Hello, I would like to find create a model and run linear regression with 3 independent variables. However, one of them should be weighted by another variable from my data. SPSS does WLS Weight, however it runs it for 2 other IV as well, which slightly ruins my model since they are usualy averaged normaly.
I will appreciate any help!
Thank you.
Use step function in R.
step(object, scope, scale = 0, direction = c("both", "backward", "forward"), trace = 1, keep = NULL, steps = 1000, k = 2, …).
Arguments
object
an object representing a model of an appropriate class (mainly "lm" and "glm"). This is used as the initial model in the stepwise search.
scope
defines the range of models examined in the stepwise search. This should be either a single formula, or a list containing components upper and lower, both formulae. See the details for how to specify the formulae and how they are used.
scale
used in the definition of the AIC statistic for selecting the models, currently only for lm, aov and glm models. The default value, 0, indicates the scale should be estimated: see extractAIC.
direction
the mode of stepwise search, can be one of "both", "backward", or "forward", with a default of "both". If the scope argument is missing the default for direction is "backward". Values can be abbreviated.
trace
if positive, information is printed during the running of step. Larger values may give more detailed information.
keep
a filter function whose input is a fitted model object and the associated AIC statistic, and whose output is arbitrary. Typically keep will select a subset of the components of the object and return them. The default is not to keep anything.
steps
the maximum number of steps to be considered. The default is 1000 (essentially as many as required). It is typically used to stop the process early.
Hi Stanislaw,
I suggest you to use Stepwise, Backward or Forward methods for finding the best model.
No No No NO PLEASE do not use STEPWISE. For reasons to not use and what you might use in place see the attached and the references:
https://www4.stat.ncsu.edu/~boos/var.select/lasso.adaptive.html
also see my comment here(especially the Austin and Tu Reference)
Preprint Dropping forward-backward algorithms for feature selection
A short session with google will give you many more good ideas, many of which are available on researchgate as are the authors..
Best wishes, David Booth
- Badges
- Science topic
- Similar topics
- Mathematical Sciences
- Statistics
- Statistical Software
- SPSS