General-to-Specific (GETS) Modelling 'lm' objects
gets.lm.RdGeneral-to-Specific (GETS) Modelling of objects of class lm.
Usage
# S3 method for class 'lm'
gets(x, keep = NULL, include.1cut = TRUE, print.searchinfo = TRUE, ...)Arguments
- x
an object of class 'lm', see
lm- keep
NULLor a vector of integers that determines which regressors to be excluded from removal in the specification search- include.1cut
logical. IfTRUE(default), then the 1-cut model is added to the list of terminal models. IfFALSE, then the 1-cut is not added, unless it is a terminal model in one of the paths- print.searchinfo
logical. IfTRUE(default), then selected info is printed during search- ...
further arguments passed on to
getsFun
Details
Internally, gets.lm invokes getsFun for the GETS-modelling, which is also invoked by getsm. See their help pages for more information.
Value
A list of class lm. Note that the 'top' of the list contains information (paths and terminal models) from the GETS modelling, see paths and terminals
Author
Genaro Sucarrat, http://www.sucarrat.net/
Examples
##generate some data:
set.seed(123) #for reproducibility
y <- rnorm(30) #generate Y
x <- matrix(rnorm(30*10), 30, 10) #matrix of Xs
colnames(x) <- paste0("var", 1:NCOL(x))
##estimate model:
mymod <- lm(y ~ x)
##do gets modelling:
gets(mymod)
#>
#> Start model (GUM):
#>
#> reg.no keep coef std.error t-stat p-value
#> (Intercept) 1 0 0.27005590 0.21026283 1.2844 0.21445
#> xvar1 2 0 -0.61303927 0.28160750 -2.1769 0.04230 *
#> xvar2 3 0 0.13398941 0.22947573 0.5839 0.56616
#> xvar3 4 0 0.30619954 0.21734292 1.4088 0.17504
#> xvar4 5 0 -0.00018761 0.19034597 -0.0010 0.99922
#> xvar5 6 0 0.16595175 0.20992401 0.7905 0.43897
#> xvar6 7 0 -0.16893171 0.21399989 -0.7894 0.43962
#> xvar7 8 0 0.51949160 0.22562893 2.3024 0.03279 *
#> xvar8 9 0 0.32756857 0.20626559 1.5881 0.12877
#> xvar9 10 0 -0.51817835 0.24477483 -2.1170 0.04768 *
#> xvar10 11 0 -0.01454824 0.19954250 -0.0729 0.94264
#> ---
#> Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
#>
#> 8 path(s) to search
#> Searching:
#> 1
#> 2
#> 3
#> 4
#> 5
#> 6
#> 7
#> 8
#>
#> Path 1: 1 6 7 8 9 10 11 4 5 2 3
#> Path 2: 3 6 7 8 9 10 11 4 5 1 2
#> Path 3: 4 6 7 8 9 10 11 3 5 1 2
#> Path 4: 5 6 7 8 9 10 11 3 4 1 2
#> Path 5: 6 5 7 8 9 10 11 3 4 1 2
#> Path 6: 7 5 6 8 9 10 11 3 4 1 2
#> Path 7: 9 5 6 7 8 10 11 3 4 1 2
#> Path 8: 11 5 6 7 8 9 10 3 4 1 2
#>
#> Terminal models:
#>
#> info(sc) logl n k
#> spec 1: 2.768546 -41.52819 30 0
#>
#> Retained regressors (final model):
#>
#> none
#>
#> Call:
#> lm(formula = y ~ NULL - 1)
#>
#> No coefficients
#>
##ensure intercept is not removed:
gets(mymod, keep=1)
#>
#> Start model (GUM):
#>
#> reg.no keep coef std.error t-stat p-value
#> (Intercept) 1 1 0.27005590 0.21026283 1.2844 0.21445
#> xvar1 2 0 -0.61303927 0.28160750 -2.1769 0.04230 *
#> xvar2 3 0 0.13398941 0.22947573 0.5839 0.56616
#> xvar3 4 0 0.30619954 0.21734292 1.4088 0.17504
#> xvar4 5 0 -0.00018761 0.19034597 -0.0010 0.99922
#> xvar5 6 0 0.16595175 0.20992401 0.7905 0.43897
#> xvar6 7 0 -0.16893171 0.21399989 -0.7894 0.43962
#> xvar7 8 0 0.51949160 0.22562893 2.3024 0.03279 *
#> xvar8 9 0 0.32756857 0.20626559 1.5881 0.12877
#> xvar9 10 0 -0.51817835 0.24477483 -2.1170 0.04768 *
#> xvar10 11 0 -0.01454824 0.19954250 -0.0729 0.94264
#> ---
#> Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
#>
#> 7 path(s) to search
#> Searching:
#> 1
#> 2
#> 3
#> 4
#> 5
#> 6
#> 7
#>
#> Path 1: 3 7 8 9 10 11 5 6 2 4
#> Path 2: 4 7 8 9 10 11 5 6 2 3
#> Path 3: 5 7 8 9 10 11 4 6 2 3
#> Path 4: 6 7 8 9 10 11 4 5 2 3
#> Path 5: 7 6 8 9 10 11 4 5 2 3
#> Path 6: 9 6 7 8 10 11 4 5 2 3
#> Path 7: 11 6 7 8 9 10 4 5 2 3
#>
#> Terminal models:
#>
#> info(sc) logl n k
#> spec 1: 2.365507 -33.78201 30 1
#>
#> Retained regressors (final model):
#>
#> (Intercept)
#>
#> Call:
#> lm(formula = y ~ NULL)
#>
#> Coefficients:
#> (Intercept)
#> -0.0471
#>