Function reference • PRECISION.array

All functions

amplify.handling.effect(): Handling effect amplification

blocking.design(): Blocking Design

calc.confounding.level(): Level of confounding calculation

clanc.intcv(): Classification to Nearest Centroids Classifier

clanc.predict(): Prediction with Classification to Nearest Centroids classifier

confounding.design(): Confounding Design

create.storage(): Create Storage for Output

dlda.intcv(): Diagonal Linear Discriminant Classifier

dlda.predict(): Prediction with Diagonal Linear Discriminant classifier

estimate.biological.effect(): Estimated Sample Effects

estimate.handling.effect(): Estimated handling effects

extract.precision.error(): Extracting errors from PRECISION (both non-FLEX and FLEX) output

knn.intcv(): K-Nearest Neighbors Classifier

knn.predict(): Prediction with K-Nearest Neighbors classifier

lasso.intcv(): Least absolute shrinkage and selection operator through internal cross validation

lasso.predict(): Prediction with least absolute shrinkage and selection operator classifier

limma.pbset(): Differential expression analysis of probe-set data

med.norm(): Median normalization

med.sum.pbset(): Probe-set median summarization

nuhdata.pl: The nonuniformly-handled probe-level dataset, 10 probes for each unique probe

pam.intcv(): Nearest shrunken centroid through internal cross validation

pam.predict(): Prediction with nearest shrunken centroid classifier

per.unipbset.truncate(): Probe-level data truncation to a fixed number of probes per unique probe-set

plot(<precision.multiclass>): plot.precision.multiclass

plot(<precision>): Plot misclassification error rates from PRECISION (both non-FLEX and FLEX) output

precision(<simulate.class>): precision simulation with classification

precision(<simulate.multiclass>): precision simulation with multi-classification

precision(<simulate>): Classification analysis of simulation study

quant.norm(): Quantile normalization

ranfor.intcv(): Random Forest Classifier

ranfor.predict(): Prediction with random forest classifier

reduce.signal(): Biological signal reduction

rehybridize(): Virtual rehybridization with an array-to-sample assignment

stratification.design(): Stratification Design

svm(<intcv>): Support Vector Machine Classifier

svm(<predict>): Prediction with support vector machine classifier

switch.classifier.funcs.class(): Switch classfication functions

switch.classifier.funcs(): Switch classfication functions

switch.norm.funcs.flex(): Switch normalization funcsions in a flexible way

switch.norm.funcs(): Switch Normalization Functions

tabulate.ext.err.func(): Tabulate.ext.err.func

uhdata.pl: The uniformly-handled probe-level dataset, 10 probes for each unique probe

uni.handled.simulate(): Classification analysis of uniformly-handled data

vs.norm(): Variance stabilizing normalization