Description Usage Arguments Details Value Author(s) Examples
Predict the value(s) for a vector or a design matrix of covariates (i.e. features)
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Model_kernel_Ridge_MM |
a Kernel_Ridge_MM object |
Matrix_covariates_target |
numeric matrix; design matrix of covariates for target data |
X_target |
numeric matrix; design matrix of predictors with fixed effects for target data (default is a vector of ones) |
Z_target |
numeric matrix; design matrix of predictors with random effects for target data (default is identity matrix) |
The matrix Matrix_covariates_target is mandatory to build the kernel matrix (with Matrix_covariates_train from Model_kernel_Ridge_MM) for prediction.
f_hat |
Predicted value for target data, i.e. f_hat = X_target*Beta_hat + Z_target*U_target where U_target=K_target_train*alpha_train and alpha_train is the BLUP of alpha for the model, i.e. alpha_train=Cov(alpha,Y_train)*Var(Y_train)^-1*(Y_train - E[Y_train]) |
Laval Jacquin <jacquin.julien@gmail.com>
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 | ## Not run:
library(KRMM)
### SIMULATE DATA
set.seed(123)
p=200
N=100
beta=rnorm(p, mean=0, sd=1.0)
X=matrix(runif(p*N, min=0, max=1), ncol=p, byrow=TRUE) #X: covariates (i.e. predictors)
f=X%*%beta #f: data generating process (i.e. DGP)
E=rnorm(N, mean=0, sd=0.5)
Y=f+E #Y: observed response data
hist(f)
hist(beta)
Nb_train=floor((2/3)*N)
###======================================================================###
### CREATE TRAINING AND TARGET SETS FOR RESPONSE AND PREDICTOR VARIABLES ###
###======================================================================###
Index_train=sample(1:N, size=Nb_train, replace=FALSE)
### Covariates (i.e. predictors) for training and target sets
Predictors_train=X[Index_train, ]
Response_train=Y[Index_train]
Predictors_target=X[-Index_train, ]
True_value_target=f[-Index_train] #True value (generated by DGP) we want to predict
###=================================================================================###
### PREDICTION WITH KERNEL RIDGE REGRESSION SOLVED WITHIN THE MIXED MODEL FRAMEWORK ###
###=================================================================================###
Gaussian_KRR_model_train = Kernel_Ridge_MM( Y_train=Response_train,
Matrix_covariates_train=Predictors_train, method="RKHS", rate_decay_kernel=5.0)
### Predict new entries for target set and measure prediction error
f_hat_target_Gaussian_KRR = Predict_kernel_Ridge_MM( Gaussian_KRR_model_train,
Matrix_covariates_target=Predictors_target )
plot(f_hat_target_Gaussian_KRR, True_value_target)
## End(Not run)
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