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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.

Usage

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

Arguments

x

An ivreg object created by a call to AER::ivreg().

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.

instruments

Logical indicating whether to return coefficients from the second-stage or diagnostics tests for each endogenous regressor (F-statistics). 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. 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.

Details

This tidier currently only supports ivreg-classed objects outputted by the AER package. The ivreg package also outputs objects of class ivreg, and will be supported in a later release.

See also

tidy(), AER::ivreg()

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

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.

p.value.Sargan

p-value for Sargan test of overidentifying restrictions.

p.value.weakinst

p-value for weak instruments test.

p.value.Wu.Hausman

p-value for Wu-Hausman weak instruments test for endogeneity.

statistic

The value of a T-statistic to use in a hypothesis that the regression term is non-zero.

statistic.Sargan

Statistic for Sargan test of overidentifying restrictions.

statistic.weakinst

Statistic for Wu-Hausman test.

statistic.Wu.Hausman

Statistic for Wu-Hausman weak instruments test for endogeneity.

std.error

The standard error of the regression term.

term

The name of the regression term.

Examples


# load libraries for models and data
library(AER)

# load data
data("CigarettesSW", package = "AER")

# fit model
ivr <- ivreg(
  log(packs) ~ income | population,
  data = CigarettesSW,
  subset = year == "1995"
)

# summarize model fit with tidiers
tidy(ivr)
#> # A tibble: 2 × 5
#>   term         estimate std.error statistic  p.value
#>   <chr>           <dbl>     <dbl>     <dbl>    <dbl>
#> 1 (Intercept)  4.61e+ 0  4.45e- 2    104.   3.74e-56
#> 2 income      -5.71e-10  2.33e-10     -2.44 1.84e- 2
tidy(ivr, conf.int = TRUE)
#> # A tibble: 2 × 7
#>   term         estimate std.error statistic  p.value conf.low conf.high
#>   <chr>           <dbl>     <dbl>     <dbl>    <dbl>    <dbl>     <dbl>
#> 1 (Intercept)  4.61e+ 0  4.45e- 2    104.   3.74e-56  4.52e+0  4.70e+ 0
#> 2 income      -5.71e-10  2.33e-10     -2.44 1.84e- 2 -1.03e-9 -1.13e-10
tidy(ivr, conf.int = TRUE, instruments = TRUE)
#> # A tibble: 1 × 5
#>   term   num.df den.df statistic  p.value
#>   <chr>   <dbl>  <dbl>     <dbl>    <dbl>
#> 1 income      1     46     3329. 1.46e-44

augment(ivr)
#> # A tibble: 48 × 6
#>    .rownames `log(packs)`    income population .fitted  .resid
#>    <chr>            <dbl>     <dbl>      <dbl>   <dbl>   <dbl>
#>  1 49                4.62  83903280    4262731    4.56  0.0522
#>  2 50                4.71  45995496    2480121    4.59  0.124 
#>  3 51                4.28  88870496    4306908    4.56 -0.285 
#>  4 52                4.04 771470144   31493524    4.17 -0.131 
#>  5 53                4.41  92946544    3738061    4.56 -0.145 
#>  6 54                4.38 104315120    3265293    4.55 -0.177 
#>  7 55                4.82  18237436     718265    4.60  0.223 
#>  8 56                4.53 333525344   14185403    4.42  0.112 
#>  9 57                4.58 159800448    7188538    4.52  0.0591
#> 10 58                4.53  60170928    2840860    4.58 -0.0512
#> # ℹ 38 more rows
augment(ivr, data = CigarettesSW)
#> # A tibble: 96 × 11
#>    state year    cpi population packs    income   tax price  taxs .fitted
#>    <fct> <fct> <dbl>      <dbl> <dbl>     <dbl> <dbl> <dbl> <dbl>   <dbl>
#>  1 AL    1985   1.08    3973000  116.  46014968  32.5 102.   33.3    4.56
#>  2 AR    1985   1.08    2327000  129.  26210736  37   101.   37      4.59
#>  3 AZ    1985   1.08    3184000  105.  43956936  31   109.   36.2    4.56
#>  4 CA    1985   1.08   26444000  100. 447102816  26   108.   32.1    4.17
#>  5 CO    1985   1.08    3209000  113.  49466672  31    94.3  31      4.56
#>  6 CT    1985   1.08    3201000  109.  60063368  42   128.   51.5    4.55
#>  7 DE    1985   1.08     618000  144.   9927301  30   102.   30      4.60
#>  8 FL    1985   1.08   11352000  122. 166919248  37   115.   42.5    4.42
#>  9 GA    1985   1.08    5963000  127.  78364336  28    97.0  28.8    4.52
#> 10 IA    1985   1.08    2830000  114.  37902896  34   102.   37.9    4.58
#> # ℹ 86 more rows
#> # ℹ 1 more variable: .resid <dbl>
augment(ivr, newdata = CigarettesSW)
#> # A tibble: 96 × 10
#>    state year    cpi population packs    income   tax price  taxs .fitted
#>    <fct> <fct> <dbl>      <dbl> <dbl>     <dbl> <dbl> <dbl> <dbl>   <dbl>
#>  1 AL    1985   1.08    3973000  116.  46014968  32.5 102.   33.3    4.59
#>  2 AR    1985   1.08    2327000  129.  26210736  37   101.   37      4.60
#>  3 AZ    1985   1.08    3184000  105.  43956936  31   109.   36.2    4.59
#>  4 CA    1985   1.08   26444000  100. 447102816  26   108.   32.1    4.36
#>  5 CO    1985   1.08    3209000  113.  49466672  31    94.3  31      4.58
#>  6 CT    1985   1.08    3201000  109.  60063368  42   128.   51.5    4.58
#>  7 DE    1985   1.08     618000  144.   9927301  30   102.   30      4.61
#>  8 FL    1985   1.08   11352000  122. 166919248  37   115.   42.5    4.52
#>  9 GA    1985   1.08    5963000  127.  78364336  28    97.0  28.8    4.57
#> 10 IA    1985   1.08    2830000  114.  37902896  34   102.   37.9    4.59
#> # ℹ 86 more rows

glance(ivr)
#> # A tibble: 1 × 8
#>   r.squared adj.r.squared sigma statistic p.value    df df.residual  nobs
#>       <dbl>         <dbl> <dbl>     <dbl>   <dbl> <int>       <int> <int>
#> 1     0.131         0.112 0.229      5.98  0.0184     2          46    48