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Plot Covariates of X Against Response With Group Assignments

Source: R/plot2.R
plot2.Rd

This function creates a 3-D plot for finite mixture regression models of class FMRM. It plots the specified covariates of x against a response (y or y_hat), with the group assignments highlighted in colour.

Usage

plot2(mod, x, y, covariate_one, covariate_two, ...)

Arguments

mod

An object of class FMRM, the result of calling FMRM() or MM_Grid().

x

Predictor/design matrix. A numeric matrix of size n x p, where the number of rows is equal to the number of observations n, and the number of columns is equal to the number of covariates p.

y

Response vector. Either a numeric vector, or something coercible to one. Can either be y or y_hat, the expected predicted responses. The latter is accessed through mod$parameters$y_hat.

covariate_one

A numeric value specifying the first covariate of x to be plotted.

covariate_two

A numeric value specifying the second covariate of x to be plotted.

...

Additional arguments for plotting (currently unused).

Value

A 3-D plotly plot of the specified covariates of x against a response, with the group assignments highlighted in colour.

Examples


set.seed(2025)

# ----Simulate data----
n <- 500   # total samples
p <- 6     # number of covariates
G <- 3     # number of mixture components
rho = 0.2  # correlation

# ----True parameters for 3 clusters----
betas <- matrix(c(
 1,  2, -1,  0.5, 0, 0, 0,  # component 1
 5, -2,  1,  0, 0, 0, 0,  # component 2
 -3, 0,  2, 0, 0, 0, 0     # component 3
), nrow = G, byrow = TRUE) / 2
pis <- c(0.4, 0.4, 0.2)
sigmas <- c(0.5, 0.4, 0.3)

# ----Generate correlation matrix----
cor_mat <- outer(1:p, 1:p, function(i, j) rho^abs(i - j))
Sigma <- cor_mat

# ----Simulate design matrix X (n × p)----
X <- mvtnorm::rmvnorm(n, mean = rep(0, p), sigma = Sigma)

# ----Generate responsibilities----
z <- rmultinom(n, size = 1, prob = pis)
groups <- apply(z, 2, which.max)

# ----b0 + b1x1 + b2x2 + ... + bkxp----
mu_vec <- rowSums(cbind(1, X) * betas[groups, ])

# ----Simulate response y----
y <- rnorm(n, mean = mu_vec, sd = sigmas[groups])

# ----Fit model----
mod <- FMRM(x = X,
            y = y,
            G = 3,
            family = gaussian(),
            parallel = TRUE,
            random = TRUE,
            verbose = FALSE)

# ----Call plot2----
plot <- plot2(mod, X, y, 1, 2)

# ----Display plot----
plot