Type:	Package
Title:	R Commander Plugin for Teaching Statistical Methods
Version:	1.1.3
Description:	R Commander plugin for teaching statistical methods. It adds a new menu for making easier the teaching of the main concepts about the main statistical methods.
License:	GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
Depends:	R (≥ 3.5.0)
Imports:	Rcmdr (≥ 2.5-1), tcltk, Hmisc, tcltk2, tseries, IndexNumR, lme4, distr, distrEx
Suggests:	tkrplot, e1071, RcmdrMisc, randtests
LazyData:	TRUE
Encoding:	UTF-8
NeedsCompilation:	no
Packaged:	2023-11-13 14:47:23 UTC; Alfredo
Author:	Tomás R. Cotos Yañez [aut], Manuel A. Mosquera Rodríguez [aut, cre], Ana Pérez González [aut], Benigno Reguengo Lareo [aut]
Maintainer:	Manuel A. Mosquera Rodríguez <mamrguez@uvigo.es>
Repository:	CRAN
Date/Publication:	2023-11-14 13:53:28 UTC

R Commander plugin for teaching statistical methods.

Description

It adds a new menu for making easier the teaching of the main concepts about the main statistical methods.

Details

Author(s)

Tomás R. Cotos Yañez <cotos@uvigo.gal>
Manuel A. Mosquera Rodríguez <mamrguez@uvigo.gal>
Ana Pérez González <anapg@uvigo.gal>
Benigno Reguengo Lareo <benireguengo@gmail.com>

See Also

Rcmdr.

Grouped or tabulated data set

Description

Grouped or tabulated data set, given by lower and upper limits and frequency. It is used as an example for the use of the Numerical Summaries - Tabulated data window of the RcmdrPlugin.TeachStat package

Usage

data("Agrupadas")

Format

Data frame with 4 cases (rows) and 3 variables (columns).

Linf

Numeric value, the lower limit of the tabulated data.

Lsup

Numeric value, the upper limit of the tabulated data.

ni

Numeric value, the frequency of the tabulated data.

Examples

data(Agrupadas)
calcularResumenDatosTabulados(l_inf=Agrupadas$Linf, l_sup=Agrupadas$Lsup,
  ni=Agrupadas$ni, statistics =c("mean", "sd", "IQR", "quantiles"), quantiles
  = c(0,0.25,0.5,0.75,1), tablaFrecuencia=FALSE)

Complex index numbers

Description

ComplexIN computes the aggregation of a set of index numbers using the arithmetic, geometric or harmonic means.

Usage

ComplexIN(data, means = c("arithmetic", "geometric", "harmonic"), zero.rm = TRUE, 
          na.rm = TRUE, ...)

Arguments

Details

Sindex, Deflat, priceIndexNum.

Value

Matrix with as many rows as columns of x and as many columns as means selected.

Examples

df <- data.frame(Index=round(runif(12,80,105),2))
ComplexIN(df, means = c("arithmetic", "geometric", "harmonic"))

Modify Variable Types

Description

In this graphical interface, the user can modify the variable type into nominal (factor), ordinal (ordered factor) or numeric type.

Test for proportions of one or two samples

Description

Performs hypothesis testing and confidence interval for a proportion or difference of two proportions. The values of the samples necessary to perform the function are the number of successes and the number of trails.

Usage

Cprop.test(ex, nx, ey = NULL, ny = NULL, p.null = 0.5,
           alternative = c("two.sided", "less", "greater"), conf.level = 0.95,
           ...)

Arguments

Details

So that the contrast can be made must be fulfilled that at least 1 hit. That is, in the case of a sample ex must be greater than or equal to 1 and in the case of two samples, ex or ey must be greater than or equal to 1.

Furthermore, for the case of a sample value p.null must be strictly positive.

Value

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

See Also

prop.test

Examples

## Proportion for a sample
Cprop.test(1,6) # 1 success in 6 attempts

#### With a data set: proportion of cars not manufactured in US
data(cars93) #data set provided with the package
exitos<-sum(cars93$USA == "nonUS")
total<-length(cars93$USA)
Cprop.test(ex=exitos, nx=total)

## Difference of proportions
Cprop.test(1,6,3,15)
  # Sample 1: 1 success in 6 attempts
  # Sample 2: 3 success in 15 attempts

#### With a data set: difference of proportions of cars not manufactured in US 
#### between manual and automatic
exitosx<-sum(cars93$USA == "nonUS" & cars93$Manual == "Yes" )
totalx<-sum(cars93$Manual == "Yes")
exitosy<-sum(cars93$USA == "nonUS" & cars93$Manual == "No" )
totaly<-sum(cars93$Manual == "No")
Cprop.test(ex=exitosx, nx=totalx,ey=exitosy, ny=totaly)

Z-test for the difference of means of two independent Normal variables with known population variances.

Description

Under the assumption that the data come from two independent Normal distributions, it performs the hypothesis test and the confidence interval for the difference of means with known population variances.

Usage

DMKV.test(x, y, difmu = 0, sdx, sdy,
          alternative = c("two.sided", "less", "greater"), conf.level = 0.95,
          ...)

Arguments

Value

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

See Also

t.test

Examples

data(cars93) # Data set provided with the package
# Maximum price difference (MaxPrice) in means between cars manufactured in the
# US and those manufactured outside, assuming that the variances are known and 
# equal to 64 and 169, respectively
var1<-subset(cars93, USA=="nonUS", select=MaxPrice)
var2<-subset(cars93, USA=="US", select=MaxPrice)
DMKV.test(var1, var2, sdx=13, sdy=8, difmu=0,
alternative="greater", conf.level=0.95)

Deflation of an economic series

Description

Deflat deflates a current value variable into a constant value variable.

Usage

Deflat(x, pervar, cvar, defl, base)

Arguments

Value

Deflat returns a data frame with one column:

See Also

Sindex, ComplexIN, priceIndexNum.

Examples

data(Depositos, package = "RcmdrPlugin.TeachStat")
Deflat(Depositos, "year", "quantity", "G_IPC_2016", "2018")

Deposits with credit institutions in Ourense

Description

Private sector deposits (in millions of euro) with credit institutions in the province of Ourense (Spain) in 2002-2018.

Usage

data("Depositos")

Format

A data frame with 17 observations on the following 4 variables.

year

a factor, year

quantity

a numeric vector, deposit (in millions of euro) with credit institutions

E_IPC_2016

a numeric vector, Consumer Price Index (CPI) with base 2016 in Spain

G_IPC_2016

a numeric vector, Consumer Price Index (CPI) with base 2016 in Galicia

Source

Galician Institute of Statistics (2019):

- https://www.ige.gal/igebdt/esqv.jsp?ruta=verTabla.jsp?OP=1&B=1&M=&COD=462&R=2%5B2002:2003:2004:2005:2006:2007:2008:2009:2010:2011:2012:2013:2014:2015:2016:2017:2018%5D&C=9928%5B32%5D;0%5B3%5D;1%5B3%5D&F=&S=&SCF=#

- https://www.ige.gal/igebdt/esqv.jsp?ruta=verTabla.jsp?OP=1&B=1&M=&COD=8547&R=0%5Ball%5D&C=2%5B0%5D;1%5B0%5D;9928%5B108:12%5D&F=&S=&SCF=

Examples

data(Depositos)

.Sindex <- Sindex(Depositos, "year", "quantity", "2010")*100
print(.Sindex)

Deflat(Depositos, "year", "quantity", "E_IPC_2016", "2011")

Index Numbers menu

Description

In this menu the user can perform some calculations related to index numbers.

This menu will call the functions for calculating simple index numbers and making base changes (Sindex), for calculating complex index numbers (ComplexIN), for calculating price indices (priceIndexNum), and for deflation of economic series (Deflat).

Z-test for the mean of a Normal variable with known population variance.

Description

Under the assumption that the data come from a Normal distribution, it makes the hypothesis testing and the confidence interval for the mean with known population variance.

Usage

MKV.test(x, mu = 0, sd, alternative = c("two.sided", "less", "greater"),
         conf.level = 0.95, ...)

Arguments

Value

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

See Also

t.test

Examples

data(cars93) # Dataset provided with the package
# Mean maximum price (MaxPrice) less than 20 thousand $ assuming that the 
# variance is known and equal to 11
MKV.test(cars93$MaxPrice, sd=11, alternative="less", mu=20, conf.level=0.95)

Data for computing price indices.

Description

Data on the sold quantity and sale price of several products through some years.

It is used as an example for the use of the Price index window of the RcmdrPlugin.TeachStat package

Usage

data("Prices")

Format

A data frame with 15 observations on the following 4 variables.

year

a factor representing the year

prodID

a factor with the ID of the products

price

the sale price

quantity

the sold quantity

Examples

data(Prices)
priceIndexNum (Prices, prodID ="prodID", pervar ="year", pvar="price", 
               qvar ="quantity", base="2001", indexMethod =c("laspeyres", "paasche", "fisher"))

One-Way ANOVA with random effects.

Description

In this menu the user can perform some calculations related to One-Way ANOVA with random effects.

This menu will call the functions for calculating the ANOVA table (aov from package stats) and the estimations of variance components using the maximum likelihood method and the REstricted Maximum Likelihood (REML) method (lmer from package lme4).

Internal RcmdrPlugin.TeachStat objects

Description

Internal RcmdrPlugin.TeachStat objects

Details

These are not to be called by the user.

Simple index numbers

Description

Sindex returns a data frame with the index numbers with a given base. An index number measures changes in a variable with respect to a characteristic (time, location, ...)

Sindex can also be used for computing the base change of an index number.

Usage

Sindex(x, pervar, vvar, base)

Arguments

Value

Sindex returns a data frame with one column:

See Also

Deflat, ComplexIN, priceIndexNum.

Examples

data(Depositos, package = "RcmdrPlugin.TeachStat")
Sindex(Depositos, "year", "quantity", "2006")

RcmdrPlugin.TeachStat Utility Functions

Description

twoOrMoreLevelFactorsP() returns TRUE if there is at least one factor in the active dataset that has two or more levels.

twoOrMoreLevelFactors() returns the object name of those factors that are active in the dataset that have at least two levels.

listDistrs(class, env) returns the object name of those distributions of class class (see package distr) in the env environment.

DiscreteDistrsP() returns TRUE if there is at least one distribution of class DiscreteDistribution (see DiscreteDistribution).

AbscontDistrsP() returns TRUE if there is at least one distribution of class AbscontDistribution (see AbscontDistribution).

Usage

twoOrMoreLevelFactors()
twoOrMoreLevelFactorsP()
listDistrs(class = "UnivariateDistribution", envir = .GlobalEnv, ...)
DiscreteDistrsP()
AbscontDistrsP()

Arguments

Chi-square test for the variance of a Normal variable with known population mean.

Description

Under the assumption that the data come from a Normal distribution, it performs the hypothesis testing and the confidence interval for the variance with known population mean.

Usage

VKM.test(x, sigma = 1, sigmasq = sigma^2, mu,
         alternative = c("two.sided", "less", "greater"), conf.level = 0.95,
         ...)

Arguments

Value

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

See Also

VUM.test , var.test

Examples

data(cars93) # Dataset provided with the package
# Variance of the maximum price (MaxPrice) assuming that the population mean
# price is known and equal to 22
VKM.test(cars93$MaxPrice, alternative="two.sided", sigma=11, mu=22, conf.level=0.95)

Chi-square test for the variance of a Normal variable with unknown population mean.

Description

Under the assumption that the data come from a Normal distribution, it performs the hypothesis testing and the confidence interval for the variance with unknown population mean.

Usage

VUM.test(x, sigma = 1, sigmasq = sigma^2,
         alternative = c("two.sided", "less", "greater"), conf.level = 0.95,
         ...)

Arguments

Value

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

See Also

VKM.test , var.test

Examples

data(cars93) # Dataset provided with the package
# Variance of the maximum price (MaxPrice) assuming that the population mean 
# price is unknown
VUM.test(cars93$MaxPrice, alternative="two.sided", sigma=11, conf.level=0.95)

Summary statistics for weighted variables

Description

W.numSummary gives the main statistical summary for weighted variables (mean, standard deviation, coefficient of variation, skewness, kurtosis and quantiles). It also allows the partition of the data by a factor variable.

Usage

W.numSummary(data, 
             statistics = c("mean", "sd", "se(mean)", "IQR",
             "quantiles", "cv", "skewness", "kurtosis"),type = c("2", "1", "3"),
             quantiles = c(0, 0.25, 0.5, 0.75, 1),groups = NULL, weights)

Arguments

Details

W.numSummary performs a descriptive analysis of quantitative variables weighted (or not) by a numeric variable which determines the importance of each subject in the data frame. Optionally it allows the partition of the data by a factor variable (groups).

Note that, unlike the numSummary function, the sample standard deviation is calculated instead of the sample standard quasideviation.

Value

An object with class "numSummary".

See Also

numSummary, skewness, kurtosis.

Examples

data(cars93)

# no weighted
W.numSummary(data=cars93[,c("CityMPG")], statistics =c("mean", "sd", "IQR", "quantiles"),
   quantiles = c(0,0.25,0.5,0.75,1), weights=NULL, groups=NULL)
# weighted
W.numSummary(data=cars93[,c("CityMPG")], statistics =c("mean", "sd", "IQR", "quantiles"),
   quantiles = c(0,0.25,0.5,0.75,1), weights=cars93$FuelCapacity, groups=NULL)
# no weighted
W.numSummary(data=cars93[,c("CityMPG")], statistics =c("mean", "sd", "IQR", "quantiles"),
   quantiles = c(0,0.25,0.5,0.75,1), weights=NULL, groups=cars93$Manual)
# weighted
bb <- W.numSummary(data=cars93[,c("CityMPG")], statistics =c("mean", "sd", "IQR", "quantiles"),
   quantiles = c(0,0.25,0.5,0.75,1), weights=cars93$FuelCapacity, groups=cars93$Manual)

bb
str(bb)
class(bb)

ANOVA with random effects using the (REstricted) Maximum Likelihood method.

Description

Print the ANOVA table with random effects and compute the point estimations of the variance components using the maximum likelihood method or the REstricted Maximum Likelihood (REML) method. It also provides some confidence intervals.

Usage

aovreml(formula, data = NULL, Lconfint = FALSE, REML = TRUE, ...)

Arguments

Value

A list

See Also

aov, lmer, aovremm.

Examples

##---- Should be DIRECTLY executable !! ----
##-- ==>  Define data, use random,
##--	or do  help(data=index)  for the standard data sets.

## The function is currently defined as
function (formula, data = NULL, Lconfint = FALSE, REML = TRUE, 
    ...) 
{
    vars <- all.vars(formula)
    formulaaov <- as.formula(paste(vars[1], "~", vars[2]))
    ANOV <- aov(formulaaov, data, ...)
    .ANOV <- summary(ANOV)
    cat("-------------------------------")
    cat("\n", gettext("ANOVA table", domain = "R-RcmdrPlugin.TeachStat"), 
        ":\n", sep = "")
    print(.ANOV)
    cat("\n-------------------------------\n")
    .sol <- lme4::lmer(formula, data = data, REML = REML, ...)
    .varcor <- lme4::VarCorr(.sol)
    .sighat2 <- unname(attr(.varcor, "sc"))^2
    .sighatalph2 <- unname(attr(.varcor[[vars[2]]], "stddev"))^2
    .prop <- .sighatalph2/(.sighatalph2 + .sighat2)
    estim <- c(.sighat2, .sighatalph2, .prop)
    names(estim) <- c("var (Error)", "var (Effect)", "% var (Effect)")
    cat("\n", gettext("Components of Variance", domain = "R-RcmdrPlugin.TeachStat"), 
        " (", lme4::methTitle(.sol@devcomp$dims), "):\n", sep = "")
    print(estim)
    if (Lconfint) {
        cat("\n", gettext("Confidence intervals", domain = "R-RcmdrPlugin.TeachStat"), 
            ":\n", sep = "")
        print(confint(.sol, oldNames = FALSE))
    }
    return(invisible(list(model = .sol, estimation = estim)))
  }

ANOVA with random effects using the Moments method.

Description

Print the ANOVA table with random effects and compute the classical point estimations of the variance components using the Moments method.

Usage

aovremm(formula, data = NULL, ...)

Arguments

Value

A list

See Also

aov, lmer, aovreml.

Examples

##---- Should be DIRECTLY executable !! ----
##-- ==>  Define data, use random,
##--	or do  help(data=index)  for the standard data sets.

## The function is currently defined as
function (formula, data = NULL, ...) 
{
    ANOV <- aov(formula, data, ...)
    .ANOV <- summary(ANOV)
    cat("-------------------------------")
    cat("\n", gettext("ANOVA table", domain = "R-RcmdrPlugin.TeachStat"), 
        ":\n", sep = "")
    print(.ANOV)
    cat("\n-------------------------------\n\n")
    .sighat2 <- .ANOV[[1]]$`Mean Sq`[2]
    .vars <- all.vars(formula)
    .groups <- data[[.vars[2]]][!is.na(data[[.vars[1]]])]
    .n <- length(.groups)
    .ni <- table(.groups)
    .c <- (.n^2 - sum(.ni^2))/(.n * (length(.ni) - 1))
    .sighatalph2 <- (.ANOV[[1]]$`Mean Sq`[1] - .sighat2)/.c
    if (.sighatalph2 < 0) 
        warning("Estimation of any variance component is not positive. The variance 
                 component model is inadequate.")
    .prop <- .sighatalph2/(.sighatalph2 + .sighat2)
    estim <- c(.sighat2, .sighatalph2, .prop)
    names(estim) <- c("var (Error)", "var (Effect)", "% var (Effect)")
    cat("\n", gettext("Components of Variance", domain = "R-RcmdrPlugin.TeachStat"), 
        ":\n", sep = "")
    print(estim)
    return(invisible(list(model = ANOV, estimation = estim)))
  }

Summary statistics for tabulated data

Description

calcularResumenDatosTabulados performs the main statistical summary for tabulated data (mean, standard deviation, coefficient of variation, skewness, kurtosis, quantile and mode) are calculated. Also it allows to obtain the frequency table (with classmark, amplitude and density).

Usage

calcularResumenDatosTabulados(l_inf, l_sup, ni, 
                              statistics = c("mean", "sd", "se(mean)", "IQR",
                              "quantiles", "cv", "skewness", "kurtosis"),
                              quantiles = c(0, 0.25, 0.5, 0.75, 1), 
                              tablaFrecuencia = FALSE)

Arguments

Details

calcularResumenDatosTabulados performs an analysis of tabulated data (frequently used in statistics when the number of distinct values is large or when dealing with continuous quantitative variables), represented by a table of statistics (arithmetic mean, standard deviation, interquartile range, coefficient of variation, asymmetry, kurtosis, and quantile).

It also allows to show the frequency table of the tabulated variable by selecting tablaFrecuencia=TRUE. The class mark, amplitude and density are added to the frequency table.

The LOWER LIMIT or L[i-1] and UPPER LIMIT or L[i] vectors, represent the data of continuous quantitative variables in class intervals of the form [L[i-1], L[i]) where i = 1, .. ., k.

Value

calcularResumenDatosTabulados() returns a list of two elements:

See Also

cut

Examples

data(cars93)
cortes <- seq(from=1500, to=4250, by=250)
aa <- cut( cars93$Weight, breaks=cortes, dig.lab=4)
ni <- table(aa)
l_inf <- cortes[-length(cortes)]
l_sup <- cortes[-1]
agrup <- data.frame(l_inf,l_sup,ni)
head(agrup)

calcularResumenDatosTabulados(agrup$l_inf, agrup$l_sup, agrup$Freq)
calcularResumenDatosTabulados(agrup$l_inf, agrup$l_sup, agrup$Freq, tabla=TRUE)

bb <- calcularResumenDatosTabulados(agrup$l_inf, agrup$l_sup, agrup$Freq,
                                    statistics=c("mean","mode") )
bb
str(bb)
class(bb$.summary)
class(bb$.table)

Summary statistics for continuous variables

Description

calcularResumenVariablesContinuas gives the main statistical summary for continuous variables (mean, standard deviation, coefficient of variation, skewness, kurtosis and quantiles). Also builds the frequency table (with classmark, amplitude and density).

Usage

calcularResumenVariablesContinuas(data,
                                  statistics = c("mean", "sd", "se(mean)", "IQR",
                                  "quantiles", "cv", "skewness", "kurtosis"),
                                  quantiles = c(0, 0.25, 0.5, 0.75, 1), groups = NULL,
                                  tablaFrecuencia = FALSE, cortes="Sturges", ...)

Arguments

Details

calcularResumenVariablesContinuas performs a descriptive analysis of continuous variables (quantitative variables that take infinite distinct values into an interval), generating a table of statistics (arithmetic mean, standard deviation, interquartile range, coefficient of variation, skewness, kurtosis, and quantiles) optionally allowing the partition of the data by a factor variable (groups).

It also allows to show the frequency table of selected continuous variables by selecting tablaFrecuencia=TRUE. Moreover it also allows to divide the range of the variables into intervals given by the argument cortes (breaks). See more info in cut and in hist.

Value

calcularResumenVariablesContinuas returns a list of two elements:

See Also

numSummary, cut, hist

Examples

## Not run: 
data(cars93)
calcularResumenVariablesContinuas(data=cars93["FuelCapacity"],group=NULL)
calcularResumenVariablesContinuas(data=cars93["FuelCapacity"],group=cars93$Airbags)
bb <- calcularResumenVariablesContinuas(data=cars93["FuelCapacity"],group=cars93$Airbags,
                                        tablaFrecuencia=TRUE)
str(bb)
bb
bb$.summary
class(bb$.summary)

calcularResumenVariablesContinuas(data=cars93["MidPrice"], tablaFrecuencia=TRUE)
calcularResumenVariablesContinuas(data=cars93["MidPrice"], tablaFrecuencia=TRUE, cortes=5)
calcularResumenVariablesContinuas(data=cars93["MidPrice"], tablaFrecuencia=TRUE,
                                  cortes=c(7,14,21,28,63))
calcularResumenVariablesContinuas(data=cars93["MidPrice"], tablaFrecuencia=TRUE,
                                  cortes="Scott")                                
calcularResumenVariablesContinuas(data=cars93["MidPrice"], groups=cars93$Airbags, 
                                  tablaFrecuencia=TRUE, cortes=5)

## End(Not run)

Summary statistics for discrete variables

Description

calcularResumenVariablesDiscretas gives the main statistical summary for discrete variables (mean, standard deviation, coefficient of variation, skewness, kurtosis and quantiles). Also builds the frequency table

Usage

calcularResumenVariablesDiscretas(data,
                                  statistics = c("mean", "sd", "se(mean)", "IQR",
                                  "quantiles", "cv", "skewness", "kurtosis"),
                                  quantiles = c(0, 0.25, 0.5, 0.75, 1), groups = NULL,
                                  tablaFrecuencia = FALSE, cortes=NULL)

Arguments

Details

calcularResumenVariablesDiscretas performs a descriptive analysis of discrete variables (quantitative variables that take as a finite or infinite numerable distinct values), generating a table of statistics (arithmetic mean, standard deviation, interquartile range, coefficient of variation, skewness, kurtosis, and quantiles) optionally allowing the partition of the data by a factor variable (groups).

It also allows to show the frequency table of selected discrete variables by selecting tablaFrecuencia=TRUE. Moreover it also allows to divide the range of the variables into intervals given by the argument cortes (breaks). See more info in cut and in hist.

Value

calcularResumenVariablesDiscretas returns a list of two elements:

See Also

cut, hist

Examples

## Not run: 
data(cars93)
calcularResumenVariablesDiscretas(data=cars93["Cylinders"],group=NULL)
calcularResumenVariablesDiscretas(data=cars93["Cylinders"],group=cars93$Airbags)
bb <- calcularResumenVariablesDiscretas(data=cars93["Cylinders"],group=cars93$Airbags,
                                        tablaFrecuencia=TRUE)
str(bb)
bb
bb$.summary
class(bb$.summary)

calcularResumenVariablesDiscretas(data=cars93["Horsepower"], tablaFrecuencia=TRUE)
calcularResumenVariablesDiscretas(data=cars93["Horsepower"], tablaFrecuencia=TRUE, cortes=5)
calcularResumenVariablesDiscretas(data=cars93["Horsepower"], tablaFrecuencia=TRUE,
                                  cortes=c(50,100,200,250,300))
calcularResumenVariablesDiscretas(data=cars93["Horsepower"], tablaFrecuencia=TRUE,
                                  cortes="Sturges")                                
calcularResumenVariablesDiscretas(data=cars93["Horsepower"], groups=cars93$Airbags, 
                                  tablaFrecuencia=TRUE, cortes=5)

## End(Not run)

Frequency distributions for qualitative variables

Description

Performs frequency distribution for qualitative, nominal and/or ordinal variables. For ordinal variables, the requested quantile is calculated.

Usage

calcular_frecuencia(df.nominal, ordenado.frec = FALSE,df.ordinal,
                    cuantil.p = 0.5,iprint = TRUE, ...)

Arguments

Value

calcular_frecuencia returns a list of three elements:

See Also

table , cumsum

Examples

data(cars93)
aa <- calcular_frecuencia(df.nominal=cars93["Type"], ordenado.frec=TRUE, df.ordinal=NULL, 
                          cuantil.p=0.5, iprint = TRUE)
calcular_frecuencia(df.nominal=NULL, ordenado.frec=TRUE, df.ordinal=cars93["Airbags"], 
                    cuantil.p=0.25, iprint = TRUE)
bb <- calcular_frecuencia(df.nominal=cars93["Type"], ordenado.frec=TRUE, 
                          df.ordinal=cars93["Airbags"], cuantil.p=0.25, iprint = FALSE)
str(bb)
bb

Data from 93 Cars on Sale in the USA in 1993

Description

The cars93 data frame has 93 rows and 26 columns.

Usage

cars93

Format

This data frame contains the following columns:

Manufacturer

Manufacturer.

Model

Model.

Type

Type: a factor with levels "Small", "Sporty", "Compact", "Midsize", "Large" and "Van".

MinPrice

Minimum Price (in $1,000): price for a basic version.

MidPrice

Midrange Price (in $1,000): average of Min.Price and Max.Price.

MaxPrice

Maximum Price (in $1,000): price for “a premium version”.

CityMPG

City MPG (miles per US gallon by EPA rating).

HighwayMPG

Highway MPG.

Airbags

Air Bags standard. Factor: none, driver only, or driver & passenger.

DriveTrain

Drive train type: rear wheel, front wheel or 4WD; (factor).

Cylinders

Number of cylinders (missing for Mazda RX-7, which has a rotary engine).

EngineSize

Engine size (litres).

Horsepower

Horsepower (maximum).

RPM

RPM (revs per minute at maximum horsepower).

EngineRevol

Engine revolutions per mile (in highest gear).

Manual

Is a manual transmission version available? (yes or no, Factor).

FuelCapacity

Fuel tank capacity (US gallons).

Passengers

Passenger capacity (persons)

Length

Length (inches).

Wheelbase

Wheelbase (inches).

Width

Width (inches).

UTurnSpace

U-turn space (feet).

RearSeatRoom

Rear seat room (inches) (missing for 2-seater vehicles).

LuggageCapacity

Luggage capacity (cubic feet) (missing for vans).

Weight

Weight (pounds).

USA

Of non-USA or USA company origins? (factor).

Details

Cars were selected at random from among 1993 passenger car models that were listed in both the Consumer Reports issue and the PACE Buying Guide. Pickup trucks and Sport/Utility vehicles were eliminated due to incomplete information in the Consumer Reports source. Duplicate models (e.g., Dodge Shadow and Plymouth Sundance) were listed at most once.

Further description can be found in Lock (1993).

Source

Lock, R. H. (1993) 1993 New Car Data. Journal of Statistics Education 1(1). doi:10.1080/10691898.1993.11910459.

References

Venables, W. N. and Ripley, B. D. (1999) Modern Applied Statistics with S-PLUS. Third Edition. Springer.

Characteristics of Random Variables.

Description

This function computes the main characteristics of a random variable (expectation, median, standard deviation, ...)

Usage

characRV(D, charact = c("expectation", "median", "sd", "IQR", "skewness", "kurtosis", 
         "moment", "cmoment"), moment = 1, cmoment = 2)

Arguments

Value

'characRV' returns a table containing the selected characteristics.

See Also

E, median, sd, IQR, skewness, kurtosis

Examples

##---- Should be DIRECTLY executable !! ----
##-- ==>  Define data, use random,
##--	or do  help(data=index)  for the standard data sets.

## The function is currently defined as
function (D, charact = c("expectation", "median", "sd", "IQR", 
    "skewness", "kurtosis", "moment", "cmoment"), moment = 1, 
    cmoment = 2) 
{
    if (missing(charact)) 
        charact <- c("expectation", "sd")
    charact <- match.arg(charact, c("expectation", "median", 
        "sd", "IQR", "skewness", "kurtosis", "moment", "cmoment"), 
        several.ok = TRUE)
    moment <- if ("moment" %in% charact) 
        moment
    else NULL
    cmoment <- if ("cmoment" %in% charact) 
        cmoment
    else NULL
    mom <- if (!is.null(moment)) 
        paste("alpha_", moment, sep = "")
    else NULL
    cmom <- if (!is.null(cmoment)) 
        paste("mu_", cmoment, sep = "")
    else NULL
    chars <- c(c("expectation", "median", "sd", "IQR", "skewness", 
        "kurtosis")[c("expectation", "median", "sd", "IQR", "skewness", 
        "kurtosis") %in% charact], mom, cmom)
    nchars <- length(chars)
    table <- matrix(0, 1, nchars)
    rownames(table) <- gsub("[[:space:]]", "", deparse(substitute(D)))
    colnames(table) <- chars
    if ("expectation" %in% chars) 
        table[, "expectation"] <- distrEx::E(D)
    if ("median" %in% chars) 
        table[, "median"] <- distrEx::median(D)
    if ("sd" %in% chars) 
        table[, "sd"] <- distrEx::sd(D)
    if ("IQR" %in% chars) 
        table[, "IQR"] <- distrEx::IQR(D)
    if ("skewness" %in% chars) 
        table[, "skewness"] <- distrEx::skewness(D)
    if ("kurtosis" %in% chars) 
        table[, "kurtosis"] <- distrEx::kurtosis(D)
    if ("moment" %in% charact) 
        table[, mom] <- distrEx::E(D, fun = function(x) {
            x^moment
        })
    if ("cmoment" %in% charact) 
        table[, cmom] <- distrEx::E(D, fun = function(x) {
            (x - distrEx::E(D))^cmoment
        })
    print(table)
    return(invisible(table))
  }

Definition of Random Variables

Description

In this window the user can define any random variable by providing its parameters (for a well-known random variable), its distribution function, its density function (for a generic absolutely continuous random variable), or its mass probability function (for a generic discrete random variable).

Confidence interval or hypothesis testing for the mean of a Normal variable

Description

In this graphical interface, the data selection is made to perform the calculation of the confidence interval or the hypothesis testing for the mean of a Normal variable.

This interface will call the statistical functions MKV.test and t.test, depending, respectively, on whether the population variance is known or not.

Confidence interval or hypothesis testing for the difference in means of two independent Normal variables

Description

In this graphical interface, the data selection is made to perform the calculation of the confidence interval or the hypothesis testing for the difference in means of two independent Normal variables.

This interface will call the statistical functions DMKV.test and t.test, depending, respectively, on whether the population variances are known or not.

Confidence interval or hypothesis testing for the Variance

Description

In this graphical interface, the data selection is made to perform the calculation of the confidence interval or the hypothesis testing for the variance of a Normal variable.

This interface will call the statistical functions VKM.test and VUM.test, depending, respectively, on whether the population mean is known or not.

List of variables and types of a Data Frame

Description

listTypesVariables returns a vector with the names and types of the variables of a data frame.

Usage

listTypesVariables(dataSet)

Arguments

Value

A character vector

See Also

names

Examples

require(datasets)
listTypesVariables("iris")

Plot regions in probability mass or density functions.

Description

This function plot regions in probability mass or density functions.

Usage

plotRegions(D, add = FALSE, regions = NULL, col = "gray", legend = TRUE, 
            legend.pos = "topright", to.draw.arg = 1, verticals = FALSE, ngrid = 1000, 
            cex.points = par("cex"), mfColRow = FALSE, lwd = par("lwd"), ...)

Arguments

Value

invisible

See Also

plot

Examples

##---- Should be DIRECTLY executable !! ----
##-- ==>  Define data, use random,
##--	or do  help(data=index)  for the standard data sets.

## The function is currently defined as
function (D, add = FALSE, regions = NULL, col = "gray", legend = TRUE, 
    legend.pos = "topright", to.draw.arg = 1, verticals = FALSE, 
    ngrid = 1000, cex.points = par("cex"), mfColRow = FALSE, 
    lwd = par("lwd"), ...) 
{
    dots <- match.call(call = sys.call(0), expand.dots = FALSE)$...
    if (!is.null(dots[["panel.first"]])) {
        pF <- .panel.mingle(dots, "panel.first")
    }
    else if (to.draw.arg == 1) {
        pF <- quote(abline(h = 0, col = "gray"))
    }
    else if (to.draw.arg == 2) {
        pF <- quote(abline(h = 0:1, col = "gray"))
    }
    else {
        pF <- NULL
    }
    dots$panel.first <- pF
    if (!add) {
        do.call(plot, c(list(D, to.draw.arg = to.draw.arg, cex.points = cex.points, 
            mfColRow = mfColRow, verticals = verticals), dots))
    }
    discrete <- is(D, "DiscreteDistribution")
    if (discrete) {
        x <- support(D)
        if (hasArg("xlim")) {
            if (length(xlim) != 2) 
                stop("Wrong length of Argument xlim")
            x <- x[(x >= xlim[1]) & (x <= xlim[2])]
        }
        if (!is.null(regions)) {
            col <- rep(col, length = length(regions))
            for (i in 1:length(regions)) {
                region <- regions[[i]]
                which.xs <- (x > region[1] & x <= region[2])
                xs <- x[which.xs]
                ps <- d(D)(x)[which.xs]
                lines(xs, ps, type = "h", col = col[i], lwd = 3 * 
                  lwd, ...)
                points(xs, ps, pch = 16, col = col[i], cex = 2 * 
                  cex.points, ...)
            }
            if (legend) {
                if (length(unique(col)) > 1) {
                  legend(legend.pos, title = if (length(regions) > 
                    1) 
                    "Regions"
                  else "Region", legend = sapply(regions, function(region) {
                    paste(round(region[1], 2), "to", round(region[2], 
                      2))
                  }), col = col, pch = 15, pt.cex = 2.5, inset = 0.02)
                }
                else {
                  legend(legend.pos, title = if (length(regions) > 
                    1) 
                    "Regions"
                  else "Region", legend = sapply(regions, function(region) {
                    paste(round(region[1], 2), "to", round(region[2], 
                      2))
                  }), inset = 0.02)
                }
            }
        }
    }
    else {
        lower0 <- getLow(D, eps = getdistrOption("TruncQuantile") * 
            2)
        upper0 <- getUp(D, eps = getdistrOption("TruncQuantile") * 
            2)
        me <- (distr::q.l(D))(1/2)
        s <- (distr::q.l(D))(3/4) - (distr::q.l(D))(1/4)
        lower1 <- me - 6 * s
        upper1 <- me + 6 * s
        lower <- max(lower0, lower1)
        upper <- min(upper0, upper1)
        dist <- upper - lower
        if (hasArg("xlim")) {
            if (length(xlim) != 2) 
                stop("Wrong length of Argument xlim")
            x <- seq(xlim[1], xlim[2], length = ngrid)
        }
        else x <- seq(from = lower - 0.1 * dist, to = upper + 
            0.1 * dist, length = ngrid)
        if (!is.null(regions)) {
            col <- rep(col, length = length(regions))
            for (i in 1:length(regions)) {
                region <- regions[[i]]
                which.xs <- (x >= region[1] & x <= region[2])
                xs <- x[which.xs]
                ps <- d(D)(x)[which.xs]
                xs <- c(xs[1], xs, xs[length(xs)])
                ps <- c(0, ps, 0)
                polygon(xs, ps, col = col[i])
            }
            if (legend) {
                if (length(unique(col)) > 1) {
                  legend(legend.pos, title = if (length(regions) > 
                    1) 
                    "Regions"
                  else "Region", legend = sapply(regions, function(region) {
                    paste(round(region[1], 2), "to", round(region[2], 
                      2))
                  }), col = col, pch = 15, pt.cex = 2.5, inset = 0.02)
                }
                else {
                  legend(legend.pos, title = if (length(regions) > 
                    1) 
                    "Regions"
                  else "Region", legend = sapply(regions, function(region) {
                    paste(round(region[1], 2), "to", round(region[2], 
                      2))
                  }), inset = 0.02)
                }
            }
        }
    }
    return(invisible(NULL))
  }

Price index numbers

Description

priceIndexNum computes price indices given data on products over time (prices and quantities)

Usage

priceIndexNum(x, prodID, pervar, pvar, qvar, base, indexMethod = "laspeyres", 
              output = "fixedBase", ...)

Arguments

Details

priceIndexNum uses the function priceIndex from package IndexNumR without restricting the argument pervar from being integers starting at period 1 (base) and increasing in increments of 1 period.

Value

priceIndexNum returns a data frame with one column with the characteristic variable plus as many columns as indexMethod selected:

See Also

priceIndex, Sindex, Deflat, ComplexIN.

Examples

library(IndexNumR)
data(Prices, package = "RcmdrPlugin.TeachStat")

priceIndexNum(Prices, prodID = "prodID", pervar = "year", pvar = "price", 
              qvar = "quantity", base = "2003", 
              indexMethod = c("laspeyres", "paasche", "fisher"))

Randomness test

Description

In the "Nonparametric Tests" menu, two new entries are provided to perform the randomness test.

The first "Randomness test for two level factor..." can be used to contrast the randomness of a factor with two levels. This option use the function runs.test from tseries package. runs.test.

The second entry in the menu "Randomness test for numeric variable..." is used to test the randomness of a numerical variable. This option use the function runs.test from randtest package.

Details

Here is an example of "Randomness test for a two level factor..." menu entry.

Load data "AMSsurvey" selecting from Rcmdr menu: "Data" -> "Data in packages" -> "Read data set from an attached package..." then double-click on "car", click on "AMSsurvey" and on "OK". Rcmdr reply with the following command in source pane (R Script)

data(AMSsurvey, package="car")

To make randomness test on variable "sex", select from Rcmdr menu: "Statistics" -> "Nonparametric tests" -> "Randomness test for two level factor..." select "sex" and "OK". Rcmdr reply with the following command in source pane (R Script)

with(AMSsurvey, twolevelfactor.runs.test(sex))

Here is an example of "Randomness test for a numeric variable..." menu entry.

Load data "sweetpotato" selecting from Rcmdr menu: "Data" -> "Data in packages" -> "Read data set from an attached package..." then double-click on "randtests", click on "sweetpotato" and on "OK". Rcmdr reply with the following command in source pane (R Script)

data(sweetpotato, package="randtests")

sweetpotato <- as.data.frame(sweetpotato)

To make randomness test on variable "yield", select from Rcmdr menu: "Statistics" -> "Nonparametric tests" -> "Randomness test for numeric variable..." select "yield" and "OK". Rcmdr reply with the following command in source pane (R Script)

with(sweetpotato, numeric.runs.test(yield))

Author(s)

Manuel Munoz-Marquez <manuel.munoz@uca.es>

See Also

For more information see Rcmdr-package.