spScottKnott {spANOVA}

The Scott-Knott Clustering Algorithm

Description

This function implements the Scott-Knott Clustering Algorithm for objects of class SARcrd, SARrcbd, and GEOanova.

Usage

spScottKnott(x, sig.level = 0.05, verbose = TRUE)

## S3 method for class 'SARanova'
spScottKnott(x, sig.level = 0.05, verbose = TRUE)

## S3 method for class 'GEOanova'
spScottKnott(x, sig.level = 0.05, verbose = TRUE)

Arguments

x

a fitted model object of class SARcrd, SARrcbd or GEOanova.

sig.level

a numeric value between zero and one giving the significance level to use.

verbose

should messages be printed during loading?

Details

For objects of class SARcrd or SARrcbd this function performs the standard Scott-Knott Clustering Algorithm provided by the function SK on the adjusted response.

For objects of class GEOanova, the method is modified to take into account the spatial dependence among the observations. The method is described in Nogueira (2017).

Value

a data frame containing the means and its group

References

Nogueira, C. H. Testes para comparações múltiplas de médias em experimentos com tendência e dependência espacial. 142 f. Tese (Doutorado em Estatística e Experimentação Agropecuária) | Universidade Federal de Lavras, Lavras, 2017

Examples

data("crd_simulated")

#Geodata object
geodados <- as.geodata(crd_simulated, coords.col = 1:2, data.col = 3,
                      covar.col = 4)
h_max <- summary(geodados)[[3]][[2]]
dist <- 0.6*h_max

# Computing the variogram
variograma <- spVariog(geodata = geodados,
                      trend = "cte", max.dist = dist, design = "crd",
                      scale = FALSE)

plot(variograma, ylab = "Semivariance", xlab = "Distance")

# Gaussian Model
ols <- spVariofit(variograma, cov.model = "gaussian", weights = "equal",
                 max.dist = dist)


# Compute the model and get the analysis of variance table
mod <- aovGeo(ols, cutoff = 0.6)

# Scott-Knott clustering algorithm
spScottKnott(mod)