Description Usage Arguments Details Value See Also Examples
Perform memory-efficient linear regression using the AS274 bounded memory QR factorization algorithm.
1 |
formula |
A symbolic description of the model to be fitted of class
|
weights |
A one-sided, single term |
... |
Ignored. |
The provided formula
must not contain any data-dependent terms to ensure
consistency across calls to fit()
. Factors are permitted, but the
levels of the factor must be the same across all data chunks. Empty factor
levels are accepted.
An oomlm
model is perpetually in an in-progress state. It is up
to the user to know when fitting is complete. Therefore, only basic
model characteristics are provided as values. Statistics are available on
demand via summary and extractor functions.
n |
The number of observations processed. |
df.residual |
The residual degrees of freedom. |
formula |
The |
terms |
The |
weights |
A one-sided, single term |
call |
The matched call. |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 | # `oomlm` are defined with a call to `oomlm()` and fit to data
# with a call to `fit()`
x <- oomlm(mpg ~ cyl + disp)
x <- fit(x, mtcars)
print(x)
# `oomlm` models can be fit with more data via subsequent calls
# to the `fit()` function
chunks <- purrr::pmap(mtcars, list)
y <- oomlm(mpg ~ cyl + disp)
for(chunk in chunks) {
y <- fit(y, chunk)
}
tidy(x)
# `oomdata_tbl()` facilitates iterating through data rows in chunks
chunks <- oomdata_tbl(mtcars, chunk_size = 1)
# `fit()` will automatically fit over all chunks in an `oomdata`
# object
z <- oomlm(mpg ~ cyl + disp)
z <- fit(z, data = chunks)
summary(z)
|
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