# tests/testthat/test_multiclass.R In fuzzyforest: Fuzzy Forests

```library(fuzzyforest)
library(mvtnorm)
context("Multi-class Logistic Regression Simulation")

test_that("Multi-Class Simulation", {
skip_on_cran()
set.seed(5)
multi_class_lr <- function(n, mod1_size=10, mod2_size=10, rho=.8, beta=NULL){
#Say there are 5 significant features.
#We will assume a multinomial logistic regression.
J <- 5 #J=number of outcomes
#signalp refers to the dimensionality of module which
#the significant covariates are a part of.
signalp <- mod1_size
if(is.null(beta)){
beta <- diag(rep(5, 4))
beta <- rbind(beta, matrix(0, signalp - 4, 4))
}
sigma <- matrix(rho, signalp, signalp)
diag(sigma) <- 1
signalX <- rmvnorm(n, mean=rep(0, signalp), sigma=sigma)

y <- rep(NA, n)
pimat <- matrix(NA, n, J)
for(i in 1:n){
lps <- signalX[i, ]%*%beta
den <- 1 + sum(exp(lps))
#first category is the reference category
#pi1 = 1/(1 + sum(exp(x_{j}'b_{j}))
pi <- c(1, exp(lps))/den
pimat[i, ] <- pi
outcm <- rmultinom(1, size=1, prob=pi)
y[i] <- which(outcm == 1)
}

noisep <- mod2_size
sigma <- matrix(rho, noisep, noisep)
diag(sigma) <- 1
noise <- rmvnorm(n, mean=rep(0, noisep), sigma=sigma)
X <- cbind(signalX, noise)
X <- as.data.frame(X)
y <- as.factor(y)
out <- list(X=X, y=y, beta=beta)
}

mc_dat <- multi_class_lr(n=1000, mod1_size=10, mod2_size=10)
X <- mc_dat\$X
y <- mc_dat\$y
sc <- screen_control(drop_fraction = 0.25, keep_fraction = 0.75,
mtry_factor = 1, min_ntree = 500, ntree_factor = 1)
se <- select_control(drop_fraction = 0.2, number_selected = 4,
mtry_factor = 1, min_ntree = 500,
ntree_factor = 1)
dat <- as.data.frame(cbind(y, X))
mod_membership <- factor(rep(1:2, times=c(10, 10)))
fit <- ff(X, y,
module_membership = mod_membership,
screen_params=sc,
select_params=se,
final_ntree=500)
expect_equal(paste("V", 1:4, sep="") %in% fit\$feature_list\$feature_name,
rep(T, 4))
fit <- ff(y ~ ., data = dat,
module_membership = mod_membership,
screen_params=sc,
select_params=se,
final_ntree=500)
expect_equal(paste("V", 1:4, sep="") %in% fit\$feature_list\$feature_name,
rep(T, 4))
if (requireNamespace("WGCNA", quietly = T)) {
library(WGCNA)
fit <- wff(X, y,
screen_params = sc,
select_params = se,
final_ntree = 500)
expect_equal(paste("V", 1:4, sep="") %in% fit\$feature_list\$feature_name,
rep(T, 4))
fit <- wff(y ~ ., data = dat,
screen_params = sc,
select_params = se,
final_ntree = 500)
expect_equal(paste("V", 1:4, sep="") %in% fit\$feature_list\$feature_name,
rep(T, 4))
}
#Now test whether Z works
Z <- X[, 1:2]
Xsub <- X[, -c(1, 2)]
mod_membership <- factor(rep(1:2, times=c(8, 10)))
fit <- ff(Xsub, y, Z,
module_membership = mod_membership,
screen_params=sc,
select_params=se,
final_ntree=500)
expect_equal(paste("V", 1:4, sep="") %in% fit\$feature_list\$feature_name,
rep(T, 4))

})
```

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fuzzyforest documentation built on March 25, 2020, 5:09 p.m.