| Type: | Package |
| Title: | Multivariate Multinomial Distribution Approximation and Imputation for Incomplete Categorical Data |
| Version: | 2.0.0 |
| Author: | Chaojie Wang |
| Maintainer: | Chaojie Wang <wang910930@163.com> |
| Description: | A method to impute the missingness in categorical data. Details see the paper <doi:10.4310/SII.2020.v13.n1.a2>. |
| License: | GPL-2 | GPL-3 [expanded from: GPL (≥ 2)] |
| Encoding: | UTF-8 |
| LazyData: | true |
| Depends: | stats |
| RoxygenNote: | 7.0.2 |
| NeedsCompilation: | no |
| Packaged: | 2020-05-02 00:53:16 UTC; saki |
| Repository: | CRAN |
| Date/Publication: | 2020-05-02 04:10:02 UTC |
Generate random dataset
Description
This function is used to generate random datasets following mixture of product multinomial distribution
Usage
GenerateData(
n,
p,
d,
k = 3,
theta = rdirichlet(1, rep(10, k)),
psi = InitialPsi(p, d, k)
)
Arguments
n |
- number of samples |
p |
- number of variables |
d |
- a vector which denotes the number of categories for each variable. It could be distinct among variables. |
k |
- number of latent classes |
theta |
- probability for latent class |
psi |
- probability for specific category |
Value
data - generated random dataset, a matrix with n rows and p columns.
Examples
# dimension parameters
n<-200; p<-5; d<-rep(2,p);
# generate complete data
Complete<-GenerateData(n, p, d, k = 3)
Imputation
Description
This function is used to perform multiple imputation for missing data given the joint distribution.
Usage
Imputation(data, theta, psi)
Arguments
data |
- incomplete dataset |
theta |
- vector of probability for each component |
psi |
- specific probability for each variable in each component |
Value
ImputedData - dataset has been imputated.
initial psi
Description
This function creates a psi list in that each component has equal weight
Usage
InitialPsi(p, d, k)
Arguments
p |
- number of variables |
d |
- a vector which denotes the number of categories for each variable. It could be distinct among variables. |
k |
- number of components |
Value
psi - a list in that each component has equal weight
Real application dataset
Description
This is a real application dataset. The source of original data is the ratings dataset in (Harper and Konstan (2016) <DOI:10.1145/2827872>). This dataset is used to evaluate the performance of package in real applications.
Author(s)
Chaojie Wang
Estimate theta and psi in multinomial mixture model
Description
This function is used to estimate theta and psi in multinomial mixture model given the number of components k.
Usage
ParEst(data, d, k, TT = 1000)
Arguments
data |
- data in matrix formation with n rows and p columns |
d |
- number of categories for each variable |
k |
- number of components |
TT |
- number of iterations in Gibbs sampler, default value is 1000. T should be an even number for 'burn-in'. |
Value
theta - vector of probability for each component
psi - specific probability for each variable in each component
Examples
# dimension parameters
n<-200; p<-5; d<-rep(2,p);
# generate complete data
Complete<-GenerateData(n, p, d, k = 3)
# mask percentage of data at MCAR
Incomplete<-Complete
Incomplete[sample(1:n*p,0.2*n*p,replace = FALSE)]<-NA
# k identify
K<-kIdentifier(data = Incomplete, d, TT = 10)
Par<-ParEst(data = Incomplete, d, k = K$k_est, TT = 10)
Identify the suitable number of components k
Description
This function is used to find the suitable number of components k.
Usage
kIdentifier(data, d, TT = 1000, alpha = 0.25)
Arguments
data |
- data in matrix formation with n rows and p columns |
d |
- number of categories for each variable |
TT |
- number of iterations in Gibbs sampler, default value is 1000. T should be an even number for 'burn-in'. |
alpha |
- hyperparameter that could be regarded as the pseudo-count of the number of samples in the new component |
Value
k_est - posterior estimation of k
k_track - track of k in the iteration process
Examples
# dimension parameters
n<-200; p<-5; d<-rep(2,p);
# generate complete data
Complete<-GenerateData(n, p, d, k = 3)
# mask percentage of data at MCAR
Incomplete<-Complete
Incomplete[sample(1:n*p,0.2*n*p,replace = FALSE)]<-NA
# k identify
K<-kIdentifier(data = Incomplete, d, TT = 10)
Estimate theta and psi in multinomial mixture model
Description
This function is generate random sample from Dirichlet distribution
Usage
rdirichlet(n = 1, alpha = c(1, 1))
Arguments
n |
- sample size |
alpha |
- parameters in Dirichlet distribution |
Value
out - generated data
Examples
# dimension parameters
rdirichlet(n=10,alpha=c(1,1,1))