The function sValues
performs the extreme bound analysis proposed by Leamer (2014) and
discussed in Leamer (2015).
For further details see the package vignette.
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 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 
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 variancecovariance matrix and the tvalues.
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). Svalues: Conventional contextminimal measures of the sturdiness of regression coefficients. Working Paper
Leamer, E. (2015). Svalues and bayesian weighted allsubsets regressions. European Economic Review.
coef.sValues
to extract coefficients or statistics;
print.sValues
for printing;
summary.sValues
for summaries;
plot.sValues
for plots.
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

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