R: Tidiers for panel regression linear models

plm_tidiers {broom} | R Documentation

## Tidiers for panel regression linear models

### Description

Tidiers for panel regression linear models

### Usage

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

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

    ## S3 method for class 'plm'
    glance(x, ...)

### Arguments

`x` | a "plm" object representing a panel 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

### Value

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

`tidy.plm` 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
`.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` | F-statistic
`p.value` | p-value from the F test, describing whether the full regression is significant
`deviance` | deviance
`df.residual` | residual degrees of freedom

### See Also

`lm_tidiers`

### Examples

    if (require("plm", quietly = TRUE)) {
        data("Produc", package = "plm")
        zz <- plm(log(gsp) ~ log(pcap) + log(pc) + log(emp) + unemp,
                  data = Produc, index = c("state","year"))

        summary(zz)

        tidy(zz)
        tidy(zz, conf.int = TRUE)
        tidy(zz, conf.int = TRUE, conf.level = .9)

        head(augment(zz))

        glance(zz)
    }

[Package broom version 0.4.2 Index]