Spatial Sampling Design and Analysis
Internal Function: Variance-Covariance Matrix Based on Local Mean Esti...
Internal Function: Local Mean Variance Estimator
Internal Function: Local Mean Variance Neighbors and Weights
Summary characteristics of a panel revisit design
Plot sampling frames, design sites, and analysis data.
Plot a cumulative distribution function (CDF)
Power calculation for multiple panel designs
Plot power curves for panel designs
Relative risk analysis
Adjust survey design weights by categories
Adjust survey design weights for non-response by categories
Compute the average shifted histogram (ASH) for one-dimensional weight...
Attributable risk analysis
Categorical variable analysis
Plot a cumulative distribution function (CDF)
Change analysis
Continuous variable analysis
Create a PDF file containing cumulative distribution functions (CDF) p...
Cumulative distribution function (CDF) inference for a probability sur...
Create a covariance matrix for a panel design
Risk difference analysis
Print errors from analysis functions
Select a generalized random tessellation stratified (GRTS) sample
Select an independent random sample (IRS)
Create a balanced incomplete block panel revisit design
Create a panel revisit design
Create a revisit design with random assignment to panels and time peri...
Calculate spatial balance metrics
sp_frame objects
Plot sampling frames, design sites, and analysis data.
Combine rows from GRTS or IRS samples.
Summarize sampling frames, design sites, and analysis data.
spsurvey: Spatial Sampling Design and Analysis
Print grts() and irs() errors.
Summarize sampling frames, design sites, and analysis data.
Trend analysis
Print grts(), irs()), and analysis function warnings
A design-based approach to statistical inference, with a focus on spatial data. Spatially balanced samples are selected using the Generalized Random Tessellation Stratified (GRTS) algorithm. The GRTS algorithm can be applied to finite resources (point geometries) and infinite resources (linear / linestring and areal / polygon geometries) and flexibly accommodates a diverse set of sampling design features, including stratification, unequal inclusion probabilities, proportional (to size) inclusion probabilities, legacy (historical) sites, a minimum distance between sites, and two options for replacement sites (reverse hierarchical order and nearest neighbor). Data are analyzed using a wide range of analysis functions that perform categorical variable analysis, continuous variable analysis, attributable risk analysis, risk difference analysis, relative risk analysis, change analysis, and trend analysis. spsurvey can also be used to summarize objects, visualize objects, select samples that are not spatially balanced, select panel samples, measure the amount of spatial balance in a sample, adjust design weights, and more. For additional details, see Dumelle et al. (2023) <doi:10.18637/jss.v105.i03>.
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