## ----setup, include = FALSE---------------------------------------------------
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>"
)
## ----libraries----------------------------------------------------------------
library(SPAS)
## ----loaddata-----------------------------------------------------------------
harrison.2011.chinook.F.csv <- textConnection("
4 , 2 , 1 , 1 , 0 , 0 , 130
12 , 7 , 14 , 1 , 3 , 0 , 330
7 , 11 , 41 , 9 , 1 , 1 , 790
1 , 13 , 40 , 12 , 9 , 1 , 667
0 , 1 , 8 , 8 , 3 , 0 , 309
0 , 0 , 0 , 0 , 0 , 1 , 65
744 , 1187 , 2136 , 951 , 608 , 127 , 0")
har.data <- as.matrix(read.csv(harrison.2011.chinook.F.csv, header=FALSE))
har.data
## ----fit1,results="hide"------------------------------------------------------
mod..1 <- SPAS.fit.model(har.data,
model.id="No restrictions",
row.pool.in=1:6, col.pool.in=1:6)
## ----mod1p--------------------------------------------------------------------
SPAS.print.model(mod..1)
## ----str1---------------------------------------------------------------------
cat("Names of objects at highest level\n")
names(mod..1)
cat("\n\nNames of estimates (both beta and real)\n")
names(mod..1$est)
cat("\n\nNames of real estimates\n")
names(mod..1$est$real)
## ----fit2,results="hide"------------------------------------------------------
mod..2 <- SPAS.fit.model(har.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(mod..2)
## ----mod3, echo=TRUE----------------------------------------------------------
mod..3 <- SPAS.fit.model(har.data, model.id="A single row",
row.pool.in=rep(1, nrow(har.data)-1),
col.pool.in=c(1,2,3,4,56,56))
SPAS.print.model(mod..3)
## -----------------------------------------------------------------------------
mod..4 <- SPAS.fit.model(har.data, model.id="Pooled Peteren",
row.pool.in=rep(1, nrow(har.data)-1),
col.pool.in=rep(1, ncol(har.data)-1))
SPAS.print.model(mod..4)
## -----------------------------------------------------------------------------
mod..5 <- SPAS.fit.model(har.data, model.id="Logical Pooling some rows",
row.pool.in=c("12","12","3","4","56","56"),
row.physical.pool=FALSE,
col.pool.in=c(1,2,3,4,56,56))
SPAS.print.model(mod..5)
## -----------------------------------------------------------------------------
mod..6 <- SPAS.fit.model(har.data, model.id="A single row - Logical Pool",
row.pool.in=rep(1, nrow(har.data)-1), row.physical.pool=FALSE,
col.pool.in=c(1,2,3,4,56,56))
SPAS.print.model(mod..6)
mod..7 <- SPAS.fit.model(har.data, model.id="Pooled Peteren - Logical Pool",
row.pool.in=rep(1, nrow(har.data)-1), row.physical.pool=FALSE,
col.pool.in=rep(1, ncol(har.data)-1))
SPAS.print.model(mod..7)
## -----------------------------------------------------------------------------
mod..8 <- SPAS.fit.model(har.data, model.id="Logical Pooling pairs rows",
row.pool.in=c("12","12","34","34","56","56"),
row.physical.pool=FALSE,
col.pool.in=c(1,2,3,4,56,56))
SPAS.print.model(mod..8)
## ----echo=TRUE----------------------------------------------------------------
model.list <- mget( ls()[grepl("^mod\\.\\.",ls())])
names(model.list)
report <- plyr::ldply(model.list, function(x){
#browser()
data.frame(#version=x$version,
date = as.Date(x$date),
model.id = x$model.info$model.id,
s.a.pool =-1+nrow(x$fit.setup$pooldata),
t.p.pool =-1+ncol(x$fit.setup$pooldata),
logL.cond = x$model.info$logL.cond,
np = x$model.info$np,
AICc = x$model.info$AICc,
gof.chisq = round(x$gof$chisq,1),
gof.df = x$gof$chisq.df,
gof.p = round(x$gof$chisq.p,3),
kappa.after.lp = round(x$kappa.after.lp),
Nhat = round(x$est$real$N),
Nhat.se = round(x$se $real$N))
})
report