What is Klovan v0.0.9 and what does it do?
The “Klovan v0.0.9” package offers a comprehensive set of
geostatistical, visual, and analytical methods, in conjunction with the
expanded version of the acclaimed J.E. Klovan’s mining dataset. This
makes the package an excellent learning resource for Principal Component
Analysis (PCA), Factor Analysis (FA), kriging, and other geostatistical
techniques. Originally published in the 1976 book ‘Geological Factor
Analysis’, the included mining dataset was assembled by Professor J. E.
Klovan of the University of Calgary. Being one of the first applications
of FA in the geosciences, this dataset has significant historical
importance. As a well-regarded and published dataset, it is an excellent
resource for demonstrating the capabilities of PCA, FA, kriging, and
other geostatistical techniques in geosciences. For those interested in
these methods, the ’Klovan’dataset provides a valuable and illustrative
resource. Note that some methods in the Klovan Package require the
Rgeostats package. Please refer to the README for installation
instructions. This package was supported by the MDS3 Center for
Materials Data Science for Stockpile Stewardship.
Here we show how to use the packages features with the use of the
suggested Rgeostats package
Install and load the package
After downloading the package file “Klovan_0.0.9.tar.gz”, put it in
your preferred working directory and run both of the following
lines:
# install.packages("Klovan_0.0.9.tar.gz", repos = NULL, type = "source")
# library(klovan)
Alternatively in your Rstudio console use this code:
# install.packages("klovan")
# library(klovan)
Loading data
#library(RGeostats)
#data("Klovan_Row80", package = "klovan")
Klovan_Row80 <- load(file = "~/CSE_MSE_RXF131/cradle-members/sdle/jeg165/git/klovan/packages/Klovan0.0.9/data/Klovan_Row80.rda")
In the code above, we load the required data from the klovan
package.
Here we create a database from our klovan dataframe to use in our
analysis.
# Building a database based on RC1 factor
db <- Rgeo_database(Klovan_Row80, 3, "RC1")
Print the created database
In this block, we build a database using the Rgeo_database()
function and then print it.
# Construct and plot the experimental variogram
Rgeo_vario_construct_plot(db, 3, "RC1", lag = 500)
Here, we construct and plot the experimental variogram for our data.
This plot will help us to determine how to build our variogram
model.
# Fit the variogram model based on experimental variogram
model <- Rgeo_vario_model(db, 3, "RC1", lag = 500, model = 13)
Print the fitted model parameters
Based on our experimental variogram, we fit a variogram model using
the Rgeo_vario_model() function. The resulting model parameters are
printed for review.
Finally, we plot the kriging estimation results. This visualization
can be used to better understand the spatial distribution of the “RC1”
variable.
Remember, this analysis is based on the “RC1” factor. To analyze
other factors, run the analysis again from the database building stage,
changing the factor as necessary.
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
