| Title: | Regression-Based Approach for Testing the Type of Missing Data |
|---|---|
| Description: | The regression-based (RB) approach is a method to test the missing data mechanism. This package contains two functions that test the type of missing data (Missing Completely At Random vs Missing At Random) on the basis of the RB approach. The first function applies the RB approach independently on each variable with missing data, using the completely observed variables only. The second function tests the missing data mechanism globally (on all variables with missing data) with the use of all available information. The algorithm is adapted both to continuous and categorical data. |
| Authors: | Serguei Rouzinov and André Berchtold |
| Maintainer: | Serguei Rouzinov <[email protected]> |
| License: | GPL-3 |
| Version: | 1.1 |
| Built: | 2024-12-06 06:49:08 UTC |
| Source: | CRAN |
This function tests the missing completely at random (MCAR) vs missing at random (MAR) by using the complete variables only.
RBtest(data)RBtest(data)
data |
Dataset with at least one complete variable. The variables could be either continuous, categorical or a mix of both. |
A list of the following elements:
abs.nbrMD The absolute number of missing data per variable.
rel.nbrMD The percentage of missing data per variable.
type Vector of the same length than the number of variables of the dataset, where '0' is for variables with MCAR data, '1' is for variables with MAR data and '-1' is for complete variables.
Serguei Rouzinov [email protected] and
André Berchtold [email protected]
Maintainer: Serguei Rouzinov [email protected]
set.seed(60) n<-100 # sample size r<-5 # number of variables mis<-0.2 # frequency of missing data mydata<-matrix(NA, nrow=n, ncol=r) # mydata is a matrix of r variables # following a U(0,1) distribution for (i in c(1:r)){ mydata[,i]<-runif(n,0,1) } bin.var<-sample(LETTERS[1:2],n,replace=TRUE, prob=c(0.3,0.7)) # binary variable [A,B]. # The probability of being in one of the categories is 0.3. cat.var<-sample(LETTERS[1:3],n,replace=TRUE, prob=c(0.5,0.3,0.2)) # categorical variable [A,B,C]. num.var<-runif(n,0,1) # Additional continuous variable following a U(0,1) distribution mydata<-cbind.data.frame(mydata,bin.var,cat.var,num.var,stringsAsFactors = TRUE) # dataframe with r+3 variables colnames(mydata)=c("v1","v2","X1","X2","X3","X4","X5", "X6") # names of columns # MCAR on X1 and X4 by using v1 and v2. MAR on X3 and X5 by using X2 and X6. mydata$X1[which(mydata$v1<=sort(mydata$v1)[mis*n])]<-NA # X1: (mis*n)% of MCAR data. # All data above the (100-mis)th percentile in v1 are selected # and the corresponding observations in X1 are replaced with missing data. mydata$X3[which(mydata$X2<=sort(mydata$X2)[mis*n])]<-NA # X3: (mis*n)% of MAR data. # All data above the (100-mis)th percentile in X2 are selected # and the corresponding observations in X3 are replaced with missing data. mydata$X4[which(mydata$v2<=sort(mydata$v2)[mis*n])]<-NA # X4: (mis*n)% of MCAR data. # All data above the (100-mis)th percentile in v2 are selected # and the corresponding observations in X4 are replaced with missing data. mydata$X5[which(mydata$X6<=sort(mydata$X6)[mis*n])]<-NA # X5: (mis*n)% of MAR data. # All data above the (100-mis)th percentile in X6 are selected # and the corresponding observations in X5 are replaced with missing data. mydata$v1=NULL mydata$v2=NULL RBtest(mydata)set.seed(60) n<-100 # sample size r<-5 # number of variables mis<-0.2 # frequency of missing data mydata<-matrix(NA, nrow=n, ncol=r) # mydata is a matrix of r variables # following a U(0,1) distribution for (i in c(1:r)){ mydata[,i]<-runif(n,0,1) } bin.var<-sample(LETTERS[1:2],n,replace=TRUE, prob=c(0.3,0.7)) # binary variable [A,B]. # The probability of being in one of the categories is 0.3. cat.var<-sample(LETTERS[1:3],n,replace=TRUE, prob=c(0.5,0.3,0.2)) # categorical variable [A,B,C]. num.var<-runif(n,0,1) # Additional continuous variable following a U(0,1) distribution mydata<-cbind.data.frame(mydata,bin.var,cat.var,num.var,stringsAsFactors = TRUE) # dataframe with r+3 variables colnames(mydata)=c("v1","v2","X1","X2","X3","X4","X5", "X6") # names of columns # MCAR on X1 and X4 by using v1 and v2. MAR on X3 and X5 by using X2 and X6. mydata$X1[which(mydata$v1<=sort(mydata$v1)[mis*n])]<-NA # X1: (mis*n)% of MCAR data. # All data above the (100-mis)th percentile in v1 are selected # and the corresponding observations in X1 are replaced with missing data. mydata$X3[which(mydata$X2<=sort(mydata$X2)[mis*n])]<-NA # X3: (mis*n)% of MAR data. # All data above the (100-mis)th percentile in X2 are selected # and the corresponding observations in X3 are replaced with missing data. mydata$X4[which(mydata$v2<=sort(mydata$v2)[mis*n])]<-NA # X4: (mis*n)% of MCAR data. # All data above the (100-mis)th percentile in v2 are selected # and the corresponding observations in X4 are replaced with missing data. mydata$X5[which(mydata$X6<=sort(mydata$X6)[mis*n])]<-NA # X5: (mis*n)% of MAR data. # All data above the (100-mis)th percentile in X6 are selected # and the corresponding observations in X5 are replaced with missing data. mydata$v1=NULL mydata$v2=NULL RBtest(mydata)
This function tests MCAR vs MAR by using both the complete and incomplete variables.
RBtest.iter(data,K)RBtest.iter(data,K)
data |
Dataset with at least one complete variable. |
K |
Maximum number of iterations. |
A list of the following elements:
abs.nbrMD The absolute quantity of missing data per variable.
rel.nbrMD The percentage of missing data per variable.
K The maximum admitted number of iterations.
iter The final number of iterations.
type.final Vector of the same length than the number of variables of the dataset, where '0' is for variables with MCAR data, '1' is for variables with MAR data and '-1' is for complete variables.
TYPE.k Dataframe containing the type of missing data after each iteration. Each row is a vector having the same length than the number of variables of the dataset, where '0' is for variables with MCAR data, '1' is for variables with MAR data and '-1' is for complete variables.
set.seed(60) n<-100 # sample size r<-5 # number of variables mis<-0.2 # frequency of missing data mydata<-matrix(NA, nrow=n, ncol=r) # mydata is a matrix of r variables # following a U(0,1) distribution for (i in c(1:r)){ mydata[,i]<-runif(n,0,1) } bin.var<-sample(LETTERS[1:2],n,replace=TRUE, prob=c(0.3,0.7)) # binary variable [A,B]. # The probability of being in one of the categories is 0.3. cat.var<-sample(LETTERS[1:3],n,replace=TRUE, prob=c(0.5,0.3,0.2)) # categorical variable [A,B,C]. # The vector of probabilities of occurence A, B and C is (0.5,0.3,0.7). num.var<-runif(n,0,1) # Additional continuous variable following a U(0,1) distribution mydata<-cbind.data.frame(mydata,bin.var,cat.var,num.var,stringsAsFactors = TRUE) # dataframe with r+3 variables colnames(mydata)=c("v1","v2","X1","X2","X3","X4","X5", "X6") # names of columns # MCAR on X1 and X4 by using v1 and v2. MAR on X3 and X5 by using X2 and X6. mydata$X1[which(mydata$v1<=sort(mydata$v1)[mis*n])]<-NA # X1: (mis*n)% of MCAR data. # All data above the (100-mis)th percentile in v1 are selected # and the corresponding observations in X1 are replaced with missing data. mydata$X3[which(mydata$X2<=sort(mydata$X2)[mis*n])]<-NA # X3: (mis*n)% of MAR data. # All data above the (100-mis)th percentile in X2 are selected # and the corresponding observations in X3 are replaced with missing data. mydata$X4[which(mydata$v2<=sort(mydata$v2)[mis*n])]<-NA # X4: (mis*n)% of MCAR data. # All data above the (100-mis)th percentile in v2 are selected # and the corresponding observations in X4 are replaced with missing data. mydata$X5[which(mydata$X6<=sort(mydata$X6)[mis*n])]<-NA # X5: (mis*n)% of MAR data. # All data above the (100-mis)th percentile in X6 are selected # and the corresponding observations in X5 are replaced with missing data. mydata$v1=NULL mydata$v2=NULL RBtest.iter(mydata,5)set.seed(60) n<-100 # sample size r<-5 # number of variables mis<-0.2 # frequency of missing data mydata<-matrix(NA, nrow=n, ncol=r) # mydata is a matrix of r variables # following a U(0,1) distribution for (i in c(1:r)){ mydata[,i]<-runif(n,0,1) } bin.var<-sample(LETTERS[1:2],n,replace=TRUE, prob=c(0.3,0.7)) # binary variable [A,B]. # The probability of being in one of the categories is 0.3. cat.var<-sample(LETTERS[1:3],n,replace=TRUE, prob=c(0.5,0.3,0.2)) # categorical variable [A,B,C]. # The vector of probabilities of occurence A, B and C is (0.5,0.3,0.7). num.var<-runif(n,0,1) # Additional continuous variable following a U(0,1) distribution mydata<-cbind.data.frame(mydata,bin.var,cat.var,num.var,stringsAsFactors = TRUE) # dataframe with r+3 variables colnames(mydata)=c("v1","v2","X1","X2","X3","X4","X5", "X6") # names of columns # MCAR on X1 and X4 by using v1 and v2. MAR on X3 and X5 by using X2 and X6. mydata$X1[which(mydata$v1<=sort(mydata$v1)[mis*n])]<-NA # X1: (mis*n)% of MCAR data. # All data above the (100-mis)th percentile in v1 are selected # and the corresponding observations in X1 are replaced with missing data. mydata$X3[which(mydata$X2<=sort(mydata$X2)[mis*n])]<-NA # X3: (mis*n)% of MAR data. # All data above the (100-mis)th percentile in X2 are selected # and the corresponding observations in X3 are replaced with missing data. mydata$X4[which(mydata$v2<=sort(mydata$v2)[mis*n])]<-NA # X4: (mis*n)% of MCAR data. # All data above the (100-mis)th percentile in v2 are selected # and the corresponding observations in X4 are replaced with missing data. mydata$X5[which(mydata$X6<=sort(mydata$X6)[mis*n])]<-NA # X5: (mis*n)% of MAR data. # All data above the (100-mis)th percentile in X6 are selected # and the corresponding observations in X5 are replaced with missing data. mydata$v1=NULL mydata$v2=NULL RBtest.iter(mydata,5)