You also need an R (>= 3.4.0) installation on your computer; run:
install.packages(c("foreach","MASS","mvtnorm","randtoolbox","stats","mclust","EQL","matlib","parallel"), dependencies=TRUE)
which should download, build and install all R tools needed.
Run R shell and load devtools library:
require(devtools)
install_github("meca7653/MAPTest")
library(MAPTest)
The Example_Data available on Biometrics website on Wiley Online Library can be generated from the following code.
library(matlib)
n_basis = 2
n_control = 10
n_treat = 10
n_rep = 3
tt_treat = c(1:n_treat)/n_treat
nt = length(tt_treat)
ind_t = sort(sample(c(1:nt), n_control))
tt = tt_treat[ind_t]
tttt = c(rep(tt, n_rep), rep(tt_treat, n_rep))
z = x = matrix(0, length(tttt), n_basis)
z[,1] = 1.224745*tttt
z[,2] = -0.7905694 + 2.371708*tttt^2
x[,1] = z[,1] - Proj(z[,1], rep(1, length(tttt)))
x[,2] = z[,2] - Proj(z[,2], rep(1, length(tttt))) - Proj(z[,2], x[,1])
p_k_real = c(0.5, 0.2, 0.2, 0.1)
set.seed(2019)
Y1 = data_generation(G = 1000,
n_control = n_control,
n_treat = n_treat,
n_rep = n_rep,
k_real = 4,
sigma2_r = rep(1, 2),
sigma1_2_r = 1,
sigma2_2_r = c(3,2),
mu1_r = 4,
phi_g_r = rep(1, 1000),
p_k_real = p_k_real,
x = x)
colnames(Y1) <- c(paste(paste("C", rep(c(1:n_control), n_rep), sep = "_"), rep(c(1:n_rep), each = n_control), sep = "_"),
paste(paste("T", rep(c(1:n_treat), n_rep), sep = "_"), rep(c(1:n_rep), each = n_treat), sep = "_"))
rownames(Y1) <- paste("Gene", c(1:1000))
save(Y1, file = "Example_Data.rda")
write.csv(Y1, file = "Example_Data.csv")
aaa <- proc.time()
est_result <- estimation_fun(n_control = n_control,
n_treat = n_treat,
n_rep = n_rep,
x = x,
Y1 = Y1,
nn = 300,
k = 4,
phi = NULL,
type = 2,
tttt = tttt)
aaa1 <- proc.time()
aaa1 - aaa
G <- 1000
k_real <- 4
p_k_real <- c(0.5, 0.2, 0.2, 0.1)
dd = rep(c(0:(k_real-1)), p_k_real * G)
result = MAP_test(est_result = est_result, Type = c(1:6), dd = dd, nn = 300)
Summary_MAP(result)
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.