Tidy summarizes information about the components of a model. A model component might be a single term in a regression, a single hypothesis, a cluster, or a class. Exactly what tidy considers to be a model component varies across models but is usually self-evident. If a model has several distinct types of components, you will need to specify which components to return.
Usage
# S3 method for class 'ivreg'
tidy(x, conf.int = FALSE, conf.level = 0.95, instruments = FALSE, ...)Arguments
- x
An
ivregobject created by a call toAER::ivreg().- conf.int
Logical indicating whether or not to include a confidence interval in the tidied output. Defaults to
FALSE.- conf.level
The confidence level to use for the confidence interval if
conf.int = TRUE. Must be strictly greater than 0 and less than 1. Defaults to 0.95, which corresponds to a 95 percent confidence interval.- instruments
Logical indicating whether to return coefficients from the second-stage or diagnostics tests for each endogenous regressor (F-statistics). Defaults to
FALSE.- ...
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.
See also
Other ivreg tidiers:
augment.ivreg(),
glance.ivreg()
Value
A tibble::tibble() with columns:
- conf.high
Upper bound on the confidence interval for the estimate.
- conf.low
Lower bound on the confidence interval for the estimate.
- estimate
The estimated value of the regression term.
- p.value
The two-sided p-value associated with the observed statistic.
- p.value.Sargan
p-value for Sargan test of overidentifying restrictions.
- p.value.weakinst
p-value for weak instruments test.
- p.value.Wu.Hausman
p-value for Wu-Hausman weak instruments test for endogeneity.
- statistic
The value of a T-statistic to use in a hypothesis that the regression term is non-zero.
- statistic.Sargan
Statistic for Sargan test of overidentifying restrictions.
- statistic.weakinst
Statistic for Wu-Hausman test.
- statistic.Wu.Hausman
Statistic for Wu-Hausman weak instruments test for endogeneity.
- std.error
The standard error of the regression term.
- term
The name of the regression term.
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