Package 'vsmi'

Generate example data for PEE

Description

This is a functoin to generate example missing data for PEE

Usage

generate_pee_missing_data(
  outcome = "binary",
  p = 20,
  n = 200,
  pt1 = 0.5,
  tbeta = c(3/4, (-3)/4, 3/4, (-3)/4, 3/4, (-3)/4, (-3)/4, 3/4),
  miss_sig = c(1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0)
)

Arguments

outcome	The type of response variable Y, choose "binary" for binary response or "count" for poisson response,defualt "binary"
p	The dimension of the independent variable X,default 20.
n	The Number of rows of generated data,default 200.
pt1	Missing rate of independent variable X,default 0.5.
tbeta	True value of the coefficient,default c(3/4,(-3)/4,3/4,(-3)/4,3/4,(-3)/4,(-3)/4,3/4).
miss_sig	A 0-1 vector of length p, where 1 means that variable at the index is with missing,while 0 means that it without missing,defualt c(1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0)

Value

A Matrix,missing data with variables X in the first p columns and response Y at the last column.

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Generate example data for PWLS

Description

This is a functoin to generate example missing data for PWLS

Usage

generate_pwls_missing_data(
  p = 20,
  n = 200,
  pt1 = 0.5,
  pt2 = 0.5,
  tbeta = c(1, -1, 1, -1, 1, -1, -1, 1),
  miss_sig = c(0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0)
)

Arguments

p	The dimension of the independent variable X,default 20.
n	The Number of rows of generated data,default 200.
pt1	Missing rate of independent variable X,default 0.5.
pt2	Missing rate of response Y, default 0.5.
tbeta	True value of the coefficient,default c(1,-1,1,-1,1,-1,-1,1).
miss_sig	A 0-1 vector of length p, where 1 means that variable at the index is with missing,while 0 means that it without missing,defualt c(0,1,0,0,1,0,0,0,0,0,1,0,0,0,1,0,0,0,0,0)

Value

A Matrix,missing data with variables X in the first p columns and response Y at the last column.

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Penalized estimating equations for generalized linear models with multiple imputation

Description

This is a function to impute missing data, estimate coefficients of generalized linear models and select variables for multiple imputed data sets, considering the correlation of multiple imputed observations.

Usage

PEE(
  missdata,
  mice_time = 5,
  penalty,
  lamda.vec = seq(1, 4, length.out = 12),
  Gamma = c(0.5, 1, 1.5)
)

Arguments

missdata	A Matrix,missing data with variables X in the first p columns and response Y at the last column.
mice_time	an integer, number of imputation.
penalty	The method for variable selection,choose from "lasso" or "alasso".
lamda.vec	Optimal tuning parameter for penalty,default seq(1,4,length.out=12).
Gamma	Parameter for adjustment of the Adaptive Weights vector in adaptive LASSO,default c(0.5,1,1.5).

Value

A Vsmi_est object, contians estcoef and index_sig , estcoef for estimate coefficients and index_sig for selected variable index.

Examples

library(MASS)
library(mice)
library(qif)

data_with_missing <- generate_pee_missing_data(outcome="binary")
est.alasso <-PEE(data_with_missing,penalty="alasso")
est.lasso <-PEE(data_with_missing,penalty="lasso")

count_data_with_missing <- generate_pee_missing_data(outcome="count")
count_est.alasso <-PEE(data_with_missing,penalty="alasso")
count_est.lasso <-PEE(data_with_missing,penalty="lasso")

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Penalized weighted least-squares estimate for variable selection on correlated multiply imputed data

Description

This is a functions to estimate coefficients of wighted leat-squares model and select variables for multiple imputed data sets ,considering the correlation of multiple imputed observations.

Usage

PWLS(
  missdata,
  mice_time = 5,
  penalty = "alasso",
  lamda.vec = seq(6, 24, length.out = 40),
  Gamma = c(0.5, 1, 2)
)

Arguments

missdata	A Matrix,missing data with variables X in the first p columns and response Y at the last column.
mice_time	An intedevger, number of imputation.
penalty	The method for variable selection,choose from "lasso" or "alasso".
lamda.vec	Optimal tuning parameter for penalty,default seq(1,4,length.out=12).
Gamma	Parameter for adjustment of the Adaptive Weights vector in adaptive LASSO,default c(0.5,1,1.5).

Value

A Vsmi_est object, contians estcoef and index_sig , estcoef for estimate coefficients and index_sig for selected variable index.

Examples

library(MASS)
library(mice)
library(qif)
entire<-generate_pwls_missing_data()
est_lasso<-PWLS(entire,penalty="lasso")
est_alasso <- PWLS(entire,penalty = "alasso")

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vsmi: Variable selection for multiple imputed data

Description

This is a package to implementation penalized weighted least-squares estimate for variable selection on correlated multiply imputed data and penalized estimating equations for generalized linear models with multiple imputation.

Functions

PEE:Penalized estimating equations for generalized linear models with multiple imputation

PWLS : Penalized weighted least-squares estimate for variable selection on correlated multiply imputed data

generate_pwls_missing_data : Generate example missing data for PWLS

generate_pee_missing_data : Generate example missing data for PEE

Author(s)

Maintainer: Haoyu Yang [email protected]

Authors:

• Mingyue Zhang

• Yang Li

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