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 plm
tidy(x, conf.int = FALSE, conf.level = 0.95,
  exponentiate = FALSE, ...)

Arguments

x

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

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(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