spMVT {spANOVA}

Multiple comparison test based on multivariate t student distribution

Description

Use a multivariate t student distribution to assess the equality of means.

Usage

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

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

## S3 method for class 'GEOanova'
spMVT(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 general linear hypothesis method provided by the function glht on the adjusted response.

For objects of class GEOanova, the test is modified to accommodate the spatial dependence among the observations as pointed out by Nogueira (2017)

Value

a data frame containing the original mean, the spatially filtered mean and its group. For the class GEOanova, the spatial dependence is filtered out using geostatistics, while for the class SARanova the adjusted response based on SAR model is employed.

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)

lines(ols, col = 1)

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

# Multivariate T test
spMVT(mod)