## ----setup, include = FALSE--------------------------------------------------- knitr::opts_chunk$set( collapse = TRUE, comment = "#>" ) ## ----libraries---------------------------------------------------------------- library(SPAS) ## ----loaddata----------------------------------------------------------------- conne.data.csv <- textConnection(" 149, 126, 0, 0, 0, 0, 1561 0, 308, 65, 0, 0, 0, 1235 0, 0, 161, 77, 0, 0, 884 0, 0, 0, 67, 7, 0, 215 0, 0, 0, 0, 17, 3, 71 0, 0, 0, 0, 0, 13, 16 1895, 8503, 2184, 525, 155, 118, 0") conne.data <- as.matrix(read.csv(conne.data.csv, header=FALSE)) ## ----fit1,results="hide"------------------------------------------------------ mod1 <- SPAS.fit.model(conne.data, model.id="No restrictions", row.pool.in=1:6, col.pool.in=1:6) ## ----mod1p-------------------------------------------------------------------- SPAS.print.model(mod1) ## ----str1--------------------------------------------------------------------- cat("Names of objects at highest level\n") names(mod1) cat("\n\nNames of estimates (both beta and real)\n") names(mod1$est) cat("\n\nNames of real estimates\n") names(mod1$est$real) ## ----fit2,results="hide"------------------------------------------------------ mod2 <- SPAS.fit.model(conne.data, model.id="Pooling some rows", row.pool.in=c("12","12","3","4","56","56"), col.pool.in=c(1,2,3,4,56,56)) ## ----mod2p-------------------------------------------------------------------- SPAS.print.model(mod2) ## ----mod3,results='hide'------------------------------------------------------ mod3 <- SPAS.fit.model(conne.data, model.id="A single row", row.pool.in=rep(1, nrow(conne.data)-1), col.pool.in=c(1,2,3,4,56,56)) ## ----mod3p-------------------------------------------------------------------- SPAS.print.model(mod3) ## ----mod4,results='hide'------------------------------------------------------ mod4 <- SPAS.fit.model(conne.data, model.id="Pooled Peteren", row.pool.in=rep(1, nrow(conne.data)-1), col.pool.in=rep(1, ncol(conne.data)-1)) ## ----mod4p-------------------------------------------------------------------- SPAS.print.model(mod4)