Package 'pgraph'

Regularized graphical model estimation

Description

greg calculate the regularized graphical model estimation using lasso, scad and adaptive lasso penalties. It report the results in the form of roc results for each method.

Usage

greg(z, A, eps = 1e-15, rholist = NULL, gamma = 0.5, trace = FALSE)

Arguments

z	n * p dimensional matrix
A	p * p true graph
eps	a tolerence level for thresholding
rholist	a sequence of penalty parameters
gamma	the adaptive lasso penalty parameter
trace	whether to trace to estimation process.

Value

a list.

roc.lasso	roc results for lasso
roc.scad	roc results for scad
roc.alasso	roc results for adaptive lasso

See Also

pgraph, roc, projcov

Examples

set.seed(0)
p = 20;
n = 300;
tmp=runif(p-1,1,3)
s=c(0,cumsum(tmp));
s1=matrix(s,p,p)
cov.mat.true=exp(-abs(s1-t(s1)))
prec.mat.true=solve(cov.mat.true);
a=matrix(rnorm(p*n),n,p)
data.sa=a%*%chol(cov.mat.true);
true.graph = outer(1:p,1:p,f<-function(x,y){(abs(x-y)==1)})
greg.fit = greg(data.sa, true.graph)
auc.lasso = sum(diff(greg.fit$roc.lasso[,1])*greg.fit$roc.lasso[-1,2])
auc.alasso = sum(diff(greg.fit$roc.alasso[,1])*greg.fit$roc.alasso[-1,2])
auc.scad = sum(diff(greg.fit$roc.scad[,1])*greg.fit$roc.scad[-1,2])
auc.lasso
auc.alasso
auc.scad

---

Calculate the Conditional Dependency Graph

Description

pgraph calculate the conditional dependency graph (with/without external factors) via projection using lasso or sparse additive model.

Usage

pgraph(
  z,
  f = NULL,
  method = c("lasso", "sam", "ols"),
  cond = TRUE,
  R = 199,
  randSeed = 0,
  trace = FALSE
)

Arguments

z	n * p dimensional matrix
f	n * q factor matrix. Default = 'NULL'.
method	projection method. Default = 'lasso'.
cond	whether to create a conditional graph or unconditional graph. Default = TRUE. If cond = FALSE, f must be provided.
R	number of random permutations for the test.
randSeed	the random seed for the program. Default = 0.
trace	whether to trace to estimation process.

Value

a list to be used to calculate the ROC curve.

statmat.pearson	matrix with pearson correlation test
statmat.dcov	matrix with distance covariance test

See Also

greg, roc, projcov

Examples

library(splines)
set.seed(0)
p = 5;
n = 100;
tmp=runif(p-1,1,3)
s=c(0,cumsum(tmp));
s1=matrix(s,p,p)
cov.mat.true=exp(-abs(s1-t(s1)))
prec.mat.true=solve(cov.mat.true);
a=matrix(rnorm(p*n),n,p)
data.sa=a%*%chol(cov.mat.true);
true.graph = outer(1:p,1:p,f<-function(x,y){(abs(x-y)==1)})
methodlist = c('lasso','sam')
fit = vector(mode='list', length=2)
info = vector(mode='list', length=2)
auc = NULL
for(i in 1:2){
method = methodlist[i]
fit[[i]] = pgraph(data.sa, method = method)
info[[i]] = roc(fit[[i]]$statmat.pearson, true.graph)
auc[i] = sum(-diff(info[[i]][,1])*info[[i]][-1,2])
  cat(method, ': auc=', auc[i],'\n')
}

---

Calculate the Projected matrix given factors

Description

projcore calculate the projected matrix given factors.

Usage

projcore(
  x,
  b,
  method = c("lasso", "sam", "ols"),
  one.SE = TRUE,
  refit = TRUE,
  randSeed = 0
)

Arguments

x	first vector
b	factor matrix
method	projection method. Default = 'lasso'.
one.SE	whether to use the 1se rule for glmnet. Default = TRUE.
refit	whether to refit the selected model. Default = TRUE.
randSeed	the random seed for the program. Default = 0.

Value

eps	the residual matrix after projection

See Also

greg, roc, pgraph

---

Calculate the Projected Covariance of Two Vectors

Description

projcov calculate the projected distance covariance of two vectors given common factors.

Usage

projcov(
  x,
  y,
  b,
  method = c("lasso", "sam", "ols"),
  one.SE = TRUE,
  refit = TRUE,
  R = 199,
  randSeed = 0,
  normalized = FALSE
)

Arguments

x	first vector
y	second vector
b	factor matrix
method	projection method. Default = 'lasso'.
one.SE	whether to use the 1se rule for glmnet. Default = TRUE.
refit	whether to refit the selected model. Default = TRUE.
R	number of random permutations for the test.
randSeed	the random seed for the program. Default = 0.
normalized	whether to normalized by S2. Default = FALSE.

Value

a list.

test.pearson	pearson correlection test statistic
test.dcov	distance covariance test statistic
xeps	residual of projection of x on b
yeps	residual of projection of y on b

See Also

greg, roc, pgraph

Examples

library(splines)
set.seed(0)
K = 3
n = 100
b = matrix(rnorm(K*n),n,K)
bx = 1:3
by = c(1,2,2)
x = b%*%bx+rnorm(n)
y = b%*%by+rnorm(n)
fit1 = projcov(x, y, b, method = 'lasso')
fit2 = projcov(x, y, b, method = 'sam')

---

Compute the Projected Graph

Description

roc calculate the fpr and tpr for the roc curve

Usage

roc(a, a0)

Arguments

a	p * p estimated graph
a0	p * p true graph

Value

a list.

tpr	tpr sequence
fpr	fpr sequence

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