R: Tidiers for ivreg models

ivreg_tidiers {broom} | R Documentation

## Tidiers for ivreg models

### Description

Tidiers for ivreg models

### Usage

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

    ## S3 method for class 'ivreg'
    augment(x, data = as.data.frame(stats::model.frame(x)),
      newdata, ...)

    ## S3 method for class 'ivreg'
    glance(x, diagnostics = FALSE, ...)

### Arguments

`x` | An "ivreg" object
`conf.int` | Whether to include a confidence interval
`conf.level` | Confidence level of the interval, used only if `conf.int=TRUE`
`exponentiate` | Whether to exponentiate the coefficient estimates and confidence intervals
`...` | extra arguments, not used
`data` | Original dataset
`newdata` | New data to make predictions from (optional)
`diagnostics` | Logical. Return results of diagnostic tests.

### Value

All tidying methods return a data.frame without rownames, whose structure depends on the method chosen.

`tidy.ivreg` returns a data frame with one row per coefficient, of the same form as `tidy.lm`.

`augment` returns a data frame with one row for each initial observation, adding the columns:

`.fitted` | predicted (fitted) values

and if `newdata` is `NULL`:

`.resid` | residuals

`glance` returns a one-row data frame with columns

`r.squared` | The percent of variance explained by the model
`adj.r.squared` | r.squared adjusted based on the degrees of freedom
`statistic` | Wald test statistic
`p.value` | p-value from the Wald test
`df` | Degrees of freedom used by the coefficients
`sigma` | The square root of the estimated residual variance
`df.residual` | residual degrees of freedom

If `diagnostics` is `TRUE`, `glance` also returns:

`p.value.Sargan` | P value of Sargan test
`p.value.Wu.Hausman` | P value of Wu-Hausman test
`p.value.weakinst` | P value of weak instruments test

### See Also

`lm_tidiers`

### Examples

    if (require("AER", quietly = TRUE)) {
        data("CigarettesSW", package = "AER")
        CigarettesSW$rprice <- with(CigarettesSW, price/cpi)
        CigarettesSW$rincome <- with(CigarettesSW, income/population/cpi)
        CigarettesSW$tdiff <- with(CigarettesSW, (taxs - tax)/cpi)
        ivr <- ivreg(log(packs) ~ log(rprice) + log(rincome) | log(rincome) + tdiff + I(tax/cpi),
              data = CigarettesSW, subset = year == "1995")

        summary(ivr)

        tidy(ivr)
        tidy(ivr, conf.int = TRUE)
        tidy(ivr, conf.int = TRUE, exponentiate = TRUE)

        head(augment(ivr))

        glance(ivr)
    }

[Package broom version 0.4.4 Index]