Description Usage Arguments Details Value See Also Examples
Apply a fitting function, with multiple models and multiple starting estimates, to one dataset.
1 |
models |
[list] A list of models to fit |
data |
[numeric data.frame/list] A list of vectors to fit |
fun |
[function] The function to use for fitting. Defaults to |
... |
Additional arguments passed to |
Finding the right model and the right starting estimates for a model is often a time consuming process, very inconvenient to do manually. This function automates it as much as possible. It takes a list of models and starting estimates, and fits them to data not stopping whenever an error occurs or a warning is issued. Error and warning messages are saved and can be inspected in the output, they just do not halt the process. multiFit
has an extension in the form of fitTable
which applies multiple models to multiple datasets.
[list.multiFit] A list of results returned by fun
or, if it ended with an error, NULL
.
fitTable
, summary.list.multiFit
1 2 3 4 5 6 7 8 9 10 11 |
To start the graphic interface, run "soundcorrsGUI()"
(requires packages "shiny" and "shinyjqui").
$`model A`
Nonlinear regression model
model: Y ~ X^a
data: dat
a
1.953
residual sum-of-squares: 291.1
Number of iterations to convergence: 5
Achieved convergence tolerance: 2.887e-06
$`model B`
Nonlinear regression model
model: Y ~ a * (X + b)
data: dat
a b
9.595 -1.845
residual sum-of-squares: 998.8
Number of iterations to convergence: 5
Achieved convergence tolerance: 1.206e-08
attr(,"class")
[1] "list.multiFit"
attr(,"depth")
[1] 1
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