySunday:month09:isHolyday NA NA
## weekdayMonday:month10:isHolyday NA NA
## weekdayTuesday:month10:isHolyday -1.182060e+05 1.947089e+06
## weekdayWednesday:month10:isHolyday NA NA
## weekdayThursday:month10:isHolyday NA NA
## weekdayFriday:month10:isHolyday NA NA
## weekdaySaturday:month10:isHolyday NA NA
## weekdaySunday:month10:isHolyday NA NA
## weekdayMonday:month11:isHolyday NA NA
## weekdayTuesday:month11:isHolyday NA NA
## weekdayWednesday:month11:isHolyday NA NA
## weekdayThursday:month11:isHolyday NA NA
## weekdayFriday:month11:isHolyday NA NA
## weekdaySaturday:month11:isHolyday NA NA
## weekdaySunday:month11:isHolyday NA NA
## weekdayMonday:month12:isHolyday NA NA
## weekdayTuesday:month12:isHolyday -3.486692e+05 1.754654e+06
## weekdayWednesday:month12:isHolyday NA NA
## weekdayThursday:month12:isHolyday NA NA
## weekdayFriday:month12:isHolyday NA NA
## weekdaySaturday:month12:isHolyday NA NA
## weekdaySunday:month12:isHolyday NA NAconfint(model4_2, level = 0.99)## Waiting for profiling to be done...## 0.5 % 99.5 %
## (Intercept) 1.785406e-11 3.581823e-11
## I(as.numeric(Date)^3) 6.400076e-24 1.279514e-23
## I(as.numeric(Date)^2) -3.406113e-19 -1.700768e-19
## weekdayMonday:month01 -2.679369e-14 -2.889350e-15
## weekdayTuesday:month01 -2.747891e-14 -3.935233e-15
## weekdayWednesday:month01 -2.566342e-14 -2.432417e-15
## weekdayThursday:month01 -2.652038e-14 -3.072107e-15
## weekdayFriday:month01 -2.568479e-14 -1.654758e-15
## weekdaySaturday:month01 -3.148875e-14 -8.078678e-15
## weekdaySunday:month01 -3.103576e-14 -7.580051e-15
## weekdayMonday:month02 -1.698996e-14 8.677796e-15
## weekdayTuesday:month02 -1.522937e-14 1.065579e-14
## weekdayWednesday:month02 -1.995388e-14 5.334703e-15
## weekdayThursday:month02 -2.222463e-14 2.812770e-15
## weekdayFriday:month02 -2.074493e-14 4.394323e-15
## weekdaySaturday:month02 -2.757494e-14 -3.272623e-15
## weekdaySunday:month02 -2.026728e-14 4.302632e-15
## weekdayMonday:month03 2.373888e-15 2.954797e-14
## weekdayTuesday:month03 6.018120e-15 3.362944e-14
## weekdayWednesday:month03 4.744020e-15 3.220327e-14
## weekdayThursday:month03 4.912728e-15 3.239314e-14
## weekdayFriday:month03 3.683684e-15 3.022726e-14
## weekdaySaturday:month03 -2.162478e-14 1.882207e-15
## weekdaySunday:month03 -2.101658e-14 3.029327e-15
## weekdayMonday:month04 5.882258e-15 3.208027e-14
## weekdayTuesday:month04 9.111874e-15 3.647531e-14
## weekdayWednesday:month04 9.681846e-15 3.778956e-14
## weekdayThursday:month04 9.280528e-15 3.734215e-14
## weekdayFriday:month04 7.663420e-15 3.553089e-14
## weekdaySaturday:month04 -1.888512e-14 5.826538e-15
## weekdaySunday:month04 -1.838272e-14 5.775991e-15
## weekdayMonday:month05 1.109885e-14 4.204172e-14
## weekdayTuesday:month05 1.412487e-14 4.295425e-14
## weekdayWednesday:month05 1.441787e-14 4.159503e-14
## weekdayThursday:month05 2.075581e-14 4.860037e-14
## weekdayFriday:month05 2.273492e-14 5.183085e-14
## weekdaySaturday:month05 -1.316326e-14 1.329075e-14
## weekdaySunday:month05 -1.165808e-14 1.521450e-14
## weekdayMonday:month06 1.571402e-14 5.005521e-14
## weekdayTuesday:month06 1.527181e-14 4.951784e-14
## weekdayWednesday:month06 1.692146e-14 6.321140e-14
## weekdayThursday:month06 1.856758e-14 5.347827e-14
## weekdayFriday:month06 1.629626e-14 4.814363e-14
## weekdaySaturday:month06 -9.429268e-15 1.800180e-14
## weekdaySunday:month06 -7.962004e-15 2.156202e-14
## weekdayMonday:month07 2.738486e-16 2.927691e-14
## weekdayTuesday:month07 3.753207e-15 3.335207e-14
## weekdayWednesday:month07 4.694777e-15 3.669304e-14
## weekdayThursday:month07 5.544968e-15 3.771547e-14
## weekdayFriday:month07 5.088025e-15 3.716404e-14
## weekdaySaturday:month07 -6.785780e-15 2.287564e-14
## weekdaySunday:month07 -6.922173e-15 2.085759e-14
## weekdayMonday:month08 -2.874605e-15 2.759146e-14
## weekdayTuesday:month08 4.722977e-15 3.673849e-14
## weekdayWednesday:month08 6.899348e-16 3.188088e-14
## weekdayThursday:month08 3.913117e-15 3.354679e-14
## weekdayFriday:month08 -2.109909e-15 2.649301e-14
## weekdaySaturday:month08 -6.916598e-15 2.272702e-14
## weekdaySunday:month08 -8.072502e-15 2.133933e-14
## weekdayMonday:month09 2.033783e-14 5.992755e-14
## weekdayTuesday:month09 1.621254e-14 5.477382e-14
## weekdayWednesday:month09 2.057092e-14 6.022992e-14
## weekdayThursday:month09 1.819186e-14 5.306339e-14
## weekdayFriday:month09 1.665185e-14 5.121301e-14
## weekdaySaturday:month09 -6.587882e-15 2.132546e-14
## weekdaySunday:month09 -1.415256e-15 2.943935e-14
## weekdayMonday:month10 1.319096e-14 4.465241e-14
## weekdayTuesday:month10 1.213887e-14 4.596820e-14
## weekdayWednesday:month10 1.261798e-14 4.656546e-14
## weekdayThursday:month10 1.316811e-14 4.718170e-14
## weekdayFriday:month10 5.445053e-15 3.788420e-14
## weekdaySaturday:month10 -1.489842e-14 1.341575e-14
## weekdaySunday:month10 -1.259688e-14 1.618698e-14
## weekdayMonday:month11 5.732140e-15 3.851491e-14
## weekdayTuesday:month11 1.128914e-14 4.522261e-14
## weekdayWednesday:month11 6.438767e-15 3.938865e-14
## weekdayThursday:month11 5.228483e-15 3.554077e-14
## weekdayFriday:month11 5.753405e-15 3.616577e-14
## weekdaySaturday:month11 -1.680273e-14 1.137415e-14
## weekdaySunday:month11 -1.539790e-14 1.307146e-14
## weekdayMonday:month12 -9.061127e-15 1.913602e-14
## weekdayTuesday:month12 -4.019990e-15 3.045964e-14
## weekdayWednesday:month12 -3.124901e-15 2.823233e-14
## weekdayThursday:month12 -5.669186e-15 2.518310e-14
## weekdayFriday:month12 -8.826306e-15 2.139724e-14
## weekdaySaturday:month12 -1.242690e-14 1.517164e-14
## weekdaySunday:month12 NA NA
## weekdayMonday:isHolyday -2.535455e-14 1.180633e-14
## weekdayTuesday:isHolyday -1.947146e-14 1.785753e-14
## weekdayWednesday:isHolyday -5.594378e-14 -7.428305e-15
## weekdayThursday:isHolyday -5.407462e-14 -1.422947e-14
## weekdayFriday:isHolyday -5.608229e-14 -1.511259e-14
## weekdaySaturday:isHolyday -1.440027e-14 3.487052e-14
## weekdaySunday:isHolyday -1.601502e-14 4.254550e-14
## weekdayMonday:month02:isHolyday -1.410311e-14 4.912056e-14
## weekdayTuesday:month02:isHolyday -6.956746e-15 6.247475e-14
## weekdayWednesday:month02:isHolyday -4.164216e-15 6.064609e-14
## weekdayThursday:month02:isHolyday 4.584478e-14 1.205388e-13
## weekdayFriday:month02:isHolyday 6.924098e-14 1.549753e-13
## weekdaySaturday:month02:isHolyday 1.819126e-14 1.036914e-13
## weekdaySunday:month02:isHolyday NA NA
## weekdayMonday:month03:isHolyday NA NA
## weekdayTuesday:month03:isHolyday NA NA
## weekdayWednesday:month03:isHolyday NA NA
## weekdayThursday:month03:isHolyday NA NA
## weekdayFriday:month03:isHolyday NA NA
## weekdaySaturday:month03:isHolyday NA NA
## weekdaySunday:month03:isHolyday NA NA
## weekdayMonday:month04:isHolyday NA NA
## weekdayTuesday:month04:isHolyday NA NA
## weekdayWednesday:month04:isHolyday NA NA
## weekdayThursday:month04:isHolyday NA NA
## weekdayFriday:month04:isHolyday NA NA
## weekdaySaturday:month04:isHolyday NA NA
## weekdaySunday:month04:isHolyday NA NA
## weekdayMonday:month05:isHolyday -4.976687e-14 4.059820e-15
## weekdayTuesday:month05:isHolyday -6.631030e-14 -1.093667e-14
## weekdayWednesday:month05:isHolyday NA NA
## weekdayThursday:month05:isHolyday NA NA
## weekdayFriday:month05:isHolyday NA NA
## weekdaySaturday:month05:isHolyday NA NA
## weekdaySunday:month05:isHolyday -3.839266e-14 3.543595e-14
## weekdayMonday:month06:isHolyday NA NA
## weekdayTuesday:month06:isHolyday NA NA
## weekdayWednesday:month06:isHolyday -4.980762e-14 1.936097e-14
## weekdayThursday:month06:isHolyday NA NA
## weekdayFriday:month06:isHolyday NA NA
## weekdaySaturday:month06:isHolyday NA NA
## weekdaySunday:month06:isHolyday NA NA
## weekdayMonday:month07:isHolyday NA NA
## weekdayTuesday:month07:isHolyday NA NA
## weekdayWednesday:month07:isHolyday NA NA
## weekdayThursday:month07:isHolyday NA NA
## weekdayFriday:month07:isHolyday NA NA
## weekdaySaturday:month07:isHolyday NA NA
## weekdaySunday:month07:isHolyday NA NA
## weekdayMonday:month08:isHolyday NA NA
## weekdayTuesday:month08:isHolyday NA NA
## weekdayWednesday:month08:isHolyday -2.196252e-14 4.433923e-14
## weekdayThursday:month08:isHolyday NA NA
## weekdayFriday:month08:isHolyday NA NA
## weekdaySaturday:month08:isHolyday NA NA
## weekdaySunday:month08:isHolyday NA NA
## weekdayMonday:month09:isHolyday -2.487176e-15 8.735645e-14
## weekdayTuesday:month09:isHolyday -3.076878e-14 4.767761e-14
## weekdayWednesday:month09:isHolyday -3.553493e-14 3.959826e-14
## weekdayThursday:month09:isHolyday NA NA
## weekdayFriday:month09:isHolyday NA NA
## weekdaySaturday:month09:isHolyday NA NA
## weekdaySunday:month09:isHolyday NA NA
## weekdayMonday:month10:isHolyday NA NA
## weekdayTuesday:month10:isHolyday -6.552769e-14 -6.163554e-15
## weekdayWednesday:month10:isHolyday NA NA
## weekdayThursday:month10:isHolyday NA NA
## weekdayFriday:month10:isHolyday NA NA
## weekdaySaturday:month10:isHolyday NA NA
## weekdaySunday:month10:isHolyday NA NA
## weekdayMonday:month11:isHolyday NA NA
## weekdayTuesday:month11:isHolyday NA NA
## weekdayWednesday:month11:isHolyday NA NA
## weekdayThursday:month11:isHolyday NA NA
## weekdayFriday:month11:isHolyday NA NA
## weekdaySaturday:month11:isHolyday NA NA
## weekdaySunday:month11:isHolyday NA NA
## weekdayMonday:month12:isHolyday NA NA
## weekdayTuesday:month12:isHolyday -5.479499e-14 4.836852e-15
## weekdayWednesday:month12:isHolyday NA NA
## weekdayThursday:month12:isHolyday NA NA
## weekdayFriday:month12:isHolyday NA NA
## weekdaySaturday:month12:isHolyday NA NA
## weekdaySunday:month12:isHolyday NA NA### 예측기간 관측치 더하기 (검증을 위한 사후 처리...)
test_btv_pre_roll2$REQ [test_btv_pre_roll2$Date >= '2019-05-01' & test_btv_pre_roll2$Date <= '2019-05-31'] <-
btv_pre_roll$REQ [btv_pre_roll$Date >= '2019-05-01' & btv_pre_roll$Date <= '2019-05-31']
test_btv_pre_roll3$REQ [test_btv_pre_roll3$Date >= '2019-06-01' & test_btv_pre_roll3$Date <= '2019-06-30'] <-
btv_pre_roll$REQ [btv_pre_roll$Date >= '2019-06-01' & btv_pre_roll$Date <= '2019-06-30']
cor(test_btv_pre_roll2$REQ, test_btv_pre_roll2$m1_fit)^2## [1] 0.7858214cor(test_btv_pre_roll2$REQ, test_btv_pre_roll2$m2_fit)^2## [1] 0.7858214cor(test_btv_pre_roll2$REQ, test_btv_pre_roll2$m3_fit)^2## [1] 0.7855516cor(test_btv_pre_roll2$REQ, test_btv_pre_roll2$m4_fit)^2## [1] 0.7841779cor(test_btv_pre_roll3$REQ, test_btv_pre_roll3$m1_fit)^2## [1] 0.841047cor(test_btv_pre_roll3$REQ, test_btv_pre_roll3$m2_fit)^2## [1] 0.841047cor(test_btv_pre_roll3$REQ, test_btv_pre_roll3$m3_fit)^2## [1] 0.8389602cor(test_btv_pre_roll3$REQ, test_btv_pre_roll3$m4_fit)^2## [1] 0.8235133# MAE
mean(abs(test_btv_pre_roll2$REQ - test_btv_pre_roll2$m1_fit))## [1] 153112.3mean(abs(test_btv_pre_roll2$REQ - test_btv_pre_roll2$m2_fit))## [1] 153112.3mean(abs(test_btv_pre_roll2$REQ - test_btv_pre_roll2$m3_fit))## [1] 154276.4mean(abs(test_btv_pre_roll2$REQ - test_btv_pre_roll2$m4_fit))## [1] 155203.9mean(abs(test_btv_pre_roll3$REQ - test_btv_pre_roll3$m1_fit))## [1] 137020.5mean(abs(test_btv_pre_roll3$REQ - test_btv_pre_roll3$m2_fit))## [1] 137020.5mean(abs(test_btv_pre_roll3$REQ - test_btv_pre_roll3$m3_fit))## [1] 138880.1mean(abs(test_btv_pre_roll3$REQ - test_btv_pre_roll3$m4_fit))## [1] 147364# MAPE
mean(abs(test_btv_pre_roll2$REQ - test_btv_pre_roll2$m1_fit)/abs(test_btv_pre_roll2$REQ))*100## [1] 4.342613mean(abs(test_btv_pre_roll2$REQ - test_btv_pre_roll2$m2_fit)/abs(test_btv_pre_roll2$REQ))*100## [1] 4.342613mean(abs(test_btv_pre_roll2$REQ - test_btv_pre_roll2$m3_fit)/abs(test_btv_pre_roll2$REQ))*100## [1] 4.375261mean(abs(test_btv_pre_roll2$REQ - test_btv_pre_roll2$m4_fit)/abs(test_btv_pre_roll2$REQ))*100## [1] 4.402082mean(abs(test_btv_pre_roll3$REQ - test_btv_pre_roll3$m1_fit)/abs(test_btv_pre_roll3$REQ))*100## [1] 3.775664mean(abs(test_btv_pre_roll3$REQ - test_btv_pre_roll3$m2_fit)/abs(test_btv_pre_roll3$REQ))*100## [1] 3.775664mean(abs(test_btv_pre_roll3$REQ - test_btv_pre_roll3$m3_fit)/abs(test_btv_pre_roll3$REQ))*100## [1] 3.825968mean(abs(test_btv_pre_roll3$REQ - test_btv_pre_roll3$m4_fit)/abs(test_btv_pre_roll3$REQ))*100## [1] 4.062741