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.

# S3 method for ivreg
tidy(x, conf.int = FALSE, conf.level = 0.95, instruments = FALSE, ...)

Arguments

x

An ivreg object created by a call to AER::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 pass conf.lvel = 0.9, all computation will proceed using conf.level = 0.95. Additionally, if you pass newdata = my_tibble to an augment() method that does not accept a newdata argument, it will use the default value for the data argument.

See also

tidy(), AER::ivreg()

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

library(AER) data("CigarettesSW", package = "AER") ivr <- ivreg( log(packs) ~ income | population, data = CigarettesSW, subset = year == "1995" ) summary(ivr)
#> #> Call: #> ivreg(formula = log(packs) ~ income | population, data = CigarettesSW, #> subset = year == "1995") #> #> Residuals: #> Min 1Q Median 3Q Max #> -0.69305 -0.12941 -0.02257 0.11723 0.58184 #> #> Coefficients: #> Estimate Std. Error t value Pr(>|t|) #> (Intercept) 4.612e+00 4.454e-02 103.549 <2e-16 *** #> income -5.705e-10 2.334e-10 -2.445 0.0184 * #> --- #> Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 #> #> Residual standard error: 0.2293 on 46 degrees of freedom #> Multiple R-Squared: 0.1308, Adjusted R-squared: 0.1119 #> Wald test: 5.976 on 1 and 46 DF, p-value: 0.01839 #>
tidy(ivr)
#> # A tibble: 2 x 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 x 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 x 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 x 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 #> # … with 38 more rows
augment(ivr, data = CigarettesSW)
#> # A tibble: 96 x 11 #> state year cpi population packs income tax price taxs .fitted .resid #> <fct> <fct> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> #> 1 AL 1985 1.08 3973000 116. 4.60e7 32.5 102. 33.3 4.56 0.0522 #> 2 AR 1985 1.08 2327000 129. 2.62e7 37 101. 37 4.59 0.124 #> 3 AZ 1985 1.08 3184000 105. 4.40e7 31 109. 36.2 4.56 -0.285 #> 4 CA 1985 1.08 26444000 100. 4.47e8 26 108. 32.1 4.17 -0.131 #> 5 CO 1985 1.08 3209000 113. 4.95e7 31 94.3 31 4.56 -0.145 #> 6 CT 1985 1.08 3201000 109. 6.01e7 42 128. 51.5 4.55 -0.177 #> 7 DE 1985 1.08 618000 144. 9.93e6 30 102. 30 4.60 0.223 #> 8 FL 1985 1.08 11352000 122. 1.67e8 37 115. 42.5 4.42 0.112 #> 9 GA 1985 1.08 5963000 127. 7.84e7 28 97.0 28.8 4.52 0.0591 #> 10 IA 1985 1.08 2830000 114. 3.79e7 34 102. 37.9 4.58 -0.0512 #> # … with 86 more rows
augment(ivr, newdata = CigarettesSW)
#> # A tibble: 96 x 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 #> # … with 86 more rows
glance(ivr)
#> # A tibble: 1 x 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