nls_tidiers {broom} | R Documentation |
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.
## S3 method for class 'nls' tidy(x, conf.int = FALSE, conf.level = 0.95, quick = FALSE, ...) ## S3 method for class 'nls' augment(x, data = NULL, newdata = NULL, ...) ## S3 method for class 'nls' glance(x, ...)
x |
An object of class "nls" |
conf.int |
whether to include a confidence interval |
conf.level |
confidence level of the interval, used only if
|
quick |
whether to compute a smaller and faster version, containing
only the |
... |
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 |
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.
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 |
nls
and summary.nls
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)