Glance accepts a model object and returns a tibble::tibble()
with exactly one row of model summaries. The summaries are typically
goodness of fit measures, p-values for hypothesis tests on residuals,
or model convergence information.
Glance never returns information from the original call to the modeling function. This includes the name of the modeling function or any arguments passed to the modeling function.
Glance does not calculate summary measures. Rather, it farms out these
computations to appropriate methods and gathers the results together.
Sometimes a goodness of fit measure will be undefined. In these cases
the measure will be reported as NA.
Glance returns the same number of columns regardless of whether the
model matrix is rank-deficient or not. If so, entries in columns
that no longer have a well-defined value are filled in with an NA
of the appropriate type.
Usage
# S3 method for class 'ivreg'
glance(x, diagnostics = FALSE, ...)Arguments
- x
An
ivregobject created by a call toAER::ivreg().- diagnostics
Logical indicating whether or not to return the Wu-Hausman and Sargan diagnostic information.
- ...
Additional arguments. Not used. Needed to match generic signature only. Cautionary note: Misspelled arguments will be absorbed in
..., where they will be ignored. If the misspelled argument has a default value, the default value will be used. For example, if you passconf.lvel = 0.9, all computation will proceed usingconf.level = 0.95. Two exceptions here are:
Details
This tidier currently only supports ivreg-classed objects
outputted by the AER package. The ivreg package also outputs
objects of class ivreg, and will be supported in a later release.
Note
Beginning 0.7.0, glance.ivreg returns statistics for the
Wu-Hausman test for endogeneity and the Sargan test of
overidentifying restrictions. Sargan test values are returned as NA
if the number of instruments is not greater than the number of
endogenous regressors.
See also
Other ivreg tidiers:
augment.ivreg(),
tidy.ivreg()
Value
A tibble::tibble() with exactly one row and columns:
- adj.r.squared
Adjusted R squared statistic, which is like the R squared statistic except taking degrees of freedom into account.
- df
Degrees of freedom used by the model.
- df.residual
Residual degrees of freedom.
- nobs
Number of observations used.
- r.squared
R squared statistic, or the percent of variation explained by the model. Also known as the coefficient of determination.
- sigma
Estimated standard error of the residuals.
- statistic
Wald test statistic.
- p.value
P-value for the Wald test.
Examples
# load libraries for models and data
library(AER)
# load data
data("CigarettesSW", package = "AER")
# fit model
ivr <- ivreg(
log(packs) ~ income | population,
data = CigarettesSW,
subset = year == "1995"
)
# summarize model fit with tidiers
tidy(ivr)
#> # A tibble: 2 × 5
#> term estimate std.error statistic p.value
#> <chr> <dbl> <dbl> <dbl> <dbl>
#> 1 (Intercept) 4.61e+ 0 4.45e- 2 104. 3.74e-56
#> 2 income -5.71e-10 2.33e-10 -2.44 1.84e- 2
tidy(ivr, conf.int = TRUE)
#> # A tibble: 2 × 7
#> term estimate std.error statistic p.value conf.low conf.high
#> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 (Intercept) 4.61e+ 0 4.45e- 2 104. 3.74e-56 4.52e+0 4.70e+ 0
#> 2 income -5.71e-10 2.33e-10 -2.44 1.84e- 2 -1.03e-9 -1.13e-10
tidy(ivr, conf.int = TRUE, instruments = TRUE)
#> # A tibble: 1 × 5
#> term num.df den.df statistic p.value
#> <chr> <dbl> <dbl> <dbl> <dbl>
#> 1 income 1 46 3329. 1.46e-44
augment(ivr)
#> # A tibble: 48 × 6
#> .rownames `log(packs)` income population .fitted .resid
#> <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 49 4.62 83903280 4262731 4.56 0.0522
#> 2 50 4.71 45995496 2480121 4.59 0.124
#> 3 51 4.28 88870496 4306908 4.56 -0.285
#> 4 52 4.04 771470144 31493524 4.17 -0.131
#> 5 53 4.41 92946544 3738061 4.56 -0.145
#> 6 54 4.38 104315120 3265293 4.55 -0.177
#> 7 55 4.82 18237436 718265 4.60 0.223
#> 8 56 4.53 333525344 14185403 4.42 0.112
#> 9 57 4.58 159800448 7188538 4.52 0.0591
#> 10 58 4.53 60170928 2840860 4.58 -0.0512
#> # ℹ 38 more rows
augment(ivr, data = CigarettesSW)
#> # A tibble: 96 × 11
#> state year cpi population packs income tax price taxs .fitted
#> <fct> <fct> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 AL 1985 1.08 3973000 116. 46014968 32.5 102. 33.3 4.56
#> 2 AR 1985 1.08 2327000 129. 26210736 37 101. 37 4.59
#> 3 AZ 1985 1.08 3184000 105. 43956936 31 109. 36.2 4.56
#> 4 CA 1985 1.08 26444000 100. 447102816 26 108. 32.1 4.17
#> 5 CO 1985 1.08 3209000 113. 49466672 31 94.3 31 4.56
#> 6 CT 1985 1.08 3201000 109. 60063368 42 128. 51.5 4.55
#> 7 DE 1985 1.08 618000 144. 9927301 30 102. 30 4.60
#> 8 FL 1985 1.08 11352000 122. 166919248 37 115. 42.5 4.42
#> 9 GA 1985 1.08 5963000 127. 78364336 28 97.0 28.8 4.52
#> 10 IA 1985 1.08 2830000 114. 37902896 34 102. 37.9 4.58
#> # ℹ 86 more rows
#> # ℹ 1 more variable: .resid <dbl>
augment(ivr, newdata = CigarettesSW)
#> # A tibble: 96 × 10
#> state year cpi population packs income tax price taxs .fitted
#> <fct> <fct> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 AL 1985 1.08 3973000 116. 46014968 32.5 102. 33.3 4.59
#> 2 AR 1985 1.08 2327000 129. 26210736 37 101. 37 4.60
#> 3 AZ 1985 1.08 3184000 105. 43956936 31 109. 36.2 4.59
#> 4 CA 1985 1.08 26444000 100. 447102816 26 108. 32.1 4.36
#> 5 CO 1985 1.08 3209000 113. 49466672 31 94.3 31 4.58
#> 6 CT 1985 1.08 3201000 109. 60063368 42 128. 51.5 4.58
#> 7 DE 1985 1.08 618000 144. 9927301 30 102. 30 4.61
#> 8 FL 1985 1.08 11352000 122. 166919248 37 115. 42.5 4.52
#> 9 GA 1985 1.08 5963000 127. 78364336 28 97.0 28.8 4.57
#> 10 IA 1985 1.08 2830000 114. 37902896 34 102. 37.9 4.59
#> # ℹ 86 more rows
glance(ivr)
#> # A tibble: 1 × 8
#> r.squared adj.r.squared sigma statistic p.value df df.residual nobs
#> <dbl> <dbl> <dbl> <dbl> <dbl> <int> <int> <int>
#> 1 0.131 0.112 0.229 5.98 0.0184 2 46 48