PomaEDA {POMA} | R Documentation |
This function automatically generates a PDF report with different exploratory plots and tables from an MSnSet object.
PomaEDA( data, imputation = "knn", normalization = "log_pareto", clean_outliers = TRUE, coeff_outliers = 1.5, username = "Username" )
data |
A MSnSet object. First |
imputation |
Imputation method. Options are "none", "half_min", "median", "mean", "min" and "knn" (default). If "none", all missing values will be replaced by zero. |
normalization |
Normalization method. Options are "none", "auto_scaling", "level_scaling", "log_scaling", "log_transformation", "vast_scaling" and "log_pareto" (default). |
clean_outliers |
Logical. If it's set to TRUE, outliers will be removed from EDA. |
coeff_outliers |
This value corresponds to the classical 1.5 in Q3 + 1.5*IQR formula to detect outliers. By changing this value, the permissiveness in outlier detection will change. |
username |
This name will be included as a report subtitle. |
An exploratory data analysis PDF report.
Pol Castellano-Escuder