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 cross 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,
  exponentiate = 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.

exponentiate

Logical indicating whether or not to exponentiate the the coefficient estimates. This is typical for logistic and multinomial regressions, but a bad idea if there is no log or logit link. 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

Value

A tibble::tibble() with columns:

conf.high

The upper end of a confidence interval for the term under consideration. Included only if `conf.int = TRUE`.

conf.low

The lower end of a confidence interval for the term under consideration. Included only if `conf.int = TRUE`.

estimate

The estimated value of the regression term.

p.value

The two-sided p-value associated with the observed statistic.

statistic

The value of a T-statistic to use in a hypothesis that the regression term is non-zero.

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, exponentiate = TRUE)
#> Warning: Exponentiating coefficients, but model did not use a log or logit link function.
#> # 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) 101. 4.45e- 2 104. 3.74e-56 92.2 110. #> 2 income 1.000 2.33e-10 -2.44 1.84e- 2 1.000 1.000
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 7 #> r.squared adj.r.squared sigma statistic p.value df df.residual #> <dbl> <dbl> <dbl> <dbl> <dbl> <int> <int> #> 1 0.131 0.112 0.229 5.98 0.0184 2 46