## ---- echo=FALSE, message=FALSE------------------------------------------
library(knitr)
library(manager)
## ---- eval = FALSE-------------------------------------------------------
# data <- read_manages3("C:/path/to/manages/Site.mdb")
## ---- eval=FALSE---------------------------------------------------------
# data("gw_data")
# data("ohio_data")
# data("indiana_data")
## ----echo=TRUE-----------------------------------------------------------
data("gw_data")
wells <- c("MW-1", "MW-2", "MW-3", "MW-4")
params <- c("Sulfate, total",
"Arsenic, dissolved",
"Boron, dissolved")
background <- lubridate::ymd(c("2007-12-20", "2012-01-01"), tz = "UTC")
# first group data by location, param, and background
# estimate percent less than
background_data <- gw_data %>%
filter(location_id %in% wells, param_name %in% params,
sample_date >= background[1] & sample_date <= background[2]) %>%
group_by(location_id, param_name, default_unit) %>%
percent_lt() %>%
est_dist(., keep_data_object = TRUE) %>%
arrange(location_id, param_name)
pred_int <- background_data %>%
mutate(pred_int = case_when(
distribution == "Normal" ~ map(.x = data,
~EnvStats::predIntNorm(x = .x$analysis_result)),
distribution == "Lognormal" ~ map(.x = data,
~EnvStats::predIntLnorm(x = .x$analysis_result)),
distribution == "Nonparametric" ~ map(.x = data,
~EnvStats::predIntNpar(x = .x$analysis_result))
)
)
pred_int_table <- pred_int %>%
mutate(distribution = distribution,
sample_size = map(.x = pred_int, ~ .x$sample.size),
lpl = map(.x = pred_int, ~ .x$interval$limits["LPL"]),
upl = map(.x = pred_int, ~ .x$interval$limits["UPL"]),
conf_level = map(.x = pred_int, ~ .x$interval$conf.level)) %>%
select(-data, -pred_int) %>%
unnest()
kable(pred_int_table)
## ----conf_int------------------------------------------------------------
conf_int <- background_data %>%
mutate(conf_int = case_when(
distribution == "Normal" ~ map(.x=data,
~EnvStats::enorm(x = .x$analysis_result,
ci = TRUE, ci.type = "lower",
conf.level = 0.99,
ci.param = "mean")),
distribution == "Lognormal" ~ map(.x = data,
~EnvStats::elnormAlt(x = .x$analysis_result,
ci = TRUE, ci.type = "lower",
ci.method = "land",
conf.level = 0.99)),
distribution == "Nonparametric" ~ map(.x = data,
~EnvStats::eqnpar(x = .x$analysis_result,
ci = TRUE, ci.type = "lower",
ci.method = "interpolate",
approx.conf.level = 0.99))
)
)
conf_int_table <- conf_int %>%
mutate(distribution = distribution,
sample_size = map(.x = conf_int, ~ .x$sample.size),
lcl = map(.x = conf_int, ~ round(.x$interval$limits["LCL"], 3)),
ucl = map(.x = conf_int, ~ .x$interval$limits["UCL"]),
conf_level = map(.x = conf_int, ~ .x$interval$conf.level)) %>%
select(-data, -conf_int) %>%
unnest()
kable(conf_int_table)
## ------------------------------------------------------------------------
manager_pred_int <- background_data %>% pred_int(.)
kable(manager_pred_int)
manager_conf_int <- background_data %>%
conf_int(., ci_type = "lower", conf_level = 0.99)
kable(manager_conf_int)
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