# R/elbow.R In celda: CEllular Latent Dirichlet Allocation

#### Defines functions .curveElbow.secondDerivativeEstimate.dist2d

```# https://stackoverflow.com/questions/35194048/using-r-how-to-calculate
#-the-distance-from-one-point-to-a-line
# http://mathworld.wolfram.com/Point-LineDistance2-Dimensional.html
# Kimberling, C. "Triangle Centers and Central Triangles." Congr.
# Numer. 129, 1-295, 1998.
.dist2d <- function(a, b, c) {
v1 <- b - c
v2 <- a - b
m <- cbind(v1, v2)
d <- abs(det(m)) / sqrt(sum(v1 * v1))
return(d)
}

.secondDerivativeEstimate <- function(v) {
nv <- length(v)
res <- rep(NA, nv)
for (i in seq(2, nv - 1)) {
res[i] <- v[i + 1] + v[i - 1] - (2 * v[i])
}
return(res)
}

.curveElbow <- function(var, perplexity, pvalCutoff = 0.05) {
len <- length(perplexity)
a <- c(var[1], perplexity[1])
b <- c(var[len], perplexity[len])
res <- rep(NA, len)
for (i in seq_along(var)) {
res[i] <- .dist2d(c(var[i], perplexity[i]), a, b)
}
elbow <- which.max(res)
ix <- var > var[elbow]
perplexitySde <- .secondDerivativeEstimate(perplexity)
perplexitySdeSd <- stats::sd(perplexitySde[ix], na.rm = TRUE)
perplexitySdeMean <- mean(perplexitySde[ix], na.rm = TRUE)
perplexitySdePval <-
stats::pnorm(perplexitySde,
mean = perplexitySdeMean,
sd = perplexitySdeSd,
lower.tail = FALSE
)
# other <- which(ix & perplexitySdePval < pvalCutoff)
return(list(elbow = var[elbow]))
}
```

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celda documentation built on Nov. 8, 2020, 8:24 p.m.