Summary method for gsym.point objects

Description

Produces a summary of a gsym.point object. The following is printed: the matched call to the gsym.point() main function; the area under the ROC curve (AUC) estimate; the Generalized Symmetry point obtained with the method(s) selected and the point estimates of the associated sensitivity and specificity indexes with their corresponding confidence intervals. All this information will be shown for each categorical covariate level (if the categorical.cov argument in the gsym.point() function is not NULL).

Usage

## S3 method for class 'gsym.point'
summary(object, ...)

Arguments

Details

The summary.gsym.point function produces a list of summary information for a fitted gsym.point object. The result depends on the two arguments, namely, methods, and categorical.cov of the gsym.point() function used in the Generalized Symmetry point computing process.

Value

Returns an object of class "summary.gsym.point" with the same components as the gsym.point function (see gsym.point)

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

See Also

gsym.point

Examples

library(GsymPoint)

data(melanoma)

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

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(),confidence.level = 0.95, 
trace = FALSE, seed = FALSE, value.seed = 3)

summary(gsym.point.GPQ.melanoma)


data(prostate)

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

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

summary(gsym.point.GPQ.prostate)


data(elastase)

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

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

summary(gsym.point.GPQ.elastase)

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/summary.gsym.point.Rd_%03d_medium.png", width=480, height=480)
> ### Name: summary.gsym.point
> ### Title: Summary method for gsym.point objects
> ### Aliases: summary.gsym.point
> 
> ### ** Examples
> 
> library(GsymPoint)
> 
> data(melanoma)
> 
> ###########################################################
> # Generalized Pivotal Quantity Method ("GPQ"): 
> ###########################################################
> 
> 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(),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(), 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         -1.3455312         -0.3282435
Specificity  0.8181698          0.7290003          0.8868421
Sensitivity  0.8181698          0.7290003          0.8868421



> 
> 
> data(prostate)
> 
> ###########################################################
> # Generalized Pivotal Quantity Method ("GPQ"): 
> ###########################################################
> 
> gsym.point.GPQ.prostate <- gsym.point (methods = "GPQ", data = prostate,
+ marker = "marker", status = "status", tag.healthy = 0, categorical.cov = NULL, 
+ CFN = 1, CFP = 1, control = control.gsym.point(), confidence.level = 0.95, 
+ trace = FALSE, seed = FALSE, value.seed = 3)
> 
> summary(gsym.point.GPQ.prostate)

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

Call:
gsym.point(methods = "GPQ", data = prostate, marker = "marker", 
    status = "status", tag.healthy = 0, categorical.cov = NULL, 
    CFN = 1, CFP = 1, control = control.gsym.point(), 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.

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: GPQ

              Estimate 95% CI lower limit 95% CI upper limit
cutoff      64.6644705         60.1174878         70.2590937
Specificity  0.6589833          0.5490465          0.7599127
Sensitivity  0.6589833          0.5490465          0.7599127



> 
> 
> data(elastase)
> 
> ###########################################################
> # Generalized Pivotal Quantity Method ("GPQ"): 
> ###########################################################
> 
> gsym.point.GPQ.elastase <- gsym.point(methods = "GPQ", data = elastase, 
+ marker = "elas", status = "status", tag.healthy = 0, categorical.cov = NULL, 
+ CFN = 1, CFP = 1, control = control.gsym.point(), confidence.level = 0.95, 
+ trace = FALSE, seed = FALSE, value.seed = 3) 
According to the Shapiro-Wilk normality test, the original marker 
can not be considered normally distributed in both groups.
After transforming the marker using the Box-Cox transformation 
estimate the Shapiro-Wilk normality test indicates that the 
transformed marker can not be considered normally distributed 
in both groups. 
Therefore, the results obtained with the GPQ method may not be 
reliable. You must use the EL method instead.

Box-Cox lambda estimate = 0.1136 

Shapiro-Wilk test p-values
                           Group 0 Group 1
Original marker             0.0746  0.0091
Box-Cox transformed marker  0.0000  0.0793
> 
> summary(gsym.point.GPQ.elastase)

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

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

According to the Shapiro-Wilk normality test, the original marker
can not be considered normally distributed in both groups.
After transforming the marker using the Box-Cox transformation
estimate the Shapiro-Wilk normality test indicates that the
transformed marker can not be considered normally distributed
in both groups.
Therefore, the results obtained with the GPQ method may not be
reliable. You must use the EL method instead.

Box-Cox lambda estimate = 0.1136 

Shapiro-Wilk test p-values
                           Group 0 Group 1
Original marker             0.0746  0.0091
Box-Cox transformed marker  0.0000  0.0793

Area under the ROC curve (AUC):  0.744 

METHOD: GPQ

              Estimate 95% CI lower limit 95% CI upper limit
cutoff      34.7162933         31.1678202         38.4205460
Specificity  0.6933122          0.6225446          0.7614595
Sensitivity  0.6933122          0.6225446          0.7614595



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> dev.off()
null device 
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