Function reference • structToolbox

All functions

ANOVA(): Analysis of Variance

AUC(): Area under ROC curve

DFA(): Discriminant Factor Analysis

DatasetExperiment_boxplot(): Feature distribution histogram

DatasetExperiment_dist(): Feature distribution histogram

DatasetExperiment_factor_boxplot(): Factor boxplot

DatasetExperiment_heatmap(): DatasetExperiment heatmap

HCA(): Hierarchical Cluster Analysis

HSD(): Tukey's Honest Significant Difference

HSDEM(): Tukey's Honest Significant Difference using estimated marginal means

MTBLS79_DatasetExperiment(): MTBLS79: Direct infusion mass spectrometry metabolomics dataset: a benchmark for data processing and quality control

OPLSDA(): Orthogonal Partial Least Squares regression

OPLSR(): Orthogonal Partial Least Squares regression

PCA(): Principal Component Analysis (PCA)

PLSDA(): Partial least squares discriminant analysis

PLSR(): Partial least squares regression

SVM(): Support Vector Machine Classifier

as_data_frame(<filter_na_count>) as_data_frame(<ttest>) as_data_frame(<wilcox_test>): Convert to data.frame

autoscale(): Autoscaling

balanced_accuracy(): Balanced Accuracy

blank_filter(): Blank filter

blank_filter_hist(): Histogram of blank filter fold changes

bootstrap(): Bootstrap resampling

calculate(<AUC>) calculate(<balanced_accuracy>) calculate(<r_squared>): Calculate metric

chart_plot(<dfa_scores_plot>,<DFA>) chart_plot(<scatter_chart>,<DatasetExperiment>) chart_plot(<pca_correlation_plot>,<PCA>) chart_plot(<pca_scores_plot>,<PCA>) chart_plot(<pca_biplot>,<PCA>) chart_plot(<pca_loadings_plot>,<PCA>) chart_plot(<pca_scree_plot>,<PCA>) chart_plot(<pca_dstat_plot>,<PCA>) chart_plot(<plsr_prediction_plot>,<PLSR>) chart_plot(<plsr_residual_hist>,<PLSR>) chart_plot(<plsr_qq_plot>,<PLSR>) chart_plot(<plsr_cook_dist>,<PLSR>) chart_plot(<pls_scores_plot>,<PLSR>) chart_plot(<plsda_predicted_plot>,<PLSDA>) chart_plot(<plsda_roc_plot>,<PLSDA>) chart_plot(<pls_vip_plot>,<PLSR>) chart_plot(<pls_regcoeff_plot>,<PLSR>) chart_plot(<blank_filter_hist>,<blank_filter>) chart_plot(<confounders_lsq_barchart>,<confounders_clsq>) chart_plot(<confounders_lsq_boxplot>,<confounders_clsq>) chart_plot(<feature_boxplot>,<DatasetExperiment>) chart_plot(<mv_histogram>,<DatasetExperiment>) chart_plot(<mv_boxplot>,<DatasetExperiment>) chart_plot(<DatasetExperiment_dist>,<DatasetExperiment>) chart_plot(<DatasetExperiment_boxplot>,<DatasetExperiment>) chart_plot(<compare_dist>,<DatasetExperiment>) chart_plot(<DatasetExperiment_heatmap>,<DatasetExperiment>) chart_plot(<DatasetExperiment_factor_boxplot>,<DatasetExperiment>) chart_plot(<feature_profile_array>,<DatasetExperiment>) chart_plot(<feature_profile>,<DatasetExperiment>) chart_plot(<fold_change_plot>,<fold_change>) chart_plot(<fs_line>,<forward_selection_by_rank>) chart_plot(<glog_opt_plot>,<glog_transform>) chart_plot(<gs_line>,<grid_search_1d>) chart_plot(<hca_dendrogram>,<HCA>) chart_plot(<kfoldxcv_grid>,<kfold_xval>) chart_plot(<kfoldxcv_metric>,<kfold_xval>) chart_plot(<kw_p_hist>,<kw_rank_sum>) chart_plot(<mv_feature_filter_hist>,<mv_feature_filter>) chart_plot(<mv_sample_filter_hist>,<mv_sample_filter>) chart_plot(<permutation_test_plot>,<permutation_test>) chart_plot(<plsda_feature_importance_plot>,<PLSDA>) chart_plot(<pqn_norm_hist>,<pqn_norm>) chart_plot(<resample_chart>,<resample>) chart_plot(<rsd_filter_hist>,<rsd_filter>) chart_plot(<feature_profile>,<sb_corr>) chart_plot(<svm_plot_2d>,<SVM>) chart_plot(<tSNE_scatter>,<tSNE>) chart_plot(<tic_chart>,<DatasetExperiment>) chart_plot(<wilcox_p_hist>,<wilcox_test>): chart_plot method

classical_lsq(): Univariate Classical Least Squares Regression

compare_dist(): Compare distributions

confounders_clsq(): Check for confounding factors

confounders_lsq_barchart(): Confounding factor relative change barchart

confounders_lsq_boxplot(): Confounding factor relative change boxplot

constant_sum_norm(): Normalisation to constant sum

corr_coef(): Correlation coefficient

dfa_scores_plot(): DFA scores plot

dratio_filter(): Dispersion ratio filter

equal_split(): Equal group sized sampling

feature_boxplot(): Feature boxplot

feature_profile(): Feature profile

feature_profile_array(): Feature profile

filter_by_name(): Filter by name

filter_na_count(): Minimum number of measured values filter

filter_smeta(): Filter by sample meta data

fisher_exact(): Fisher Exact Test

fold_change(): Fold change

fold_change_int(): Fold change for interactions between factors

fold_change_plot(): Fold change plot

forward_selection_by_rank(): Forward selection by rank

fs_line(): Forward selection line plot

glog_opt_plot(): Glog optimisation

glog_transform(): Generalised logarithmic transform

grid_search_1d(): One dimensional grid search

gs_line(): Grid search line plot

hca_dendrogram(): HCA dendrogram

kfold_xval(): k-fold cross-validation

kfoldxcv_grid(): k-fold cross validation plot

kfoldxcv_metric(): kfoldxcv metric plot

knn_impute(): kNN missing value imputation

kw_p_hist(): Histogram of p values

kw_rank_sum(): Kruskal-Wallis rank sum test

linear_model(): Linear model

log_transform(): logarithm transform

mean_centre(): Mean centre

mean_of_medians(): Mean of medians

mixed_effect(): Mixed effects model

model_apply(<ANOVA>,<DatasetExperiment>) model_apply(<HSD>,<DatasetExperiment>) model_apply(<mixed_effect>,<DatasetExperiment>) model_apply(<HSDEM>,<DatasetExperiment>) model_apply(<classical_lsq>,<DatasetExperiment>) model_apply(<confounders_clsq>,<DatasetExperiment>) model_apply(<constant_sum_norm>,<DatasetExperiment>) model_apply(<corr_coef>,<DatasetExperiment>) model_apply(<split_data>,<DatasetExperiment>) model_apply(<equal_split>,<DatasetExperiment>) model_apply(<filter_smeta>,<DatasetExperiment>) model_apply(<fisher_exact>,<DatasetExperiment>) model_apply(<fold_change>,<DatasetExperiment>) model_apply(<fold_change_int>,<DatasetExperiment>) model_apply(<HCA>,<DatasetExperiment>) model_apply(<knn_impute>,<DatasetExperiment>) model_apply(<kw_rank_sum>,<DatasetExperiment>) model_apply(<log_transform>,<DatasetExperiment>) model_apply(<mean_of_medians>,<DatasetExperiment>) model_apply(<nroot_transform>,<DatasetExperiment>) model_apply(<pairs_filter>,<DatasetExperiment>) model_apply(<prop_na>,<DatasetExperiment>) model_apply(<rsd_filter>,<DatasetExperiment>) model_apply(<sb_corr>,<DatasetExperiment>) model_apply(<stratified_split>,<DatasetExperiment>) model_apply(<tSNE>,<DatasetExperiment>) model_apply(<ttest>,<DatasetExperiment>) model_apply(<vec_norm>,<DatasetExperiment>) model_apply(<wilcox_test>,<DatasetExperiment>): Apply method

model_predict(<DFA>,<DatasetExperiment>) model_predict(<PCA>,<DatasetExperiment>) model_predict(<PLSR>,<DatasetExperiment>) model_predict(<PLSDA>,<DatasetExperiment>) model_predict(<autoscale>,<DatasetExperiment>) model_predict(<blank_filter>,<DatasetExperiment>) model_predict(<constant_sum_norm>,<DatasetExperiment>) model_predict(<dratio_filter>,<DatasetExperiment>) model_predict(<filter_by_name>,<DatasetExperiment>) model_predict(<filter_na_count>,<DatasetExperiment>) model_predict(<filter_smeta>,<DatasetExperiment>) model_predict(<glog_transform>,<DatasetExperiment>) model_predict(<linear_model>,<DatasetExperiment>) model_predict(<mean_centre>,<DatasetExperiment>) model_predict(<mv_feature_filter>,<DatasetExperiment>) model_predict(<mv_sample_filter>,<DatasetExperiment>) model_predict(<OPLSR>,<DatasetExperiment>) model_predict(<OPLSDA>,<DatasetExperiment>) model_predict(<pareto_scale>,<DatasetExperiment>) model_predict(<pqn_norm>,<DatasetExperiment>) model_predict(<SVM>,<DatasetExperiment>) model_predict(<vec_norm>,<DatasetExperiment>): Model prediction

model_reverse(<autoscale>,<DatasetExperiment>) model_reverse(<mean_centre>,<DatasetExperiment>): Reverse preprocessing

model_train(<DFA>,<DatasetExperiment>) model_train(<PCA>,<DatasetExperiment>) model_train(<PLSR>,<DatasetExperiment>) model_train(<PLSDA>,<DatasetExperiment>) model_train(<autoscale>,<DatasetExperiment>) model_train(<blank_filter>,<DatasetExperiment>) model_train(<constant_sum_norm>,<DatasetExperiment>) model_train(<dratio_filter>,<DatasetExperiment>) model_train(<filter_by_name>,<DatasetExperiment>) model_train(<filter_na_count>,<DatasetExperiment>) model_train(<filter_smeta>,<DatasetExperiment>) model_train(<glog_transform>,<DatasetExperiment>) model_train(<linear_model>,<DatasetExperiment>) model_train(<mean_centre>,<DatasetExperiment>) model_train(<mv_feature_filter>,<DatasetExperiment>) model_train(<mv_sample_filter>,<DatasetExperiment>) model_train(<OPLSR>,<DatasetExperiment>) model_train(<OPLSDA>,<DatasetExperiment>) model_train(<pareto_scale>,<DatasetExperiment>) model_train(<pqn_norm>,<DatasetExperiment>) model_train(<SVM>,<DatasetExperiment>) model_train(<vec_norm>,<DatasetExperiment>): Train a model

mv_boxplot(): Missing value boxplots

mv_feature_filter(): Filter features by missing values

mv_feature_filter_hist(): Histogram of missing values per feature

mv_histogram(): Missing value histogram

mv_sample_filter(): Missing value sample filter

mv_sample_filter_hist(): Histogram of missing values per sample

nroot_transform(): nth root transform

ontology_cache(): ontology cache

pairs_filter(): Pairs filter

pareto_scale(): Pareto scaling

pca_biplot(): PCA biplot

pca_correlation_plot(): PCA correlation plot

pca_dstat_plot(): d-statistic plot

pca_loadings_plot(): PCA loadings plot

pca_scores_plot(): PCA scores plot

pca_scree_plot(): Scree plot

permutation_test(): Permutation test

permutation_test_plot(): permutation_test_plot class

permute_sample_order(): Permute Sample Order

pls_regcoeff_plot(): pls_regcoeff_plot class

pls_scores_plot() plsda_scores_plot(): PLSDA scores plot

pls_vip_plot(): PLSDA VIP plot

plsda_feature_importance_plot(): PLSDA feature importance summary plot

plsda_predicted_plot(): PLSDA predicted plot

plsda_roc_plot(): PLSDA ROC plot

plsr_cook_dist(): Cook's distance barchart

plsr_prediction_plot(): PLSR prediction plot

plsr_qq_plot(): PLSR QQ plot

plsr_residual_hist(): PLSR residuals histogram

pqn_norm(): Probabilistic Quotient Normalisation (PQN)

pqn_norm_hist(): PQN coefficient histogram

prop_na(): Fisher's exact test for missing values

r_squared(): Coefficient of determination (R-squared)

resample(): Data resampling

resample_chart(): resample_chart class

rsd_filter(): RSD filter

rsd_filter_hist(): RSD histogram

run(<bootstrap>,<DatasetExperiment>,<metric>) run(<forward_selection_by_rank>,<DatasetExperiment>,<metric>) run(<grid_search_1d>,<DatasetExperiment>,<metric>) run(<kfold_xval>,<DatasetExperiment>,<metric>) run(<permutation_test>,<DatasetExperiment>,<metric>) run(<permute_sample_order>,<DatasetExperiment>,<metric>) run(<resample>,<DatasetExperiment>,<metric>): Runs an iterator, applying the chosen model multiple times.

sb_corr(): Signal/batch correction for mass spectrometry data

scatter_chart(): Group scatter chart

split_data(): Split data

stratified_split(): Stratified sampling

structToolbox: structToolbox: Examples of tools built using the Statistics in R Using Class Templates (struct) package

svm_plot_2d(): SVM scatter plot

tSNE(): tSNE

tSNE_scatter(): Feature boxplot

tic_chart(): Total Ion Count chart.

ttest(): t-test

vec_norm(): Vector normalisation

wilcox_p_hist(): Histogram of p values

wilcox_test(): wilcoxon signed rank test