glmvsd {glmvsd} | R Documentation |
The package calculates the variable selection deviation (VSD) to measure the uncertainty of the selection in terms of inclusion of predictors in the model.
glmvsd(x, y, n_train = ceiling(n/2), no_rep = 100,
n_train_bound = n_train - 2, n_bound = n - 2,
model_check, psi = 1, family = c("gaussian",
"binomial"), method = c("union", "customize"),
candidate_models, weight_type = c("BIC", "AIC",
"ARM"), prior = TRUE, reduce_bias = FALSE)
x |
Matrix of predictors. |
y |
Response variable. |
n_train |
Size of training set when the weight function is ARM or ARM with prior. The default value is |
no_rep |
Number of replications when the weight function is ARM and ARM with prior. The default value is |
n_train_bound |
When computing the weights using |
n_bound |
When computing the weights using |
model_check |
The index of the model to be assessed by calculating the VSD measures. |
psi |
A positive number to control the improvement of the prior weight. The default value is 1. |
family |
Choose the family for GLM models. So far only |
method |
User chooses one of the |
candidate_models |
Only available when |
weight_type |
Options for computing weights for VSD measure. User chooses one of the |
prior |
Whether use prior in the weight function. The default is |
reduce_bias |
If the binomial model is used, occasionally the algorithm might has convergence issue when the problem of so-called complete separation or quasi-complete separation happens. Users can set |
See Reference section.
A "glmvsd" object is retured. The components are:
candidate_models_cleaned |
Cleaned candidate models: the duplicated candidate models are cleaned; When computing VSD weights using AIC and BIC, the models with more than n-2 variables are removed (n is the number of observaitons); When computing VSD weights using ARM, the models with more than n_train-2 variables are removed (n_train is the number of training observations). |
VSD |
Variable selection deviation (VSD) value. |
VSD_minus |
The lower VSD value of |
VSD_plus |
The upper VSD value of |
Precision |
A vector of precision values computed using each candidate model. |
Recall |
A vector of recall values computed using each candidate model. |
Fmeasure |
F-measure for the given model under check. |
Gmeasure |
G-measure for the given model under check. |
sd.F |
Estimated standard deviation of F-measure for the given model under check. |
sd.G |
Estimated standard deviation of G-measure for the given model under check. |
weight |
The weight for each candidate model. |
Nan, Y. and Yang, Y. (2013), "Variable Selection Diagnostics Measures for High-dimensional Regression," Journal of Computational and Graphical Statistics, 23:3, 636-656.
BugReport: https://github.com/emeryyi/glmvsd
# REGRESSION CASE
# generate simulation data
n <- 50
p <- 8
beta <- c(3,1.5,0,0,2,0,0,0)
sigma <- matrix(0,p,p)
for(i in 1:p){
for(j in 1:p) sigma[i,j] <- 0.5^abs(i-j)
}
x <- mvrnorm(n, rep(0,p), sigma)
e <- rnorm(n)
y <- x %*% beta + e
# user provide a model to be checked
model_check <- c(0,1,1,1,0,0,0,1)
# compute VSD for model_check using ARM with prior
v_ARM <- glmvsd(x, y, n_train = ceiling(n/2),
no_rep=50, model_check = model_check, psi=1,
family = "gaussian", method = "union",
weight_type = "ARM", prior = TRUE)
# compute VSD for model_check using AIC
v_AIC <- glmvsd(x, y,
model_check = model_check,
family = "gaussian", method = "union",
weight_type = "AIC", prior = TRUE)
# compute VSD for model_check using BIC
v_BIC <- glmvsd(x, y,
model_check = model_check,
family = "gaussian", method = "union",
weight_type = "BIC", prior = TRUE)
# user supplied candidate models
candidate_models = rbind(c(0,0,0,0,0,0,0,1),
c(0,1,0,0,0,0,0,1), c(0,1,1,1,0,0,0,1),
c(0,1,1,0,0,0,0,1), c(1,1,0,1,1,0,0,0),
c(1,1,0,0,1,0,0,0))
v1_BIC <- glmvsd(x, y,
model_check = model_check, psi=1,
family = "gaussian",
method = "customize",
candidate_models = candidate_models,
weight_type = "BIC", prior = TRUE)
# CLASSIFICATION CASE
# generate simulation data
n = 300
p = 8
b <- c(1,1,1,-3*sqrt(2)/2)
x=matrix(rnorm(n*p, mean=0, sd=1), n, p)
feta=x[, 1:4]%*%b
fprob=exp(feta)/(1+exp(feta))
y=rbinom(n, 1, fprob)
# user provide a model to be checked
model_check <- c(0,1,1,1,0,0,0,1)
# compute VSD for model_check using BIC with prior
b_BIC <- glmvsd(x, y, n_train = ceiling(n/2),
family = "binomial",
no_rep=50, model_check = model_check, psi=1,
method = "union", weight_type = "BIC",
prior = TRUE)
candidate_models =
rbind(c(0,0,0,0,0,0,0,1),
c(0,1,0,0,0,0,0,1),
c(1,1,1,1,0,0,0,0),
c(0,1,1,0,0,0,0,1),
c(1,1,0,1,1,0,0,0),
c(1,1,0,0,1,0,0,0),
c(0,0,0,0,0,0,0,0),
c(1,1,1,1,1,0,0,0))
# compute VSD for model_check using AIC
# user supplied candidate models
b_AIC <- glmvsd(x, y,
family = "binomial",
model_check = model_check, psi=1,
method = "customize",
candidate_models = candidate_models,
weight_type = "AIC")