Tidy a(n) plm object

Source: R/plm-tidiers.R

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

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.
...

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(), plm::plm(), tidy.lm()

Other plm tidiers: augment.plm(), glance.plm()

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.

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 = 0.9)
#> # 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      
#>    <pseries>  <pseries>   <pseries> <pseries>  <pseries>   <dbl> <pseries>   
#>  1 10.25478   9.617981    10.48553  6.918201   4.7          10.3 -0.046561413
#>  2 10.28790   9.648720    10.52675  6.929419   5.2          10.3 -0.030640422
#>  3 10.35147   9.678618    10.56283  6.977561   4.7          10.4 -0.016454312
#>  4 10.41721   9.705418    10.59873  7.034828   3.9          10.4 -0.008726974
#>  5 10.42671   9.726910    10.64679  7.064588   5.5          10.5 -0.027084312
#>  6 10.42240   9.759401    10.69130  7.052202   7.7          10.4 -0.022368930
#>  7 10.48470   9.783175    10.82420  7.095893   6.8          10.5 -0.036587629
#>  8 10.53111   9.804326    10.84125  7.146142   7.4          10.6 -0.030020604
#>  9 10.59573   9.824430    10.87055  7.197810   6.3          10.6 -0.018942497
#> 10 10.62082   9.845937    10.90643  7.216709   7.1          10.6 -0.014057170
#> # … with 806 more rows
glance(zz)
#> # A tibble: 1 x 7
#>   r.squared adj.r.squared statistic p.value deviance df.residual  nobs
#>       <dbl>         <dbl>     <dbl>   <dbl>    <dbl>       <int> <int>
#> 1     0.941         0.937     3065.       0     1.11         764   816