Glance at a(n) plm object

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

Glance returns the same number of columns regardless of whether the model matrix is rank-deficient or not. If so, entries in columns that no longer have a well-defined value are filled in with an NA of the appropriate type.

Usage

# S3 method for class '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. Two exceptions here are:

tidy() methods will warn when supplied an exponentiate argument if it will be ignored.
augment() methods will warn when supplied a newdata argument if it will be ignored.

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.
nobs: Number of observations used.
p.value: P-value corresponding to the test statistic.
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


# load libraries for models and data
library(plm)

# load data
data("Produc", package = "plm")

# fit model
zz <- plm(log(gsp) ~ log(pcap) + log(pc) + log(emp) + unemp,
  data = Produc, index = c("state", "year")
)

# summarize model fit with tidiers
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 × 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 × 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 × 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 × 7
#>    `log(gsp)` `log(pcap)` `log(pc)` `log(emp)` unemp  .fitted .resid      
#>    <pseries>  <pseries>   <pseries> <pseries>  <pser>   <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
#> # ℹ 806 more rows
glance(zz)
#> # A tibble: 1 × 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

Usage

Arguments

See also

Value

Examples