broom (version 0.3.4)

nls_tidiers: Tidying methods for a nonlinear model

Description

These methods tidy the coefficients of a nonlinear model into a summary, augment the original data with information on the fitted values and residuals, and construct a one-row glance of the model's statistics.

Usage

## S3 method for class 'nls':
tidy(x, conf.int = FALSE, conf.level = 0.95, ...)
## S3 method for class 'nls':
augment(x, data = NULL, newdata = NULL, ...)
## S3 method for class 'nls':
glance(x, ...)

Arguments

x

An object of class "nls"

conf.int

whether to include a confidence interval

conf.level

confidence level of the interval, used only if conf.int=TRUE

...

extra arguments (not used)

data

original data this was fitted on; if not given this will attempt to be reconstructed from nls (may not be successful)

newdata

new data frame to use for predictions

Value

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

  tidy returns one row for each coefficient in the model, with five columns:

• term
  The term in the nonlinear model being estimated and tested
• estimate
  The estimated coefficient
• std.error
  The standard error from the linear model
• statistic
  t-statistic
• p.value
  two-sided p-value
• augment returns one row for each original observation, with two columns added:
• .fitted
  Fitted values of model
• .resid
  Residuals
• If newdata is provided, these are computed on based on predictions of the new data.

  glance returns one row with the columns

• sigma
  the square root of the estimated residual variance
• isConv
  whether the fit successfully converged
• finTol
  the achieved convergence tolerance
• logLik
  the data's log-likelihood under the model
• AIC
  the Akaike Information Criterion
• BIC
  the Bayesian Information Criterion
• deviance
  deviance
• df.residual
  residual degrees of freedom

Details

When the modeling was performed with na.action = "na.omit" (as is the typical default), rows with NA in the initial data are omitted entirely from the augmented data frame. When the modeling was performed with na.action = "na.exclude", one should provide the original data as a second argument, at which point the augmented data will contain those rows (typically with NAs in place of the new columns). If the original data is not provided to augment and na.action = "na.exclude", a warning is raised and the incomplete rows are dropped.

See Also

na.action

nls and summary.nls

Examples

n <- nls(mpg ~ k * e ^ wt, data = mtcars, start = list(k = 1, e = 2))

tidy(n)
augment(n)
glance(n)

library(ggplot2)
ggplot(augment(n), aes(wt, mpg)) + geom_point() + geom_line(aes(y = .fitted))

# augment on new data
newdata <- head(mtcars)
newdata$wt <- newdata$wt + 1
augment(n, newdata = newdata)