#' Create standard columns and types from data in BITS shapefiles
#'
#' Data in different tiles has diferent columns, this function standardizes the
#' information and variable types.
#' @param estados string ot vector of strings with the name of the state as
#' defined in sampling frame
#' @param bits_df data.frame with BITS classifications.
#' @param edo_val data.frame with expert classification.
#' @return A list with estimates for percentage of area correcrly classified and
#' percentage of polygons correctly classified.
#' @examples
#' sampling_frame <- data.frame(id = 1:100,
#' str = sample(1:5, 100, replace = TRUE),
#' val = rnorm(100))
#' allo <- sampling_frame %>%
#' group_by(str) %>%
#' summarise(n = 0.4 * n())
#' select_sample(allo, sampling_frame, n, str)
#' @importFrom magrittr %>%
#' @export
calcular_est <- function(estados, edo_val, bits_df, marco_muestral){
# variables:
# predicted - MADMEX
# Interpr1_p, Interpr2_p (interpretaciones Pedro)
# necesitamos calcular M_h el tamaƱo de cada cluster
# load("datos_procesados/2017-08-18_marco_muestral.Rdata")
estratos_area <- marco_muestral %>%
dplyr::filter(edo %in% estados) %>%
dplyr::group_by(estrato) %>%
dplyr::summarise(
M_h = sum(area),
N_h = n()
)
edo_val_df <- edo_val %>%
dplyr::left_join(marco_muestral, by = c("edo", "oid", "tile")) %>%
dplyr::left_join(select(bits_df, -predicted), by = c("edo", "oid", "tile")) %>%
# dplyr::left_join(estratos_area, by = c("estrato")) %>%
dplyr::mutate(
id = stringr::str_c(oid, tile, sep = "-"),
class_madmex = factor(predicted, levels = 1:32),
class_bits = factor(interpreta, levels = 1:32),
class_expert_1 = factor(Interpr1_p, levels = 1:32),
class_expert_2 = factor(Interpr2_p, levels = 1:32),
y_madmex = (predicted == Interpr1_p | predicted == Interpr2_p),
y_bits = (interpreta == Interpr1_p | interpreta == Interpr2_p),
y_M_madmex = y_madmex * area,
y_M_bits = y_bits * area
) %>%
dplyr::select(id, edo, estrato, area, class_madmex:y_M_bits)
ests_madmex_area_sep <- separate_ratio_est(data = edo_val_df,
estratos_area = estratos_area, stratum = estrato, y = y_madmex, M = area)
ests_bits_area_sep <- separate_ratio_est(data = edo_val_df,
estratos_area = estratos_area, stratum = estrato, y = y_bits, M = area)
# usando srvyr para calcular el combinado
edo_svy <- edo_val_df %>%
dplyr::left_join(estratos_area, by = "estrato")
edo_design <- edo_svy %>%
srvyr::as_survey_design(ids = id, strata = estrato, fpc = N_h)
ests_area_combined <- edo_design %>%
srvyr::summarise(
est_area_madmex = srvyr::survey_ratio(numerator = y_M_madmex,
denominator = area),
est_area_bits = srvyr::survey_ratio(numerator = y_M_bits,
denominator = area)
)
ests_area_edo_combined <- edo_design %>%
srvyr::group_by(edo) %>%
srvyr::summarise(
est_area_madmex = srvyr::survey_ratio(numerator = y_M_madmex,
denominator = area),
est_area_bits = srvyr::survey_ratio(numerator = y_M_bits,
denominator = area)
)
ests_area_clase_madmex_combined <- edo_design %>%
srvyr::group_by(class_madmex) %>%
srvyr::summarise(
est_area_madmex = srvyr::survey_ratio(numerator = y_M_madmex,
denominator = area)
)
ests_area_clase_bits_combined <- edo_design %>%
srvyr::group_by(class_bits) %>%
srvyr::summarise(
est_area_bits = srvyr::survey_ratio(numerator = y_M_bits,
denominator = area)
)
ests_area_clase_experto_combined <- edo_design %>%
srvyr::group_by(class_expert_1) %>%
srvyr::summarise(
est_area_madmex = srvyr::survey_ratio(numerator = y_M_madmex, denominator = area),
est_area_bits = srvyr::survey_ratio(numerator = y_M_bits, denominator = area)
)
ests_porcent_combined <- edo_design %>%
srvyr::summarise(
est_porcent_madmex = srvyr::survey_mean(y_madmex),
est_porcent_bits = srvyr::survey_mean(y_bits)
)
ests_porcent_edo_combined <- edo_design %>%
srvyr::group_by(edo) %>%
srvyr::summarise(
est_porcent_madmex = srvyr::survey_mean(y_madmex),
est_porcent_bits = srvyr::survey_mean(y_bits)
)
ests_porcent_clase_madmex_combined <- edo_design %>%
srvyr::group_by(class_madmex) %>%
srvyr::summarise(
est_porcent_madmex = srvyr::survey_mean(y_madmex)
)
ests_porcent_clase_bits_combined <- edo_design %>%
srvyr::group_by(class_bits) %>%
srvyr::summarise(
est_porcent_bits = srvyr::survey_mean(y_bits)
)
ests_porcent_clase_experto_combined <- edo_design %>%
srvyr::group_by(class_expert_1) %>%
srvyr::summarise(
est_area_madmex = srvyr::survey_mean(y_madmex),
est_area_bits = srvyr::survey_mean(y_bits)
)
return(list(est_madmex_area_sep = ests_madmex_area_sep,
ests_bits_area_sep = ests_bits_area_sep,
ests_area_combined = ests_area_combined,
ests_area_edo_combined = ests_area_edo_combined,
ests_area_clase_madmex_combined = ests_area_clase_madmex_combined,
ests_area_clase_bits_combined = ests_area_clase_bits_combined,
ests_area_clase_experto_combined = ests_area_clase_experto_combined,
ests_porcent_combined = ests_porcent_combined,
ests_porcent_edo_combined = ests_porcent_edo_combined,
ests_porcent_clase_madmex_combined = ests_porcent_clase_madmex_combined,
ests_porcent_clase_bits_combined = ests_porcent_clase_bits_combined,
ests_porcent_clase_experto_combined = ests_porcent_clase_experto_combined
))
}
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