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# https://link.springer.com/article/10.1057/jma.2014.3#Tab2 | |
# using the exgaus distribution to model response time | |
library(tidyverse) | |
data.frame(x=c(0, 20)) %>% | |
ggplot(aes(x)) + | |
stat_function(fun=function(x) dexGAUS(x, mu=1, sigma=2, nu=3), | |
linetype = 1) + | |
stat_function(fun=function(x) dexGAUS(x, mu=4, sigma=0.04, nu=4), | |
linetype = 2) + |
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GGally::ggpairs(upper = list(continuous = "points"), | |
lower = list(continuous = "cor")) |
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# load libraries | |
library(purrr) | |
library(tidyverse) | |
# positive_round function | |
positive_round <- function(...) round(pmax(..., 0), 0) | |
# adstock function | |
adstock<-function(x,rate=0){ | |
return(as.numeric(stats::filter(x=x,filter=rate,method="recursive"))) |
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```{r} | |
# load libraries | |
library(purrr) | |
library(tidyverse) | |
# positive_round function | |
positive_round <- function(...) round(pmax(..., 0), 0) | |
# adstock function | |
adstock<-function(x,rate=0){ |
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mutate(date = as.Date(paste(fiscal_year, fiscal_week_number, 1, sep="-"), "%Y-%U-%u")) %>% |
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trans %>% | |
clean_names() %>% | |
group_by(rest_number) %>% | |
filter(!any(!complete.cases(comp_trans_ty_ty))) |
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tq_mutate(select = usd,mutate_fun = rollapply,FUN = mean,width = 13, | |
col_rename = "roll13") %>% | |
tq_mutate(select = usd,mutate_fun = rollapply,FUN = mean,width = 2, | |
col_rename = "roll2") |
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mp <- plot(brms::marginal_effects(bfit), plot = F) | |
mp$x_promo_items + theme_ipsum() |
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`%ni%` = Negate(`%in%`) |
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library(modelr) | |
library(tidyverse) | |
library(gapminder) | |
# nest data by continent and label test/train data | |
nested_gap <- gapminder %>% | |
mutate(test_train = ifelse(year < 1992, "train", "test")) %>% | |
group_by(continent) %>% | |
nest() |
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