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runningFile.R
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runningFile.R
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library(BAS)
mround <- function(x, base) base * round(x / base)
data(galileo, package = "UsingR")
polynomial = function(x,coefs, raw = FALSE) {
if (inherits(coefs, "lm"))
coefs <- coef(coefs)
z <- cbind(1, poly(x, length(coefs) - 1))
tcrossprod(z, coefs)
dim(z)
length(coefs)
coefs %*% z
sum(coefs * z[1, ])
sum(coefs * z[2, ])
coefs %*% z
sum <- 0
for(i in 0:(length(coefs)-1)) {
sum <- sum + coefs[i+1]*x^i
}
sum
}
maxDegree <- 3L
resLm <- vector("list", maxDegree + 1)
resLm[[1]] <- lm(h.d ~ 1, data = galileo)
for (i in 1:maxDegree) {
fNew <- as.formula(sprintf("h.d ~ poly(init.h, %d, raw = TRUE)", i))
resLm[[i + 1]] <- lm(fNew, data = galileo)
}
xBreaks <- pretty(galileo$init.h)
xLimits <- range(xBreaks)
xValues <- seq(xLimits[1L], xLimits[2L], length.out = 2^10)
yValues <- do.call(cbind, lapply(resLm, polynomial, x = xValues))
df <- data.frame(
x = rep(xValues, maxDegree + 1L),
y = c(yValues),
order = rep(factor(seq_len(maxDegree + 1L)), each = length(xValues))
)
ggplot2::ggplot() +
ggplot2::geom_line(data = df, mapping = ggplot2::aes(x = x, y = y, group = order, color = order), inherit.aes = FALSE) +
jaspGraphs::geom_point(data = galileo, mapping = ggplot2::aes(x = init.h, y = h.d)) +
jaspGraphs::geom_rangeframe() +
jaspGraphs::themeJaspRaw(legend.position = "right")
raw <- FALSE
fullModel <- h.d ~ poly(init.h, maxDegree, raw = raw)
basObject <- BAS::bas.lm(
formula = fullModel,
data = galileo,
prior = "JZS",
modelprior = BAS::uniform()
)
# dput(names(which(lengths(basObject) == 8)))
namesToSubset <- c("which", "logmarg", "postprobs", "priorprobs", "sampleprobs",
"mse", "mle", "mle.se", "shrinkage", "size", "R2", "rank", "postprobs.RN",
"df")
idx <- extractModelIndices(basObject, maxDegree)
for (nm in namesToSubset)
basObject[[nm]] <- basObject[[nm]][idx]
basObject$postprobs <- basObject$postprobs / sum(basObject$postprobs)
basObject$priorprobs <- basObject$priorprobs / sum(basObject$priorprobs)
posteriorInclProb <- rev(cumsum(rev(basObject$postprobs)))
priorInclProb <- rev(cumsum(rev(basObject$priorprobs)))
inclBF <- (posteriorInclProb / (1 - posteriorInclProb)) / (priorInclProb / (1 - priorInclProb))
inclBF[1] <- NA_real_ # no evidence
debugonce(BAS:::predict.bas)
newData <- data.frame(init.h = xValues)
predictionsObj <- predict(basObject, newdata = newData, estimator = "BMA", top = length(basObject[["which"]]), se.fit = TRUE)
predictionsObj$Ypred
df <- data.frame(
x = rep(xValues, maxDegree + 1L),
y = c(t(predictionsObj[["Ypred"]])),
order = rep(factor(seq_len(maxDegree + 1L)), each = length(xValues))
)
ggplot2::ggplot() +
ggplot2::geom_line(data = df, mapping = ggplot2::aes(x = x, y = y, group = order, color = order), inherit.aes = FALSE) +
jaspGraphs::geom_point(data = galileo, mapping = ggplot2::aes(x = init.h, y = h.d)) +
jaspGraphs::geom_rangeframe() +
jaspGraphs::themeJaspRaw(legend.position = "right")
basObject$postprobs[3] + basObject$postprobs[4]
cumsum(basObject$postprobs)
basObject$X[, -1] == resLm[[5]]$model[, -1]
basObject$which <- basObject$which[idx]
basObject$mle[idx]
lapply(resLm, \(x) unname(coef(x)))
coefMat <- matrix(0, length(idx), maxDegree + 1L)
for (i in seq_along(idx)) {
coefs <- basObject$mle[[idx[i]]]
coefMat[i, seq_along(coefs)] <- coefs
}
xBreaks <- pretty(galileo$init.h)
xLimits <- range(xBreaks)
xValues <- seq(xLimits[1L], xLimits[2L], length.out = 2^10)
coefsList <- galileoExtractCoefficientsList(list(basObject = basObject), maxDegree)
coefs <- coefsList[[4]]
z <- poly(galileo$init.h, degree = maxDegree, raw = raw)
coefs[1] + scale(z, scale = FALSE) %*% coefs[-1]
ee <- basObject
ee$pos
predict()
debugonce(BAS:::predict.bas)
predict(basObject, estimator = "HPM")$fit
newData <- model.matrix(~ 0 + poly(x, maxDegree, raw = raw), data.frame(x = galileo$init.h))
newData <- sweep(newData, 2L, colMeans(newData))
(newData %*% coefs[-1]) * basObject$shrinkage[idx[length(idx)]] + coefs[1]
model.matrix(~ 0 + poly(x, 3), data.frame(x = 1:5))
model.matrix(~ 1 + poly(x, 3), data.frame(x = 1:5))
basObject
(basObject$X %*% coefs) * basObject$shrinkage
yValues <- do.call(cbind, lapply(resLm, polynomial, x = xValues, raw = raw))
debugonce(galileoExtractCoefficientsList)
df <- data.frame(
x = rep(xValues, maxDegree + 1L),
y = c(yValues),
order = rep(factor(seq_len(maxDegree + 1L)), each = length(xValues))
)
ggplot2::ggplot() +
ggplot2::geom_line(data = df, mapping = ggplot2::aes(x = x, y = y, group = order, color = order), inherit.aes = FALSE) +
jaspGraphs::geom_point(data = galileo, mapping = ggplot2::aes(x = init.h, y = h.d)) +
jaspGraphs::geom_rangeframe() +
jaspGraphs::themeJaspRaw(legend.position = "right")
basObject$postprobs
predict(basObject, estimator = "HPM")$fit
predictBasSpecificModel <- function(basObject, modelOrder) {
modelIndex <- 3
newdata <- basObject$X
models <- basObject$which[modelIndex]
beta <- basObject$mle[modelIndex]
gg <- basObject$shrinkage[modelIndex]
intercept <- basObject$intercept[modelIndex]
beta.m <- beta[[1]]
model.m <- models[[1]]
Ypred <- (newdata[, model.m[-1], drop = FALSE] %*% beta.m[-1]) * gg[1] + beta.m[1]
}
raw <- FALSE
fullModel <- h_d ~ poly(init_h, maxDegree, raw = raw)
galileo_jasp_bas <- galileo
galileo_jasp_bas$init_h <- ordered(galileo_jasp_bas$init_h)
galileo_jasp_bas$h_d <- ordered(galileo_jasp_bas$h_d)
basObject <- BAS::bas.lm(
formula = fullModel,
data = galileo_jasp_bas,
prior = "JZS",
modelprior = BAS::uniform()
)