Summarising Smooth Information Criterion (SIC) Fits

summary method class “smoothic”

Usage

# S3 method for smoothic
summary(object, ...)

Arguments

object

an object of class “smoothic” which is the result of a call to smoothic.

...

further arguments passed to or from other methods.

Value

A list containing the following components:

• model - the matched model from the smoothic object.

• coefmat - a typical coefficient matrix whose columns are the estimated regression coefficients, estimated standard errors (SEE) and p-values.

• plike - value of the penalized likelihood function.

Author

Meadhbh O'Neill

Examples

# Sniffer Data --------------------
# MPR Model ----
results <- smoothic(
  formula = y ~ .,
  data = sniffer,
  family = "normal",
  model = "mpr"
)
summary(results)
#> Call:
#> smoothic(formula = y ~ ., data = sniffer, family = "normal", 
#>     model = "mpr")
#> Family:
#> [1] "normal"
#> Model:
#> [1] "mpr"
#> 
#> Coefficients:
#> 
#> Location:
#>                   Estimate        SE       Z    Pvalue    
#> intercept_0_beta  0.742017  0.921733  0.8050  0.176466    
#> tanktemp_1_beta  -0.089265  0.040390 -2.2100  0.001228 ** 
#> gastemp_2_beta    0.226331  0.028111  8.0514 < 2.2e-16 ***
#> tankpres_3_beta          0         0       0         0    
#> gaspres_4_beta    5.199452  0.836829  6.2133 < 2.2e-16 ***
#> 
#> Scale:
#>                    Estimate        SE       Z    Pvalue    
#> intercept_0_alpha -0.647524  0.724492 -0.8938    0.1427    
#> tanktemp_1_alpha          0         0       0         0    
#> gastemp_2_alpha    0.056681  0.011276  5.0268 8.045e-13 ***
#> tankpres_3_alpha          0         0       0         0    
#> gaspres_4_alpha           0         0       0         0    
#> 
#> ---
#> Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
#> 
#> Penalized Likelihood:
#> [1] -310.6329
#> IC Value:
#> [1] 621.2658