predict.tsglm {TensorTest2D}

Predict by Second-order Tensor Generalized Regression

Description

predict method for self-defined class "tsglm".

Usage

## S3 method for class 'tsglm'
predict(object, newx, neww = NULL, type = c("link", "response"), ...)

Arguments

object

Fitted "tsglm" object.

newx

a 3-dimensional array for x for which the prediction are interested.

neww

a numerical matrix for W for which the prediction are interested.

type

the type of prediction required. The default is type = "link" that returns prediction values on the scale of the linear predictors (eta). Alternatively, set type = "response" for returning predictions on the scale of the response variable.

...

further arguments passed to or from other methods.

Value

There are two types of the output of predict.tsglm function. By setting type = "link", it returns the values of the linear predictors; and by setting type = "response", it returns the the expected values of response variable. For example, for a binomial model, the predictions are log-odds (probabilities on logit scale) if type = "link", and type = "response" gives the predicted probabilities of Y=1.

Author(s)

Ping-Yang Chen

See Also

tensorReg2D, summary.tsglm

Examples

# Predefined function: sum of hadamard product in each array
`%hp%` <- function(X, B) sapply(1:dim(X)[3], function(i) sum(X[,,i]*B))

# Simulation data
n <- 500 # number of observations
n_P <- 3; n_G <- 64 # dimension of 3-D tensor variables.
n_d <- 1 # number of numerical variable, if n_d == 1,  numerical variable equals to intercept.
beta_True <- rep(1, n_d)
B_True <- c(1,1,1)%*%t(rnorm(n_G)) + c(0, .5, .5)%*%t(rnorm(n_G))
B_True <- B_True / 10
W <- matrix(rnorm(n*n_d), n, n_d); W[,1] <- 1
X <- array(rnorm(n*n_P*n_G), dim=c(n_P, n_G, n))
## Regression
y_R<- as.vector(W%*%beta_True + X%hp%B_True + rnorm(n))
DATA_R <- list(y = y_R, X = X, W = W)
## Binomial
p_B <- exp(W%*%beta_True + X%hp%B_True); p_B <- p_B/(1+p_B)
y_B <- rbinom(n, 1, p_B)
DATA_B <- list(y = y_B, W = W, X = X)
## Poisson
p_P <- exp(W%*%beta_True + X%hp%B_True)
y_P <- rpois(n, p_P)
y_P[which(y_P > 170)] <- 170 # If y_P > 170, factorial(y_P) == inf.
DATA_P <- list(y = y_P, W = W, X = X)

# Execution
## Regression
result_R <- tensorReg2D(y = DATA_R$y, X = DATA_R$X, W=NULL, n_R = 1, family = "gaussian",
opt = 1, max_ite = 100, tol = 10^(-7) )
## Prediction
head(predict(result_R, DATA_R$X))

## Binomial
result_B <- tensorReg2D(y = DATA_B$y, X = DATA_B$X, W=NULL, n_R = 1, family = "binomial",
opt = 1, max_ite = 100, tol = 10^(-7) )
## Prediction
head(predict(result_B, DATA_B$X))

## Poisson
result_P <- tensorReg2D(y = DATA_P$y, X = DATA_P$X, W=NULL, n_R = 1, family = "poisson",
opt = 1, max_ite = 100, tol = 10^(-7) )
## Prediction
head(predict(result_P, DATA_P$X))