1d:T7e9,# Re-scales the category scale values and Phi after convergence of the nominal model This auxillary function only applies to nominal models after estimating the parameters of the model. During estimation that scaling identification constraint is put on conditional variances (i.e., phi_mm) such that they equal 1. This function provide an alternative identification constraint after the algorithm has converged. This function allow a user to tease apart the strength and structure of associations. The alternative scaling identification constraint is to set the sum of category scale values equals 0 and the sum of squares equal to 1. The phi parameters are adjusted accordingly. Only one item per trait should be selected for the identification constraint and this is indicated by the object anchor. ```r reScaleItem(model.fit, anchor) ``` ## Arguments - `model.fit`: Model object for a nominal model - `anchor`: Indicator of item(s) to place scaling constraint on. ## Returns sNu Re-scaled category scale values sPhi.mat Re-scale phi matrix (conditional covariance matrix) ## Examples ```r #--- 3 items from depression, anxiety, and stress scales # for 250 cases out of possible 1000 data(dass) inData <- dass[1:250,c("d1", "d2", "d3", "a1","a2","a3","s1","s2","s3")] inTraitAdj <- matrix(1, nrow=1, ncol=1) inItemTraitAdj <- matrix(1, nrow=9, ncol=1) #--- nominal response model n1 <- ple.lma(inData, model.type="nominal",inItemTraitAdj,inTraitAdj,tol=1e-03) anchor <- c(1,0,0,0,0,0,0,0,0) reScaleItem(model.fit=n1, anchor) #--- Multidimensional models inTraitAdj <- matrix(1, nrow=3, ncol=3) dpress <- matrix(c(1,0,0), nrow=3, ncol=3, byrow=TRUE) anxiety <- matrix(c(0,1,0), nrow=3, ncol=3, byrow=TRUE) stress <- matrix(c(0,0,1), nrow=3, ncol=3, byrow=TRUE) das <- list(dpress, anxiety, stress) inItemTraitAdj <- rbind(das[[1]], das[[2]], das[[3]]) n3 <- ple.lma(inData, model.type="nominal", inItemTraitAdj, inTraitAdj, tol=1e-03) anchor <- c(1,0,0, 0,1,0, 1,0,0) reScaleItem(model.fit=n3, anchor) ```

reScaleItem function