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 plm
glance(x, ...)

Arguments

x

A plm objected returned by plm::plm().

...

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.

deviance

Deviance of the model.

df.residual

Residual degrees of freedom for the model.

p.value

Needs custom info.

r.squared

R squared statistic, or the percent of variation explained by the model. Also known as the coefficient of determination.

statistic

F-statistic

Examples

library(plm) data("Produc", package = "plm") zz <- plm(log(gsp) ~ log(pcap) + log(pc) + log(emp) + unemp, data = Produc, index = c("state","year")) summary(zz)
#> Oneway (individual) effect Within Model #> #> Call: #> plm(formula = log(gsp) ~ log(pcap) + log(pc) + log(emp) + unemp, #> data = Produc, index = c("state", "year")) #> #> Balanced Panel: n = 48, T = 17, N = 816 #> #> Residuals: #> Min. 1st Qu. Median 3rd Qu. Max. #> -0.120456 -0.023741 -0.002041 0.018144 0.174718 #> #> Coefficients: #> Estimate Std. Error t-value Pr(>|t|) #> log(pcap) -0.02614965 0.02900158 -0.9017 0.3675 #> log(pc) 0.29200693 0.02511967 11.6246 < 2.2e-16 *** #> log(emp) 0.76815947 0.03009174 25.5273 < 2.2e-16 *** #> unemp -0.00529774 0.00098873 -5.3582 1.114e-07 *** #> --- #> Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 #> #> Total Sum of Squares: 18.941 #> Residual Sum of Squares: 1.1112 #> R-Squared: 0.94134 #> Adj. R-Squared: 0.93742 #> F-statistic: 3064.81 on 4 and 764 DF, p-value: < 2.22e-16
tidy(zz)
#> # A tibble: 4 x 5 #> term estimate std.error statistic p.value #> <chr> <dbl> <dbl> <dbl> <dbl> #> 1 log(pcap) -0.0261 0.0290 -0.902 3.68e- 1 #> 2 log(pc) 0.292 0.0251 11.6 7.08e- 29 #> 3 log(emp) 0.768 0.0301 25.5 2.02e-104 #> 4 unemp -0.00530 0.000989 -5.36 1.11e- 7
tidy(zz, conf.int = TRUE)
#> # A tibble: 4 x 7 #> term estimate std.error statistic p.value conf.low conf.high #> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> #> 1 log(pcap) -0.0261 0.0290 -0.902 3.68e- 1 -0.0830 0.0307 #> 2 log(pc) 0.292 0.0251 11.6 7.08e- 29 0.243 0.341 #> 3 log(emp) 0.768 0.0301 25.5 2.02e-104 0.709 0.827 #> 4 unemp -0.00530 0.000989 -5.36 1.11e- 7 -0.00724 -0.00336
tidy(zz, conf.int = TRUE, conf.level = .9)
#> # A tibble: 4 x 7 #> term estimate std.error statistic p.value conf.low conf.high #> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> #> 1 log(pcap) -0.0261 0.0290 -0.902 3.68e- 1 -0.0739 0.0216 #> 2 log(pc) 0.292 0.0251 11.6 7.08e- 29 0.251 0.333 #> 3 log(emp) 0.768 0.0301 25.5 2.02e-104 0.719 0.818 #> 4 unemp -0.00530 0.000989 -5.36 1.11e- 7 -0.00692 -0.00367
augment(zz)
#> # A tibble: 816 x 7 #> log.gsp. log.pcap. log.pc. log.emp. unemp .fitted .resid #> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> #> 1 10.3 9.62 10.5 6.92 4.7 10.3 -0.0466 #> 2 10.3 9.65 10.5 6.93 5.2 10.3 -0.0306 #> 3 10.4 9.68 10.6 6.98 4.7 10.4 -0.0165 #> 4 10.4 9.71 10.6 7.03 3.9 10.4 -0.00873 #> 5 10.4 9.73 10.6 7.06 5.5 10.5 -0.0271 #> 6 10.4 9.76 10.7 7.05 7.7 10.4 -0.0224 #> 7 10.5 9.78 10.8 7.10 6.8 10.5 -0.0366 #> 8 10.5 9.80 10.8 7.15 7.4 10.6 -0.0300 #> 9 10.6 9.82 10.9 7.20 6.3 10.6 -0.0189 #> 10 10.6 9.85 10.9 7.22 7.1 10.6 -0.0141 #> # ... with 806 more rows
glance(zz)
#> # A tibble: 1 x 6 #> r.squared adj.r.squared statistic p.value deviance df.residual #> <dbl> <dbl> <dbl> <dbl> <dbl> <int> #> 1 0.941 0.937 3065. 0 1.11 764