lin.RdThis smooth constructor implements simple linear effects. The columns of the design matrix
are automatically scaled. The main advantage of this constructor is speed when used in
the BAMLSS boosting algorithm boost. Optionally, a ridge penalty can be
added, please see the example.
## Linear smooth constructor.
lin(...)
## For mgcv.
# S3 method for linear.smooth.spec
smooth.construct(object, data, knots, ...)For function lin() a formula of the type ~x1+x2+x3 that specifies
the covariates that should be modeled.
See smooth.construct.
Function lin(), similar to function s a simple smooth specification
object.
if (FALSE) ## Simulate data.
set.seed(123)
d <- GAMart()
## Estimate model.
f <- num ~ lin(~x1+x2+x3+fac,ridge=TRUE)
b <- bamlss(f, data = d)
#> AICc 328.2219 logPost -168.649 logLik -157.258 edf 6.7460 eps 1.5021 iteration 1
#> AICc 306.1394 logPost -159.383 logLik -146.058 edf 6.9002 eps 0.3385 iteration 2
#> AICc 295.5924 logPost -155.273 logLik -140.746 edf 6.9379 eps 0.2123 iteration 3
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#> AICc 285.5040 logPost -152.305 logLik -135.670 edf 6.9689 eps 0.0007 iteration 18
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#> AICc 285.5008 logPost -152.307 logLik -135.668 edf 6.9689 eps 0.0002 iteration 21
#> AICc 285.5004 logPost -152.308 logLik -135.668 edf 6.9689 eps 0.0001 iteration 22
#> AICc 285.5001 logPost -152.308 logLik -135.668 edf 6.9689 eps 0.0001 iteration 23
#> AICc 285.5001 logPost -152.308 logLik -135.668 edf 6.9689 eps 0.0001 iteration 23
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#> |********************| 100% 0.00sec 1.62sec
#> .. not computing effect plot for term lin(~x1+x2+x3+fac), use predict() instead!
summary(b)
#>
#> Call:
#> bamlss(formula = f, data = d)
#> ---
#> Family: gaussian
#> Link function: mu = identity, sigma = log
#> *---
#> Formula mu:
#> ---
#> num ~ lin(~x1 + x2 + x3 + fac, ridge = TRUE)
#> -
#> Parametric coefficients:
#> Mean 2.5% 50% 97.5% parameters
#> (Intercept) -0.1745 -0.2057 -0.1748 -0.1461 -0.185
#> -
#> Acceptance probability:
#> Mean 2.5% 50% 97.5%
#> alpha 1 1 1 1
#> -
#> Smooth terms:
#> Mean 2.5% 50% 97.5% parameters
#> lin(~x1+x2+x3+fac).tau21 NaN NA NA NA 0.058
#> lin(~x1+x2+x3+fac).alpha 1 1 1 1 NA
#> lin(~x1+x2+x3+fac).edf NaN NA NA NA 4.969
#> ---
#> Formula sigma:
#> ---
#> sigma ~ 1
#> -
#> Parametric coefficients:
#> Mean 2.5% 50% 97.5% parameters
#> (Intercept) -1.127 -1.186 -1.128 -1.065 -1.148
#> -
#> Acceptance probability:
#> Mean 2.5% 50% 97.5%
#> alpha 0.9815 0.8596 1.0000 1
#> ---
#> Sampler summary:
#> -
#> DIC = 293.9441 logLik = -145.9162 pd = 2.1116
#> runtime = 1.629
#> ---
#> Optimizer summary:
#> -
#> AICc = 285.5001 edf = 6.9689 logLik = -135.6683
#> logPost = -152.3082 nobs = 500 runtime = 0.133
#>