Type:	Package
Title:	Semi-Parametric Dimension Reduction Models Using Orthogonality Constrained Optimization
Version:	0.6.8
Description:	Utilize an orthogonality constrained optimization algorithm of Wen & Yin (2013) <doi:10.1007/s10107-012-0584-1> to solve a variety of dimension reduction problems in the semiparametric framework, such as Ma & Zhu (2012) <doi:10.1080/01621459.2011.646925>, Ma & Zhu (2013) <doi:10.1214/12-AOS1072>, Sun, Zhu, Wang & Zeng (2019) <doi:10.1093/biomet/asy064> and Zhou, Zhu & Zeng (2021) <doi:10.1093/biomet/asaa087>. The package also implements some existing dimension reduction methods such as hMave by Xia, Zhang, & Xu (2010) <doi:10.1198/jasa.2009.tm09372> and partial SAVE by Feng, Wen & Zhu (2013) <doi:10.1080/01621459.2012.746065>. It also serves as a general purpose optimization solver for problems with orthogonality constraints, i.e., in Stiefel manifold. Parallel computing for approximating the gradient is enabled through 'OpenMP'.
License:	GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
Encoding:	UTF-8
LazyData:	TRUE
Imports:	Rcpp (≥ 1.0.9), survival, dr, pracma, plot3D, rgl, MASS
LinkingTo:	Rcpp, RcppArmadillo
URL:	https://github.com/teazrq/orthoDr
BugReports:	https://github.com/teazrq/orthoDr/issues
RoxygenNote:	7.3.1
NeedsCompilation:	yes
Packaged:	2024-03-13 02:44:48 UTC; zrq
Author:	Ruilin Zhao [aut, cph], Ruoqing Zhu [aut, cre, cph], Jiyang Zhang [aut, cph], Wenzhuo Zhou [aut, cph], Peng Xu [aut, cph], James Joseph Balamuta [ctb]
Maintainer:	Ruoqing Zhu <teazrq@gmail.com>
Repository:	CRAN
Date/Publication:	2024-03-13 05:10:02 UTC

orthoDr: Semi-Parametric Dimension Reduction Models Using Orthogonality Constrained Optimization

Description

Utilize an orthogonality constrained optimization algorithm of Wen & Yin (2013) doi:10.1007/s10107-012-0584-1 to solve a variety of dimension reduction problems in the semiparametric framework, such as Ma & Zhu (2012) doi:10.1080/01621459.2011.646925, Ma & Zhu (2013) doi:10.1214/12-AOS1072, Sun, Zhu, Wang & Zeng (2019) doi:10.1093/biomet/asy064 and Zhou, Zhu & Zeng (2021) doi:10.1093/biomet/asaa087. The package also implements some existing dimension reduction methods such as hMave by Xia, Zhang, & Xu (2010) doi:10.1198/jasa.2009.tm09372 and partial SAVE by Feng, Wen & Zhu (2013) doi:10.1080/01621459.2012.746065. It also serves as a general purpose optimization solver for problems with orthogonality constraints, i.e., in Stiefel manifold. Parallel computing for approximating the gradient is enabled through 'OpenMP'.

Author(s)

Maintainer: Ruoqing Zhu teazrq@gmail.com (ORCID) [copyright holder]

Authors:

• Ruilin Zhao rzhao15@seas.upenn.edu [copyright holder]

• Jiyang Zhang jiyangz2@illinois.edu [copyright holder]

• Wenzhuo Zhou wenzhuo3@illinois.edu [copyright holder]

• Peng Xu px2132@columbia.edu [copyright holder]

Other contributors:

• James Joseph Balamuta balamut2@illinois.edu (ORCID) [contributor]

See Also

Useful links:

• https://github.com/teazrq/orthoDr

• Report bugs at https://github.com/teazrq/orthoDr/issues

Counting process based sliced inverse regression model

Description

The CP-SIR model for right-censored survival outcome. This model is correct only under very strong assumptions, however, since it only requires an SVD, the solution is used as the initial value in the orthoDr optimization.

Usage

CP_SIR(x, y, censor, bw = silverman(1, length(y)))

Arguments

Value

A list consisting of

References

Sun, Q., Zhu, R., Wang, T. and Zeng, D. (2019) "Counting Process Based Dimension Reduction Method for Censored Outcomes." Biometrika, 106(1), 181-196. DOI: doi:10.1093/biomet/asy064

Examples

# This is setting 1 in Sun et. al. (2017) with reduced sample size
library(MASS)
set.seed(1)
N <- 200
P <- 6
V <- 0.5^abs(outer(1:P, 1:P, "-"))
dataX <- as.matrix(mvrnorm(N, mu = rep(0, P), Sigma = V))
failEDR <- as.matrix(c(1, 0.5, 0, 0, 0, rep(0, P - 5)))
censorEDR <- as.matrix(c(0, 0, 0, 1, 1, rep(0, P - 5)))
T <- rexp(N, exp(dataX %*% failEDR))
C <- rexp(N, exp(dataX %*% censorEDR - 1))
ndr <- 1
Y <- pmin(T, C)
Censor <- (T < C)

# fit the model
cpsir.fit <- CP_SIR(dataX, Y, Censor)
distance(failEDR, cpsir.fit$vectors[, 1:ndr, drop = FALSE], "dist")

KernelDist_cross

Description

Calculate the kernel distance between testing data and training data

Usage

KernelDist_cross(TestX, X)

Arguments

Cross distance matrix

Description

Calculate the Gaussian kernel distance between rows of X1 and rows of X2. As a result, this is an extension to the stats::dist() function.

Usage

dist_cross(x1, x2)

Arguments

Value

A distance matrix with its (i, j)th element being the Gaussian kernel distance between ith row of X1 jth row of X2.

Examples

# two matrices
set.seed(1)
x1 <- matrix(rnorm(10), 5, 2)
x2 <- matrix(rnorm(6), 3, 2)
dist_cross(x1, x2)

Compute Distance Correlation

Description

Calculate the distance correlation between two linear spaces.

Usage

distance(s1, s2, type = "dist", x = NULL)

Arguments

Value

The distance between s1 and s2.

Examples

# two spaces
failEDR <- as.matrix(cbind(
  c(1, 1, 0, 0, 0, 0),
  c(0, 0, 1, -1, 0, 0)
))
B <- as.matrix(cbind(
  c(0.1, 1.1, 0, 0, 0, 0),
  c(0, 0, 1.1, -0.9, 0, 0)
))

distance(failEDR, B, "dist")
distance(failEDR, B, "trace")

N <- 300
P <- 6
dataX <- matrix(rnorm(N * P), N, P)
distance(failEDR, B, "canonical", dataX)

The prediction function for the personalized direct learning dose model

Description

Predict the fitted dose from the direct learning dose model

Usage

dosepred(B, X, X_test, bw, w)

Arguments

Value

The predicted dose

General solver C++ function

Description

A general purpose optimization solver with orthogonality constraint. For details, please see the original MATLAB code by Wen and Yin (2013). This is an internal function and should not be called directly.

Usage

gen_solver(
  B,
  f,
  g,
  env,
  useg,
  rho,
  eta,
  gamma,
  tau,
  epsilon,
  btol,
  ftol,
  gtol,
  maxitr,
  verbose
)

Arguments

References

Wen, Z., & Yin, W. (2013). A feasible method for optimization with orthogonality constraints. Mathematical Programming, 142(1), 397-434. DOI: doi:10.1007/s10107-012-0584-1

Zhang, H., & Hager, W. W. (2004). A nonmonotone line search technique and its application to unconstrained optimization. SIAM journal on Optimization, 14(4), 1043-1056. DOI: doi:10.1137/S1052623403428208

Examples

# This function should be called internally. When having all objects pre-computed, one can call
# gen_solver(B, f, g, env, useg, rho, eta, gamma, tau, epsilon, btol, ftol, gtol, maxitr, verbose)
# to solve for the parameters B.

Hazard Mave for Censored Survival Data

Description

This is an almost direct R translation of Xia, Zhang & Xu's (2010) hMave MATLAB code. We implemented further options for setting a different initial value. The computational algorithm does not utilize the orthogonality constrained optimization.

Usage

hMave(x, y, censor, m0, B0 = NULL)

Arguments

Value

A list consisting of

References

Xia, Y., Zhang, D., & Xu, J. (2010). Dimension reduction and semiparametric estimation of survival models. Journal of the American Statistical Association, 105(489), 278-290. DOI: doi:10.1198/jasa.2009.tm09372

Examples

# generate some survival data
set.seed(1)
P <- 7
N <- 150
dataX <- matrix(runif(N * P), N, P)
failEDR <- as.matrix(cbind(c(1, 1.3, -1.3, 1, -0.5, 0.5, -0.5, rep(0, P - 7))))
T <- exp(dataX %*% failEDR + rnorm(N))
C <- runif(N, 0, 15)
Y <- pmin(T, C)
Censor <- (T < C)

# fit the model
hMave.fit <- hMave(dataX, Y, Censor, 1)

initB

Description

regression initiation function

Usage

initB(x, y, ndr, bw, method, ncore)

Kernel Weight

Description

Calculate the Gaussian kernel weights between rows of X1 and rows of X2.

Usage

kernel_weight(x1, x2, kernel = "gaussian", dist = "euclidean")

Arguments

Value

A distance matrix, with its (i, j)th element being the kernel weights for the i th row of X1 jth row of X2.

Examples

# two matrices
set.seed(1)
x1 <- matrix(rnorm(10), 5, 2)
x2 <- matrix(rnorm(6), 3, 2)
kernel_weight(x1, x2)

local_f

Description

local method f value function

Usage

local_f(B, X, Y, bw, ncore)

local semi regression solver C++ function

Description

Sovling the local semiparametric estimating equations. This is an internal function and should not be called directly.

Usage

local_solver(
  B,
  X,
  Y,
  bw,
  rho,
  eta,
  gamma,
  tau,
  epsilon,
  btol,
  ftol,
  gtol,
  maxitr,
  verbose,
  ncore
)

Arguments

References

Ma, Y., & Zhu, L. (2013). Efficient estimation in sufficient dimension reduction. Annals of statistics, 41(1), 250. DOI: doi:10.1214/12-AOS1072

Direct Learning & Pseudo-direct Learning Model

Description

Performs the "Direct Learning & Pseudo-direct Learning" Method for personalized medicine.

Usage

orthoDr_pdose(
  x,
  a,
  r,
  ndr = ndr,
  B.initial = NULL,
  bw = NULL,
  lambda = 0.1,
  K = sqrt(length(r)),
  method = c("direct", "pseudo_direct"),
  keep.data = FALSE,
  control = list(),
  maxitr = 500,
  verbose = FALSE,
  ncore = 0
)

Arguments

Value

A orthoDr object consisting of list with named elements:

References

Zhou, W., Zhu, R., & Zeng, D. (2021). A parsimonious personalized dose-finding model via dimension reduction. Biometrika, 108(3), 643-659. DOI: doi:10.1093/biomet/asaa087

Examples

# generate some personalized dose scenario

exampleset <- function(size, ncov) {
  X <- matrix(runif(size * ncov, -1, 1), ncol = ncov)
  A <- runif(size, 0, 2)

  Edr <- as.matrix(c(0.5, -0.5))

  D_opt <- X %*% Edr + 1

  mu <- 2 + 0.5 * (X %*% Edr) - 7 * abs(D_opt - A)

  R <- rnorm(length(mu), mu, 1)

  R <- R - min(R)

  datainfo <- list(X = X, A = A, R = R, D_opt = D_opt, mu = mu)
  return(datainfo)
}

# generate data

set.seed(123)
n <- 150
p <- 2
ndr <- 1
train <- exampleset(n, p)
test <- exampleset(500, p)

# the direct learning method
orthofit <- orthoDr_pdose(train$X, train$A, train$R,
  ndr = ndr, lambda = 0.1,
  method = "direct", K = sqrt(n), keep.data = TRUE,
  maxitr = 150, verbose = FALSE, ncore = 2
)

dose <- predict(orthofit, test$X)

# ` # compare with the optimal dose
dosedistance <- mean((test$D_opt - dose$pred)^2)
print(dosedistance)

# the pseudo direct learning method
orthofit <- orthoDr_pdose(train$X, train$A, train$R,
  ndr = ndr, lambda = seq(0.1, 0.2, 0.01),
  method = "pseudo_direct", K = as.integer(sqrt(n)), keep.data = TRUE,
  maxitr = 150, verbose = FALSE, ncore = 2
)

dose <- predict(orthofit, test$X)

# compare with the optimal dose

dosedistance <- mean((test$D_opt - dose$pred)^2)
print(dosedistance)

Semiparametric dimension reduction method from Ma & Zhu (2012).

Description

Performs the semiparametric dimension reduction method associated with Ma & Zhu (2012).

Usage

orthoDr_reg(
  x,
  y,
  method = "sir",
  ndr = 2,
  B.initial = NULL,
  bw = NULL,
  keep.data = FALSE,
  control = list(),
  maxitr = 500,
  verbose = FALSE,
  ncore = 0
)

Arguments

Value

A orthoDr object consisting of list with named elements:

References

Ma, Y., & Zhu, L. (2012). A semiparametric approach to dimension reduction. Journal of the American Statistical Association, 107(497), 168-179. DOI: doi:10.1080/01621459.2011.646925

Ma, Y., & Zhu, L. (2013). Efficient estimation in sufficient dimension reduction. Annals of statistics, 41(1), 250. DOI: doi:10.1214/12-AOS1072

Examples

# generate some regression data
set.seed(1)
N <- 100
P <- 4
dataX <- matrix(rnorm(N * P), N, P)
Y <- -1 + dataX[, 1] + rnorm(N)

# fit the semi-sir model
orthoDr_reg(dataX, Y, ndr = 1, method = "sir")

# fit the semi-phd model
Y <- -1 + dataX[, 1]^2 + rnorm(N)
orthoDr_reg(dataX, Y, ndr = 1, method = "phd")

Counting Process based semiparametric dimension reduction (IR-CP) model

Description

Models the data according to the counting process based semiparametric dimension reduction (IR-CP) model for right censored survival outcome.

Usage

orthoDr_surv(
  x,
  y,
  censor,
  method = "dm",
  ndr = ifelse(method == "forward", 1, 2),
  B.initial = NULL,
  bw = NULL,
  keep.data = FALSE,
  control = list(),
  maxitr = 500,
  verbose = FALSE,
  ncore = 0
)

Arguments

Value

A orthoDr object consisting of list with named elements:

References

Sun, Q., Zhu, R., Wang, T., & Zeng, D. (2019). Counting process-based dimension reduction methods for censored outcomes. Biometrika, 106(1), 181-196. DOI: doi:10.1093/biomet/asy064

Examples

# This is setting 1 in Sun et. al. (2017) with reduced sample size
library(MASS)
set.seed(1)
N <- 200
P <- 6
V <- 0.5^abs(outer(1:P, 1:P, "-"))
dataX <- as.matrix(mvrnorm(N, mu = rep(0, P), Sigma = V))
failEDR <- as.matrix(c(1, 0.5, 0, 0, 0, rep(0, P - 5)))
censorEDR <- as.matrix(c(0, 0, 0, 1, 1, rep(0, P - 5)))
T <- rexp(N, exp(dataX %*% failEDR))
C <- rexp(N, exp(dataX %*% censorEDR - 1))
ndr <- 1
Y <- pmin(T, C)
Censor <- (T < C)

# fit the model
forward.fit <- orthoDr_surv(dataX, Y, Censor, method = "forward")
distance(failEDR, forward.fit$B, "dist")

dn.fit <- orthoDr_surv(dataX, Y, Censor, method = "dn", ndr = ndr)
distance(failEDR, dn.fit$B, "dist")

dm.fit <- orthoDr_surv(dataX, Y, Censor, method = "dm", ndr = ndr)
distance(failEDR, dm.fit$B, "dist")

Orthogonality constrained optimization

Description

A general purpose optimization solver with orthogonality constraint. The orthogonality constrained optimization method is a nearly direct translation from Wen and Yin (2010)'s MATLAB code.

Usage

ortho_optim(
  B,
  fn,
  grad = NULL,
  ...,
  maximize = FALSE,
  control = list(),
  maxitr = 500,
  verbose = FALSE
)

Arguments

Value

A orthoDr object that consists of a list with named entries of:

References

Wen, Z., & Yin, W. (2013). A feasible method for optimization with orthogonality constraints. Mathematical Programming, 142(1), 397-434. DOI: doi:10.1007/s10107-012-0584-1

Examples

# an eigen value problem
library(pracma)
set.seed(1)
n <- 100
k <- 6
A <- matrix(rnorm(n * n), n, n)
A <- t(A) %*% A
B <- gramSchmidt(matrix(rnorm(n * k), n, k))$Q

fx <- function(B, A) -0.5 * sum(diag(t(B) %*% A %*% B))
gx <- function(B, A) -A %*% B
fit <- ortho_optim(B, fx, gx, A = A)
fx(fit$B, A)

# compare with the solution from the eigen function
sol <- eigen(A)$vectors[, 1:k]
fx(sol, A)

Partial Sliced Averaged Variance Estimation

Description

The partial-SAVE model. This model is correct only under very strong assumptions, the solution is used as the initial value in the orthoDr optimization.

Usage

pSAVE(x, a, r, ndr = 2, nslices0 = 2)

Arguments

Value

A list consisting of:

References

Feng, Z., Wen, X. M., Yu, Z., & Zhu, L. (2013). On partial sufficient dimension reduction with applications to partially linear multi-index models. Journal of the American Statistical Association, 108(501), 237-246. DOI: doi:10.1080/01621459.2012.746065

pdose_direct_solver

Description

The direct learning optimization function for personalized dose finding.

Usage

pdose_direct_solver(
  B,
  X,
  A,
  a_dist,
  a_seq,
  R,
  lambda,
  bw,
  rho,
  eta,
  gamma,
  tau,
  epsilon,
  btol,
  ftol,
  gtol,
  maxitr,
  verbose,
  ncore
)

Arguments

Value

The optimizer B for the esitmating equation.

References

Zhou, W., Zhu, R., & Zeng, D. (2021). A parsimonious personalized dose-finding model via dimension reduction. Biometrika, 108(3), 643-659. DOI: doi:10.1093/biomet/asaa087

pdose_semi_solver

Description

The pseudo direct learning optimization function for personalized dose finding with dimension reduction.

Usage

pdose_semi_solver(
  B,
  X,
  R,
  A,
  a_dist,
  a_seq,
  lambda,
  bw,
  rho,
  eta,
  gamma,
  tau,
  epsilon,
  btol,
  ftol,
  gtol,
  maxitr,
  verbose,
  ncore
)

Arguments

Value

The optimizer B for the esitmating equation.

References

Zhou, W., Zhu, R., & Zeng, D. (2021). A parsimonious personalized dose-finding model via dimension reduction. Biometrika, 108(3), 643-659. DOI: doi:10.1093/biomet/asaa087

phd_init

Description

phd initial value function

Usage

phd_init(B, X, Y, bw, ncore)

semi-phd solver C++ function

Description

Sovling the semi-phd estimating equations. This is an internal function and should not be called directly.

Usage

phd_solver(
  B,
  X,
  Y,
  bw,
  rho,
  eta,
  gamma,
  tau,
  epsilon,
  btol,
  ftol,
  gtol,
  maxitr,
  verbose,
  ncore
)

Arguments

References

Ma, Y., & Zhu, L. (2012). A semiparametric approach to dimension reduction. Journal of the American Statistical Association, 107(497), 168-179. DOI: doi:10.1080/01621459.2011.646925

Predictions under orthoDr models

Description

The prediction function for orthoDr fitted models

Usage

## S3 method for class 'orthoDr'
predict(object, testx, ...)

Arguments

Value

The predicted object

Examples

# generate some survival data
N <- 100
P <- 4
dataX <- matrix(rnorm(N * P), N, P)
Y <- exp(-1 + dataX[, 1] + rnorm(N))
Censor <- rbinom(N, 1, 0.8)

# fit the model with keep.data = TRUE
orthoDr.fit <- orthoDr_surv(dataX, Y, Censor,
  ndr = 1,
  method = "dm", keep.data = TRUE
)

# predict 10 new observations
predict(orthoDr.fit, matrix(rnorm(10 * P), 10, P))

# generate some personalized dose scenario

exampleset <- function(size, ncov) {
  X <- matrix(runif(size * ncov, -1, 1), ncol = ncov)
  A <- runif(size, 0, 2)

  Edr <- as.matrix(c(0.5, -0.5))

  D_opt <- X %*% Edr + 1

  mu <- 2 + 0.5 * (X %*% Edr) - 7 * abs(D_opt - A)

  R <- rnorm(length(mu), mu, 1)

  R <- R - min(R)

  datainfo <- list(X = X, A = A, R = R, D_opt = D_opt, mu = mu)
  return(datainfo)
}

# generate data

set.seed(123)
n <- 150
p <- 2
ndr <- 1
train <- exampleset(n, p)
test <- exampleset(500, p)

# the direct learning method
orthofit <- orthoDr_pdose(train$X, train$A, train$R,
  ndr = ndr, lambda = 0.1,
  method = "direct", K = as.integer(sqrt(n)), keep.data = TRUE,
  maxitr = 150, verbose = FALSE, ncore = 2
)

predict(orthofit, test$X)

# the pseudo direct learning method
orthofit <- orthoDr_pdose(train$X, train$A, train$R,
  ndr = ndr, lambda = seq(0.1, 0.2, 0.01),
  method = "pseudo_direct", K = as.integer(sqrt(n)), keep.data = TRUE,
  maxitr = 150, verbose = FALSE, ncore = 2
)

predict(orthofit, test$X)

Print a orthoDr object

Description

Provides a custom print wrapper for displaying orthoDr fitted models.

Usage

## S3 method for class 'orthoDr'
print(x, ...)

Arguments

Value

Sliently returns the orthoDr object supplied into the function to allow for use with pipes.

Examples

# generate some survival data
N <- 100
P <- 4
dataX <- matrix(rnorm(N * P), N, P)
Y <- exp(-1 + dataX[, 1] + rnorm(N))
Censor <- rbinom(N, 1, 0.8)

# fit the model
orthoDr_surv(dataX, Y, Censor, ndr = 1, method = "dm")

reg_init

Description

regression initiation function to get better initial value

Usage

reg_init(method, ...)

reg_solve

Description

regression solver switch function

Usage

reg_solver(method, ...)

save_init

Description

save initial value function

Usage

save_init(B, X, Y, bw, ncore)

semi-save solver C++ function

Description

Sovling the semi-save estimating equations. This is an internal function and should not be called directly.

Usage

save_solver(
  B,
  X,
  Y,
  bw,
  rho,
  eta,
  gamma,
  tau,
  epsilon,
  btol,
  ftol,
  gtol,
  maxitr,
  verbose,
  ncore
)

Arguments

References

Ma, Y., & Zhu, L. (2012). A semiparametric approach to dimension reduction. Journal of the American Statistical Association, 107(497), 168-179. DOI: doi:10.1080/01621459.2011.646925

seff_init

Description

semiparametric efficient method initial value function

Usage

seff_init(B, X, Y, bw, ncore)

Eff semi regression solver C++ function

Description

Sovling the semiparametric efficient estimating equations. This is an internal function and should not be called directly.

Usage

seff_solver(
  B,
  X,
  Y,
  bw,
  rho,
  eta,
  gamma,
  tau,
  epsilon,
  btol,
  ftol,
  gtol,
  maxitr,
  verbose,
  ncore
)

Arguments

References

Ma, Y., & Zhu, L. (2013). Efficient estimation in sufficient dimension reduction. Annals of statistics, 41(1), 250. DOI: doi:10.1214/12-AOS1072

Silverman's rule of thumb

Description

A simple Silverman's rule of thumb bandwidth calculation.

Usage

silverman(d, n)

Arguments

Value

A simple bandwidth choice

Examples

silverman(1, 300)

sir_init

Description

sir initial value function

Usage

sir_init(B, X, Y, bw, ncore)

semi-sir solver C++ function

Description

Sovling the semi-sir estimating equations. This is an internal function and should not be called directly.

Usage

sir_solver(
  B,
  X,
  Y,
  bw,
  rho,
  eta,
  gamma,
  tau,
  epsilon,
  btol,
  ftol,
  gtol,
  maxitr,
  verbose,
  ncore
)

Arguments

References

Ma, Y., & Zhu, L. (2012). A semiparametric approach to dimension reduction. Journal of the American Statistical Association, 107(497), 168-179. DOI: doi:10.1080/01621459.2011.646925

Skin Cutaneous Melanoma Data set

Description

The clinical variables of the SKCM dataset. The original data was obtained from The Cancer Genome Atlas (TCGA).

Usage

skcm.clinical

Format

Contains 469 subjects with 156 failures. Each row contains one subject, subject ID is indicated by row name. Variables include:

• Time

• Censor

• Gender

• Age

Note: Age has 8 missing values.

References

https://www.cancer.gov/ccg/research/genome-sequencing/tcga

Genes associated with Melanoma given by the MelGene Database

Description

The expression of top 20 genes of cutaneous melanoma literature based on the MelGene Database.

Usage

skcm.melgene

Format

Each row contains one subject, subject ID is indicated by row name. Gene names in the columns. The columns are scaled.

References

Chatzinasiou, Foteini, Christina M. Lill, Katerina Kypreou, Irene Stefanaki, Vasiliki Nicolaou, George Spyrou, Evangelos Evangelou et al. "Comprehensive field synopsis and systematic meta-analyses of genetic association studies in cutaneous melanoma." Journal of the National Cancer Institute 103, no. 16 (2011): 1227-1235. Emmanouil I. Athanasiadis, Kyriaki Antonopoulou, Foteini Chatzinasiou, Christina M. Lill, Marilena M. Bourdakou, Argiris Sakellariou, Katerina Kypreou, Irene Stefanaki, Evangelos Evangelou, John P.A. Ioannidis, Lars Bertram, Alexander J. Stratigos, George M. Spyrou, A Web-based database of genetic association studies in cutaneous melanoma enhanced with network-driven data exploration tools, Database, Volume 2014, 2014, bau101, https://doi.org/10.1093/database/bau101 https://www.cancer.gov/ccg/research/genome-sequencing/tcga

surv_dm_solver C++ function

Description

The main optimization function for survival dimensional reduction, the IR-Semi method. This is an internal function and should not be called directly.

Usage

surv_dm_solver(
  B,
  X,
  Phit,
  Fail_Ind,
  bw,
  rho,
  eta,
  gamma,
  tau,
  epsilon,
  btol,
  ftol,
  gtol,
  maxitr,
  verbose,
  ncore
)

Arguments

Value

The optimizer B for the estimating equation.

References

Sun, Q., Zhu, R., Wang, T., & Zeng, D. (2019). Counting process-based dimension reduction methods for censored outcomes. Biometrika, 106(1), 181-196. DOI: doi:10.1093/biomet/asy064

Examples

# This function should be called internally. When having all objects pre-computed, one can call
# surv_solver(B, X, Phit, Fail.Ind,
#             rho, eta, gamma, tau, epsilon, btol, ftol, gtol, maxitr, verbose)
# to solve for the parameters B.

surv_dn_solver C++ function

Description

The main optimization function for survival dimensional reduction, the IR-CP method. This is an internal function and should not be called directly.

Usage

surv_dn_solver(
  B,
  X,
  Phit,
  Fail_Ind,
  bw,
  rho,
  eta,
  gamma,
  tau,
  epsilon,
  btol,
  ftol,
  gtol,
  maxitr,
  verbose,
  ncore
)

Arguments

Value

The optimizer B for the estimating equation.

References

Sun, Q., Zhu, R., Wang, T., & Zeng, D. (2019). Counting process-based dimension reduction methods for censored outcomes. Biometrika, 106(1), 181-196. DOI: doi:10.1093/biomet/asy064

Examples

# This function should be called internally. When having all objects pre-computed, one can call
# surv_solver(B, X, Phit, Fail.Ind,
#             rho, eta, gamma, tau, epsilon, btol, ftol, gtol, maxitr, verbose)
# to solve for the parameters B.

surv_forward_solver C++ function

Description

The main optimization function for survival dimensional reduction, the forward method. This is an internal function and should not be called directly.

Usage

surv_forward_solver(
  B,
  X,
  Fail_Ind,
  bw,
  rho,
  eta,
  gamma,
  tau,
  epsilon,
  btol,
  ftol,
  gtol,
  maxitr,
  verbose,
  ncore
)

Arguments

Value

The optimizer B for the esitmating equation.

References

Sun, Q., Zhu, R., Wang, T., & Zeng, D. (2019). Counting process-based dimension reduction methods for censored outcomes. Biometrika, 106(1), 181-196. DOI: doi:10.1093/biomet/asy064

Examples

# This function should be called internally. When having all objects pre-computed, one can call
# surv_forward_solver(B, X, Fail.Ind, bw,
#                     rho, eta, gamma, tau, epsilon, btol, ftol, gtol, maxitr, verbose)
# to solve for the parameters B.

2D or 2D view of survival data on reduced dimension

Description

Produce 2D or 3D plots of right censored survival data based on a given dimension reduction space

Usage

view_dr_surv(
  x,
  y,
  censor,
  B = NULL,
  bw = NULL,
  FUN = "log",
  type = "2D",
  legend.add = TRUE,
  xlab = "Reduced Direction",
  ylab = "Time",
  zlab = "Survival"
)

Arguments

Value

An rgl object that is rendered.

References

Sun, Q., Zhu, R., Wang, T., & Zeng, D. (2019). Counting process-based dimension reduction methods for censored outcomes. Biometrika, 106(1), 181-196. DOI: doi:10.1093/biomet/asy064

Examples

# generate some survival data
N <- 100
P <- 4
dataX <- matrix(rnorm(N * P), N, P)
Y <- exp(-1 + dataX[, 1] + rnorm(N))
Censor <- rbinom(N, 1, 0.8)

orthoDr.fit <- orthoDr_surv(dataX, Y, Censor, ndr = 1, method = "dm")
view_dr_surv(dataX, Y, Censor, orthoDr.fit$B)