## ----setup, include=FALSE-----------------------------------------------------
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  fig.width = 6.5,
  fig.height = 4,
  fig.align = "center",
  warning = FALSE,
  message = FALSE
)
library(NRMSampling)
set.seed(123)

## -----------------------------------------------------------------------------
sample_nrm <- data.frame(
  plot_id   = 1:100,
  biomass   = round(runif(100, 15, 65), 1),
  soil_loss = round(runif(100, 0, 12), 2),
  rainfall  = round(runif(100, 800, 1500)),
  slope     = round(runif(100, 1, 35), 1),
  strata    = sample(c("forest", "grassland", "agriculture"), 100, replace = TRUE),
  cluster   = sample(1:10, 100, replace = TRUE),
  size      = round(runif(100, 0.5, 5.0), 2)
)
head(sample_nrm)

## -----------------------------------------------------------------------------
srs <- srs_sample(sample_nrm, n = 25)
head(srs)

## -----------------------------------------------------------------------------
st <- stratified_sample(sample_nrm, strata_var = "strata", n_per_stratum = 8)
table(st$strata)

## -----------------------------------------------------------------------------
cl <- cluster_sample(sample_nrm, cluster_var = "cluster", n_clusters = 4)
length(unique(cl$cluster))

## -----------------------------------------------------------------------------
pps <- pps_sample(sample_nrm, size_var = "size", n = 20)
summary(pps$.inclusion_prob)

## -----------------------------------------------------------------------------
conv <- convenience_sample(sample_nrm, n = 10)

purp <- purposive_sample(sample_nrm, "biomass > 45 & strata == 'forest'")

quot <- quota_sample(sample_nrm, strata_var = "strata", quota = 6)
table(quot$strata)

## -----------------------------------------------------------------------------
N <- nrow(sample_nrm)
srs_est <- srs_sample(sample_nrm, n = 30)

estimate_mean(srs_est$biomass)
estimate_total(srs_est$biomass, N = N)
estimate_se(srs_est$biomass, N = N)

## -----------------------------------------------------------------------------
X_total <- sum(sample_nrm$size)
X_mean  <- mean(sample_nrm$size)

ratio_estimator(srs_est$biomass, srs_est$size, X_total)
regression_estimator(srs_est$biomass, srs_est$size, X_mean)

## -----------------------------------------------------------------------------
ht_estimator(pps$biomass, pps$.inclusion_prob)
ht_variance(pps$biomass, pps$.inclusion_prob)

## -----------------------------------------------------------------------------
N_h <- table(sample_nrm$strata)
stratified_estimator(st$biomass, st$strata, N_h)

## -----------------------------------------------------------------------------
bio <- biomass_estimate(srs_est, biomass_var = "biomass", area = 1500)
bio$total_biomass

carbon_stock_estimate(srs_est, biomass_var = "biomass", area = 1500)

## -----------------------------------------------------------------------------
sl <- soil_loss_estimate(srs_est, loss_var = "soil_loss", area = 1500)
sl$total_loss

## -----------------------------------------------------------------------------
srs1 <- srs_sample(sample_nrm, n = 20)
srs2 <- srs_sample(sample_nrm, n = 40)

sampling_efficiency(srs1$biomass, srs2$biomass, N = 100)

## ----eval=FALSE---------------------------------------------------------------
# library(sf)
# 
# sample_spatial <- data.frame(
#   lon = runif(50, 77.8, 78.2),
#   lat = runif(50, 30.1, 30.4)
# )
# 
# pts_sf <- to_sf_points(sample_spatial, lon = "lon", lat = "lat")
# head(pts_sf)