summarize_stratified: Summarize stratified sample

In SilviaTerra/forestsamplr: Standard Forest Sampling Design Workups

Description

Summarizes strata- and population-level statistics for stratified sample data. The calculations are derived from Chapter 5 in Gregoire and Valentine's (2008) Sampling Strategies for Natural Resources and the Environment. The variance terms refer to the variance of the mean, hence the n terms in the denominators. This function has two options: (1) stratified and (2) post-stratified.

Usage

summarize_stratified(trainingData, attribute, stratumTab, 
                            desiredConfidence = 0.95, post = T)

Arguments

trainingData	data frame containing observations of variable of interest, and stratum assignment for each plot
attribute	character name of attribute to be summarized
stratumTab	data frame containing acreages for each stratum
desiredConfidence	numeric desired confidence level (e.g. 0.9)
post	logical true if post-stratification was used

Value

a data frame of population mean, variance, standard error, and high and low confidence limits.

Author(s)

Henry Rodman

Examples

## Not run: 

# See Forest Sampling vignette for more details

# Data can be expressed as:

trainingData <- data.frame(bapa = c(120, 140, 160, 110, 100, 90),
                           stratum = c(1, 1, 1, 2, 2, 2))
stratumTab <- data.frame(stratum = c(1, 2), acres = c(200, 50))
attribute = 'bapa'
desiredConfidence = 0.9


## End(Not run)

SilviaTerra/forestsamplr documentation built on Jan. 3, 2020, 2:33 p.m.