best_Spatial_CorrStruct: Find the best spatial correlation structure in GLMM run with...

In MarcoPittarello/AnalysisTools: A set of support tools for data analysis

Find the best spatial correlation structure in GLMM run with 'lme' function in 'nlme' package.

Description

When through the function (modelCheck_plots) spatial autocorrelation is found in the residuals of a model performed with (gls) or (lme), it may be necessary to specify the structure of this spatial correlation through the 'correlation' argument.
Among the different correlation structures (see (CorStruct)), those supported by the present function are:

• corGaus –> Gaussian spatial correlation

• corExp –> Exponential spatial correlation

• corSpher –> Spherical spatial correlation

• corLin –> Linear spatial correlation.
  

See @examples section for operative details

Usage

best_Spatial_CorrStruct(
  data,
  X,
  Y,
  var.res,
  range,
  sill,
  nugget,
  spatial.formula
)

Arguments

data	a data.frame used as data input in the model
X	column within the dataset containing the coordinates X (e.g.: dataset$X)
Y	column within the dataset containing the coordinates Y (e.g.: dataset$Y)
var.res	column within the dataset containing the response variable or model residuals obtained with residuals(model,type="pearson") see @examples
range	value of the 'range' identifiable from the semivariogram obtained with (modelCheck_plots).
sill	value of the 'sill' identifiable from the semivariogram obtained with (modelCheck_plots).
nugget	value of the 'nugget' identifiable from the semivariogram obtained with (modelCheck_plots).
spatial.formula	a one sided formula of the form ~ S1+...+Sp, or ~ S1+...+Sp | g, specifying spatial covariates S1 through Sp and, optionally, a grouping factor g. For more details see the 'form' argument of e.g. (corGaus).

Value

Linear, exponential, gaussian and spherical models plotted on the semivariogram.
It is also provided in the console the string to copy and paste in 'correlation' argument in the functions: (gls), (lme) of 'nlme' package"

References

• Gałecki, A., & Burzykowski, T. (2013). Linear mixed-effects model. In Linear mixed-effects models using R (pp. 245-273). Springer, New York, NY.

• Jack Weiss tutorial (2012). Fitting theoretical models to the empirical semivariogram

Examples

1- define from (modelCheck_plots) which are the values of range, sill, and nugget. 
For a definition of range, sill, and nugget terminology see here 


2- run 'best_Spatial_CorrStruct' function:
best_Spatial_CorrStruct(data=Dataset, 
X = Dataset$X,
Y = Dataset$Y,
var.res = residuals(model,type="pearson"),
range = 5,
sill = 5,
nugget = 4,
spatial.formula = "~ X + Y")

MarcoPittarello/AnalysisTools documentation built on Nov. 4, 2022, 7:35 p.m.