sValues: S-values: conventional model ambiguity measures
In sValues: Measures of the Sturdiness of Regression Coefficients

Description Usage Arguments Value References See Also Examples

View source: R/sValues.R

The function sValues performs the extreme bound analysis proposed by Leamer (2014) and discussed in Leamer (2015). For further details see the package vignette.

sValues(..., R2_bounds = c(0.1, 0.5, 1), favorites = NULL,
  R2_favorites = NULL, scale = TRUE)

## S3 method for class 'formula'
sValues(formula, data, R2_bounds = c(0.1, 0.5, 1),
  favorites = NULL, R2_favorites = NULL, scale = TRUE, ...)

## S3 method for class 'matrix'
sValues(m, R2_bounds = c(0.1, 0.5, 1), favorites = NULL,
  R2_favorites = NULL, scale = TRUE, ...)

## S3 method for class 'data.frame'
sValues(df, R2_bounds = c(0.1, 0.5, 1),
  favorites = NULL, R2_favorites = NULL, scale = TRUE, ...)

`...`	arguments passed to other methods. The first argument should be a `formula` followed by a `data.frame`; alternatively, as a shortcut, you can omit the `formula` and provide only a `matrix` or a `data.frame`: in that case, the function will automatically consider the first column as the dependent variable and the rest as the independent variables.
`R2_bounds`	a numeric vector with two or more R2 bounds to be considered in the analysis. The default values are `c(0.1, 0.5, 1)`, proposed by Leamer (2014).
`favorites`	optional - a character vector that specifies the "favorite" varibles to be used in the analysis. These variables will have different lower and upper R2 bounds as defined in the `R_favorites` argument.
`R2_favorites`	optional - a numeric vector with two or more R2 bounds for the "favorite" variables.
`scale`	should the variables be scaled/standardized to zero mean and unit variance? The default is `TRUE`. If your data is already scaled/standardized you should set this to `FALSE`.
`formula`	an object of the class `formula`: a symbolic description of the model to be fitted.
`data`	needed only when you pass a formula as first parameter. An object of the class `data.frame` containing the variables used in the analysis.
`m`	an object of class `matrix` with the dependent variable in the first column followed by the covariates. The matrix must have column names.
`df`	an object of class `data.frame` with the dependent variable in the first column followed by the covariates.

sValues returns an object a list of class "sValues" containing the main results of the analysis:

info: a list with the general information about the paramaters used in the analysis, such as the formula, the data, the bounds and favorite variables.
simple: a list with the results of the simple linear regressions for each variable.
all: the results of the linear regression with all variables.
bayes: a list with the results of the bayesian regression for each combination of the R2 bounds. Each bayesian regression includes the coefficient estimates, the variance-covariance matrix and the t-values.
ext_bounds: a list with the extreme bounds estimates for each combination of the R2 bounds.
s_values: a data.frame with the s_values for each combination of the R2 bounds.

Leamer, E. (2014). S-values: Conventional context-minimal measures of the sturdiness of regression coefficients. Working Paper

Leamer, E. (2015). S-values and bayesian weighted all-subsets regressions. European Economic Review.

coef.sValues to extract coefficients or statistics;

print.sValues for printing;

summary.sValues for summaries;

plot.sValues for plots.

# growth regressions example
## All variables, No favorites
data(economic_growth)
eg_sv <- sValues(GR6096 ~ ., data = economic_growth)
eg_sv # prints results
plot(eg_sv, R2_bounds = c(0.5, 1))
plot(eg_sv, type = "beta_plot", variable = "P60", error_bar = TRUE)
coefs_eg <- coef(eg_sv) # extract coefficients
coefs_eg

##  only 14 variables
eg_sv_14 <-  sValues(GR6096 ~GDPCH60L + OTHFRAC + ABSLATIT + 
                       LT100CR + BRIT + GOVNOM1 + WARTIME + 
                       SCOUT + P60 + PRIEXP70 + OIL + 
                       H60 + POP1560 + POP6560, data = economic_growth)
eg_sv_14
coefs_eg_14 <- coef(eg_sv_14)

## With 14 favorites among all variables
favorites <- c("GDPCH60L", "OTHFRAC", "ABSLATIT", "LT100CR", 
              "BRIT", "GOVNOM1", "WARTIME", "SCOUT", 
              "P60", "PRIEXP70", "OIL", "H60", 
              "POP1560", "POP6560")
eg_sv_fav <- sValues(GR6096 ~ ., data = economic_growth, R2_bounds = c(0.5, 1),
                    favorites = favorites, R2_favorites = c(0.4, 0.8))
eg_sv_fav
plot(eg_sv_fav, R2_bounds = c(0.5, 1))
plot(eg_sv_fav, type = "beta_plot", variable = "P60", error_bar = TRUE)
coefs_eg_fav <- coef(eg_sv_fav)
coefs_eg_fav