Svalues: conventional model ambiguity measures
Description
The function sValues
performs the extreme bound analysis proposed by Leamer (2014) and
discussed in Leamer (2015).
For further details see the package vignette.
Usage
1 2 3 4 5 6 7 8 9 10 11 12 13 14  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
... 
arguments passed to other methods. The first argument should be a 
R2_bounds 
a numeric vector with two or more R2 bounds to be considered in the analysis. The default values are

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 
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 
formula 
an object of the class 
data 
needed only when you pass a formula as first parameter. An object of the class 
m 
an object of class 
df 
an object of class 
Value
sValues
returns an object a list of class "sValues" containing the main results of the analysis:

info
: alist
with the general information about the paramaters used in the analysis, such as the formula, the data, the bounds and favorite variables. 
simple
: alist
with the results of the simple linear regressions for each variable. 
all
: the results of the linear regression with all variables. 
bayes
: alist
with the results of the bayesian regression for each combination of the R2 bounds. Each bayesian regression includes the coefficient estimates, the variancecovariance matrix and the tvalues. 
ext_bounds
: alist
with the extreme bounds estimates for each combination of the R2 bounds. 
s_values
: adata.frame
with the s_values for each combination of the R2 bounds.
References
Leamer, E. (2014). Svalues: Conventional contextminimal measures of the sturdiness of regression coefficients. Working Paper
Leamer, E. (2015). Svalues and bayesian weighted allsubsets regressions. European Economic Review.
See Also
coef.sValues
to extract coefficients or statistics;
print.sValues
for printing;
summary.sValues
for summaries;
plot.sValues
for plots.
Examples
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30  # 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
