revdep/checks.noindex/OmicsMarkeR/old/OmicsMarkeR.Rcheck/OmicsMarkeR-Ex.R
In gbm-developers/gbm: Generalized Boosted Regression Models
pkgname <- "OmicsMarkeR"
source(file.path(R.home("share"), "R", "examples-header.R"))
options(warn = 1)
library('OmicsMarkeR')
base::assign(".oldSearch", base::search(), pos = 'CheckExEnv')
base::assign(".old_wd", base::getwd(), pos = 'CheckExEnv')
cleanEx()
nameEx("RPT")
### * RPT
flush(stderr()); flush(stdout())
### Name: RPT
### Title: Robustness-Performance Trade-Off
### Aliases: RPT
### ** Examples
# RPT demo
RPT(stability=0.85, performance=0.90, beta=1)
cleanEx()
nameEx("aggregation")
### * aggregation
flush(stderr()); flush(stdout())
### Name: aggregation
### Title: Feature Aggregation
### Aliases: aggregation
### ** Examples
# test data
ranks <- replicate(5, sample(seq(50), 50))
row.names(ranks) <- paste0("V", seq(50))
aggregation(ranks, "CLA")
cleanEx()
nameEx("canberra")
### * canberra
flush(stderr()); flush(stdout())
### Name: canberra
### Title: Canberra Distance
### Aliases: canberra
### ** Examples
# Canberra demo
v1 <- seq(10)
v2 <- sample(v1, 10)
canberra(v1, v2)
canberra_stability(v1, v2)
cleanEx()
nameEx("canberra_stability")
### * canberra_stability
flush(stderr()); flush(stdout())
### Name: canberra_stability
### Title: Canberra Stability
### Aliases: canberra_stability
### ** Examples
# Canberra demo
v1 <- seq(10)
v2 <- sample(v1, 10)
canberra(v1, v2)
canberra_stability(v1, v2)
cleanEx()
nameEx("create.corr.matrix")
### * create.corr.matrix
flush(stderr()); flush(stdout())
### Name: create.corr.matrix
### Title: Correlated Multivariate Data Generator
### Aliases: create.corr.matrix
### ** Examples
# Create Multivariate Matrices
# Random Multivariate Matrix
# 50 variables, 100 samples, 1 standard devation, 0.2 noise factor
rand.mat <- create.random.matrix(nvar = 50,
nsamp = 100,
st.dev = 1,
perturb = 0.2)
# Induce correlations in a numeric matrix
# Default settings
# minimum and maximum block sizes (min.block.size = 2, max.block.size = 5)
# default correlation purturbation (k=4)
# see ?create.corr.matrix for citation for methods
corr.mat <- create.corr.matrix(rand.mat)
# Induce Discriminatory Variables
# 10 discriminatory variables (D = 10)
# default discrimination level (l = 1.5)
# default number of groups (num.groups=2)
# default correlation purturbation (k = 4)
dat.discr <- create.discr.matrix(corr.mat, D=10)
cleanEx()
nameEx("create.discr.matrix")
### * create.discr.matrix
flush(stderr()); flush(stdout())
### Name: create.discr.matrix
### Title: Discriminatory Multivariate Data Generator
### Aliases: create.discr.matrix
### ** Examples
# Create Multivariate Matrices
# Random Multivariate Matrix
# 50 variables, 100 samples, 1 standard devation, 0.2 noise factor
rand.mat <- create.random.matrix(nvar = 50,
nsamp = 100,
st.dev = 1,
perturb = 0.2)
# Induce correlations in a numeric matrix
# Default settings
# minimum and maximum block sizes (min.block.size = 2, max.block.size = 5)
# default correlation purturbation (k=4)
# see ?create.corr.matrix for citation for methods
corr.mat <- create.corr.matrix(rand.mat)
# Induce Discriminatory Variables
# 10 discriminatory variables (D = 10)
# default discrimination level (l = 1.5)
# default number of groups (num.groups=2)
# default correlation purturbation (k = 4)
dat.discr <- create.discr.matrix(corr.mat, D=10)
cleanEx()
nameEx("create.random.matrix")
### * create.random.matrix
flush(stderr()); flush(stdout())
### Name: create.random.matrix
### Title: Random Multivariate Data Generator
### Aliases: create.random.matrix
### ** Examples
# Create Multivariate Matrices
# Random Multivariate Matrix
# 50 variables, 100 samples, 1 standard devation, 0.2 noise factor
rand.mat <- create.random.matrix(nvar = 50,
nsamp = 100,
st.dev = 1,
perturb = 0.2)
# Induce correlations in a numeric matrix
# Default settings
# minimum and maximum block sizes (min.block.size = 2, max.block.size = 5)
# default correlation purturbation (k=4)
# see ?create.corr.matrix for citation for methods
corr.mat <- create.corr.matrix(rand.mat)
# Induce Discriminatory Variables
# 10 discriminatory variables (D = 10)
# default discrimination level (l = 1.5)
# default number of groups (num.groups=2)
# default correlation purturbation (k = 4)
dat.discr <- create.discr.matrix(corr.mat, D=10)
cleanEx()
nameEx("denovo.grid")
### * denovo.grid
flush(stderr()); flush(stdout())
### Name: denovo.grid
### Title: Denovo Grid Generation
### Aliases: denovo.grid
### ** Examples
# random test data
set.seed(123)
dat.discr <- create.discr.matrix(
create.corr.matrix(
create.random.matrix(nvar = 50,
nsamp = 100,
st.dev = 1,
perturb = 0.2)),
D = 10
)
df <- data.frame(dat.discr$discr.mat, .classes = dat.discr$classes)
# create tuning grid
denovo.grid(df, "gbm", 3)
cleanEx()
nameEx("feature.table")
### * feature.table
flush(stderr()); flush(stdout())
### Name: feature.table
### Title: Feature Consistency Table
### Aliases: feature.table
### ** Examples
dat.discr <- create.discr.matrix(
create.corr.matrix(
create.random.matrix(nvar = 50,
nsamp = 100,
st.dev = 1,
perturb = 0.2)),
D = 10
)
vars <- dat.discr$discr.mat
groups <- dat.discr$classes
fits <- fs.stability(vars,
groups,
method = c("plsda", "rf"),
f = 10,
k = 3,
k.folds = 10,
verbose = 'none')
feature.table(fits, "plsda")
cleanEx()
nameEx("fit.only.model")
### * fit.only.model
flush(stderr()); flush(stdout())
### Name: fit.only.model
### Title: Fit Models without Feature Selection
### Aliases: fit.only.model
### ** Examples
dat.discr <- create.discr.matrix(
create.corr.matrix(
create.random.matrix(nvar = 50,
nsamp = 100,
st.dev = 1,
perturb = 0.2)),
D = 10
)
vars <- dat.discr$discr.mat
groups <- dat.discr$classes
fit <- fit.only.model(X=vars,
Y=groups,
method="plsda",
p = 0.9)
cleanEx()
nameEx("fs.ensembl.stability")
### * fs.ensembl.stability
flush(stderr()); flush(stdout())
### Name: fs.ensembl.stability
### Title: Ensemble Classification & Feature Selection
### Aliases: fs.ensembl.stability
### ** Examples
## Not run:
##D fits <- fs.ensembl.stability(vars,
##D groups,
##D method = c("plsda", "rf"),
##D f = 10,
##D k = 3,
##D k.folds = 10,
##D verbose = 'none')
## End(Not run)
cleanEx()
nameEx("fs.stability")
### * fs.stability
flush(stderr()); flush(stdout())
### Name: fs.stability
### Title: Classification & Feature Selection
### Aliases: fs.stability
### ** Examples
dat.discr <- create.discr.matrix(
create.corr.matrix(
create.random.matrix(nvar = 50,
nsamp = 100,
st.dev = 1,
perturb = 0.2)),
D = 10
)
vars <- dat.discr$discr.mat
groups <- dat.discr$classes
fits <- fs.stability(vars,
groups,
method = c("plsda", "rf"),
f = 10,
k = 3,
k.folds = 10,
verbose = 'none')
cleanEx()
nameEx("jaccard")
### * jaccard
flush(stderr()); flush(stdout())
### Name: jaccard
### Title: Jaccard Index
### Aliases: jaccard
### ** Examples
# Jaccard demo
v1 <- paste("Metabolite", seq(10), sep="_")
v2 <- sample(v1, 10)
jaccard(v1, v2)
cleanEx()
nameEx("kuncheva")
### * kuncheva
flush(stderr()); flush(stdout())
### Name: kuncheva
### Title: Kuncheva's Index
### Aliases: kuncheva
### ** Examples
# Kuncheva demo
# Assuming 50 metabolites were measured
# But only 10 were found significant
# For demonstration purposes only!!!
some.numbers <- seq(20)
# Metabolites identified from one run
v1 <- paste("Metabolite", sample(some.numbers, 10), sep="_")
# Metabolites identifed from second run
v2 <- paste("Metabolite", sample(some.numbers, 10), sep="_")
kuncheva(v1, v2, 50)
cleanEx()
nameEx("modelList")
### * modelList
flush(stderr()); flush(stdout())
### Name: modelList
### Title: Model List
### Aliases: modelList
### ** Examples
modelList()
cleanEx()
nameEx("ochiai")
### * ochiai
flush(stderr()); flush(stdout())
### Name: ochiai
### Title: Ochiai's Index
### Aliases: ochiai
### ** Examples
# Ochiai demo
v1 <- paste("Metabolite", seq(10), sep="_")
v2 <- sample(v1, 10)
ochiai(v1, v2)
cleanEx()
nameEx("pairwise.model.stability")
### * pairwise.model.stability
flush(stderr()); flush(stdout())
### Name: pairwise.model.stability
### Title: Pairwise Model Stability Metrics
### Aliases: pairwise.model.stability
### ** Examples
# pairwise.model.stability demo
# For demonstration purposes only!!!
some.numbers <- seq(20)
# A list containing the metabolite matrices for each algorithm
# As an example, let's say we have the output from two different models
# such as plsda and random forest.
# matrix of Metabolites identified (e.g. 5 trials)
plsda <-
replicate(5, paste("Metabolite", sample(some.numbers, 10), sep="_"))
rf <-
replicate(5, paste("Metabolite", sample(some.numbers, 10), sep="_"))
features <- list(plsda=plsda, rf=rf)
# nc may be omitted unless using kuncheva
pairwise.model.stability(features, "kuncheva", nc=20)
cleanEx()
nameEx("pairwise.stability")
### * pairwise.stability
flush(stderr()); flush(stdout())
### Name: pairwise.stability
### Title: Pairwise Stability Metrics
### Aliases: pairwise.stability
### ** Examples
# pairwise.stability demo
# For demonstration purposes only!!!
some.numbers <- seq(20)
# matrix of Metabolites identified (e.g. 5 trials)
features <-
replicate(5, paste("Metabolite", sample(some.numbers, 10), sep="_"))
# nc may be omitted unless using kuncheva
pairwise.stability(features, "jaccard")
cleanEx()
nameEx("params")
### * params
flush(stderr()); flush(stdout())
### Name: params
### Title: Model Parameters and Properties
### Aliases: params
### ** Examples
params("plsda")
cleanEx()
nameEx("performance.metrics")
### * performance.metrics
flush(stderr()); flush(stdout())
### Name: performance.metrics
### Title: Performance Metrics of fs.stability or fs.ensembl.stability
### object
### Aliases: performance.metrics
### ** Examples
dat.discr <- create.discr.matrix(
create.corr.matrix(
create.random.matrix(nvar = 50,
nsamp = 100,
st.dev = 1,
perturb = 0.2)),
D = 10
)
vars <- dat.discr$discr.mat
groups <- dat.discr$classes
fits <- fs.stability(vars,
groups,
method = c("plsda", "rf"),
f = 10,
k = 3,
k.folds = 10,
verbose = 'none')
performance.metrics(fits)
cleanEx()
nameEx("perm.class")
### * perm.class
flush(stderr()); flush(stdout())
### Name: perm.class
### Title: Monte Carlo Permutation of Model Performance
### Aliases: perm.class
### ** Examples
dat.discr <- create.discr.matrix(
create.corr.matrix(
create.random.matrix(nvar = 50,
nsamp = 100,
st.dev = 1,
perturb = 0.2)),
D = 10
)
vars <- dat.discr$discr.mat
groups <- dat.discr$classes
fits <- fs.stability(vars,
groups,
method = c("plsda", "rf"),
f = 10,
k = 3,
k.folds = 10,
verbose = 'none')
perm.class(fits, vars, groups, "rf", k.folds=5,
metric="Accuracy", nperm=10)
cleanEx()
nameEx("perm.features")
### * perm.features
flush(stderr()); flush(stdout())
### Name: perm.features
### Title: Feature Selection via Monte Carlo Permutation
### Aliases: perm.features
### ** Examples
dat.discr <- create.discr.matrix(
create.corr.matrix(
create.random.matrix(nvar = 50,
nsamp = 100,
st.dev = 1,
perturb = 0.2)),
D = 10
)
vars <- dat.discr$discr.mat
groups <- dat.discr$classes
fits <- fs.stability(vars,
groups,
method = c("plsda", "rf"),
f = 10,
k = 3,
k.folds = 10,
verbose = 'none')
# permute variables/features
perm.features(fits, vars, groups, "rf",
sig.level = .05, nperm = 10)
cleanEx()
nameEx("pof")
### * pof
flush(stderr()); flush(stdout())
### Name: pof
### Title: Percentage of Overlapping Features
### Aliases: pof
### ** Examples
# Percent-Overlapping Features demo
v1 <- paste("Metabolite", seq(10), sep="_")
v2 <- sample(v1, 10)
pof(v1, v2)
cleanEx()
nameEx("predictNewClasses")
### * predictNewClasses
flush(stderr()); flush(stdout())
### Name: predictNewClasses
### Title: Class Prediction
### Aliases: predictNewClasses
### ** Examples
dat.discr <- create.discr.matrix(
create.corr.matrix(
create.random.matrix(nvar = 50,
nsamp = 100,
st.dev = 1,
perturb = 0.2)),
D = 10
)
vars <- dat.discr$discr.mat
groups <- dat.discr$classes
fits <- fs.stability(vars,
groups,
method = c("plsda", "rf"),
f = 10,
k = 3,
k.folds = 10,
verbose = 'none')
newdata <- create.discr.matrix(
create.corr.matrix(
create.random.matrix(nvar = 50,
nsamp = 100,
st.dev = 1,
perturb = 0.2)),
D = 10
)$discr.mat
orig.df <- data.frame(vars, groups)
# see what the PLSDA predicts for the new data
# NOTE, newdata does not require a .classes column
predictNewClasses(fits, "plsda", orig.df, newdata)
cleanEx()
nameEx("sorensen")
### * sorensen
flush(stderr()); flush(stdout())
### Name: sorensen
### Title: Dice-Sorensen's Index
### Aliases: sorensen
### ** Examples
# Dice-Sorensen demo
v1 <- paste("Metabolite", seq(10), sep="_")
v2 <- sample(v1, 10)
sorensen(v1, v2)
cleanEx()
nameEx("spearman")
### * spearman
flush(stderr()); flush(stdout())
### Name: spearman
### Title: Spearman Rank Correlation Coefficient
### Aliases: spearman
### ** Examples
# Spearman demo
v1 <- seq(10)
v2 <- sample(v1, 10)
spearman(v1, v2)
cleanEx()
nameEx("svmrfeFeatureRanking")
### * svmrfeFeatureRanking
flush(stderr()); flush(stdout())
### Name: svmrfeFeatureRanking
### Title: SVM Recursive Feature Extraction (Binary)
### Aliases: svmrfeFeatureRanking
### ** Examples
dat.discr <- create.discr.matrix(
create.corr.matrix(
create.random.matrix(nvar = 50,
nsamp = 100,
st.dev = 1,
perturb = 0.2)),
D = 10
)
vars <- dat.discr$discr.mat
groups <- dat.discr$classes
# binary class feature ranking
svmrfeFeatureRanking(x = vars,
y = groups,
c = 0.1,
perc.rem = 10)
cleanEx()
nameEx("svmrfeFeatureRankingForMulticlass")
### * svmrfeFeatureRankingForMulticlass
flush(stderr()); flush(stdout())
### Name: svmrfeFeatureRankingForMulticlass
### Title: SVM Recursive Feature Extraction (Multiclass)
### Aliases: svmrfeFeatureRankingForMulticlass
### ** Examples
dat.discr <- create.discr.matrix(
create.corr.matrix(
create.random.matrix(nvar = 50,
nsamp = 100,
st.dev = 1,
perturb = 0.2)),
D = 10,
num.groups=4
)
vars <- dat.discr$discr.mat
groups <- dat.discr$classes
# multiclass
svmrfeFeatureRankingForMulticlass(x = vars,
y = groups,
c = 0.1,
perc.rem = 10)
### * <FOOTER>
###
cleanEx()
options(digits = 7L)
base::cat("Time elapsed: ", proc.time() - base::get("ptime", pos = 'CheckExEnv'),"\n")
grDevices::dev.off()
###
### Local variables: ***
### mode: outline-minor ***
### outline-regexp: "\\(> \\)?### [*]+" ***
### End: ***
quit('no')
gbm-developers/gbm documentation built on Feb. 16, 2024, 6:13 p.m.
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pkgname <- "OmicsMarkeR"
source(file.path(R.home("share"), "R", "examples-header.R"))
options(warn = 1)
library('OmicsMarkeR')
base::assign(".oldSearch", base::search(), pos = 'CheckExEnv')
base::assign(".old_wd", base::getwd(), pos = 'CheckExEnv')
cleanEx()
nameEx("RPT")
### * RPT
flush(stderr()); flush(stdout())
### Name: RPT
### Title: Robustness-Performance Trade-Off
### Aliases: RPT
### ** Examples
# RPT demo
RPT(stability=0.85, performance=0.90, beta=1)
cleanEx()
nameEx("aggregation")
### * aggregation
flush(stderr()); flush(stdout())
### Name: aggregation
### Title: Feature Aggregation
### Aliases: aggregation
### ** Examples
# test data
ranks <- replicate(5, sample(seq(50), 50))
row.names(ranks) <- paste0("V", seq(50))
aggregation(ranks, "CLA")
cleanEx()
nameEx("canberra")
### * canberra
flush(stderr()); flush(stdout())
### Name: canberra
### Title: Canberra Distance
### Aliases: canberra
### ** Examples
# Canberra demo
v1 <- seq(10)
v2 <- sample(v1, 10)
canberra(v1, v2)
canberra_stability(v1, v2)
cleanEx()
nameEx("canberra_stability")
### * canberra_stability
flush(stderr()); flush(stdout())
### Name: canberra_stability
### Title: Canberra Stability
### Aliases: canberra_stability
### ** Examples
# Canberra demo
v1 <- seq(10)
v2 <- sample(v1, 10)
canberra(v1, v2)
canberra_stability(v1, v2)
cleanEx()
nameEx("create.corr.matrix")
### * create.corr.matrix
flush(stderr()); flush(stdout())
### Name: create.corr.matrix
### Title: Correlated Multivariate Data Generator
### Aliases: create.corr.matrix
### ** Examples
# Create Multivariate Matrices
# Random Multivariate Matrix
# 50 variables, 100 samples, 1 standard devation, 0.2 noise factor
rand.mat <- create.random.matrix(nvar = 50,
nsamp = 100,
st.dev = 1,
perturb = 0.2)
# Induce correlations in a numeric matrix
# Default settings
# minimum and maximum block sizes (min.block.size = 2, max.block.size = 5)
# default correlation purturbation (k=4)
# see ?create.corr.matrix for citation for methods
corr.mat <- create.corr.matrix(rand.mat)
# Induce Discriminatory Variables
# 10 discriminatory variables (D = 10)
# default discrimination level (l = 1.5)
# default number of groups (num.groups=2)
# default correlation purturbation (k = 4)
dat.discr <- create.discr.matrix(corr.mat, D=10)
cleanEx()
nameEx("create.discr.matrix")
### * create.discr.matrix
flush(stderr()); flush(stdout())
### Name: create.discr.matrix
### Title: Discriminatory Multivariate Data Generator
### Aliases: create.discr.matrix
### ** Examples
# Create Multivariate Matrices
# Random Multivariate Matrix
# 50 variables, 100 samples, 1 standard devation, 0.2 noise factor
rand.mat <- create.random.matrix(nvar = 50,
nsamp = 100,
st.dev = 1,
perturb = 0.2)
# Induce correlations in a numeric matrix
# Default settings
# minimum and maximum block sizes (min.block.size = 2, max.block.size = 5)
# default correlation purturbation (k=4)
# see ?create.corr.matrix for citation for methods
corr.mat <- create.corr.matrix(rand.mat)
# Induce Discriminatory Variables
# 10 discriminatory variables (D = 10)
# default discrimination level (l = 1.5)
# default number of groups (num.groups=2)
# default correlation purturbation (k = 4)
dat.discr <- create.discr.matrix(corr.mat, D=10)
cleanEx()
nameEx("create.random.matrix")
### * create.random.matrix
flush(stderr()); flush(stdout())
### Name: create.random.matrix
### Title: Random Multivariate Data Generator
### Aliases: create.random.matrix
### ** Examples
# Create Multivariate Matrices
# Random Multivariate Matrix
# 50 variables, 100 samples, 1 standard devation, 0.2 noise factor
rand.mat <- create.random.matrix(nvar = 50,
nsamp = 100,
st.dev = 1,
perturb = 0.2)
# Induce correlations in a numeric matrix
# Default settings
# minimum and maximum block sizes (min.block.size = 2, max.block.size = 5)
# default correlation purturbation (k=4)
# see ?create.corr.matrix for citation for methods
corr.mat <- create.corr.matrix(rand.mat)
# Induce Discriminatory Variables
# 10 discriminatory variables (D = 10)
# default discrimination level (l = 1.5)
# default number of groups (num.groups=2)
# default correlation purturbation (k = 4)
dat.discr <- create.discr.matrix(corr.mat, D=10)
cleanEx()
nameEx("denovo.grid")
### * denovo.grid
flush(stderr()); flush(stdout())
### Name: denovo.grid
### Title: Denovo Grid Generation
### Aliases: denovo.grid
### ** Examples
# random test data
set.seed(123)
dat.discr <- create.discr.matrix(
create.corr.matrix(
create.random.matrix(nvar = 50,
nsamp = 100,
st.dev = 1,
perturb = 0.2)),
D = 10
)
df <- data.frame(dat.discr$discr.mat, .classes = dat.discr$classes)
# create tuning grid
denovo.grid(df, "gbm", 3)
cleanEx()
nameEx("feature.table")
### * feature.table
flush(stderr()); flush(stdout())
### Name: feature.table
### Title: Feature Consistency Table
### Aliases: feature.table
### ** Examples
dat.discr <- create.discr.matrix(
create.corr.matrix(
create.random.matrix(nvar = 50,
nsamp = 100,
st.dev = 1,
perturb = 0.2)),
D = 10
)
vars <- dat.discr$discr.mat
groups <- dat.discr$classes
fits <- fs.stability(vars,
groups,
method = c("plsda", "rf"),
f = 10,
k = 3,
k.folds = 10,
verbose = 'none')
feature.table(fits, "plsda")
cleanEx()
nameEx("fit.only.model")
### * fit.only.model
flush(stderr()); flush(stdout())
### Name: fit.only.model
### Title: Fit Models without Feature Selection
### Aliases: fit.only.model
### ** Examples
dat.discr <- create.discr.matrix(
create.corr.matrix(
create.random.matrix(nvar = 50,
nsamp = 100,
st.dev = 1,
perturb = 0.2)),
D = 10
)
vars <- dat.discr$discr.mat
groups <- dat.discr$classes
fit <- fit.only.model(X=vars,
Y=groups,
method="plsda",
p = 0.9)
cleanEx()
nameEx("fs.ensembl.stability")
### * fs.ensembl.stability
flush(stderr()); flush(stdout())
### Name: fs.ensembl.stability
### Title: Ensemble Classification & Feature Selection
### Aliases: fs.ensembl.stability
### ** Examples
## Not run:
##D fits <- fs.ensembl.stability(vars,
##D groups,
##D method = c("plsda", "rf"),
##D f = 10,
##D k = 3,
##D k.folds = 10,
##D verbose = 'none')
## End(Not run)
cleanEx()
nameEx("fs.stability")
### * fs.stability
flush(stderr()); flush(stdout())
### Name: fs.stability
### Title: Classification & Feature Selection
### Aliases: fs.stability
### ** Examples
dat.discr <- create.discr.matrix(
create.corr.matrix(
create.random.matrix(nvar = 50,
nsamp = 100,
st.dev = 1,
perturb = 0.2)),
D = 10
)
vars <- dat.discr$discr.mat
groups <- dat.discr$classes
fits <- fs.stability(vars,
groups,
method = c("plsda", "rf"),
f = 10,
k = 3,
k.folds = 10,
verbose = 'none')
cleanEx()
nameEx("jaccard")
### * jaccard
flush(stderr()); flush(stdout())
### Name: jaccard
### Title: Jaccard Index
### Aliases: jaccard
### ** Examples
# Jaccard demo
v1 <- paste("Metabolite", seq(10), sep="_")
v2 <- sample(v1, 10)
jaccard(v1, v2)
cleanEx()
nameEx("kuncheva")
### * kuncheva
flush(stderr()); flush(stdout())
### Name: kuncheva
### Title: Kuncheva's Index
### Aliases: kuncheva
### ** Examples
# Kuncheva demo
# Assuming 50 metabolites were measured
# But only 10 were found significant
# For demonstration purposes only!!!
some.numbers <- seq(20)
# Metabolites identified from one run
v1 <- paste("Metabolite", sample(some.numbers, 10), sep="_")
# Metabolites identifed from second run
v2 <- paste("Metabolite", sample(some.numbers, 10), sep="_")
kuncheva(v1, v2, 50)
cleanEx()
nameEx("modelList")
### * modelList
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### Name: modelList
### Title: Model List
### Aliases: modelList
### ** Examples
modelList()
cleanEx()
nameEx("ochiai")
### * ochiai
flush(stderr()); flush(stdout())
### Name: ochiai
### Title: Ochiai's Index
### Aliases: ochiai
### ** Examples
# Ochiai demo
v1 <- paste("Metabolite", seq(10), sep="_")
v2 <- sample(v1, 10)
ochiai(v1, v2)
cleanEx()
nameEx("pairwise.model.stability")
### * pairwise.model.stability
flush(stderr()); flush(stdout())
### Name: pairwise.model.stability
### Title: Pairwise Model Stability Metrics
### Aliases: pairwise.model.stability
### ** Examples
# pairwise.model.stability demo
# For demonstration purposes only!!!
some.numbers <- seq(20)
# A list containing the metabolite matrices for each algorithm
# As an example, let's say we have the output from two different models
# such as plsda and random forest.
# matrix of Metabolites identified (e.g. 5 trials)
plsda <-
replicate(5, paste("Metabolite", sample(some.numbers, 10), sep="_"))
rf <-
replicate(5, paste("Metabolite", sample(some.numbers, 10), sep="_"))
features <- list(plsda=plsda, rf=rf)
# nc may be omitted unless using kuncheva
pairwise.model.stability(features, "kuncheva", nc=20)
cleanEx()
nameEx("pairwise.stability")
### * pairwise.stability
flush(stderr()); flush(stdout())
### Name: pairwise.stability
### Title: Pairwise Stability Metrics
### Aliases: pairwise.stability
### ** Examples
# pairwise.stability demo
# For demonstration purposes only!!!
some.numbers <- seq(20)
# matrix of Metabolites identified (e.g. 5 trials)
features <-
replicate(5, paste("Metabolite", sample(some.numbers, 10), sep="_"))
# nc may be omitted unless using kuncheva
pairwise.stability(features, "jaccard")
cleanEx()
nameEx("params")
### * params
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### Name: params
### Title: Model Parameters and Properties
### Aliases: params
### ** Examples
params("plsda")
cleanEx()
nameEx("performance.metrics")
### * performance.metrics
flush(stderr()); flush(stdout())
### Name: performance.metrics
### Title: Performance Metrics of fs.stability or fs.ensembl.stability
### object
### Aliases: performance.metrics
### ** Examples
dat.discr <- create.discr.matrix(
create.corr.matrix(
create.random.matrix(nvar = 50,
nsamp = 100,
st.dev = 1,
perturb = 0.2)),
D = 10
)
vars <- dat.discr$discr.mat
groups <- dat.discr$classes
fits <- fs.stability(vars,
groups,
method = c("plsda", "rf"),
f = 10,
k = 3,
k.folds = 10,
verbose = 'none')
performance.metrics(fits)
cleanEx()
nameEx("perm.class")
### * perm.class
flush(stderr()); flush(stdout())
### Name: perm.class
### Title: Monte Carlo Permutation of Model Performance
### Aliases: perm.class
### ** Examples
dat.discr <- create.discr.matrix(
create.corr.matrix(
create.random.matrix(nvar = 50,
nsamp = 100,
st.dev = 1,
perturb = 0.2)),
D = 10
)
vars <- dat.discr$discr.mat
groups <- dat.discr$classes
fits <- fs.stability(vars,
groups,
method = c("plsda", "rf"),
f = 10,
k = 3,
k.folds = 10,
verbose = 'none')
perm.class(fits, vars, groups, "rf", k.folds=5,
metric="Accuracy", nperm=10)
cleanEx()
nameEx("perm.features")
### * perm.features
flush(stderr()); flush(stdout())
### Name: perm.features
### Title: Feature Selection via Monte Carlo Permutation
### Aliases: perm.features
### ** Examples
dat.discr <- create.discr.matrix(
create.corr.matrix(
create.random.matrix(nvar = 50,
nsamp = 100,
st.dev = 1,
perturb = 0.2)),
D = 10
)
vars <- dat.discr$discr.mat
groups <- dat.discr$classes
fits <- fs.stability(vars,
groups,
method = c("plsda", "rf"),
f = 10,
k = 3,
k.folds = 10,
verbose = 'none')
# permute variables/features
perm.features(fits, vars, groups, "rf",
sig.level = .05, nperm = 10)
cleanEx()
nameEx("pof")
### * pof
flush(stderr()); flush(stdout())
### Name: pof
### Title: Percentage of Overlapping Features
### Aliases: pof
### ** Examples
# Percent-Overlapping Features demo
v1 <- paste("Metabolite", seq(10), sep="_")
v2 <- sample(v1, 10)
pof(v1, v2)
cleanEx()
nameEx("predictNewClasses")
### * predictNewClasses
flush(stderr()); flush(stdout())
### Name: predictNewClasses
### Title: Class Prediction
### Aliases: predictNewClasses
### ** Examples
dat.discr <- create.discr.matrix(
create.corr.matrix(
create.random.matrix(nvar = 50,
nsamp = 100,
st.dev = 1,
perturb = 0.2)),
D = 10
)
vars <- dat.discr$discr.mat
groups <- dat.discr$classes
fits <- fs.stability(vars,
groups,
method = c("plsda", "rf"),
f = 10,
k = 3,
k.folds = 10,
verbose = 'none')
newdata <- create.discr.matrix(
create.corr.matrix(
create.random.matrix(nvar = 50,
nsamp = 100,
st.dev = 1,
perturb = 0.2)),
D = 10
)$discr.mat
orig.df <- data.frame(vars, groups)
# see what the PLSDA predicts for the new data
# NOTE, newdata does not require a .classes column
predictNewClasses(fits, "plsda", orig.df, newdata)
cleanEx()
nameEx("sorensen")
### * sorensen
flush(stderr()); flush(stdout())
### Name: sorensen
### Title: Dice-Sorensen's Index
### Aliases: sorensen
### ** Examples
# Dice-Sorensen demo
v1 <- paste("Metabolite", seq(10), sep="_")
v2 <- sample(v1, 10)
sorensen(v1, v2)
cleanEx()
nameEx("spearman")
### * spearman
flush(stderr()); flush(stdout())
### Name: spearman
### Title: Spearman Rank Correlation Coefficient
### Aliases: spearman
### ** Examples
# Spearman demo
v1 <- seq(10)
v2 <- sample(v1, 10)
spearman(v1, v2)
cleanEx()
nameEx("svmrfeFeatureRanking")
### * svmrfeFeatureRanking
flush(stderr()); flush(stdout())
### Name: svmrfeFeatureRanking
### Title: SVM Recursive Feature Extraction (Binary)
### Aliases: svmrfeFeatureRanking
### ** Examples
dat.discr <- create.discr.matrix(
create.corr.matrix(
create.random.matrix(nvar = 50,
nsamp = 100,
st.dev = 1,
perturb = 0.2)),
D = 10
)
vars <- dat.discr$discr.mat
groups <- dat.discr$classes
# binary class feature ranking
svmrfeFeatureRanking(x = vars,
y = groups,
c = 0.1,
perc.rem = 10)
cleanEx()
nameEx("svmrfeFeatureRankingForMulticlass")
### * svmrfeFeatureRankingForMulticlass
flush(stderr()); flush(stdout())
### Name: svmrfeFeatureRankingForMulticlass
### Title: SVM Recursive Feature Extraction (Multiclass)
### Aliases: svmrfeFeatureRankingForMulticlass
### ** Examples
dat.discr <- create.discr.matrix(
create.corr.matrix(
create.random.matrix(nvar = 50,
nsamp = 100,
st.dev = 1,
perturb = 0.2)),
D = 10,
num.groups=4
)
vars <- dat.discr$discr.mat
groups <- dat.discr$classes
# multiclass
svmrfeFeatureRankingForMulticlass(x = vars,
y = groups,
c = 0.1,
perc.rem = 10)
### * <FOOTER>
###
cleanEx()
options(digits = 7L)
base::cat("Time elapsed: ", proc.time() - base::get("ptime", pos = 'CheckExEnv'),"\n")
grDevices::dev.off()
###
### Local variables: ***
### mode: outline-minor ***
### outline-regexp: "\\(> \\)?### [*]+" ***
### End: ***
quit('no')
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