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 modelling function. This includes the name of the modelling function or any arguments passed to the modelling 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.

# S3 method for ivreg
glance(x, diagnostics = FALSE, ...)

Arguments

x

An ivreg object created by a call to AER::ivreg().

diagnostics

Logical indicating whether to include statistics and p-values for Sargan, Wu-Hausman and weak instrument tests. 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 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 for the model.

p.value.Sargan

P-value for Sargan test.

p.value.weakinst

P-value for weak instruments test.

p.value.Wu.Hausman

P-value for Wu-Hausman test.

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.Sargan

Statistic for Sargan test.

statistic.weakinst

Statistic for Wu-Hausman test.

statistic.Wu.Hausman

Statistic for Wu-Hausman test.

statistic

Wald test statistic.

p.value

P-value for the Wald test.

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