R: Tidiers for linear model II objects from the lmodel2 package

lmodel2_tidiers {broom} | R Documentation

## Tidiers for linear model II objects from the lmodel2 package

### Description

Tidy or glance an lmodel2 object. An lmodel2 represents model II simple linear regression, where both variables in the regression equation are random.

### Usage

    ## S3 method for class 'lmodel2'
    tidy(x, ...)

    ## S3 method for class 'lmodel2'
    glance(x, ...)

### Arguments

`x` | lmodel2 object
`...` | Extra arguments, not used

### Details

Note that unlike linear regression, there are always only two terms in an lmodel2: Intercept and Slope. Furthermore, these are computed by four methods: OLS (ordinary least squares), MA (major axis), SMA (standard major axis), and RMA (ranged major axis). See the lmodel2 documentation for more.

Note that there is no `augment` method for lmodel2 objects because lmodel2 does not provide a `predict` or `residuals` method (and since when both observations are random, fitted values and residuals have a less clear meaning).

### Value

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

`tidy` returns a data frame with one row for each combination of method (OLS/MA/SMA/RMA) and term (always Intercept/Slope). Its columns are:

method

  Either OLS/MA/SMA/RMA

term

  Either "Intercept" or "Slope"

estimate

  Estimated coefficient

conf.low

  Lower bound of 95% confidence interval

conf.high

  Upper bound of 95% confidence interval

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

r.squared

  OLS R-squared

p.value

  OLS parametric p-value

theta

  Angle between OLS lines `lm(y ~ x)` and `lm(x ~ y)`

H

  H statistic for computing confidence interval of major axis slope

### Examples

    if (require("lmodel2", quietly = TRUE)) {
      data(mod2ex2)
      Ex2.res <- lmodel2(Prey ~ Predators, data=mod2ex2, "relative", "relative", 99)
      Ex2.res

      tidy(Ex2.res)
      glance(Ex2.res)

      # this allows coefficient plots with ggplot2
      library(ggplot2)
      ggplot(tidy(Ex2.res), aes(estimate, term, color = method)) +
        geom_point() +
        geom_errorbarh(aes(xmin = conf.low, xmax = conf.high)) +
        geom_errorbarh(aes(xmin = conf.low, xmax = conf.high))
    }

[Package broom version 0.4.4 Index]