plot.tsglm {TensorTest2D} | R Documentation |
Plot Effective Image Pixels for A "tsglm" Object
Description
plot method for self-defined class "tsglm".
Usage
## S3 method for class 'tsglm'
plot(
x,
method = p.adjust.methods,
alpha = NULL,
type = c("coef", "tval"),
background = NULL,
showlabels = TRUE,
plot.legend = TRUE,
...
)
Arguments
x |
an object of class "tsglm". |
method |
p-value correction method. See |
alpha |
double. The value of significance level.
Its default value is |
type |
string. The type of values shown on the image pixels when |
background |
an image data that used as the background of the effectiveness markers.
If |
showlabels |
boolean. For |
plot.legend |
boolean. Set |
... |
further arguments passed to the |
Author(s)
Ping-Yang Chen
See Also
Examples
# Simulation data
n <- 500 # number of observations
n_P <- 3; n_G <- 16 # 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))
# Binomial Responses
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)
# Binomial Model
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) )
# Plot the effect size of the pixels
plot(result_B, method = "fdr", alpha = 0.05, type = "coef")
# Plot the t-statistics of the coefficients of the pixels
plot(result_B, method = "fdr", alpha = 0.05, type = "tval")
# Plot the effective pixels with data image as the background
x0 <- DATA_B$X[,,which(DATA_B$y == 0)]
x1 <- DATA_B$X[,,which(DATA_B$y == 1)]
m0 <- m1 <- matrix(0, dim(DATA_B$X)[1], dim(DATA_B$X)[2])
for (i in 1:dim(x0)[3]) m0 <- m0 + x0[,,i]/dim(x0)[3]
for (i in 1:dim(x1)[3]) m1 <- m1 + x1[,,i]/dim(x1)[3]
par(mfrow = c(1, 2), mar = c(2, 2, 2, 2))
plot(result_B, method = "fdr", alpha = 0.05,
background = m0, col = gray(seq(0, 1, 0.05)))
title("Category 0")
plot(result_B, method = "fdr", alpha = 0.05,
background = m1, col = gray(seq(0, 1, 0.05)))
title("Category 1")