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Last data update: 2014.03.03

R: Controlling the Generalized Symmetry point computing process

control.gsym.point

R Documentation

Controlling the Generalized Symmetry point computing process

Description

Used to set various parameters controlling the Generalized Symmetry point computing process

Usage

control.gsym.point(B = 499, c_sampling = 0.25, c_F = 0.25, c_ELq = 0.25, 
c_R = 0.25, I = 2500)

Arguments

`B`	a numerical value meaningful only in the "EL" method. It specifies the number of bootstrap resamples in the Empirical Likelihood method. The default value is 499.
`c_sampling`	a numerical value meaningful only in the "EL" method. It specifies the constant needed for resampling in the Empirical Likelihood method. The default value is 0.25.
`c_F`	a numerical value meaningful only in the "EL" method. It specifies the constant needed for estimating the distribution in the Empirical Likelihood method. The default value is 0.25.
`c_ELq`	a numerical value meaningful only in the "EL" method. It specifies the constant needed for estimating the empirical likelihood function in the Empirical Likelihood method. The default value is 0.25.
`c_R`	a numerical value meaningful only in the "EL" method. It specifies the constant needed for estimating the ROC Curve in the Empirical Likelihood method. The default value is 0.25.
`I`	a numerical value meaningful only in the "GPQ" method. It specifies the number of replicates in the Generalized Pivotal Quantity method. The default value is 2500.

Details

The value yielded by this function is used as the control argument of the gsym.point()function.

Author(s)

M<c3><b3>nica L<c3><b3>pez-Rat<c3><b3>n, Carmen Cadarso-Su<c3><a1>rez, Elisa M. Molanes-L<c3><b3>pez and Emilio Let<c3><b3>n

Examples

library(GsymPoint)

data(melanoma)

###########################################################
# Generalized Pivotal Quantity Method ("GPQ"): 
###########################################################

# Change the number of replicates:

gsym.point.GPQ.melanoma<-gsym.point(methods = "GPQ", data = melanoma,
marker = "X", status = "group", tag.healthy = 0, categorical.cov = NULL, 
CFN = 1, CFP = 1, control = control.gsym.point(I = 2000),
confidence.level = 0.95, trace = FALSE, seed = FALSE, value.seed = 3)

summary(gsym.point.GPQ.melanoma)


data(prostate)

###########################################################
# Empirical Likelihood Method ("EL")
###########################################################

# Change the number of bootstrap resamples:

gsym.point.EL.prostate <- gsym.point (methods = "EL", data = prostate,
marker = "marker", status = "status", tag.healthy = 0, categorical.cov = NULL, 
CFN = 1, CFP = 1, control = control.gsym.point(B=99), confidence.level = 0.95, 
trace = FALSE, seed = FALSE, value.seed = 3)

summary(gsym.point.EL.prostate)

Results


R version 3.3.1 (2016-06-21) -- "Bug in Your Hair"
Copyright (C) 2016 The R Foundation for Statistical Computing
Platform: x86_64-pc-linux-gnu (64-bit)

R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.

R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.

Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.

> library(GsymPoint)
Loading required package: truncnorm
Loading required package: Rsolnp
> png(filename="/home/ddbj/snapshot/RGM3/R_CC/result/GsymPoint/control.gsym.point.Rd_%03d_medium.png", width=480, height=480)
> ### Name: control.gsym.point
> ### Title: Controlling the Generalized Symmetry point computing process
> ### Aliases: control.gsym.point
> 
> ### ** Examples
> 
> library(GsymPoint)
> 
> data(melanoma)
> 
> ###########################################################
> # Generalized Pivotal Quantity Method ("GPQ"): 
> ###########################################################
> 
> # Change the number of replicates:
> 
> gsym.point.GPQ.melanoma<-gsym.point(methods = "GPQ", data = melanoma,
+ marker = "X", status = "group", tag.healthy = 0, categorical.cov = NULL, 
+ CFN = 1, CFP = 1, control = control.gsym.point(I = 2000),
+ confidence.level = 0.95, trace = FALSE, seed = FALSE, value.seed = 3)
> 
> summary(gsym.point.GPQ.melanoma)

*************************************************
OPTIMAL CUTOFF: GENERALIZED SYMMETRY POINT
*************************************************

Call:
gsym.point(methods = "GPQ", data = melanoma, marker = "X", status = "group", 
    tag.healthy = 0, categorical.cov = NULL, CFN = 1, CFP = 1, 
    control = control.gsym.point(I = 2000), confidence.level = 0.95, 
    trace = FALSE, seed = FALSE, value.seed = 3)

According to the Shapiro-Wilk normality test, the marker can be
considered normally distributed in both groups.

Shapiro-Wilk test p-values

                Group 0 Group 1
Original marker  0.4719  0.9084

Area under the ROC curve (AUC):  0.906 

METHOD: GPQ

              Estimate 95% CI lower limit 95% CI upper limit
cutoff      -0.8273306         -1.3429871         -0.3542391
Specificity  0.8181698          0.7324915          0.8853894
Sensitivity  0.8181698          0.7324915          0.8853894



> 
> 
> data(prostate)
> 
> ###########################################################
> # Empirical Likelihood Method ("EL")
> ###########################################################
> 
> # Change the number of bootstrap resamples:
> 
> gsym.point.EL.prostate <- gsym.point (methods = "EL", data = prostate,
+ marker = "marker", status = "status", tag.healthy = 0, categorical.cov = NULL, 
+ CFN = 1, CFP = 1, control = control.gsym.point(B=99), confidence.level = 0.95, 
+ trace = FALSE, seed = FALSE, value.seed = 3)
According to the Shapiro-Wilk normality test, the marker can not 
be considered normally distributed in both groups.
However, after transforming the marker using the Box-Cox 
transformation estimate, the Shapiro-Wilk normality test 
indicates that the transformed marker can be considered 
normally distributed in both groups.
Therefore the GPQ method would be more suitable for this dataset.

Box-Cox lambda estimate = -1.2494 

Shapiro-Wilk test p-values
                           Group 0 Group 1
Original marker             0.0000  0.0232
Box-Cox transformed marker  0.3641  0.2118
> 
> summary(gsym.point.EL.prostate)

*************************************************
OPTIMAL CUTOFF: GENERALIZED SYMMETRY POINT
*************************************************

Call:
gsym.point(methods = "EL", data = prostate, marker = "marker", 
    status = "status", tag.healthy = 0, categorical.cov = NULL, 
    CFN = 1, CFP = 1, control = control.gsym.point(B = 99), confidence.level = 0.95, 
    trace = FALSE, seed = FALSE, value.seed = 3)

According to the Shapiro-Wilk normality test, the marker can not
be considered normally distributed in both groups.
However, after transforming the marker using the Box-Cox
transformation estimate, the Shapiro-Wilk normality test
indicates that the transformed marker can be considered
normally distributed in both groups.
Therefore the GPQ method would be more suitable for this dataset.

Box-Cox lambda estimate = -1.2494 

Shapiro-Wilk test p-values
                           Group 0 Group 1
Original marker             0.0000  0.0232
Box-Cox transformed marker  0.3641  0.2118

Area under the ROC curve (AUC):  0.725 

METHOD: EL

              Estimate 95% CI lower limit 95% CI upper limit
cutoff      67.1199201         58.5560314         74.7152000
Specificity  0.7170686          0.5534702          0.7998167
Sensitivity  0.7170686          0.5534702          0.7998167



> 
> 
> 
> 
> 
> dev.off()
null device 
          1 
>