Glance at a(n) spatialreg 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 'sarlm'
glance(x, ...)

Arguments

x

An object returned from spatialreg::lagsarlm() or spatialreg::errorsarlm().

...

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:

AIC: Akaike's Information Criterion for the model.
BIC: Bayesian Information Criterion for the model.
deviance: Deviance of the model.
logLik: The log-likelihood of the model. [stats::logLik()] may be a useful reference.
nobs: Number of observations used.

Examples



# load libraries for models and data
library(spatialreg)
library(spdep)

# load data
data(oldcol, package = "spdep")

listw <- nb2listw(COL.nb, style = "W")

# fit model
crime_sar <-
  lagsarlm(CRIME ~ INC + HOVAL,
    data = COL.OLD,
    listw = listw,
    method = "eigen"
  )

# summarize model fit with tidiers
tidy(crime_sar)
#> # A tibble: 4 × 5
#>   term        estimate std.error statistic  p.value
#>   <chr>          <dbl>     <dbl>     <dbl>    <dbl>
#> 1 rho            0.431    0.118       3.66 2.50e- 4
#> 2 (Intercept)   45.1      7.18        6.28 3.37e-10
#> 3 INC           -1.03     0.305      -3.38 7.23e- 4
#> 4 HOVAL         -0.266    0.0885     -3.00 2.66e- 3
tidy(crime_sar, 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 rho            0.431    0.118       3.66 2.50e- 4    0.200    0.662 
#> 2 (Intercept)   45.1      7.18        6.28 3.37e-10   31.0     59.1   
#> 3 INC           -1.03     0.305      -3.38 7.23e- 4   -1.63    -0.434 
#> 4 HOVAL         -0.266    0.0885     -3.00 2.66e- 3   -0.439   -0.0925
glance(crime_sar)
#> # A tibble: 1 × 6
#>   r.squared   AIC   BIC deviance logLik  nobs
#>       <dbl> <dbl> <dbl>    <dbl>  <dbl> <int>
#> 1     0.652  375.  384.    4679.  -182.    49
augment(crime_sar)
#> # A tibble: 49 × 6
#>    `(Intercept)`   INC HOVAL  CRIME .fitted .resid
#>            <dbl> <dbl> <dbl>  <dbl>   <dbl>  <dbl>
#>  1             1 21.2   44.6 18.8      22.6  -3.84
#>  2             1  4.48  33.2 32.4      46.6 -14.2 
#>  3             1 11.3   37.1 38.4      41.4  -2.97
#>  4             1  8.44  75    0.178    37.9 -37.7 
#>  5             1 19.5   80.5 15.7      14.2   1.54
#>  6             1 16.0   26.4 30.6      34.3  -3.66
#>  7             1 11.3   23.2 50.7      44.7   5.99
#>  8             1 16.0   28.8 26.1      38.4 -12.3 
#>  9             1  9.87  18   48.6      51.7  -3.12
#> 10             1 13.6   96.4 34.0      16.3  17.7 
#> # ℹ 39 more rows

# fit another model
crime_sem <- errorsarlm(CRIME ~ INC + HOVAL, data = COL.OLD, listw)

# summarize model fit with tidiers
tidy(crime_sem)
#> # A tibble: 4 × 5
#>   term        estimate std.error statistic   p.value
#>   <chr>          <dbl>     <dbl>     <dbl>     <dbl>
#> 1 (Intercept)   59.9      5.37       11.2  0        
#> 2 INC           -0.941    0.331      -2.85 0.00441  
#> 3 HOVAL         -0.302    0.0905     -3.34 0.000836 
#> 4 lambda         0.562    0.134       4.20 0.0000271
tidy(crime_sem, 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 (Intercept)   59.9      5.37       11.2  0           49.4      70.4  
#> 2 INC           -0.941    0.331      -2.85 0.00441     -1.59     -0.293
#> 3 HOVAL         -0.302    0.0905     -3.34 0.000836    -0.480    -0.125
#> 4 lambda         0.562    0.134       4.20 0.0000271    0.299     0.824
glance(crime_sem)
#> # A tibble: 1 × 6
#>   r.squared   AIC   BIC deviance logLik  nobs
#>       <dbl> <dbl> <dbl>    <dbl>  <dbl> <int>
#> 1     0.658  377.  386.    4683.  -183.    49
augment(crime_sem)
#> # A tibble: 49 × 6
#>    `(Intercept)`   INC HOVAL  CRIME .fitted  .resid
#>            <dbl> <dbl> <dbl>  <dbl>   <dbl>   <dbl>
#>  1             1 21.2   44.6 18.8      22.5  -3.70 
#>  2             1  4.48  33.2 32.4      44.9 -12.5  
#>  3             1 11.3   37.1 38.4      38.2   0.223
#>  4             1  8.44  75    0.178    35.0 -34.8  
#>  5             1 19.5   80.5 15.7      13.3   2.45 
#>  6             1 16.0   26.4 30.6      35.0  -4.33 
#>  7             1 11.3   23.2 50.7      42.3   8.41 
#>  8             1 16.0   28.8 26.1      39.4 -13.3  
#>  9             1  9.87  18   48.6      49.3  -0.721
#> 10             1 13.6   96.4 34.0      16.6  17.4  
#> # ℹ 39 more rows

# fit another model
crime_sac <- sacsarlm(CRIME ~ INC + HOVAL, data = COL.OLD, listw)

# summarize model fit with tidiers
tidy(crime_sac)
#> # A tibble: 5 × 5
#>   term        estimate std.error statistic    p.value
#>   <chr>          <dbl>     <dbl>     <dbl>      <dbl>
#> 1 rho            0.368    0.197      1.87  0.0613    
#> 2 (Intercept)   47.8      9.90       4.83  0.00000140
#> 3 INC           -1.03     0.326     -3.14  0.00167   
#> 4 HOVAL         -0.282    0.0900    -3.13  0.00176   
#> 5 lambda         0.167    0.297      0.562 0.574     
tidy(crime_sac, conf.int = TRUE)
#> # A tibble: 5 × 7
#>   term        estimate std.error statistic    p.value conf.low conf.high
#>   <chr>          <dbl>     <dbl>     <dbl>      <dbl>    <dbl>     <dbl>
#> 1 rho            0.368    0.197      1.87  0.0613      -0.0174     0.754
#> 2 (Intercept)   47.8      9.90       4.83  0.00000140  28.4       67.2  
#> 3 INC           -1.03     0.326     -3.14  0.00167     -1.67      -0.386
#> 4 HOVAL         -0.282    0.0900    -3.13  0.00176     -0.458     -0.105
#> 5 lambda         0.167    0.297      0.562 0.574       -0.415      0.748
glance(crime_sac)
#> # A tibble: 1 × 6
#>   r.squared   AIC   BIC deviance logLik  nobs
#>       <dbl> <dbl> <dbl>    <dbl>  <dbl> <int>
#> 1     0.652  376.  388.    4685.  -182.    49
augment(crime_sac)
#> # A tibble: 49 × 6
#>    `(Intercept)`   INC HOVAL  CRIME .fitted .resid
#>            <dbl> <dbl> <dbl>  <dbl>   <dbl>  <dbl>
#>  1             1 21.2   44.6 18.8      22.2  -3.37
#>  2             1  4.48  33.2 32.4      46.4 -14.0 
#>  3             1 11.3   37.1 38.4      40.4  -2.00
#>  4             1  8.44  75    0.178    37.5 -37.3 
#>  5             1 19.5   80.5 15.7      13.5   2.25
#>  6             1 16.0   26.4 30.6      34.4  -3.74
#>  7             1 11.3   23.2 50.7      44.1   6.60
#>  8             1 16.0   28.8 26.1      39.0 -12.9 
#>  9             1  9.87  18   48.6      51.5  -2.93
#> 10             1 13.6   96.4 34.0      15.8  18.2 
#> # ℹ 39 more rows

Usage

Arguments

See also

Value

Examples