Anova tables for fitted trend surface objects

Compute analysis of variance tables for one or more fitted trend surface model objects; where anova.trls is called with multiple objects, it passes on the arguments to anovalist.trls.

Usage

# S3 method for class 'trls'
anova(object, ...)
anovalist.trls(object, ...)

Arguments

object

A fitted trend surface model object from surf.ls

...

Further objects of the same kind

Value

anova.trls and anovalist.trls return objects corresponding to their printed tabular output.

References

Venables, W. N. and Ripley, B. D. (2002) Modern Applied Statistics with S. Fourth edition. Springer.

See also

surf.ls

Examples

library(stats)
data(topo, package="MASS")
topo0 <- surf.ls(0, topo)
topo1 <- surf.ls(1, topo)
topo2 <- surf.ls(2, topo)
topo3 <- surf.ls(3, topo)
topo4 <- surf.ls(4, topo)
anova(topo0, topo1, topo2, topo3, topo4)
#> Analysis of Variance Table
#> 
#> Model 1: surf.ls(np = 0, x = topo)
#> Model 2: surf.ls(np = 1, x = topo)
#> Model 3: surf.ls(np = 2, x = topo)
#> Model 4: surf.ls(np = 3, x = topo)
#> Model 5: surf.ls(np = 4, x = topo)
#>   Res.Df Res.Sum Sq Df Sum Sq F value    Pr(>F)
#> 1     51     196030                            
#> 2     49      67186  2 128844 46.9843 4.040e-12
#> 3     46      39958  3  27228 10.4482 2.325e-05
#> 4     42      21577  4  18381  8.9447 2.558e-05
#> 5     37      14886  5   6691  3.3265     0.014
summary(topo4)
#> Analysis of Variance Table
#>  Model: surf.ls(np = 4, x = topo)
#>              Sum Sq Df    Mean Sq  F value     Pr(>F)
#> Regression 181144.0 14 12938.8567 32.16092 2.2204e-16
#> Deviation   14885.7 37   402.3162                    
#> Total      196029.7 51                               
#> Multiple R-Squared: 0.9241,	Adjusted R-squared: 0.8953 
#> AIC: (df = 15) 324.1594
#> Fitted:
#>    Min     1Q Median     3Q    Max 
#>  702.1  785.0  836.3  880.5  939.1 
#> Residuals:
#>     Min      1Q  Median      3Q     Max 
#> -34.077 -12.568  -2.085  14.056  50.161