Cross Validation

Description

This function allows you to compute the average of misdiagnoses rate for viral failure and the optimal risk under min-\lambda rules from K-fold cross-validation.

Usage

CV.TGST(Obj, lambda, K = 10)

Arguments

Value

Cross-validation results.

Examples

d = Simdata
Z = d$Z # True Disease Status
S = d$S # Risk Score
phi = 0.1 #10% of patients taking viral load test
Obj = TGST(Z, S, phi, method="nonpar")
lambda = 0.8
CV.TGST(Obj, lambda, K=10)

Check exponential tilt model assumption

Description

This function provides graphical assessment to the suitability of the exponential tilt model for risk score in finding optimal tripartite rules by semiparametric approach.

g1(s)=exp(\tilde{\beta}_{0}+\beta_{1}*s)*g0(s)

Usage

Check.exp.tilt(Z, S)

Arguments

Value

Plot of empirical density for risk score S, joint empirical density for (S,Z=1) and (S,Z=0), and the density under the exponential tilt model assumption for (S,Z=1) and (S,Z=0).

Examples

d = Simdata
Z = d$Z # True Disease Status
S = d$S # Risk Score
Check.exp.tilt( Z, S)

Optimal Nonparametric Rule

Description

This function gives you the optimal nonparametric tripartite rule that minimizes the min-\lambda rules.

Usage

Opt.nonpar.rule(Z, S, phi, lambda)

Arguments

Value

Optimal nonparametric rule and its associated misclassification rates (FNR, FPR), optimal lambda risk, and total misclassification rate (TMR).

Examples

d = Simdata
Z = d$Z # True Disease Status
S = d$S # Risk Score
phi = 0.1 #10% of patients taking viral load test
lambda = 0.5
Opt.nonpar.rule( Z, S, phi, lambda)

Optimal Semiparametric Rule

Description

This function gives you the optimal semiparametric tripartite rule that minimizes the min-\lambda rules.

Usage

Opt.semipar.rule(Z, S, phi, lambda)

Arguments

Value

Optimal semiparametric rule and its associated misclassification rates (FNR, FPR), optimal lambda risk, and total misclassification rate (TMR).

Examples

d = Simdata
Z = d$Z # True Disease Status
S = d$S # Risk Score
phi = 0.1 #10% of patients taking viral load test
lambda = 0.5
Opt.semipar.rule( Z, S, phi, lambda)

Optimal Tripartite Rule

Description

OptimalRule is the main function of TGST and it gives you the optimal tripartite rule that minimizes the min-\lambda risk based on the type of user selected approach. The function takes the risk score and true disease status from a training data set and returns the optimal tripartite rule under the specified proportion of patients able to take gold standard test.

Usage

OptimalRule(Obj, lambda)

Arguments

Value

Optimal tripartite rule.

Examples

d = Simdata
Z = d$Z # True Disease Status
S = d$S # Risk Score
phi = 0.1 #10% of patients taking viral load test
lambda = 0.5
Obj = TGST(Z, S, phi, method="nonpar")
OptimalRule(Obj, lambda)

Constructor of Output class

Description

This class contains invisible results of OptimalRule function.

Details

phi

Percentage of patients taking viral load test.

Z

A vector of true disease status (Failure Status coded as Z=1).

S

A vector of risk Score.

Rules

A matrix of all possible tripartite rules (two cutoffs) derived from the training data set.

Nonparametric

A boolean indicating if nonparametric approach should be used in calculating the misclassfication rates. If FALSE, semiparametric approach would be used.

FNR.FPR

A matrix with two columns of misclassification rates, FNR and FPR.

OptRule

A numeric vector with two elements, the lower and upper cutoffs of the optimal tripartite rule.

The Output class adds optimal rule to the TGST class.

Nonparametric ROC Analysis

Description

This function performs ROC analysis for tripartite rules by nonparametric approach. If plot=TRUE, the ROC curve is returned.

Usage

ROC.nonpar(Z, S, phi, plot = TRUE)

Arguments

Value

AUC The area under the ROC curve. FNR Misdiagnoses rate for viral failure (false negative rate). FPR Misdiagnoses rate for treatment failure (false positive rate).

Examples

d = Simdata
Z = d$Z # True Disease Status
S = d$S # Risk Score
phi = 0.1 #10% of patients taking viral load test
a = ROC.nonpar( Z, S, phi,plot=TRUE)
a$AUC
a$FNR
a$FPR

Semiparametric ROC Analysis

Description

This function performs ROC analysis on the rules from nonparametric approach. If plot=TRUE, the ROC curve is returned.

Usage

ROC.semipar(Z, S, phi, plot = TRUE)

Arguments

Value

AUC The area under the ROC curve. FNR Misdiagnoses rate for viral failure (false negative rate). FPR Misdiagnoses rate for treatment failure (false positive rate).

Examples

d = Simdata
Z = d$Z # True Disease Status
S = d$S # Risk Score
phi = 0.1 #10% of patients taking viral load test
a = ROC.semipar( Z, S, phi,plot=TRUE)
a$AUC
a$FNR
a$FPR

ROC Analysis

Description

This function performs ROC analysis for tripartite rules. If plot=TRUE, the ROC curve is returned.

Usage

ROCAnalysis(Obj, plot = TRUE)

Arguments

Value

AUC (the area under ROC curve) and ROC curve.

Examples

d = Simdata
Z = d$Z # True Disease Status
S = d$S # Risk Score
phi = 0.1 #10% of patients taking viral load test
lambda = 0.5
Obj = TGST(Z, S, phi, method="nonpar")
ROCAnalysis(Obj, plot=TRUE)

Min TMR Semiparametric Rule

Description

This function gives you the optimal semiparametric tripartite rule that minimizes TMR (total misclassification risk).

Usage

Semi.par.rule(Z, S, phi)

Arguments

Value

Semiparametric rule and its associated TMR.

Examples

d = Simdata
Z = d$Z # True Disease Status
S = d$S # Risk Score
phi = 0.1 #10% of patients taking viral load test
Semi.par.rule( Z, S, phi)

Simulated data for package illustration

Description

A simulated dataset containing true disease status and risk score. See details for simulation setting.

Usage

data(Simdata)

Format

A data frame with 8000 simulated observations on the following 2 variables.

Z

True disease status (No disease / treatment success coded as Z=0, diseased / treatment failure coded as Z=1).

S

Risk score. Higher risk score indicates larger tendency of diseased / treatment failure.

Details

We first simulate viral status Z assuming Z\sim Bernoulli(p) with p=0.25; and then conditional on Z, simulate {S|Z=z}=ceiling(W) with W\sim Gamma(\eta_z,\kappa_z) where \eta and \kappa are shape and scale parameters.(\eta0,\kappa0)=(2.3,80) and (\eta1,\kappa1)=(9.2,62).

Examples

data(Simdata)
## maybe str(Simdata) ; plot(Simdata) ...

Create a TGST Object

Description

Create a TGST object, usually used as an input for optimal rule search and ROC analysis.

Usage

TGST(Z, S, phi, method = "nonpar")

Arguments

Value

An object of class TGST.The class contains 6 slots: phi (percentage of gold standard tests), Z (true failure status), S (risk score), Rules (all possible tripartite rules), Nonparametric (logical indicator of the approach), and FNR.FPR (misclassification rates).

Examples

d = Simdata
Z = d$Z # True Disease Status
S = d$S # Risk Score
phi = 0.1 #10% of patients taking viral load test
TGST( Z, S, phi, method="nonpar")

Constructor of TGST class

Description

This class contains results of a run to search for all possible rules.

Details

phi

Percentage of patients taking viral load test.

Z

A vector of true disease status (Failure Status coded as Z=1).

S

A vector of risk Score.

Rules

A matrix of all possible tripartite rules (two cutoffs) derived from the training data set.

Nonparametric

A boolean indicating if nonparametric approach should be used in calculating the misclassfication rates. If FALSE, semiparametric approach would be used.

FNR.FPR

A matrix with two columns of misclassification rates, FNR and FPR.

If res is the result of rankclust(), each slot of results can be reached by res[k]@slotname, where k is the number of clusters and slotname is the name of the slot we want to reach (see Output-class). For the slots ll, bic, icl, res["slotname"] returns a vector of size K containing the values of the slot for each number of clusters.

Summary of Disease Status and Risk Score

Description

This function allows you to compute the percentage of diseased (equivalent to treatment failure Z=1) and show distribution summary of risk score (S) by the true disease status (Z).

Usage

TGST.summ(Z, S)

Arguments

Value

Percentage of treatment failure; Summary statistics (mean, standard deviation, minimum, median, maximum and IQR) of risk score by true disease status; Distribution plot.

Examples

d = Simdata
Z = d$Z # True Disease Status
S = d$S # Risk Score
TGST.summ(Z,S)

Calculate AUC

Description

This function gives you the AUC associated with the rules set.

Usage

cal.AUC(Z, S, l, u)

Arguments

Value

AUC.

Examples

d = Simdata
Z = d$Z # True Disease Status
S = d$S # Risk Score
phi = 0.1 #10% of patients taking viral load test
rules = nonpar.rules( Z, S, phi)
cal.AUC(Z,S,rules[,1],rules[,2])

Nonparametric FNR FPR of the rules

Description

This function gives you the nonparametric FNR and FPR associated with a given tripartite rule.

Usage

nonpar.fnr.fpr(Z, S, l, u)

Arguments

Value

Matrix with 2 columns. Each row is a set of nonparametric (FNR, FPR) on an associated tripartite rule.

Examples

d = Simdata
Z = d$Z # True Disease Status
S = d$S # Risk Score
phi = 0.1 #10% of patients taking viral load test
rules = nonpar.rules( Z, S, phi)
nonpar.fnr.fpr(Z,S,rules[1,1],rules[1,2])

Nonparametric Rules Set

Description

This function gives you all possible cutoffs [l,u] for tripartite rules, by applying nonparametric search to the given data.

P(S in [l,u]) \le \phi

Usage

nonpar.rules(Z, S, phi)

Arguments

Value

Matrix with 2 columns. Each row is a possible tripartite rule, with output on lower and upper cutoff.

Examples

d = Simdata
Z = d$Z # True Disease Status
S = d$S # Risk Score
phi = 0.1 #10% of patients taking viral load test
nonpar.rules( Z, S, phi)

plot function.

Description

This function This function gives visualize object of class TGST or Output.

This function This function gives visualize object of class TGST or Output.

Usage

## S4 method for signature 'TGST'
plot(x)

## S4 method for signature 'Output'
plot(x)

Arguments

Value

Distribution plot.

Distribution plot.

Semiparametric FNR FPR of the rules

Description

This function gives you the semiparametric FNR and FPR associated with a given tripartite rule.

Usage

semipar.fnr.fpr(Z, S, l, u)

Arguments

Value

Matrix with 2 columns. Each row is a set of semiparametric (FNR, FPR) on an associated tripartite rule.

Examples

d = Simdata
Z = d$Z # True Disease Status
S = d$S # Risk Score
phi = 0.1 #10% of patients taking viral load test
rules = nonpar.rules( Z, S, phi)
semipar.fnr.fpr(Z,S,rules[1,1],rules[1,2])

summary function.

Description

This function gives the summary of the data from TGST.

Usage

## S4 method for signature 'TGST'
summary(object)

Arguments

Value

Percentage of treatment failure; Summary statistics (mean, standard deviation, minimum, median, maximum and IQR) of risk score by true disease status; Distribution plot.