These methods tidy the coefficients of spatial autoregression models generated by functions in the spatialreg package.

## Usage

# S3 method for sarlm
tidy(x, conf.int = FALSE, conf.level = 0.95, ...)

## Arguments

x

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

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. 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.

tidy(), spatialreg::lagsarlm(), spatialreg::errorsarlm(), spatialreg::sacsarlm()

Other spatialreg tidiers: augment.sarlm(), glance.sarlm()

## 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



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

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