PLADMM.R
In PlackettLuce: Plackett-Luce Models for Rankings

## ----setup, include = FALSE---------------------------------------------------
library(knitr)
# if render using rmarkdown, use output format to decide table format
table.format <- opts_knit$get("rmarkdown.pandoc.to")
if (!identical(table.format, "latex")) table.format <- "html"
opts_knit$set(knitr.table.format = table.format)
opts_chunk$set(message = FALSE)

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library(prefmod)
head(salad, 4)

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features <- data.frame(salad = LETTERS[1:4],
                       acetic = c(0.5, 0.5, 1, 0),
                       gluconic = c(0, 10, 0, 10))

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library(PlackettLuce)
standardPL <- pladmm(salad, ~ salad, data = features, rho = 8)
summary(standardPL)

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sum(exp(standardPL$x %*% coef(standardPL)))

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standardPL_PlackettLuce <- PlackettLuce(salad, npseudo = 0)
summary(standardPL_PlackettLuce)

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standardPL <- pladmm(salad, ~ salad, data = features, rho = 8, rtol = 1e-6)
summary(standardPL)

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itempar(standardPL)

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regressionPL <- pladmm(salad, ~ acetic + gluconic, data = features, rho = 8)
summary(regressionPL)

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anova(standardPL, regressionPL)

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features2 <- data.frame(salad = LETTERS[5:6],
                        acetic = c(0.5, 0),
                        gluconic = c(5, 5))

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predict(regressionPL, features2)

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fitted(regressionPL)

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predict(regressionPL, features2, type = "itempar", log  = FALSE, ref = NULL)

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predict(regressionPL, features2, type = "itempar", log  = FALSE, ref = NULL,
        se.fit = TRUE)

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set.seed(1)
judge_features <- data.frame(varC = rpois(nrow(salad), lambda = salad$C^2))

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grouped_salad <- group(as.rankings(salad), 1:nrow(salad))

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tree <- pltree(grouped_salad ~ .,
               worth = ~acetic + gluconic,
               data = list(judge_features, features),
               rho = 2, minsize = 10)
plot(tree, ylines = 2)

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tree

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regressionPL$pi

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regressionPL$tilde_pi