Class

The Gaussian Processes object

Objects from the Class

Objects can be created by calls of the form new("lssvm", ...). or by calling the lssvm function

Slots

kernelf:: Object of class "kfunction" contains the kernel function used
kpar:: Object of class "list" contains the kernel parameter used
param:: Object of class "list" contains the regularization parameter used.
kcall:: Object of class "call" contains the used function call
type:: Object of class "character" contains type of problem
coef:: Object of class "ANY" contains the model parameter
terms:: Object of class "ANY" contains the terms representation of the symbolic model used (when using a formula)
xmatrix:: Object of class "matrix" containing the data matrix used
ymatrix:: Object of class "output" containing the response matrix
fitted:: Object of class "output" containing the fitted values
b:: Object of class "numeric" containing the offset
lev:: Object of class "vector" containing the levels of the response (in case of classification)
scaling:: Object of class "ANY" containing the scaling information performed on the data
nclass:: Object of class "numeric" containing the number of classes (in case of classification)
alpha:: Object of class "listI" containing the computes alpha values
alphaindex: Object of class "list" containing the indexes for the alphas in various classes (in multi-class problems).
error:: Object of class "numeric" containing the training error
cross:: Object of class "numeric" containing the cross validation error
n.action:: Object of class "ANY" containing the action performed in NA
nSV:: Object of class "numeric" containing the number of model parameters

Methods

alpha: signature(object = "lssvm"): returns the alpha vector
cross: signature(object = "lssvm"): returns the cross validation error
error: signature(object = "lssvm"): returns the training error
fitted: signature(object = "vm"): returns the fitted values
kcall: signature(object = "lssvm"): returns the call performed
kernelf: signature(object = "lssvm"): returns the kernel function used
kpar: signature(object = "lssvm"): returns the kernel parameter used
param: signature(object = "lssvm"): returns the regularization parameter used
lev: signature(object = "lssvm"): returns the response levels (in classification)
type: signature(object = "lssvm"): returns the type of problem
scaling: signature(object = "ksvm"): returns the scaling values
xmatrix: signature(object = "lssvm"): returns the data matrix used
ymatrix: signature(object = "lssvm"): returns the response matrix used

Author

Alexandros Karatzoglou
alexandros.karatzoglou@ci.tuwien.ac.at

Examples


# train model
data(iris)
test <- lssvm(Species~.,data=iris,var=2)
#> Using automatic sigma estimation (sigest) for RBF or laplace kernel 
test
#> Least Squares Support Vector Machine object of class "lssvm" 
#> 
#> problem type : classification 
#>  parameter : tau = 0.01 
#> 
#> Gaussian Radial Basis kernel function. 
#>  Hyperparameter : sigma =  1.23831652091826 
#> 
#> Number of data points used for training : 49 
#> Training error : 0.872483 
alpha(test)
#>               [,1]          [,2]        [,3]
#>  [1,] -249.4674408   537.6808843 -288.213444
#>  [2,]  -53.6237095    12.0722884   41.551421
#>  [3,]   87.5298574   -40.8226261  -46.707231
#>  [4,]  866.4171632  -489.4987431 -376.918420
#>  [5,] -456.5714804   656.2924607 -199.720980
#>  [6,]  431.6853600   123.6639801 -555.349340
#>  [7,]   36.2795896   149.0955959 -185.375186
#>  [8,] -126.3246709  -345.0103908  471.335062
#>  [9,]  595.7168577 -1063.0516054  467.334748
#> [10,] -324.1581825   540.7280357 -216.569853
#> [11,]  -21.9289818   -49.6864666   71.615448
#> [12,] -553.4952812  1328.8536176 -775.358336
#> [13,] -345.6095546   -54.7433562  400.352911
#> [14,] -121.6847131   624.3491461 -502.664433
#> [15,]  158.6313903  -225.3348889   66.703499
#> [16,]  221.5774389  -176.4932470  -45.084192
#> [17,]    3.6668477    -2.3395646   -1.327283
#> [18,]  -17.4082623    16.4041754    1.004087
#> [19,]   87.7259559  -127.7395515   40.013596
#> [20,]  223.2992966   -17.0291710 -206.270126
#> [21,]  287.2470833  -432.4277626  145.180679
#> [22,]   67.5836525  -359.4043651  291.820713
#> [23,]  -21.8216719  -226.1714428  247.993115
#> [24,]  141.5005198  -117.4922291  -24.008291
#> [25,] -300.4892006   336.6331954  -36.143995
#> [26,] -280.4537624   316.2110201  -35.757258
#> [27,]  -24.6020002    91.5704874  -66.968487
#> [28,] -198.9784672    -0.7377473  199.716215
#> [29,]   60.2855672  -206.9306462  146.645079
#> [30,]  -50.4645463    86.3254212  -35.860875
#> [31,]  -54.3274579    34.7421005   19.585357
#> [32,]  524.8143695   -64.4445128 -460.369857
#> [33,] -472.6604713   421.8127551   50.847716
#> [34,]    0.1231057    -1.7834319    1.660326
#> [35,]  336.2928622  -596.7505475  260.457685
#> [36,] -852.6342445   -22.9601035  875.594348
#> [37,] 1104.6687231  -969.5082315 -135.160492
#> [38,]  179.1141601  -564.3234923  385.209332
#> [39,]  100.8452465   -75.9141190  -24.931128
#> [40,]   19.3359474   -33.3525841   14.016637
#> [41,]  177.7290460  -232.4282923   54.699246
#> [42,]   -4.2449106   -10.1658074   14.410718
#> [43,]  -37.8896396    79.8721466  -41.982507
#> [44,]  -17.0978742    41.5041592  -24.406285
#> [45,]   13.6187448   -16.2811524    2.662408
#> [46,]  -39.1561852   109.8293031  -70.673118
#> [47,]   66.7208559   -31.2009358  -35.519920
#> [48,]   48.8589706   -79.4821070   30.623136
#> [49,]  -28.7182776   -10.5575107   39.275788
error(test)
#> [1] 0.8724832
lev(test)
#> [1] "setosa"     "versicolor" "virginica"

Class "lssvm"

Objects from the Class

Slots

Methods

Author

See also

Examples