Convert Between Index and Model Indicator
These functions convert the binary vector, indicating which terms are in the current model, to the hexadecimal model indicator, and vice versa.
index2model(index) model2index(model,dig)
index |
A binary vector, of the same length as the number of log-linear parameters in the maximal model, indicating which parameters are present in the model to be converted to a hexadecimal. |
dig |
A scalar argument giving the number of columns of the design matrix for the maximal model. |
model |
A character string giving a hexadecimal model indicator. |
index2model will return a hexadecimal model indicator.
model2index will return a binary vector, of the same length as the number of log-linear parameters
in the maximal model, indicating which parameters are present in the model converted from hexadecimal.
This function will not typically be called by the user.
Antony M. Overstall A.M.Overstall@soton.ac.uk.
data(ScotPWID) ## Load the ScotPWID data maximal.mod<-glm(y~(S1+S2+S3+S4+Region+Gender+Age)^2,family=poisson,contrasts=list( S1="contr.sum",S2="contr.sum",S3="contr.sum",S4="contr.sum", Region="contr.sum",Gender="contr.sum",Age="contr.sum"),data=ScotPWID,x=TRUE) ## Fit the maximal model containing all two-way interactions. big.X<-maximal.mod$x ## Set the design matrix under the maximal model index<-formula2index(big.X=big.X, formula=~S1+S2+S3+S4+Region+Gender+Age+S1:S2+S1:Age+S2:Gender+S3:S4+S4:Age, data=ScotPWID) ## Find the index under the model with the following interactions: ## S1:S2 ## S1:Age ## S2:Gender ## S3:S4 ## S4:Age index ## Print the index, will get: # [1] 1 1 1 1 1 1 1 1 1 0 0 0 0 1 0 0 0 1 0 1 0 0 0 0 0 1 0 0 0 modind<-index2model(index) ## Find the hexadecimal model indicator modind ## Print it, will get: #[1] "1ff08a08" ## Convert back to index model2index(model=modind,dig=length(index)) ## Will get: # [1] 1 1 1 1 1 1 1 1 1 0 0 0 0 1 0 0 0 1 0 1 0 0 0 0 0 1 0 0 0
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