rTableICC (version 1.0.9)

rTableICC-package: Random Generation of R x C and 2 x 2 x K Contingency Tables

Description

Contains functions for random generation of R x C and 2 x 2 x K contingency tables. In addition to the generation of contingency tables over predetermined intraclass-correlated clusters, it is possible to generate contingency tables without intraclass correlations under product multinomial, multinomial, and Poisson sampling plans. It also consists of a function for generation of random data from a given discrete probability distribution function (Demirhan, 2016).

Author

Haydar Demirhan

Maintainer: Haydar Demirhan <[e-mail]>

Details

To generate 2 x 2 x K and R x C contingency tables with intraclass-correlated observations under product multinomial, multinomial or Poisson sampling plans, respectively use rTableICC.2x2xK and rTableICC.RxC functions.

To generate 2 x 2 x K and R x C contingency tables without intraclass-correlated observations product multinomial, multinomial or Poisson sampling plans, respectively use rTable.2x2xK and rTable.RxC functions.

To generate random data from an empirical probability function, use rDiscrete function.

References

Agresti A. (2002) Categorical Data Analysis, Wiley, New York.

Altham, P.M. (1976) Discrete variable analysis for individuals grouped into families, Biometrika 63, 263--269.

Nandram, B. and Choi, J.W. (2006) Bayesian analysis of a two-way categorical table incorporating intraclass correlation, Journal of Statistical Computation and Simulation 76, 233--249.

Demirhan, H. (2016) rTableICC: An R package for random generation of 2x2xK and RxC contingency tables, The R Journal 8, 1, 48--63.

Demirhan, H. (2013) Bayesian estimation of log odds ratios over two-way contingency tables with intraclass-correlated cells, Journal of Applied Statistics 40, 2303--2316.

Demirhan, H. and Hamurkaroglu, C. (2008) Bayesian estimation of log odds ratios from RxC and 2 x 2 x K contingency tables, Statistica Neerlandica 62, 405--424.

Kroese D.P., Taimre T., Botev Z.I. (2011) Handbook of Monte Carlo Methods, Wiley, New York.

See Also

rTableICC.2x2xK, rTableICC.RxC, rTable.2x2xK, rTable.RxC, rDiscrete

Examples

# --- For more examples, please refer to specific functions ---

# --- Generate a random value from given probability function ---
p = c(0.23,0.11,0.05,0.03,0.31,0.03,0.22,0.02)
rDiscrete(n=2,pf=p)

# --- Generate a 2x2x4 contingency table under multinomial sampling plan with ICCs ---
num.centers=4                                # Number of centers
max.cluster.size=9                           # Maximum allowed cluster size
num.cluster=95                               # Total number of clusters under each  
                                             #  center is equal across the centers 
ICCs=array(0.1,dim=max.cluster.size)         # Assign equal ICCs for this exmaple
ICCs[1]=0                                    # Assign zero ICC to clusters with 
                                             #  one individual 
sampl="Multinomial"                          # Generate table under multinomial 
                                             #  sampling plan
num.obs=900                                  # Number of observations to be generated
cell.prob=array(0.0625,dim=c(num.centers,4)) # Cell probabilities sum up to one 

x=rTableICC.2x2xK(p=cell.prob,theta=ICCs,M=num.cluster,sampling=sampl,
                   N=num.obs,print.regular=TRUE,print.raw=FALSE)
print(x)       

# --- Generate a 2x3 contingency table under product multinomial sampling plan  ---
# --- with fixed row margins with ICCs                                          ---
max.cluster.size=9                           # Maximum allowed cluster size
num.cluster=12                               # Total number of clusters 
ICCs=array(0.1,dim=max.cluster.size)         # Assign equal ICCs for this exmaple
ICCs[1]=0                                    # Assign zero ICC to clusters with 
                                             #  one individual 
sampl="Product"                              # Generate table under product 
                                             #  multinomial sampling plan
row=c(12,12)                                 # Fixed row margins
cell.prob=array(0,dim=c(2,3))                # Cell probabilities sum up to one 
cell.prob[1,1:2]=0.2
cell.prob[1,3]=0.1
cell.prob[2,1:2]=0.1
cell.prob[2,3]=0.3                           # Marginal and cell probabilities 
                                             #  should match to each other

y=rTableICC.RxC(p=cell.prob,theta=ICCs,row.margins=row,M=num.cluster,
                 sampling=sampl,print.regular=TRUE,print.raw=FALSE)
print(y)

# --- Generate a 2x2x8 contingency table under Poisson sampling plan without ICC ---
num.centers=8                               # Number of centers
sampl="Poisson"                             # Generate table under Poisson 
                                            #  sampling plan
cell.mean=array(3,dim=c(2,2,num.centers))   # Enter mean number of individuals 
                                            #  in each cell

z=rTable.2x2xK(sampling=sampl,lambda=cell.mean)
print(z)

# --- Generate a 5x7 contingency table under multinomial sampling plan without ICC ---
num.row=5                                   # Number of rows
num.col=7                                   # Number of columns
sampl="Multinomial"                         # Generate table under multinomial 
                                            #  sampling plan
cell.prob=array(1/35,dim=c(num.row,num.col))# Enter cell probabilities in RxC 
                                            #  format 
num.obs=124                                 # Number of observations

u=rTable.RxC(p=cell.prob,sampling=sampl,N=num.obs)
print(u)