A Minimal Book Example
1
Data processing
1.1
Objective
1.2
Useful libraries for data handling
1.3
Québec potato data set
1.4
Selection of useful variables
1.5
Arranging the data frame
1.6
Cultivar classes correction
1.7
Summarise and backup
2
Ionome analysis
2.1
Objective
2.2
Useful libraries and custom functions
2.3
Leaves processed compositions data set
2.4
The yield cut-off, low and high yielders delimiter
2.5
Centered log-ratio (clr) centroids computation
2.6
Clustering potato cultivars with leaf ionome
2.7
Axis reduction
2.8
Do clrs affect potato tuber yield?
3
Predicting tuber yield category
3.1
Objective
3.2
Useful libraries
3.3
Machine learning data set
3.4
Machine learning
3.4.1
Data train and test splits
3.4.2
Building the Models
3.4.3
Goodness of fit on training set
3.4.4
Models’ evaluation (test set)
3.5
Variable importance estimation
3.6
Make predictions on the test set with
kknn
model
3.7
Comparison with non-informative classification
3.8
Nutritionally balanced compositions
4
Ionome perturbation concept
4.1
Objective
4.2
Data set and useful libraries
4.3
Euclidean distance from nutritionally balanced compositions
4.4
Perturbation effect of some elements on the whole
4.5
Rebalancing a misbalanced sample by perturbation
References
Published with bookdown
Balancing the nutritional status of potato crops.
References