### R code from vignette source 'RSGHB_HowTo.Rnw'

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### code chunk number 1: RSGHB_HowTo.Rnw:44-72 (eval = FALSE)
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## 
## library(RSGHB)
## 
## # Load example data
## data(choicedata)
## 
## # This data set is organized as one row per choice observation. This isn't necessary 
## # but it does make writing the likelihood function straightforward.
## head(choicedata)
## 
## # Any variables or transformations of variables from the choicedata data.frame that are
## # needed for evaluating the likelihood function can be extracted as a series of vectors
## # for convenience (e.g., TT1, TT2, TOLL2). Alternatively, the likelihood function could
## # refer to the choicedata data.frame directly. In this example, each alternative is
## # defined by travel times and toll costs.
## TT1   <- choicedata$tt1
## TT2   <- choicedata$tt2
## TOLL2 <- choicedata$toll2
## 
## # Similarly for the vectors of choices. Note that in this example, there are only two 
## # alternatives. Dummying coding the choice vector is not necessary but again makes
## # writing the likelihood function straightforward.
## choice1 <- (choicedata$Choice==1)
## choice2 <- (choicedata$Choice==2)
## 
## # Frequency of choices
## table(choicedata$Choice)
## 


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### code chunk number 2: RSGHB_HowTo.Rnw:83-102 (eval = FALSE)
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## 
## # The likelihood function
## likelihood <- function(fc, b) {
## 
##   # Assign fixed parameters to named variables for convenience    
##   cc <- 1
##   ASC1  <- fc[cc]; cc <- cc + 1
##   Btime <- fc[cc]; cc <- cc + 1
##   Btoll <- fc[cc]; cc <- cc + 1
## 
##   # Utility functions
##   v1 <- ASC1 + Btime * TT1                   
##   v2 <-        Btime * TT2 + Btoll * TOLL2   
##  
##   # MNL probability statement
##   p  <- (exp(v1) * choice1 + exp(v2) * choice2) / (exp(v1) + exp(v2))
## 	
##   return(p)
## }


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### code chunk number 3: RSGHB_HowTo.Rnw:109-146 (eval = FALSE)
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## 
## ### Setting control list for estimation (see ?doHB for more estimation options)
## 
## # The model name/description
## modelname <- "MNL"          
## 
## # gVarNamesFixed contains the names for the fixed (non-random) variables in the model
## # These will be used in the output and also when displaying iteration detail to 
## # the screen
## gVarNamesFixed <- c("ASC1", "BTime", "BCost")
## 
## # FC contains the starting values for the fixed coefficients
## FC <- c(0, 0, 0)                 
## 
## # gNCREP contains the number of iterations to use prior to convergence
## gNCREP <- 2500
## # gNEREP contains the number of iterations to keep for averaging after convergence 
## # has been reached
## gNEREP <- 2500
## # gNSKIP contains the number of iterations between retaining draws for averaging
## gNSKIP <- 1		       
## # gINFOSKIP controls how frequently to print info about the iteration process
## gINFOSKIP <- 10
## # gSeed ensures reproducible results
## gSeed <- 1987
## 
## # To simplify the doHB function call, all of the control arguments are placed in 
## # a single list that can be passed directly to doHB
## control <- list(modelname = modelname,
##                 gVarNamesFixed = gVarNamesFixed,
##                 FC = FC,
##                 gNCREP = gNCREP,
##                 gNEREP = gNEREP,
##                 gNSKIP = gNSKIP,
##                 gINFOSKIP = gINFOSKIP,
##                 gSeed = gSeed)
## 


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### code chunk number 4: RSGHB_HowTo.Rnw:157-163 (eval = FALSE)
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## 
## # Estimate model
## control$verbose <- FALSE
## control$nodiagnostics <- TRUE
## model <- doHB(likelihood, choicedata, control)
## 


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### code chunk number 5: RSGHB_HowTo.Rnw:185-189 (eval = FALSE)
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## 
## # Model iteration details
## head(model[["iter.detail"]])
## 


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### code chunk number 6: RSGHB_HowTo.Rnw:194-198 (eval = FALSE)
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## 
## # Plot model statistics
## plot(model)
## 


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### code chunk number 7: RSGHB_HowTo.Rnw:203-207 (eval = FALSE)
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## 
## # Plot parameter estimates (see ?plot.RSGHB for more uses)
## plot(model, type = "F")
## 


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### code chunk number 8: RSGHB_HowTo.Rnw:222-243 (eval = FALSE)
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## 
## likelihood <- function(fc, b) {
##      
##      # Note that the fc argument is still supplied, but is unused
##      
##      # Using b instead of fc is the only change
##      cc     <- 1
##      ASC1   <- b[, cc]; cc <- cc + 1
##      Btime  <- b[, cc]; cc <- cc + 1
##      Btoll  <- b[, cc]; cc <- cc + 1  
##   
##      # Utility functions
##      v1 <- ASC1 + Btime * TT1                   
##      v2 <-        Btime * TT2 + Btoll * TOLL2   
##  
##      # MNL probability statement
##      p  <- (exp(v1) * choice1 + exp(v2) * choice2) / (exp(v1) + exp(v2))
##      
##      return(p)
## }
## 


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### code chunk number 9: RSGHB_HowTo.Rnw:250-301 (eval = FALSE)
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## ### Setting control list for estimation (see ?doHB for more estimation options)
## 
## # The model name/description
## modelname <- "MMNL"
## 
## # gVarNamesNormal provides names for the random parameters in the same way
## # gVarNamesFixed does for the fixed parameters
## gVarNamesNormal <- c("ASC1","BTime","BCost")
## 
## # svN contains the starting values for the means of the normal distributions for each 
## # of the random parameters
## svN <- c(0, 0, 0)
## 
## # gDIST specifies the type of continuous distribution to use for the random parameters
## # gDIST must have an entry for each value in gVarNamesNormal
## # The options are:
## # 1. normal
## # 2. log-nomal
## # 3. negative log-normal
## # 4. normal with all values below zero massed at zero
## # 5. normal with all values greater than zero massed at zero
## # 6. Johnson SB with a specified min and max
## 
## # In this example, normal distributions are used for all 3 parameters
## gDIST <- c(1, 1, 1)
## 
## # gNCREP contains the number of iterations to use prior to convergence
## gNCREP <- 2500
## # gNEREP contains the number of iterations to keep for averaging after convergence 
## # has been reached
## gNEREP <- 2500
## # gNSKIP contains the number of iterations between retaining draws for averaging
## gNSKIP <- 1		       
## # gINFOSKIP controls how frequently to print info about the iteration process
## gINFOSKIP <- 10
## # gSeed ensures reproducible results
## gSeed <- 1987
## 
## # To simplify the doHB function call, all of the control parameters are placed in
## # a single list that can be passed directly to doHB
## control <- list(modelname = modelname,
##                 gVarNamesNormal = gVarNamesNormal,
##                 gDIST = gDIST,
##                 svN = svN,
##                 gNCREP = gNCREP,
##                 gNEREP = gNEREP,
##                 gNSKIP = gNSKIP,
##                 gINFOSKIP = gINFOSKIP,
##                 gSeed = gSeed)
## 
## 


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### code chunk number 10: RSGHB_HowTo.Rnw:312-318 (eval = FALSE)
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## 
## # Estimate model
## control$verbose <- FALSE
## control$nodiagnostics <- TRUE
## model <- doHB(likelihood, choicedata, control)
## 


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### code chunk number 11: RSGHB_HowTo.Rnw:336-343 (eval = FALSE)
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## 
## # Model iteration details
## head(model[["iter.detail"]])
## 
## # Plot model statistics
## plot(model)
## 


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### code chunk number 12: RSGHB_HowTo.Rnw:348-355 (eval = FALSE)
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## 
## # Sample-level means
## head(model[["A"]])
## 
## # Plot sample-level means
## plot(model, type = "A")
## 


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### code chunk number 13: RSGHB_HowTo.Rnw:360-367 (eval = FALSE)
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## 
## # Average individual-level draws
## head(model[["B"]])
## 
## # Standard deviations of individual-level draws
## head(model[["Bsd"]])
## 


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### code chunk number 14: RSGHB_HowTo.Rnw:374-381 (eval = FALSE)
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## 
## # Average individual-level draws (transformed; if applicable)
## head(model[["C"]])
## 
## # Standard deviations of individual-level draws (transformed; if applicable)
## head(model[["Csd"]])
##