pred_int | R Documentation |
Function to calculate prediction interval
pred_int(
df,
analysis_result = "analysis_result",
distribution = "distribution",
method = "Bonferroni",
n_mean = 1,
k = 1,
pi_type = "upper",
conf_level = 0.95
)
df |
df data frame of groundwater data in tidy format |
analysis_result |
the analysis result column |
distribution |
the distribution column |
method |
default is "Bonferroni" |
n_mean |
n.mean positive integer specifying the sample size associated with the future averages. The default value is n.mean=1 (i.e., individual observations). Note that all future averages must be based on the same sample size. |
k |
k positive integer specifying the number of future observations or averages the prediction interval should contain with confidence level conf.level. The default value is k=1. |
pi_type |
character string indicating what kind of prediction interval to compute. The possible values are pi_type="two-sided" (the default), pi_type="upper", and pi_type="lower". |
conf_level |
a scalar between 0 and 1 indicating the confidence level of the prediction interval. The default value is conf.level=0.95 |
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 and distribution
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)
background_data %>%
pred_int(., pi_type = "upper", conf_level = 0.99)
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