Package 'Deducer'

Title:	A Data Analysis GUI for R
Description:	An intuitive, cross-platform graphical data analysis system. It uses menus and dialogs to guide the user efficiently through the data manipulation and analysis process, and has an excel like spreadsheet for easy data frame visualization and editing. Deducer works best when used with the Java based R GUI JGR, but the dialogs can be called from the command line. Dialogs have also been integrated into the Windows Rgui.
Authors:	Ian Fellows with contributions from others (see documentation).
Maintainer:	Ian Fellows <[email protected]>
License:	GPL-2
Version:	0.7-9
Built:	2024-11-26 06:45:59 UTC
Source:	CRAN

Help Index

Apply a Stratified test to a Contingency Table
Apply the Mantel-Haenszel test to a Contingency Table
Apply a test to a Contingency Tables object
as.matrix method
Non-central Chi-Squared Confidence Interval
contin.tests.to.table
Contingency Tables
cor.matrix
wrapper for data.frame
GUI Access functions
Controls Deducer's command line menus
Table of Descriptives
Deducer's plug-in development tools
dich
Extract Contingency Table Arrays
Frequency Tables
get objects
Correlation matrix
K Sample Test
Likelihood Ratio (G test) for contingency tables
multi.test
One Sample Test
onesample.plot
One Way PLot
Vector Permutations
Permutation t-test
Plot method
Print method
Print method
Print method
Print method
Print method
Print method
qscatter_array
Recode
ROC Plot for a logistic regression model
Sort Data
Summary table for a linear model
Table –> data.frame
Two Sample Test

Apply a Stratified test to a Contingency Table

Description

Applies and adds a hypothesis test to a contingency.tables object.

Usage

add.cross.strata.test(tables,name,htests,types=c("asymptotic","monte.carlo","exact"))
add.cross.strata.test(tables,name,htests,types=c("asymptotic","monte.carlo","exact"))

Arguments

`tables`	An object of class `contingency.tables`
`name`	The name of the hypothesis test
`htests`	A function or list of functions which take a three dimensional array as it's argument and returns an object of class `htest`
`types`	A character vector with the same number of items as htests, indicating what type of test was done

Value

A contingency.tables object identical to tables, but with the test applied to each table.

Examples

dat<-data.frame(a=rnorm(100)>.5,b=rnorm(100)>0,c=rnorm(100)>(-.5))
tables<-contingency.tables(
 row.vars=a,
 col.vars=b,
 stratum.var=c,data=dat)
add.cross.strata.test(tables,"Mantel-Haenszel",list(function(x) mantelhaen.test(x,correct=FALSE)),
 "asymptotic")
tables
dat<-data.frame(a=rnorm(100)>.5,b=rnorm(100)>0,c=rnorm(100)>(-.5))
tables<-contingency.tables(
 row.vars=a,
 col.vars=b,
 stratum.var=c,data=dat)
add.cross.strata.test(tables,"Mantel-Haenszel",list(function(x) mantelhaen.test(x,correct=FALSE)),
 "asymptotic")
tables

Apply the Mantel-Haenszel test to a Contingency Table

Description

Applies and adds the Cochran-Mantel-Haenzsel test to a contingency.tables object. The Cochran-Mantel-Haenzsel tests the independence of two nominal variables, stratified by a third nominal variable, assuming no three way interaction.

Usage

add.mantel.haenszel(tables,conservative=FALSE)
add.mantel.haenszel(tables,conservative=FALSE)

Arguments

`tables`	An object of class `contingency.tables`
`conservative`	Should a continuity 'correction' be applied

Details

This is a convenience function wrapping mantelhaen.test in a add.cross.strata.test call. See mantelhaen.test for further details.

Value

A contingency.tables object identical to tables, but with the test applied to each table.

Examples

dat<-data.frame(a=rnorm(100)>.5,b=rnorm(100)>0,c=rnorm(100)>(-.5))
tables1<-contingency.tables(
 row.vars=a,
 col.vars=b,
 stratum.var=c,data=dat)
tables1<-add.mantel.haenszel(tables1)
print(tables1,prop.r=TRUE,prop.c=TRUE,prop.t=FALSE)
dat<-data.frame(a=rnorm(100)>.5,b=rnorm(100)>0,c=rnorm(100)>(-.5))
tables1<-contingency.tables(
 row.vars=a,
 col.vars=b,
 stratum.var=c,data=dat)
tables1<-add.mantel.haenszel(tables1)
print(tables1,prop.r=TRUE,prop.c=TRUE,prop.t=FALSE)

Apply a test to a Contingency Tables object

Description

Applies and adds a test to a contingency.tables object.

Usage

add.test(tables,name,htests,types=c("asymptotic","monte.carlo","exact"))

add.chi.squared(tables, simulate.p.value = FALSE, B = 10000)

add.likelihood.ratio(tables, conservative = FALSE, simulate.p.value = FALSE, B = 10000)

add.fishers.exact(tables, simulate.p.value = FALSE, B = 10000)

add.correlation(tables,method=c("spearman","kendall"))

add.kruskal(tables,nominal=c("both","rows","cols"))
add.test(tables,name,htests,types=c("asymptotic","monte.carlo","exact"))

add.chi.squared(tables, simulate.p.value = FALSE, B = 10000)

add.likelihood.ratio(tables, conservative = FALSE, simulate.p.value = FALSE, B = 10000)

add.fishers.exact(tables, simulate.p.value = FALSE, B = 10000)

add.correlation(tables,method=c("spearman","kendall"))

add.kruskal(tables,nominal=c("both","rows","cols"))

Arguments

`tables`	An object of class `contingency.tables`
`name`	Name of the test
`htests`	A function or list of functions which take a matrix as it's argument and returns an object of class `htest`
`types`	A character vector with the same number of items as `htests`, indicating what type of test was done
`conservative`	Should a conservative p-value be computed. i.e. One with a continuity correction for asymptotic tests and not using the mid p-value for exact and approximate tests
`simulate.p.value`	If `TRUE` p-values will be computed via monte carlo simulation
`B`	the number of samples for the monte carlo simulation
`method`	the type of correlation
`nominal`	Should the rows or columns be considered nominal.

Details

add.test applies a supplied list of tests to all of the tables in tables.

add.chi.squared is a wrapper function applying the chisq.test function to each table. add.likelihood.ratio is a wrapper function applying the likelihood.test function to each table. add.fishers.exact is a wrapper function applying the fisher.test function to each table. add.correlation is a wrapper function applying the cor.test function to each table. add.kruskal is a wrapper function applying the kruskal.test function to each table.

Value

A contingency.tables object identical to tables, but with the test applied to each table.

Examples

dat<-data.frame(a=rnorm(100)>.5,b=rnorm(100)>0,c=rnorm(100)>(-.5))
tables<-contingency.tables(
	row.vars=a,
	col.vars=b,
	stratum.var=c,data=dat)
tables<-add.chi.squared(tables,simulate.p.value=TRUE,B=10000)
tables<-add.likelihood.ratio(tables)
tables<-add.fishers.exact(tables)
tables<-add.correlation(tables,method='kendall')
tables<-add.kruskal(tables)
tables<-add.mantel.haenszel(tables)
print(tables)
remove(tables)
dat<-data.frame(a=rnorm(100)>.5,b=rnorm(100)>0,c=rnorm(100)>(-.5))
tables<-contingency.tables(
	row.vars=a,
	col.vars=b,
	stratum.var=c,data=dat)
tables<-add.chi.squared(tables,simulate.p.value=TRUE,B=10000)
tables<-add.likelihood.ratio(tables)
tables<-add.fishers.exact(tables)
tables<-add.correlation(tables,method='kendall')
tables<-add.kruskal(tables)
tables<-add.mantel.haenszel(tables)
print(tables)
remove(tables)

as.matrix method

Description

as matrix

Usage

## S3 method for class 'cor.matrix'
as.matrix(x,...)
## S3 method for class 'cor.matrix'
as.matrix(x,...)

Arguments

`x`	Object of class `"cor.matrix"`.
`...`	further arguments. unsued

Value

a matrix

Non-central Chi-Squared Confidence Interval

Description

Confidence interval for the Non-centrality parameter of Non-central chi-squared distribution

Usage

chi.noncentral.conf(chival,df,conf,prec=.00001)
chi.noncentral.conf(chival,df,conf,prec=.00001)

Arguments

`chival`	The observed Chi-Squared value
`conf`	The confidence level (e.g. .95)
`df`	Degrees of freedom
`prec`	Precision of estimate

Value

A 2X2 matrix whose rows represent the upper and lower bounds, and whose columns represent the parameter value and upper tail percentiles.

References

Smithson, M.J. (2003). Confidence Intervals, Quantitative Applications in the Social Sciences Series, No. 140. Thousand Oaks, CA: Sage.

Examples


chi.noncentral.conf(6,1,.95)
#            Result:

#      Non-Central          %
#Lower   0.2089385 0.97500899
#Upper  19.4443359 0.02499302
chi.noncentral.conf(6,1,.95)
#            Result:

#      Non-Central          %
#Lower   0.2089385 0.97500899
#Upper  19.4443359 0.02499302

contin.tests.to.table

Description

Makes a nice table out of a contin.tests object

Usage

contin.tests.to.table(tests,test.digits=3,...)
contin.tests.to.table(tests,test.digits=3,...)

Arguments

`tests`	a `contin.tests` object
`test.digits`	The number of digits to round to
`...`	other paramaters

Value

A nice table

Contingency Tables

Description

Creates a contingency.tables object

Usage

contingency.tables(row.vars, col.vars, stratum.var, data=NULL, missing.include=FALSE )
contingency.tables(row.vars, col.vars, stratum.var, data=NULL, missing.include=FALSE )

Arguments

`row.vars`	A variable or data frame evaluated in data
`col.vars`	A variable or data frame evaluated in data
`stratum.var`	A variable evaluated in data
`data`	A `data.frame`
`missing.include`	A logical indicating whether a missing category should be included in the table

Value

A list with class "contingency.tables." Each element of the list is a single contingency table of class "contin.table" corresponding to each combination of elements of row.vars and col.vars stratified by stratum.var

Examples

temp.data<-data.frame(a=rnorm(100)>0,b=rnorm(100)>0,gender=rep(c("male","female"),50))
#a vs. b stratified by gender
tab<-contingency.tables(a,b,gender,data=temp.data)
tab

##add in chi-squared tests
tab<-add.chi.squared(tab)
tab
temp.data<-data.frame(a=rnorm(100)>0,b=rnorm(100)>0,gender=rep(c("male","female"),50))
#a vs. b stratified by gender
tab<-contingency.tables(a,b,gender,data=temp.data)
tab

##add in chi-squared tests
tab<-add.chi.squared(tab)
tab

cor.matrix

Description

Creates a correlation matrix

Usage

cor.matrix(variables,with.variables,data=NULL,test=cor.test,...)
cor.matrix(variables,with.variables,data=NULL,test=cor.test,...)

Arguments

`variables`	variables
`with.variables`	An optional set of variables to correlate with `variables`. If nothing is specified, all variables in `variables` are correlated with themselves.
`data`	A data.frame from which the variables and factor will be selected.
`test`	A function whose first two arguments are the variables upon which the correlation will be calculated, and whose result is an object of class `htest`.
`...`	further arguments for `test`.

Value

A multi.test object, representing a table of the results of func applied to each of the variables.

Examples

dat<-data.frame(aa=rnorm(100),bb=rnorm(100),cc=rnorm(100),dd=rnorm(100))
dat$aa<-dat$aa+dat$dd
dat$cc<-dat$cc+dat$aa
cor.matrix(dat,test=cor.test)
cor.matrix(d(aa,cc),data=dat,test=cor.test,method="kendall")
cor.matrix(d(aa,cc),d(dd,bb),data=dat,test=cor.test,method="spearman")
dat<-data.frame(aa=rnorm(100),bb=rnorm(100),cc=rnorm(100),dd=rnorm(100))
dat$aa<-dat$aa+dat$dd
dat$cc<-dat$cc+dat$aa
cor.matrix(dat,test=cor.test)
cor.matrix(d(aa,cc),data=dat,test=cor.test,method="kendall")
cor.matrix(d(aa,cc),d(dd,bb),data=dat,test=cor.test,method="spearman")

wrapper for data.frame

Description

This function creates data frames, tightly coupled collections of variables which share many of the properties of matrices and of lists, used as the fundamental data structure by most of R's modeling software. It is a keystroke saving wrapper for the data.frame function. The only difference is that check.names and stringsAsFactors are FALSE by default.

Usage

d(..., row.names = NULL, check.rows = FALSE,
                check.names = FALSE,
                stringsAsFactors = FALSE)
d(..., row.names = NULL, check.rows = FALSE,
                check.names = FALSE,
                stringsAsFactors = FALSE)

Arguments

`...`	items
`row.names`	NULL or a single integer or character string specifying a column to be used as row names, or a character or integer vector giving the row names for the data frame.
`check.rows`	if TRUE then the rows are checked for consistency of length and names.
`check.names`	logical. If TRUE then the names of the variables in the data frame are checked to ensure that they are syntactically valid variable names and are not duplicated. If necessary they are adjusted (by make.names) so that they are.
`stringsAsFactors`	logical: should character vectors be converted to factors?

Examples

x <- d(rnorm(10),1:10)
x <- d(rnorm(10),1:10)

GUI Access functions

Description

splits a variable into two groups

Usage

deducer(cmd=NULL)

data.viewer()
deducer(cmd=NULL)

data.viewer()

Arguments

cmd

The command to be executed

Controls Deducer's command line menus

Description

Controls Deducer's command line menus

Usage

deducer.addMenu(name, pos=length(menus)+1)
deducer.setMenus(newMenus)
deducer.getMenus()
deducer.addMenuItem(name, pos=NULL, command, menuName, silent=TRUE)
menuFunctions()
deducer.addMenu(name, pos=length(menus)+1)
deducer.setMenus(newMenus)
deducer.getMenus()
deducer.addMenuItem(name, pos=NULL, command, menuName, silent=TRUE)
menuFunctions()

Arguments

`name`	name of item or menu to add
`pos`	position at which to add the item or menu
`menuName`	the name of the menu to add the item to
`command`	A character vector representing the R command to be run
`silent`	Should the command be executed silently
`newMenus`	new menus

Examples


#add a menu with two items
deducer.addMenu("TestMenu")
deducer.addMenuItem("test1",,"cat('test1 selected')","TestMenu")
deducer.addMenuItem("test2",,"print(summary(lm(rnorm(100)~rnorm(100))))","TestMenu")

#Add menu to gui if applicable
if(.windowsGUI){
	winMenuAdd("TestMenu")
	winMenuAddItem("TestMenu", "test1", "cat('test1 selected')")
	winMenuAddItem("TestMenu", "test2", "print(summary(lm(rnorm(100)~rnorm(100))))")
}else if(.jgr){
	jgr.addMenu("TestMenu")
	jgr.addMenuItem("TestMenu", "test1", "cat('test1 selected')")
	jgr.addMenuItem("TestMenu", "test2", "print(summary(lm(rnorm(100)~rnorm(100))))")	
}


#add a menu with two items
deducer.addMenu("TestMenu")
deducer.addMenuItem("test1",,"cat('test1 selected')","TestMenu")
deducer.addMenuItem("test2",,"print(summary(lm(rnorm(100)~rnorm(100))))","TestMenu")

#Add menu to gui if applicable
if(.windowsGUI){
	winMenuAdd("TestMenu")
	winMenuAddItem("TestMenu", "test1", "cat('test1 selected')")
	winMenuAddItem("TestMenu", "test2", "print(summary(lm(rnorm(100)~rnorm(100))))")
}else if(.jgr){
	jgr.addMenu("TestMenu")
	jgr.addMenuItem("TestMenu", "test1", "cat('test1 selected')")
	jgr.addMenuItem("TestMenu", "test2", "print(summary(lm(rnorm(100)~rnorm(100))))")	
}

Table of Descriptives

Description

Table of descriptive statistics, possibly stratified

Usage

descriptive.table(vars,
	strata,
	data,
	func.names = c("Mean","St. Deviation","Median",
		"25th Percentile","75th Percentile",
		"Minimum","Maximum","Skew","Kurtosis","Valid N"),
	func.additional)
descriptive.table(vars,
	strata,
	data,
	func.names = c("Mean","St. Deviation","Median",
		"25th Percentile","75th Percentile",
		"Minimum","Maximum","Skew","Kurtosis","Valid N"),
	func.additional)

Arguments

`vars`	A variable or data.frame containing variables on which to run descriptive statistics.
`data`	The data frame in which vars is evaluated
`strata`	A variable or data.frame containing variables on which to stratify
`func.names`	A character vector of built-in statistics
`func.additional`	A named list of functions. Each function should take a numeric vector as its argument, and return a single value

Value

Returns a list of matrix objects containing descriptive information on all variables in dat. One for each level or combination of levels in strata.

Examples

data(mtcars)
##means and standard deviations
descriptive.table(vars = d(mpg,hp),data= mtcars,
	func.names =c("Mean","St. Deviation","Valid N"))
##stratifying by cyl
descriptive.table(vars = d(mpg,hp) ,
	strata = d(cyl),data= mtcars,
	func.names =c("Mean","St. Deviation","Valid N"))
	
	
func.list=list(mean.deviance=function(x) mean(abs(x-mean(x))))

##Adding deviance as a statistic
descriptive.table(vars = d(mpg,hp) ,
	strata = d(cyl),data= mtcars,
	func.names =c("Mean","St. Deviation","Valid N"),func.additional=func.list)
data(mtcars)
##means and standard deviations
descriptive.table(vars = d(mpg,hp),data= mtcars,
	func.names =c("Mean","St. Deviation","Valid N"))
##stratifying by cyl
descriptive.table(vars = d(mpg,hp) ,
	strata = d(cyl),data= mtcars,
	func.names =c("Mean","St. Deviation","Valid N"))
	
	
func.list=list(mean.deviance=function(x) mean(abs(x-mean(x))))

##Adding deviance as a statistic
descriptive.table(vars = d(mpg,hp) ,
	strata = d(cyl),data= mtcars,
	func.names =c("Mean","St. Deviation","Valid N"),func.additional=func.list)

Deducer's plug-in development tools

Description

functions pertaining to GUI development

Usage

addComponent(container, component, top, right, bottom, 
	left, topType = "REL", rightType = "REL", bottomType = "REL", 
	leftType = "REL")
getSize(component)
setSize(component,width,height)
execute(cmd)

ButtonGroupWidget 
CheckBoxesWidget 
DeducerMain 
JLabel 
RDialog
SimpleRDialog
SimpleRSubDialog 
SingleVariableWidget 
SliderWidget 
TextAreaWidget
VariableListWidget 
VariableSelectorWidget
ComboBoxWidget
RDialogMonitor
ListWidget
AddRemoveButtons
TextFieldWidget
ObjectChooserWidget
addComponent(container, component, top, right, bottom, 
	left, topType = "REL", rightType = "REL", bottomType = "REL", 
	leftType = "REL")
getSize(component)
setSize(component,width,height)
execute(cmd)

ButtonGroupWidget 
CheckBoxesWidget 
DeducerMain 
JLabel 
RDialog
SimpleRDialog
SimpleRSubDialog 
SingleVariableWidget 
SliderWidget 
TextAreaWidget
VariableListWidget 
VariableSelectorWidget
ComboBoxWidget
RDialogMonitor
ListWidget
AddRemoveButtons
TextFieldWidget
ObjectChooserWidget

Arguments

`container`	A Java Swing container with Anchor layout
`component`	a Java Swing component
`top`	location of top of component 0 - 1000
`right`	location of right of component 0 - 1000
`bottom`	location of bottom of component 0 - 1000
`left`	location of left of component 0 - 1000
`topType`	Type of constraint on top of component. Can be "REL", "ABS", or "NONE"
`rightType`	Type of constraint on right of component. Can be "REL", "ABS", or "NONE"
`bottomType`	Type of constraint on bottom of component. Can be "REL", "ABS", or "NONE"
`leftType`	Type of constraint on left of component. Can be "REL", "ABS", or "NONE"
`height`	new height of component or window in pixels
`width`	new width of component or window in pixels
`cmd`	the command to be executed

Details

addComponent adds a Java object of class Component to a container (usually an RDialog or SimpleRDialog). the location of the component is determined by the top, right, bottom, and left arguments, which are numbers between 1 and 1000 indicating the distance from either the top (or left) of the container, with 1000 indicating the opposite side of the container. Each side can be constrained in three different ways. If the Type is "REL", the side will scale proportional to the container when the container is resized. If it is "ABS", it is not rescaled. If it is "NONE", the location of that side is determined by the componet's preferred size, which can be set with the "setPreferedSize" method.

getSize gets the height and width

setSize sets the height and width

execute executes a character representing a command, as if it were entered into the console

The rest of the items are references to the Java classes of commonly used GUI components. see www.deducer.org for more details and usage.

dich

Description

splits a variable into two groups

Usage

dich(variables,data=NULL,cut=NULL,group1=NULL,group2=NULL)
dich(variables,data=NULL,cut=NULL,group1=NULL,group2=NULL)

Arguments

`variables`	variables to be dichotomized
`data`	A data.frame
`cut`	An optional cut point dividing `factor` into two groups.
`group1`	An optional vector of levels of `factor` defining group 1.
`group2`	An optional vector of levels of `factor` defining group 2.

Value

a data.frame containing the variables, recoded into two groups.

Extract Contingency Table Arrays

Description

Extracts the counts of a contingency.tables object

Usage

extract.counts(tables)
extract.counts(tables)

Arguments

tables

A contingency.table object

Value

A named list of three dimensional arrays. One for each contin.table in tables

Examples

temp.data<-data.frame(a=rnorm(100)>0,b=rnorm(100)>0,gender=rep(c("male","female"),50))
#a vs. b stratified by gender
tab<-contingency.tables(a,b,gender,data=temp.data)
tab

##extract counts
extract.counts(tab)

##Yields something like the following:
#$`a by b`
#, , female
#
#      FALSE TRUE
#FALSE    11    9
#TRUE     15   15
#
#, , male
#
#      FALSE TRUE
#FALSE    10   10
#TRUE     22    8

temp.data<-data.frame(a=rnorm(100)>0,b=rnorm(100)>0,gender=rep(c("male","female"),50))
#a vs. b stratified by gender
tab<-contingency.tables(a,b,gender,data=temp.data)
tab

##extract counts
extract.counts(tab)

##Yields something like the following:
#$`a by b`
#, , female
#
#      FALSE TRUE
#FALSE    11    9
#TRUE     15   15
#
#, , male
#
#      FALSE TRUE
#FALSE    10   10
#TRUE     22    8

Frequency Tables

Description

Creates a set of frequency tables.

Usage

frequencies(data,r.digits=1)
frequencies(data,r.digits=1)

Arguments

`data`	A data.frame containing the variables on which to run frequencies
`r.digits`	how many digits should the percentages be rounded to

Value

Returns a list of freq.table objects. One for each variable in data.

Examples

dat<-data.frame(rnorm(100)>0,trunc(runif(100,0,5)))
##rounding to 1
frequencies(dat)
##rounding to 4
frequencies(dat,4)
dat<-data.frame(rnorm(100)>0,trunc(runif(100,0,5)))
##rounding to 1
frequencies(dat)
##rounding to 4
frequencies(dat,4)

get objects

Description

Enumerates all objects of a certain class

Usage

get.objects(cn,env = globalenv(),includeInherited=TRUE)
get.objects(cn,env = globalenv(),includeInherited=TRUE)

Arguments

`cn`	The name of the class
`env`	environment to look in
`includeInherited`	Should objects inheriting cn be included

Value

a character vector

Correlation matrix

Description

Plots a correlation matrix

Usage

ggcorplot(cor.mat,data=NULL,lines=TRUE,line.method=c("lm","loess"),type="points",
		alpha=.25,main="auto",var_text_size=5,
		cor_text_limits=c(5,25),level=.05)
ggcorplot(cor.mat,data=NULL,lines=TRUE,line.method=c("lm","loess"),type="points",
		alpha=.25,main="auto",var_text_size=5,
		cor_text_limits=c(5,25),level=.05)

Arguments

`cor.mat`	a `cor.matrix` object to plot
`data`	the data.frame used to compute the correlation matrix
`lines`	Logical. Should regression lines be drawn.
`type`	type of plot. "points" or "bins"
`line.method`	Character. Type of regression line.
`alpha`	numeric. level of alpha transparency for the points.
`main`	Title of the plot. defaults to the method of cor.mat.
`var_text_size`	size of the diagonal variable names.
`cor_text_limits`	lower and upper bounds for the size of the correlation text.
`level`	the size of the test differentiated by text color.

Author(s)

Mike Lawrence and Ian Fellows

Examples

data(mtcars)
corr.mat1<-cor.matrix(variables=d(mpg,carb,carb+rnorm(length(carb))),,
	 data=mtcars,
	 test=cor.test,
	 method='spearman',
	alternative="two.sided",exact=FALSE)
	
p<-ggcorplot(corr.mat1,data = mtcars)
print(p)
## Not run: 

has.hex<-require("hexbin")
if(has.hex){
	data(diamonds)
	corr.mat<-cor.matrix(variables=d(price,carat,color),,
		 data=diamonds,
		 test=cor.test,
		 method='spearman',
		alternative="two.sided")

	p1 <- ggcorplot(cor.mat=corr.mat,data=diamonds,type="bins",
		cor_text_limits=c(5,15),
		lines=FALSE)
	print(p1)
	rm('corr.mat')

}

## End(Not run)

data(mtcars)
corr.mat1<-cor.matrix(variables=d(mpg,carb,carb+rnorm(length(carb))),,
	 data=mtcars,
	 test=cor.test,
	 method='spearman',
	alternative="two.sided",exact=FALSE)
	
p<-ggcorplot(corr.mat1,data = mtcars)
print(p)
## Not run: 

has.hex<-require("hexbin")
if(has.hex){
	data(diamonds)
	corr.mat<-cor.matrix(variables=d(price,carat,color),,
		 data=diamonds,
		 test=cor.test,
		 method='spearman',
		alternative="two.sided")

	p1 <- ggcorplot(cor.mat=corr.mat,data=diamonds,type="bins",
		cor_text_limits=c(5,15),
		lines=FALSE)
	print(p1)
	rm('corr.mat')

}

## End(Not run)

K Sample Test

Description

Performs a K independent sample test.

Usage

k.sample.test(formula,data,test=oneway.test,...)
k.sample.test(formula,data,test=oneway.test,...)

Arguments

`formula`	A formula, the left hand side of which indicated the outcomes, and the right hand side of which contains the factor
`data`	A data.frame
`test`	A function whose first argument is a formula with the outcome on the lhs and the factor on the rhs. The second argument should be the data to be used for the formula. The result of the function should be an object of class `htest`.
`...`	further arguments for func

Value

A multi.test object, representing a table of the results of func applied to each of the variables.

Examples

dat<-data.frame(a=rnorm(100),b=rnorm(100),c=rnorm(100),d=cut(rnorm(100),4))
k.sample.test(d(a,b)~d,dat)
k.sample.test(dat[,-4]~dat$d,var.equal=TRUE)
k.sample.test(d(a,c)~d,dat,kruskal.test)
dat<-data.frame(a=rnorm(100),b=rnorm(100),c=rnorm(100),d=cut(rnorm(100),4))
k.sample.test(d(a,b)~d,dat)
k.sample.test(dat[,-4]~dat$d,var.equal=TRUE)
k.sample.test(d(a,c)~d,dat,kruskal.test)

Likelihood Ratio (G test) for contingency tables

Description

Performs a likelihood ratio test of independence

Usage

likelihood.test(x,y=NULL,conservative=FALSE)
likelihood.test(x,y=NULL,conservative=FALSE)

Arguments

`x`	A vector or a matrix
`y`	A vector that is ignored if x is a matrix and required if x is a vector
`conservative`	If `TRUE`, the Williams' continuity correction is used

Value

A list with class "htest" containing the following components:

`statistic`	the value the chi-squared test statistic.
`parameter`	the degrees of freedom of the approximate chi-squared distribution of the test statistic.
`p.value`	the p-value for the test.
`method`	a character string indicating the type of test performed, and whether the continuity correction was used.
`data.name`	a character string giving the name(s) of the data.

Author(s)

Pete Hurd and Ian Fellows

Examples

data(InsectSprays)
likelihood.test(InsectSprays$count>7,InsectSprays$spray)
data(InsectSprays)
likelihood.test(InsectSprays$count>7,InsectSprays$spray)

multi.test

Description

Creates a table from a list of htests

Usage

multi.test(tests)
multi.test(tests)

Arguments

tests

A named list of htest objects representing the same test applied to a number of different conditions or variables.

Value

A multi.test object, representing a table of the htest objects.

One Sample Test

Description

Performs a one sample test.

Usage

one.sample.test(variables,data=NULL,test=t.test,...)
one.sample.test(variables,data=NULL,test=t.test,...)

Arguments

`variables`	A variable or dataframe of variables
`data`	The data frame in which variables is evaluated
`test`	A function whose first argument is the sample to be tested, and whose result is an object of class `htest`.
`...`	further arguments for func

Value

A multi.test object, representing a table of the results of test applied to each of the variables.

Examples

data(anorexia)

#are subjects' weights at baseline and endpoint significantly different from normal
one.sample.test(variables=d(Prewt,Postwt),
	data=anorexia,
	test=shapiro.test)

#does CBT work at increasing mean wt
anorexia.sub<-subset(anorexia,Treat=="CBT")
one.sample.test(variables=Postwt-Prewt,
	data=anorexia.sub,
	test=t.test)

data(anorexia)

#are subjects' weights at baseline and endpoint significantly different from normal
one.sample.test(variables=d(Prewt,Postwt),
	data=anorexia,
	test=shapiro.test)

#does CBT work at increasing mean wt
anorexia.sub<-subset(anorexia,Treat=="CBT")
one.sample.test(variables=Postwt-Prewt,
	data=anorexia.sub,
	test=t.test)

onesample.plot

Description

plots for one sample tests

Usage

onesample.plot(variables,data=NULL,test.value,scale=FALSE,type="hist",alpha=.2)
onesample.plot(variables,data=NULL,test.value,scale=FALSE,type="hist",alpha=.2)

Arguments

`variables`	An expression denoting a set of variable.
`data`	A data.frame from which the variables will be selected.
`test.value`	null hypothesis test value
`scale`	scale variables
`type`	type of plot. 'hist' or 'box' are allowed
`alpha`	transparency of points for box plot

Examples


data(mtcars)
onesample.plot(variables=d(mpg,cyl,disp,hp,drat,wt,qsec,vs,am,
gear,carb),data=mtcars,type='hist')

onesample.plot(variables=d(mpg,cyl,disp,hp,drat,wt,qsec,vs,am,
gear,carb),data=mtcars,type='box',alpha=1)

data(mtcars)
onesample.plot(variables=d(mpg,cyl,disp,hp,drat,wt,qsec,vs,am,
gear,carb),data=mtcars,type='hist')

onesample.plot(variables=d(mpg,cyl,disp,hp,drat,wt,qsec,vs,am,
gear,carb),data=mtcars,type='box',alpha=1)

One Way PLot

Description

plots a categorical variable against a series of continuous variables

Usage

oneway.plot(formula,data=NULL,alpha=.2,
		box=TRUE,points=TRUE,scale=FALSE)
oneway.plot(formula,data=NULL,alpha=.2,
		box=TRUE,points=TRUE,scale=FALSE)

Arguments

`formula`	A formula, the left hand side of which indicated the outcomes, and the right hand side of which contains the factor
`data`	A data.frame
`alpha`	alpha transparency level for the points.
`box`	prints boxplot
`points`	prints jitter plot
`scale`	standardize the variables prior to plotting

Value

a ggplot object

Examples


oneway.plot(d(DriversKilled, drivers, front, rear,   kms, PetrolPrice)~law,as.data.frame(Seatbelts))

oneway.plot(d(DriversKilled, drivers, front, rear,   kms, PetrolPrice)~law,as.data.frame(Seatbelts))

Vector Permutations

Description

Enumerates all permutations of a vector

Usage

perm(vec,duplicates=FALSE)
perm(vec,duplicates=FALSE)

Arguments

`vec`	The vector to permute
`duplicates`	Should duplicate permutations be listed

Value

Returns a matrix where each row is a permutation of vec. All possible permutations are listed, and if duplicates=TRUE non-unique permutations are also listed.

Examples

perm(1:4)
perm(LETTERS[4:8])

perm(1:4)
perm(LETTERS[4:8])

Permutation t-test

Description

Two Sample t-test via monte-carlo permutation

Usage

perm.t.test(x,y,statistic=c("t","mean"),
			alternative=c("two.sided", "less", "greater"), midp=TRUE, B=10000)
perm.t.test(x,y,statistic=c("t","mean"),
			alternative=c("two.sided", "less", "greater"), midp=TRUE, B=10000)

Arguments

`x`	a numeric vector containing the first sample
`y`	a numeric vector containing the second sample
`statistic`	The statistic to be permuted. See details
`alternative`	The alternative hypothesis
`midp`	should the mid p-value be used
`B`	The number of monte-carlo samples to be generated

Details

This function performs a two sample permutation test. If the mean is permuted, then the test assumes exchangability between the two samples. if the t-statistic is used, the test assumes either exchangability or a sufficiently large sample size. Because there is little lost in the way of power, and the assumptions are weaker, the t-statistic is used by default.

Value

A list with class "htest" containing the following components:

`statistic`	The observed value of the statistic.
`p.value`	the p-value for the test.
`method`	a character string indicating the type of test performed.
`data.name`	a character string giving the name(s) of the data.
`B`	The number of samples generated
`alternative`	the direction of the test

Examples

perm.t.test(rnorm(100),runif(100,-.5,.5))
perm.t.test(rnorm(100),runif(100,-.5,.5))

Plot method

Description

Produces a circle plot for an object of class "plot.cor.matrix"

Usage

## S3 method for class 'cor.matrix'
plot(x,y=NULL,size=10,...)
## S3 method for class 'cor.matrix'
plot(x,y=NULL,size=10,...)

Arguments

`x`	Object of class `"cor.matrix"`.
`y`	unused
`size`	maximum radius size
`...`	further arguments. unsued

Value

a ggplot object

Print method

Description

Print object of class "contin.table" in nice layout.

Usage

## S3 method for class 'contin.table'
print(
	x,digits=3,prop.r=TRUE,prop.c=TRUE,prop.t=TRUE,
	expected.n=FALSE,residuals=FALSE,std.residuals=FALSE,
	adj.residuals=FALSE,no.tables=FALSE,...)
## S3 method for class 'contin.table'
print(
	x,digits=3,prop.r=TRUE,prop.c=TRUE,prop.t=TRUE,
	expected.n=FALSE,residuals=FALSE,std.residuals=FALSE,
	adj.residuals=FALSE,no.tables=FALSE,...)

Arguments

`x`	Object of class `"contin.table"`.
`digits`	Number of digits to round to.
`prop.r`	Logical. print row proportions.
`prop.c`	Logical. print column proportions.
`prop.t`	Logical. print proportions.
`expected.n`	Logical print expected cell counts.
`residuals`	Logical. print residuals.
`std.residuals`	Logical. print standardized residuals.
`adj.residuals`	Logical. Print Adjusted residuals
`no.tables`	Logical. Suppress tables
`...`	further arguments

Value

none

Author(s)

Ian Fellows based on the CrossTable function from the gmodels package maintained by Gregory R. Warnes

Print method

Description

Print object of class "contin.tests" in nice layout.

Usage

## S3 method for class 'contin.tests'
print(x,test.digits, ...)
## S3 method for class 'contin.tests'
print(x,test.digits, ...)

Arguments

`x`	Object of class `"contin.tests"`.
`test.digits`	Number of digits to be printed
`...`	further arguments to be passed to or from methods.

Value

none

Print method

Description

Print object of class "contingency.tables" in nice layout.

Usage

## S3 method for class 'contingency.tables'
print(x,digits=3,prop.r=TRUE,prop.c=TRUE,prop.t=TRUE,
						expected.n=FALSE,no.tables=FALSE,...)
## S3 method for class 'contingency.tables'
print(x,digits=3,prop.r=TRUE,prop.c=TRUE,prop.t=TRUE,
						expected.n=FALSE,no.tables=FALSE,...)

Arguments

`x`	Object of class `"contin.table"`.
`digits`	Number of digits to round to.
`prop.r`	Logical. print row proportions.
`prop.c`	Logical. print column proportions.
`prop.t`	Logical. print proportions.
`expected.n`	Logical print expected cell counts.
`no.tables`	Logical. Suppress tables
`...`	further arguments

Value

none

Print method

Description

Print object of class "cor.matrix" in nice layout.

Usage

## S3 method for class 'cor.matrix'
print(x,digits=4,N=TRUE,CI=TRUE,stat=TRUE,p.value=TRUE,...)
## S3 method for class 'cor.matrix'
print(x,digits=4,N=TRUE,CI=TRUE,stat=TRUE,p.value=TRUE,...)

Arguments

`x`	Object of class `"cor.matrix"`.
`digits`	Number of digits to round to.
`N`	Logical. print a row for sample size.
`CI`	Logical. print a row for confidence intervals if they exist.
`stat`	Logical. print a row for test statistics.
`p.value`	Logical. print a row for p-values.
`...`	further arguments

Value

none

Print method

Description

Print object of class "freq.table" in nice layout.

Usage

## S3 method for class 'freq.table'
print(x,...)
## S3 method for class 'freq.table'
print(x,...)

Arguments

`x`	Object of class `"freq.table"`.
`...`	further arguments

Value

none

Print method

Description

Print object of class "multi.test" in nice layout.

Usage

## S3 method for class 'multi.test'
print(x,...)
## S3 method for class 'multi.test'
print(x,...)

Arguments

`x`	Object of class `"multi.test"`.
`...`	further arguments

Value

none

qscatter_array

Description

Creates an array of scatterplots

Usage

qscatter_array(variables,with.variables,data,x.lab="",y.lab="",
				main="Correlation Array",common.scales=TRUE,alpha=.25)
qscatter_array(variables,with.variables,data,x.lab="",y.lab="",
				main="Correlation Array",common.scales=TRUE,alpha=.25)

Arguments

`variables`	variables
`with.variables`	An optional set of variables to correlate with `variables`. If nothing is specified, all variables in `variables` are correlated with themselves.
`data`	A data.frame from which the variables will be selected.
`x.lab`	A label for the x axis
`y.lab`	A label for the y axis
`main`	A label for the plot
`common.scales`	should common x and y scales be used.
`alpha`	alpha transparency

Examples

data(mtcars)
qscatter_array(d(cyl,disp,hp,drat),
	data=mtcars) + geom_smooth(method="lm")
qscatter_array(d(cyl,disp,hp,drat),d(wt,carb),data=mtcars,common.scales=FALSE)
data(mtcars)
qscatter_array(d(cyl,disp,hp,drat),
	data=mtcars) + geom_smooth(method="lm")
qscatter_array(d(cyl,disp,hp,drat),d(wt,carb),data=mtcars,common.scales=FALSE)

Recode

Description

Recodes a set of variables according to a set of rules

Usage

recode.variables(data,recodes)
recode.variables(data,recodes)

Arguments

`data`	A `data.frame` to be recoded
`recodes`	Definition of the recoding rules. See details

Details

recodes contains a set of recoding rules separated by ";". There are three different types of recoding rules:

1. The simplest codes one value to another. If we wish to recode 1 into 2, we could use the rule "1->2;".

2. A range of values can be coded to a single value using "1:3->4;". This rule would code all values between 1 and 3 inclusive into 4. For factors, a value is between two levels if it is between them in the factor ordering. One sided ranges can be specified using the Lo and Hi key words (e.g."Lo:3->0; 4:Hi->1")

3. Default conditions can be coded using "else." For example, if we wish to recode all values >=0 to 1 and all values <0 to missing, we could use ("0:Hi->1; else->NA")

Value

returns a recoded data.frame

Author(s)

Ian Fellows adapted from code by John Fox

Examples

data<-data.frame(a=rnorm(100),b=rnorm(100),male=rnorm(100)>0)
recode.variables(data[c("a","b")] , "Lo:0 -> 0;0:Hi -> 1;")
data[c("male")] <- recode.variables(data[c("male")] , "1 -> 'Male';0 -> 'Female';else -> NA;")
data<-data.frame(a=rnorm(100),b=rnorm(100),male=rnorm(100)>0)
recode.variables(data[c("a","b")] , "Lo:0 -> 0;0:Hi -> 1;")
data[c("male")] <- recode.variables(data[c("male")] , "1 -> 'Male';0 -> 'Female';else -> NA;")

ROC Plot for a logistic regression model

Description

Plots the ROC Curve

Usage

rocplot(logistic.model,diag=TRUE,pred.prob.labels=FALSE,prob.label.digits=3,AUC=TRUE)
rocplot(logistic.model,diag=TRUE,pred.prob.labels=FALSE,prob.label.digits=3,AUC=TRUE)

Arguments

`logistic.model`	a glm object with binomial link function.
`diag`	a logical value indicating whether a diagonal reference line should be displayed.
`pred.prob.labels`	a logical value indicating whether the predictive probabilities should be displayed
`prob.label.digits`	The number of digits of the predictive probabilities to be displayed.
`AUC`	a logical value indicating whether the estimated area under the curve should be displayed

Value

a ggplot object

Author(s)

Ian Fellows adapted from the lroc function by Virasakdi Chongsuvivatwong

Examples

model.glm <- glm(formula=income>5930.5 ~ education + women + type,
	family=binomial(),data=Prestige,na.action=na.omit)
rocplot(model.glm)
model.glm <- glm(formula=income>5930.5 ~ education + women + type,
	family=binomial(),data=Prestige,na.action=na.omit)
rocplot(model.glm)

Sort Data

Description

Sorts a data frame

Usage

## S3 method for class 'data.frame'
sort(x, decreasing, by, ...)
## S3 method for class 'data.frame'
sort(x, decreasing, by, ...)

Arguments

`x`	A `data.frame` to be sorted
`decreasing`	unused
`by`	A character, a one sided formula, or an expression indicating the sorting order
`...`	further arguments

Details

If by is a formula, or a character vector coerce-able into a formula, x is sorted by each element of the formula, with ties broken by subsequent elements. Elements preceded by a '-' indicate descending order, otherwise ascending order is used. Parentheses or any formula operator other than + and - are ignored, so sorting by a*b will sort based on the product of a and b.

If by is not a formula, a ~ is appended to the left hand side of the call, and coerced into a formula.

The decreasing argument is included for generic method consistency, and is not used.

Value

returns x, sorted.

Author(s)

Ian Fellows adapted from code by Ari Friedman and Kevin Wright

Examples

data(mtcars)

#sort by the number of cylenders
sort(mtcars, by= ~cyl)
sort(mtcars, by= cyl) #identical: no need for ~

#sort in descending order
sort(mtcars, by= -cyl)

#break ties with horse power
sort(mtcars,by= cyl +hp )
sort(mtcars,by= cyl -hp )

#randomly permute the data
sort(mtcars,by= rnorm(nrow(mtcars)) )

#reverse order
sort(mtcars,by= nrow(mtcars):1 )

#sort by squared deviation from mean hp
sort(mtcars,by= -(hp-mean(hp))^2 )
sort(mtcars,by= "-(hp-mean(hp))^2" ) #identical
data(mtcars)

#sort by the number of cylenders
sort(mtcars, by= ~cyl)
sort(mtcars, by= cyl) #identical: no need for ~

#sort in descending order
sort(mtcars, by= -cyl)

#break ties with horse power
sort(mtcars,by= cyl +hp )
sort(mtcars,by= cyl -hp )

#randomly permute the data
sort(mtcars,by= rnorm(nrow(mtcars)) )

#reverse order
sort(mtcars,by= nrow(mtcars):1 )

#sort by squared deviation from mean hp
sort(mtcars,by= -(hp-mean(hp))^2 )
sort(mtcars,by= "-(hp-mean(hp))^2" ) #identical

Summary table for a linear model

Description

Computes the coefficients, std. errors, t values, and p-values for a linear model in the presence of possible heteroskedasticity.

Usage

summarylm(object,correlation=FALSE,symbolic.cor = FALSE,white.adjust=FALSE,...)
summarylm(object,correlation=FALSE,symbolic.cor = FALSE,white.adjust=FALSE,...)

Arguments

`object`	an object of class lm.
`correlation`	a logical value indicating whether parameter correlations should be printed.
`symbolic.cor`	logical. If TRUE, print the correlations in a symbolic form (see symnum) rather than as numbers. Effective only if white.adjust is FALSE.
`white.adjust`	value passed to `hccm` indicating the type of robust adjustment to be used. If TRUE, type is assumed to be 'hc3'
`...`	additional parameters passed to stats::summary.lm

Details

If white.adjust is false, the function returns a value identical to stats::summary.lm. Otherwise, robust summaries are computed

Value

A summary table

Examples

ctl <- c(4.17,5.58,5.18,6.11,4.50,4.61,5.17,4.53,5.33,5.14)
trt <- c(4.81,4.17,4.41,3.59,5.87,3.83,6.03,4.89,4.32,4.69)
group <- gl(2,10,20, labels=c("Ctl","Trt"))
weight <- c((ctl-mean(ctl))*10+mean(ctl), trt)
lm.D9 <- lm(weight ~ group)
summarylm(lm.D9,white.adjust=TRUE)
ctl <- c(4.17,5.58,5.18,6.11,4.50,4.61,5.17,4.53,5.33,5.14)
trt <- c(4.81,4.17,4.41,3.59,5.87,3.83,6.03,4.89,4.32,4.69)
group <- gl(2,10,20, labels=c("Ctl","Trt"))
weight <- c((ctl-mean(ctl))*10+mean(ctl), trt)
lm.D9 <- lm(weight ~ group)
summarylm(lm.D9,white.adjust=TRUE)

Table –> data.frame

Description

Creates a data.frame from a table

Usage

table.to.data(x)
table.to.data(x)

Arguments

`x`	A matrix or table representing the cross tabulation of two variables

Value

A two column data.frame where each row is an observation and each column is a variable.

Examples

tab<-matrix(c(4,5,6,9,7,3),ncol=3)
tab
table.to.data(tab)

tab<-matrix(c(4,5,6,9,7,3),ncol=3)
tab
table.to.data(tab)

Two Sample Test

Description

Performs a two independent sample test.

Usage

two.sample.test(formula,data=NULL,test=t.test,...)
two.sample.test(formula,data=NULL,test=t.test,...)

Arguments

`formula`	A formula, the left hand side of which indicated the outcomes, and the right hand side of which contains the factor
`data`	A data.frame
`test`	A function whose first two arguments are the two-samples to be tested, and whose result is an object of class `htest`.
`...`	further arguments for test

Value

A multi.test object, representing a table of the results of test applied to each of the variables.

Examples

dat<-data.frame(a=rnorm(100),b=rnorm(100),c=rnorm(100),d=rnorm(100)>(-.5))
two.sample.test(d(a,b) ~ d,dat,ks.test)
two.sample.test(a ~ dich(b,cut=0) ,dat,t.test)
two.sample.test(d(a^2,abs(b),c)~d,dat,wilcox.test)
dat<-data.frame(a=rnorm(100),b=rnorm(100),c=rnorm(100),d=rnorm(100)>(-.5))
two.sample.test(d(a,b) ~ d,dat,ks.test)
two.sample.test(a ~ dich(b,cut=0) ,dat,t.test)
two.sample.test(d(a^2,abs(b),c)~d,dat,wilcox.test)

Package 'Deducer'

Help Index

Apply a Stratified test to a Contingency Table

Description

Usage

Arguments

Value

See Also

Examples

Apply the Mantel-Haenszel test to a Contingency Table

Description

Usage

Arguments

Details

Value

See Also

Examples

Apply a test to a Contingency Tables object

Description

Usage

Arguments

Details

Value

See Also

Examples

as.matrix method

Description

Usage

Arguments

Value

Non-central Chi-Squared Confidence Interval

Description

Usage

Arguments

Value

References

See Also

Examples

contin.tests.to.table

Description

Usage

Arguments

Value

Contingency Tables

Description

Usage

Arguments

Value

See Also

Examples

cor.matrix

Description

Usage

Arguments

Value

See Also

Examples

wrapper for data.frame

Description

Usage

Arguments

See Also

Examples

GUI Access functions

Description

Usage

Arguments

Controls Deducer's command line menus

Description

Usage

Arguments

Examples

Table of Descriptives

Description

Usage

Arguments

Value

See Also

Examples

Deducer's plug-in development tools