Description Usage Arguments Details Value References See Also Examples
View source: R/seq_GEE_model.R
seq_GEE_model
estimates the the effective variables and chooses the
subjects sequentially by the generalized estimating equations with adaptive
shrinkage estimate method.
1 2 3 
formula 
An object of class "formula" (or one that can be coerced to that class): a symbolic description of the model to be fitted. 
data 
A data frame containing the initial random samples to obtain the initial estimate of the coefficient. Note that the first column of the data frame is the response variable, and the rest is the explanatory variables. 
clusterID 
The id for each subject in the initial samples. Note that the subjects in the same cluster will have identical id. 
data_pool 
A data frame containing all the random samples which we will choose subject from. The first column of the data frame is the response variable, and the rest is the explanatory variables. 
clusterID_pool 
The id for each subject in the data_pool. Note that the subjects in the same cluster will have identical id. 
strategy 
A character string that determines the sample selection criterion to be used, matching one of 'random' or 'D_optimal. The default value is 'D_optimal'. 
d 
A numeric number specifying the length of the fixed size confidence set for our model. The default value is 0.4. 
family 
A description of the error distribution and link function to be
used in the model. See family for details of 
corstr 
A character string specifying the correlation structure. The following are permitted: "independence", "exchangeable" and "ar1". 
contrasts 
An optional list. See the contrasts.arg of

... 
Further arguments passed to or from other methods. 
seq_GEE_model fits the clustered data sequentially by generalized estimating equations with adaptive shrinkage estimate. It can detect the effective variables which have the impact on the response and choose the most representative sample point at the same time. Specifically, we fit a initial sample data and determine if the stop condition is reached. If not, we will select the most informative subjects by some criterion. Iteration stops once it meets our requirements.
a list containing the following components
d 
the length of the fixed size confidence set that we specify 
n 
the current sample size when the stopping criterion is satisfied 
is_stopped 
the label of sequential iterations stop or not. When the value of is_stopped is TRUE, it means the iteration stops 
beta_est 
the parameters that we estimate when the the iteration is finished 
cov 
the covariance matrix between the estimated parameters 
rho 
estimate of correlation coefficient 
nonZeroIdx 
the index of the non zero coefficient 
corstr 
the correlation structure. The following are permitted: "independence", "exchangeable" and "ar1" 
family 
a description of the error distribution and link function to be used in the model 
Chen, Z., Wang, Z., & Chang, Y. I. (2019). Sequential adaptive variables and subject selection for GEE methods. Biometrics. doi:10.1111/biom.13160
seq_cat_model
for categorical case
seq_bin_model
for binary classification case
seq_ord_model
for ordinal case.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22  # generate the toy example
data < gen_GEE_data(numClusters = 75, clusterSize = 5,
clusterCorstr = 'ar1', clusterRho = 0.3,
beta = c(1, 1.1, 1.5, 2, rep(0, 50)), family = gaussian(),
intercept = TRUE, xCorstr = 'ar1',
xCorRho = 0.5, xVariance = 0.2)
df < data.frame(y = data$y, data$x)
clusterID < data$clusterID
pool < gen_GEE_data(numClusters = 8000, clusterSize = 5,
clusterCorstr = 'ar1', clusterRho = 0.3,
beta = c(1, 1.1, 1.5, 2, rep(0, 50)), family = gaussian(),
intercept = TRUE, xCorstr = 'ar1',
xCorRho = 0.5, xVariance = 0.2)
df_pool < data.frame(y = pool$y, pool$x)
clusterID_pool < pool$clusterID
d< 0.25
# use seq_GEE_model to generalized estimating equations case.
# You can remove #' to run the command.
# seqRes.ASED < seq_GEE_model(y ~ .1, data = df, clusterID = clusterID,
# data_pool = df_pool, clusterID_pool = clusterID_pool,
# strategy = "Doptimal", d = d, family = gaussian(), corstr = 'ar1')

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