Tidy a(n) nls object

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 cross models but is usually self-evident. If a model has several distinct types of components, you will need to specify which components to return.

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

Arguments

x	An nls object returned from stats::nls().
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.
quick	Logical indiciating if the only the term and estimate columns should be returned. Often useful to avoid time consuming covariance and standard error calculations. 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. Additionally, if you pass newdata = my_tibble to an augment() method that does not accept a newdata argument, it will use the default value for the data argument.

Value

A tibble::tibble() with one row for each term in the regression. The tibble has columns:

term

The name of the regression term.

estimate

The estimated value of the regression term.

std.error

The standard error of the regression term.

statistic

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

p.value

The two-sided p-value associated with the observed statistic.

conf.low

The low end of a confidence interval for the regression term. Included only if conf.int = TRUE.

conf.high

The high end of a confidence interval for the regression term. Included only if conf.int = TRUE.

See also

tidy, stats::nls(), stats::summary.nls()

Other nls tidiers: augment.nls, glance.nls

Examples

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

tidy(n)
#> # A tibble: 2 x 5
#>   term  estimate std.error statistic  p.value
#>   <chr>    <dbl>     <dbl>     <dbl>    <dbl>
#> 1 k       49.7      3.79        13.1 5.96e-14
#> 2 e        0.746    0.0199      37.5 8.86e-27
augment(n)
#> # A tibble: 32 x 4
#>      mpg    wt .fitted .resid
#>    <dbl> <dbl>   <dbl>  <dbl>
#>  1  21    2.62    23.0 -2.01 
#>  2  21    2.88    21.4 -0.352
#>  3  22.8  2.32    25.1 -2.33 
#>  4  21.4  3.22    19.3  2.08 
#>  5  18.7  3.44    18.1  0.611
#>  6  18.1  3.46    18.0  0.117
#>  7  14.3  3.57    17.4 -3.11 
#>  8  24.4  3.19    19.5  4.93 
#>  9  22.8  3.15    19.7  3.10 
#> 10  19.2  3.44    18.1  1.11 
#> # … with 22 more rows
glance(n)
#> # A tibble: 1 x 8
#>   sigma isConv     finTol logLik   AIC   BIC deviance df.residual
#>   <dbl> <lgl>       <dbl>  <dbl> <dbl> <dbl>    <dbl>       <int>
#> 1  2.67 TRUE   0.00000204  -75.8  158.  162.     214.          30

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

newdata <- head(mtcars)
newdata$wt <- newdata$wt + 1
augment(n, newdata = newdata)
#> # A tibble: 6 x 13
#>   .rownames   mpg   cyl  disp    hp  drat    wt  qsec    vs    am  gear  carb
#>   <chr>     <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 1          21       6   160   110  3.9   3.62  16.5     0     1     4     4
#> 2 2          21       6   160   110  3.9   3.88  17.0     0     1     4     4
#> 3 3          22.8     4   108    93  3.85  3.32  18.6     1     1     4     1
#> 4 4          21.4     6   258   110  3.08  4.22  19.4     1     0     3     1
#> 5 5          18.7     8   360   175  3.15  4.44  17.0     0     0     3     2
#> 6 6          18.1     6   225   105  2.76  4.46  20.2     1     0     3     1
#> # … with 1 more variable: .fitted <dbl>