Description Usage Arguments Details Value References See Also Examples
Best-subsets regression for ordinary linear models.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 | lmSelect(formula, ...)
## Default S3 method:
lmSelect(formula, data, subset, weights, na.action,
model = TRUE, x = FALSE, y = FALSE, contrasts = NULL, offset, ...)
## S3 method for class 'lmSubsets'
lmSelect(formula, ...)
lmSelect_fit(x, y, weights = NULL, offset = NULL,
include = NULL, exclude = NULL, penalty = "BIC", tolerance = 0,
pradius = NULL, nbest = 1, ..., .algo = "phbba")
lmSubsets_select(object, penalty = "BIC", ...)
|
object |
An object of class |
formula, data, subset, weights, na.action, model, contrasts,
offset |
Standard formula interface. |
x, y |
The model matrix and response. |
include, exclude |
Force regressors in or out. |
penalty |
Penalty per parameter (see |
tolerance |
Heuristic tolerance. |
pradius |
Preordering radius. |
nbest |
Number of best subsets. |
... |
Ignored. |
.algo |
Internal use. |
The generic lmSelect
computes best-variable-subsets regression
for ordinary linear models: The nbest
best subset models are
computed according to an information criterion of the AIC family.
See lmSubsets
for further information.
An object of class "lmSelect"
, i.e. a list with the following
components:
nobs |
Number of observations. |
nvar |
Number of variables (not including intercept, if any). |
weights |
Weights vector. |
offset |
Offset component. |
intercept |
|
include |
Included variables. |
exclude |
Excluded variables. |
nmin, nmax |
Minimum and maximum subset sizes. |
penalty |
Penalty per parameter. |
tolerance |
Heuristic tolerance. |
nbest |
Number of best subsets. |
df |
Degrees of freedom. |
rss |
Residual sum of squares. |
val |
Subset values. |
which |
Selected variables. |
Hofmann M, Gatu C, Kontoghiorghes EJ (2007). Efficient Algorithms for Computing the Best Subset Regression Models for Large-Scale Problems. Computational Statistics \& Data Analysis, 52, 16–29.
Gatu C, Kontoghiorghes EJ (2006). Branch-and-Bound Algorithms for Computing the Best Subset Regression Models. Journal of Computational and Graphical Statistics, 15, 139–156.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 | ## load data (with logs for relative potentials)
data("AirPollution", package = "lmSubsets")
#################
## basic usage ##
#################
## canonical example: fit best subsets
best.AirPoll <- lmSelect(mortality ~ ., data = AirPollution, nbest = 20)
## equivalent to:
## Not run:
all.AirPoll <- lmSubsets(mortality ~ ., data = AirPollution, nbest = 20)
best.AirPoll <- lmSelect(all.AirPoll)
## End(Not run)
## visualize RSS
plot(best.AirPoll)
## summarize
summary(best.AirPoll)
|
Call:
lmSelect(formula = mortality ~ ., data = AirPollution, nbest = 20,
penalty = "BIC")
Statistics:
SIZE BEST sigma R2 R2adj pval Cp AIC BIC
5 1 35.45910 0.6971012 0.6750722 1.0975e-13 6.683675 605.2575 617.8236
6 2 34.59656 0.7169005 0.6906875 1.1157e-13 4.978493 603.2015 617.8619
7 3 33.79705 0.7348369 0.7048185 1.1482e-13 3.621912 601.2743 618.0290
6 4 34.87009 0.7124062 0.6857771 1.6918e-13 5.819540 604.1465 618.8069
6 5 35.10688 0.7084870 0.6814951 2.4190e-13 6.552961 604.9587 619.6191
7 6 34.28204 0.7271721 0.6962859 2.3951e-13 5.056290 602.9841 619.7388
6 7 35.18135 0.7072491 0.6801425 2.7055e-13 6.784624 605.2129 619.8733
8 8 33.50824 0.7442674 0.7098418 2.4093e-13 3.857124 601.1015 619.9506
7 9 34.46458 0.7242590 0.6930431 3.1495e-13 5.601433 603.6213 620.3761
7 10 34.47918 0.7240254 0.6927829 3.2190e-13 5.645161 603.6721 620.4269
8 11 33.64706 0.7421441 0.7074327 2.9669e-13 4.254470 601.5977 620.4468
5 12 36.24421 0.6835396 0.6605243 3.5924e-13 9.221560 607.8855 620.4516
6 13 35.38715 0.7038141 0.6763895 3.6815e-13 7.427446 605.9128 620.5732
7 14 34.53343 0.7231562 0.6918154 3.4908e-13 5.807818 603.8608 620.6156
6 15 35.45422 0.7026903 0.6751616 4.0686e-13 7.637752 606.1401 620.8005
6 16 35.49316 0.7020369 0.6744477 4.3113e-13 7.760027 606.2718 620.9322
7 17 34.64911 0.7212983 0.6897472 4.1474e-13 6.155493 604.2621 621.0169
7 18 34.67023 0.7209585 0.6893689 4.2797e-13 6.219089 604.3352 621.0900
7 19 34.68636 0.7206987 0.6890797 4.3835e-13 6.267703 604.3911 621.1458
6 20 35.55912 0.7009283 0.6732365 4.7553e-13 7.967486 606.4946 621.1550
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.