summarize_all_cluster: Summarize all cluster sample

In SilviaTerra/forestsamplr: Standard Forest Sampling Design Workups

Description

Summarizes population-level statistics for cluster sample data. This function has two options: (1) Cluster sample with a normal distribution and (2) Cluster sample with a Bernoulli distribution.

Usage

summarize_all_cluster(data, attribute = NA, element = TRUE, 
                             plotTot = NA, 
                             desiredConfidence = 0.95,
                             bernoulli = F)

Arguments

data	data frame containing observations of variable of interest for either cluster-level or plot-level data.
attribute	character name of attribute to be summarized.
element	logical true if parameter data is plot-level, false if parameter data is cluster-level. Default is True.
plotTot	numeric population size. Equivalent to the total number of possible elements in the population.
desiredConfidence	numeric desired confidence level (e.g. 0.9).
bernoulli	logical TRUE if data fitting the Bernoulli distribution is used.

Value

data frame of stand-level statistics including standard error and confidence interval limits.

Author(s)

Karin Wolken

Examples

## Not run: 

# See Forest Sampling vignette for more details

# Plot level data can be expressed as:

plotLevelDataExample <- data.frame(clusterID = c(1, 1, 1, 1, 1, 2,  
                                                 2, 3, 4, 4, 4, 4, 
                                                 4, 4, 5, 5, 5, 5, 
                                                 5), 
                                   attr = c(1000, 1250, 950, 900, 
                                            1005, 1000, 1250, 950,
                                            900, 1005, 1000, 1250, 
                                            950, 900, 1005, 1000, 
                                            1250, 950, 900), 
                                   isUsed = c(T, T, T, T, T, T, T, 
                                              T, T, T, T, T, T, T, 
                                              F, F, F, F, F))

# Cluster level data can be expressed as: 

clusterLevelDataExample <- data.frame(clusterID = c(1, 2, 3, 4, 5), 
                                      clusterElements = c(4, 2, 9, 
                                                          4, 10), 
                                      sumAttr = c(1000, 1250, 950,
                                                  900, 1005), 
                                      isUsed = c(T, T, F, T, T))
# Set element = FALSE


# Bernoulli data can be expressed as: 

bernoulliData <- data.frame(plots = c(1, 2, 3, 4, 5, 6, 7, 8, 9, 10),
                            propAlive = c(0.75, 0.80, 0.80, 0.85, 
                                          0.70, 0.90, 0.70, 0.75, 
                                          0.80, 0.65))
# Set parameter bernoulli = TRUE


## End(Not run)

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