Compare Fitted Objects

The columns in object1 and object2 are put together in matrices which allow direct comparison of the individual elements for each object. Missing columns in either object are replaced by NAs.

compareFits(object1, object2, which)

Arguments

object1,object2

data frames, or matrices, with the same row names, but possibly different column names. These will usually correspond to coefficients from fitted objects with a grouping structure (e.g. lme and lmList objects).

which

an optional integer or character vector indicating which columns in object1 and object2 are to be used in the returned object. Defaults to all columns.

Value

a three-dimensional array, with the third dimension given by the number of unique column names in either object1 or object2. To each column name there corresponds a matrix with as many rows as the rows in object1 and two columns, corresponding to object1 and object2. The returned object inherits from class compareFits.

Author

José Pinheiro and Douglas Bates bates@stat.wisc.edu

See also

plot.compareFits, pairs.compareFits, comparePred, coef, random.effects

Examples

fm1 <- lmList(Orthodont)
fm2 <- lme(fm1)
(cF12 <- compareFits(coef(fm1), coef(fm2)))
#> , , (Intercept)
#> 
#>     coef(fm1) coef(fm2)
#> M16     16.95  16.57335
#> M05     13.65  15.58444
#> M02     14.85  16.03361
#> M11     20.05  17.65160
#> M07     14.95  16.15314
#> M08     19.75  17.62141
#> M03     16.00  16.58721
#> M12     13.25  15.76312
#> M13      2.80  12.63156
#> M14     19.10  17.66546
#> M09     14.40  16.31671
#> M15     13.50  16.22614
#> M06     18.95  17.97875
#> M04     24.70  19.76157
#> M01     17.30  17.81269
#> M10     21.25  19.41435
#> F10     13.55  14.47973
#> F09     18.10  16.47016
#> F06     17.00  16.14053
#> F01     17.25  16.27515
#> F05     19.60  17.27792
#> F07     16.95  16.57335
#> F02     14.20  15.74926
#> F08     21.45  18.01143
#> F03     14.40  15.98832
#> F04     19.65  17.83028
#> F11     18.95  17.97875
#> 
#> , , age
#> 
#>     coef(fm1) coef(fm2)
#> M16     0.550 0.5913314
#> M05     0.850 0.6857856
#> M02     0.775 0.6746931
#> M11     0.325 0.5413591
#> M07     0.800 0.6950853
#> M08     0.375 0.5654488
#> M03     0.750 0.6960376
#> M12     1.000 0.7747494
#> M13     1.950 1.0738543
#> M14     0.525 0.6460653
#> M09     0.975 0.7960939
#> M15     1.125 0.8683630
#> M06     0.675 0.7433764
#> M04     0.175 0.5943001
#> M01     0.950 0.8758698
#> M10     0.750 0.8713317
#> F10     0.450 0.4095945
#> F09     0.275 0.4421434
#> F06     0.375 0.4736281
#> F01     0.375 0.4819754
#> F05     0.275 0.4922274
#> F07     0.550 0.5913314
#> F02     0.800 0.6700432
#> F08     0.175 0.4857847
#> F03     0.850 0.7108276
#> F04     0.475 0.6303229
#> F11     0.675 0.7433764
#>