Package 'RcmdrPlugin.TeachStat' reference manual

Title:	R Commander Plugin for Teaching Statistical Methods
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.
Authors:	Tomás R. Cotos Yañez [aut] , Manuel A. Mosquera Rodríguez [aut, cre] , Ana Pérez González [aut] , Benigno Reguengo Lareo [aut]
Maintainer:	Manuel A. Mosquera Rodríguez <[email protected]>
License:	GPL (>= 2)
Version:	1.1.3
Built:	2024-12-09 06:58:46 UTC
Source:	CRAN

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

Package:	`RcmdrPlugin.TeachStat`
Type:	Package
Version:	1.1.2
Date:	2023-11-13
License:	GPL version 2 or newer

Author(s)

Tomás R. Cotos Yañez <[email protected]>
Manuel A. Mosquera Rodríguez <[email protected]>
Ana Pérez González <[email protected]>
Benigno Reguengo Lareo <[email protected]>

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")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)

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)

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

Arguments

`formula`	A formula specifying the model. Random-effects terms are distinguished by vertical bars (\|) separating expressions for design matrices from grouping factors. Two vertical bars (\|\|) can be used to specify multiple uncorrelated random effects for the same grouping variable. (Because of the way it is implemented, the \|\|-syntax works only for design matrices containing numeric (continuous) predictors.)
`data`	an optional data frame containing the variables named in `formula`. By default the variables are taken from the environment of `formula`.
`Lconfint`	logical scalar - Should the confidence intervals be printed?
`REML`	logical scalar - Should the estimates be chosen to optimize the REML criterion (as opposed to the log-likelihood)?
`...`	Arguments to be passed to other functions.

Value

A list

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)))
  }
##---- 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, ...)
aovremm(formula, data = NULL, ...)

Arguments

`formula`	A formula specifying the model.
`data`	A data frame in which the variables specified in the `formula` will be found. If missing, the variables are searched for in the standard way.
`...`	Arguments to be passed to other functions.

Value

A list

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)))
  }
##---- 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)))
  }

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

Arguments

`df.nominal`	`data.frame` with factor type components (including character type) that are interpreted as nominal variables.
`ordenado.frec`	table ordered frequencies depending on their frequency (only used for nominal variables).
`df.ordinal`	`data.frame` with factor type components (including character type) that are interpreted as ordinal variables.
`cuantil.p`	requested quantile value (only used for ordinal variables).
`iprint`	logical value indicating whether or not to display the frequency table.
`...`	further arguments to be passed to or from methods.

Value

calcular_frecuencia returns a list of three elements:

`.nominal`	a matrix containing the table of frequency distribution for nominal variables (`ni` = absolute frequencies and `fi` = relative frequencies).
`.ordinal`	a matrix containing the table of frequency distribution for ordinal variables (`ni` = absolute frequencies, `fi` = relative frequencies, `Ni` = absolute cumulative frequency, `Fi` = cumulative absolute frequencies).
`df.cuantil`	data frame containing the quantiles.

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(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

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)
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

`l_inf`	numeric vector with the lower limit of each interval.
`l_sup`	numeric vector with the upper limit of each interval.
`ni`	numeric vector with the frequency of occurrence of values in the range between the lower limit and upper limit [l_inf [i-1], l_sup [i]).
`statistics`	any of `"mean"`, `"sd"`, `"se(mean)"`, `"quantiles"`, `"cv"` (coefficient of variation - sd/mean), `"skewness"`, `"kurtosis"` or `"mode"`; defaulting to `c("mean", "sd", "quantiles", "IQR")`.
`quantiles`	quantiles to report; by default is `c(0, 0.25, 0.5, 0.75, 1)`.
`tablaFrecuencia`	logical value indicating whether or not to display the frequency table, by default is `FALSE`.

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:

`.numsummary`	an object of class `"numSummary"` containing the numerical summary of the tabulated variable.
`.table`	a matrix containing the values of the frequency table.

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$.table)
data(cars93)
cortes <- seq(from=1500, to=4250, by=250)
aa <- cut( cars93$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", ...)
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

`data`	`data.frame` with the continuous variables.
`statistics`	any of `"mean"`, `"sd"`, `"se(mean)"`, `"quantiles"`, `"cv"` (coefficient of variation - sd/mean), `"skewness"` or `"kurtosis"`; defaulting to `c("mean", "sd", "quantiles", "IQR")`.
`quantiles`	quantiles to report; by default is `c(0, 0.25, 0.5, 0.75, 1)`.
`groups`	optional variable, typically a factor, to be used to partition the data. By default is `NULL`.
`tablaFrecuencia`	logical value indicating whether or not to display the frequency table, by default is `FALSE`.
`cortes`	one of: a numeric vector of two or more unique cut points, a single number (greater than or equal to 2) giving the number of intervals into which data is to be cut, a character string naming an algorithm to compute the number of intervals into which data is to be cut (see 'Details') by default is `Sturges`.
`...`	further arguments to be passed to `numSummary`.

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:

`.numsummary`	an object of class `"numSummary"` containing the numerical summary of the continuous variables.
`.table`	a matrix containing the values of the frequency table.

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)
## 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)
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

`data`	`data.frame` with the discrete variables.
`statistics`	any of `"mean"`, `"sd"`, `"se(mean)"`, `"quantiles"`, `"cv"` (coefficient of variation - sd/mean), `"skewness"` or `"kurtosis"`; defaulting to `c("mean", "sd", "quantiles", "IQR")`.
`quantiles`	quantiles to report; by default is `c(0, 0.25, 0.5, 0.75, 1)`.
`groups`	optional variable, typically a factor, to be used to partition the data. By default is `NULL`.
`tablaFrecuencia`	logical value indicating whether or not to display the frequency table, by default is `FALSE`.
`cortes`	one of: a numeric vector of two or more unique cut points, a single number (greater than or equal to 2) giving the number of intervals into which data is to be cut, a character string naming an algorithm to compute the number of intervals into which data is to be cut (see 'Details') by default is `NULL`.

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:

`.numsummary`	an object of class `"numSummary"` containing the numerical summary of the discrete variables.
`.table`	a matrix containing the values of the frequency table.

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)
## 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)

Data from 93 Cars on Sale in the USA in 1993

Description

The cars93 data frame has 93 rows and 26 columns.

Usage

cars93
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)
characRV(D, charact = c("expectation", "median", "sd", "IQR", "skewness", "kurtosis", 
         "moment", "cmoment"), moment = 1, cmoment = 2)

Arguments

`D`	An object of the class `Distribution`.
`charact`	any of "`expectation`", "`median`", "`sd`", "`IQR`", "`skewness`", "`kurtosis`", "`moment`", "`cmoment`"
`moment`	an integer indicating the moment with respect to the origin to be calculated.
`cmoment`	an integer indicating the moment with respect to the expectation to be calculated.

Value

'characRV' returns a table containing the selected characteristics.

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))
  }
##---- 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))
  }

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

Arguments

`data`	Data frame containing, the index numbers to aggregate.
`means`	Character vector with the name of the mean to compute. `mean` can be `arithmetic`, `geometric` or `harmonic`.
`zero.rm`	Logical string for geometric and harmonic means. `TRUE` (default) indicates that negative and zero values should be deleted before computing the geometric mean and that zero values should be deleted before computing the harmonic mean.
`na.rm`	Logical value indicating whether NA values should be stripped before the computation proceeds. It is `TRUE` by default.
`...`	Further arguments passed to or from other methods.

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

Arguments

`ex`	numeric value that represents the number of successes of the first sample (see Details).
`nx`	numerical value representing the total number of trails of the first sample.
`ey`	(optional) numerical value representing the number of success of the second sample (see Details).
`ny`	(optional) numerical value representing the total number of trails of the second sample.
`p.null`	numeric value that represents the value of the population proportion or the difference between the two population proportions, depending on whether there are one or two samples (see Details).
`alternative`	a character string specifying the alternative hypothesis, must be one of `"two.sided"` (default), `"greater"` or `"less"`. You can specify just the initial letter.
`conf.level`	confidence level of the interval.
`...`	further arguments to be passed to or from methods.

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:

`statistic`	the value of the test statistic.
`parameter`	number of trails and value of the population proportion or the difference in population proportions.
`p.value`	the p-value for the test.
`conf.int`	a confidence interval for the proportion or for the difference in proportions, appropriate to the specified alternative hypothesis.
`estimate`	a value with the sample proportions.
`null.value`	the value of the null hypothesis.
`alternative`	a character string describing the alternative.
`method`	a character string indicating the method used, and whether Yates' continuity correction was applied.
`data.name`	a character string giving the names of the data.

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)
## 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)

Deflation of an economic series

Description

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

Usage

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

Arguments

`x`	Data frame containing, at least, the characteristics (time, location, ...), the current value and the deflator variables.
`pervar`	Character string for the name of the factor variable with the characteristics.
`cvar`	Character string for the name of the numeric variable with the current values.
`defl`	Character string for the name of the numeric variable with the index number used as deflator.
`base`	Character string for the name of the base characteristic.

Value

Deflat returns a data frame with one column:

const_base

The variable with constant values at base base

Examples

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

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

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

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).

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

Arguments

`x`	numerical vector (non-empty) that contains the data of the first sample.
`y`	numerical vector (non-empty) containing the data of the second sample.
`difmu`	numeric value indicating the value of the difference in population means between the two samples.
`sdx`	numerical value indicating the population standard deviation of the first sample, which is assumed to be known (mandatory).
`sdy`	numeric value indicating the population standard deviation of the second sample, which is assumed to be known (mandatory).
`alternative`	a character string specifying the alternative hypothesis, must be one of `"two.sided"` (default), `"greater"` or `"less"`. You can specify just the initial letter.
`conf.level`	confidence level of the interval.
`...`	further arguments to be passed to or from methods.

Value

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

`statistic`	the value of the test statistic.
`parameter`	sample lengths and population standard deviations.
`p.value`	the p-value for the test.
`conf.int`	confidence interval for the difference of means with known population variances associated with the specified alternative hypothesis.
`estimate`	the estimated difference in means.
`null.value`	the specified hypothesized value of the mean difference.
`alternative`	a character string describing the alternative hypothesis.
`method`	a character string indicating what type of statistical method was performed.
`data.name`	a character string giving the name(s) of the data.

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)

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)

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).

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)
listTypesVariables(dataSet)

Arguments

dataSet

the quoted name of a data frame in memory.

Value

A character vector

Examples

require(datasets)
listTypesVariables("iris")
require(datasets)
listTypesVariables("iris")

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

Arguments

`x`	a (non-empty) numeric vector of data values.
`mu`	a number indicating the true value of the mean - Null hypothesis.
`sd`	numerical value indicating the population standard deviation assumed to be known (mandatory).
`alternative`	a character string specifying the alternative hypothesis, must be one of `"two.sided"` (default), `"greater"` or `"less"`. You can specify just the initial letter.
`conf.level`	confidence level of the interval.
`...`	further arguments to be passed to or from methods.

Value

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

`statistic`	the value of the test statistic.
`parameter`	sample length, population standard deviation and sample standard deviation.
`p.value`	the p-value for the test.
`conf.int`	a confidence interval for the mean appropriate to the specified alternative hypothesis.
`estimate`	the estimated mean.
`null.value`	the specified hypothesized value of the mean.
`alternative`	a character string describing the alternative hypothesis.
`method`	a character string indicating what type of statistical test was performed.
`data.name`	a character string giving the name of the data.

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(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)

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"), ...)
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

`D`	object of class "`AffLinUnivarLebDecDistribution`" or class "`UnivarLebDecDistribution`" or class "`AbscontDistribution`" or class "`DiscreteDistribution`" or class "`DistrList`": (list of) distribution(s) to be plotted
`add`	logical; if `TRUE` only add to an existing plot.
`regions`	a list of regions to fill with color `col`; each element of the list is a pair of x values with the minimum and maximum horizontal coordinates of the corresponding region.
`col`	may be a single value or a vector indicating the colors of the regions.
`legend`	plot a legend of the regions (default `TRUE`).
`legend.pos`	position for the `legend` (see `legend`, default "`topright`").
`to.draw.arg`	Either `NULL` (default; everything is plotted) or a vector of either integers (the indices of the subplots to be drawn) or characters - the names of the subplots to be drawn: in case of an object x of class "`DiscreteDistribution`" or "`AbscontDistribution`" c("`d`","`p`","`q`") for density, c.d.f. and quantile function; in case of x a proper "`UnivarLebDecDistribution`" (with pos. weights for both discrete and abs. continuous part) names are c("`p`","`q`","`d.c`","`p.c`","`q.c`","`d.d`","`p.d`","`q.d`")) for c.d.f. and quantile function of the composed distribution and the respective three panels for the absolutely continuous and the discrete part, respectively;
`verticals`	logical: if TRUE, draw vertical lines at steps; as in plot.stepfun
`ngrid`	integer: number of grid points used for plots of absolutely continuous distributions
`cex.points`	numeric; character expansion factor; as in plot.stepfun
`mfColRow`	shall default partition in panels be used – defaults to TRUE
`lwd`	a vector of line widths, see par.
`...`	arguments to be passed to plot.

Value

invisible

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

Arguments

`x`	Data frame containing, at least, the characteristics (time, location, ...), the product identifiers, the prices and the quantities.
`prodID`	Character string for the name of the product identifier.
`pervar`	Character string for the name of the factor variable with the characteristics.
`pvar`	Character string for the name of the price variable.
`qvar`	Character string for the name of the quantity variable.
`base`	Character string for the name of the base characteristic.
`indexMethod`	Character vector to select the price index method. Tipical price index methods are laspeyres (default), paasche, and fisher, but it can also be use those in function `priceIndex` from package `IndexNumR` (dutot, carli, jevons, cswd, harmonic, tornqvist, satovartia, walsh and CES).
`output`	A character string specifying whether a chained (output="chained") , fixed base (output="fixedBase") or period-on-period (output="pop") price index numbers should be returned. Default is fixed base.
`...`	Further arguments passed to or from other methods.

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:

`period`	The characteristic variable.
`laspeyres`	The price index computed by the Laspeyres method.
`paasche`	The price index computed by the Paasche method.
`fisher`	The price index computed by the Fisher method.
`...`

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

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

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")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"))
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).

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 <[email protected]>

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)
Sindex(x, pervar, vvar, base)

Arguments

`x`	Data frame containing, at least, a factor and a numeric variables.
`pervar`	Character string for the name of the factor variable with the characteristics.
`vvar`	Character string for the name of the numeric variable for which you want to calculate the index number or representing the index number for which you want to compute a base change.
`base`	Character string for the name of the base characteristic.

Value

Sindex returns a data frame with one column:

index_base

The index number with base base

Examples

data(Depositos, package = "RcmdrPlugin.TeachStat")
Sindex(Depositos, "year", "quantity", "2006")
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()
twoOrMoreLevelFactors()
twoOrMoreLevelFactorsP()
listDistrs(class = "UnivariateDistribution", envir = .GlobalEnv, ...)
DiscreteDistrsP()
AbscontDistrsP()

Arguments

`class`	string with the name of the class to be listed.
`envir`	string with the name of the environment.
`...`	further 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,
         ...)
VKM.test(x, sigma = 1, sigmasq = sigma^2, mu,
         alternative = c("two.sided", "less", "greater"), conf.level = 0.95,
         ...)

Arguments

`x`	a (non-empty) numeric vector of data values.
`sigma`	a number indicating the true value of the population standard deviation - Null hypothesis.
`sigmasq`	control argument.
`mu`	numerical value indicating the population mean assumed to be known (mandatory).
`alternative`	a character string specifying the alternative hypothesis, must be one of `"two.sided"` (default), `"greater"` or `"less"`. You can specify just the initial letter.
`conf.level`	confidence level of the interval.
`...`	further arguments to be passed to or from methods.

Value

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

`statistic`	the value of the ctest statistic.
`parameter`	the degrees of freedom for the test statistic.
`p.value`	the p-value for the test.
`conf.int`	confidence interval for variance with known population mean associated with the specified alternative hypothesis.
`estimate`	the estimated variance.
`null.value`	the specified hypothesized value of the variance.
`alternative`	a character string describing the alternative hypothesis.
`method`	a character string indicating what type of statistical test was performed.
`data.name`	a character string giving the name of the data.

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$MaxPrice, alternative="two.sided", sigma=11, mu=22, conf.level=0.95)
data(cars93) # Dataset provided with the package
# Variance of the maximum price (MaxPrice) assuming that the population mean
# price is known and equal to 22
VKM.test(cars93$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,
         ...)
VUM.test(x, sigma = 1, sigmasq = sigma^2,
         alternative = c("two.sided", "less", "greater"), conf.level = 0.95,
         ...)

Arguments

`x`	a (non-empty) numeric vector of data values.
`sigma`	a number indicating the true value of the population standard deviation - Null hypothesis.
`sigmasq`	control argument.
`alternative`	a character string specifying the alternative hypothesis, must be one of `"two.sided"`, `"greater"` o `"less"`. You can specify just the initial letter.
`conf.level`	confidence level of the interval.
`...`	further arguments to be passed to or from methods.

Value

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

`statistic`	the value of the test statistic
`parameter`	the degrees of freedom for the test statistic
`p.value`	the p-value for the test.
`conf.int`	confidence interval for variance with unknown population mean associated with the specified alternative hypothesis.
`estimate`	the estimated variance.
`null.value`	the specified hypothesized value of the variance.
`alternative`	a character string describing the alternative hypothesis.
`method`	a character string indicating what type of statistical test was performed.
`data.name`	a character string giving the name of the data.

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

`data`	`data.frame` with the variables.
`statistics`	any of `"mean"`, `"sd"`, `"se(mean)"`, `"quantiles"`, `"cv"` (coefficient of variation - sd/mean), `"skewness"` or `"kurtosis"`; defaulting to `c("mean", "sd", "quantiles", "IQR")`.
`type`	definition to use in computing skewness and kurtosis; see the `skewness` and `kurtosis` functions in the e1071 package. The default is "2".
`quantiles`	quantiles to report; by default is `c(0, 0.25, 0.5, 0.75, 1)`.
`groups`	optional variable, typically a factor, to be used to partition the data. By default is `NULL`.
`weights`	numeric vector of weights. Zero values are allowed.

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".

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)
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)

Package 'RcmdrPlugin.TeachStat'

Help Index

R Commander plugin for teaching statistical methods.

Description

Details

Author(s)

See Also

Grouped or tabulated data set

Description

Usage

Format

Examples

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

Description

Usage

Arguments

Value

See Also

Examples

ANOVA with random effects using the Moments method.

Description

Usage

Arguments

Value

See Also

Examples

Frequency distributions for qualitative variables

Description

Usage

Arguments

Value

See Also

Examples

Summary statistics for tabulated data

Description

Usage

Arguments

Details

Value

See Also

Examples

Summary statistics for continuous variables

Description

Usage

Arguments

Details

Value

See Also

Examples

Summary statistics for discrete variables

Description

Usage

Arguments

Details

Value

See Also

Examples

Data from 93 Cars on Sale in the USA in 1993

Description

Usage

Format

Details

Source

References

Characteristics of Random Variables.

Description

Usage

Arguments

Value

See Also

Examples

Complex index numbers

Description

Usage

Arguments

Details

Value

Examples

Modify Variable Types

Description