Function to simulate big data under linear, logistic and Poisson regression for sampling. Covariate data X is through Normal or Uniform distribution for linear regression. Covariate data X is through Exponential or Normal or Uniform distribution for logistic regression. Covariate data X is through Normal or Uniform distribution for Poisson regression.
Arguments
- Dist
a character value for the distribution "Normal" or "Uniform or "Exponential"
- Dist_Par
a list of parameters for the distribution that would generate data for covariate X
- No_Of_Var
number of variables
- Beta
a vector for the model parameters, including the intercept
- N
the big data size
- family
a character vector for "linear", "logistic" and "poisson" regression from Generalised Linear Models
Value
The output of GenGLMData gives a list of
Basic a list of outputs based on the inputs and Beta Estimates for all models
Complete_Data a matrix for Y and X
Details
Big data for the Generalised Linear Models are generated by the "linear", "logistic" and "poisson" regression types.
We have limited the covariate data generation for linear regression through normal and uniform distribution, logistic regression through exponential, normal and uniform and Poisson regression through normal and uniform distribution.
References
Lee Y, Nelder JA (1996). “Hierarchical generalized linear models.” Journal of the Royal Statistical Society Series B: Statistical Methodology, 58(4), 619--656.
Examples
Dist<-"Normal"; Dist_Par<-list(Mean=0,Variance=1,Error_Variance=0.5)
No_Of_Var<-2; Beta<-c(-1,2,1); N<-5000; Family<-"linear"
Results<-GenGLMdata(Dist,Dist_Par,No_Of_Var,Beta,N,Family)
Dist<-"Normal"; Dist_Par<-list(Mean=0,Variance=1)
No_Of_Var<-2; Beta<-c(-1,2,1); N<-5000; Family<-"logistic"
Results<-GenGLMdata(Dist,Dist_Par,No_Of_Var,Beta,N,Family)
Dist<-"Normal";
No_Of_Var<-2; Beta<-c(-1,2,1); N<-5000; Family<-"poisson"
Results<-GenGLMdata(Dist,NULL,No_Of_Var,Beta,N,Family)