在R中寻找局部最大值和最小值
Finding local maxima and minima in R
我正在尝试创建一个函数来查找 "maxima" 和 "minima"。我有以下数据：
y
157
144
80
106
124
46
207
188
190
208
143
170
162
178
155
163
162
149
135
160
149
147
133
146
126
120
151
74
122
145
160
155
173
126
172
93
我试过这个函数来寻找"maxima"
localMaxima <- function(x) {
# Use -Inf instead if x is numeric (non-integer)
y <- diff(c(-.Machine$integer.max, x)) > 0L
rle(y)$lengths
y <- cumsum(rle(y)$lengths)
y <- y[seq.int(1L, length(y), 2L)]
if (x[[1]] == x[[2]]) {
y <- y[-1]
}
y
}
maks <- localMaxima(x)
和查找函数"minima"
localMinima <- function(x) {
# Use -Inf instead if x is numeric (non-integer)
y <- diff(c(.Machine$integer.max, x)) > 0L
rle(y)$lengths
y <- cumsum(rle(y)$lengths)
y <- y[seq.int(1L, length(y), 2L)]
if (x[[1]] == x[[2]]) {
y <- y[-1]
}
y
}
mins <- localMinima(x)
结果并非 100% 正确
maks = 1 5 7 10 12 14 16 20 24 27 31 33 35
mins = 3 6 8 11 13 15 19 23 26 28 32 34 36
结果应该
maks = 5 7 10 12 14 16 20 24 27 31 33 35
mins = 3 6 8 11 13 15 19 23 26 28 32 34
Finding local maxima and minima in R 接近，但不太合适。
我该如何解决这个问题？
非常感谢
您可以定义如下两个函数来生成您需要的向量：
library(data.table)
#shift lags or leads a vector by a certain amount defined as the second argument
#the default is to lag a vector.
#The rationale behind the below code is that each local minimum's adjucent
#values will be greater than itself. The opposite is true for a local
#maximum. I think this is what you are trying to achieve and one way to do
#it is the following code
maximums <- function(x) which(x - shift(x, 1) > 0 & x - shift(x, 1, type='lead') > 0)
minimums <- function(x) which(x - shift(x, 1) < 0 & x - shift(x, 1, type='lead') < 0)
输出：
> maximums(y)
[1] 5 7 10 12 14 16 20 24 27 31 33 35
> minimums(y)
[1] 3 6 8 11 13 15 19 23 26 28 32 34
这是我不久前写的一个函数（它比您需要的更通用）。它在顺序数据 x 中找到峰值，我将峰值定义为局部最大值，其两侧的 m 点的值都低于它（因此更大的 m 导致更严格的标准寻找峰值）：
find_peaks <- function (x, m = 3){
shape <- diff(sign(diff(x, na.pad = FALSE)))
pks <- sapply(which(shape < 0), FUN = function(i){
z <- i - m + 1
z <- ifelse(z > 0, z, 1)
w <- i + m + 1
w <- ifelse(w < length(x), w, length(x))
if(all(x[c(z : i, (i + 2) : w)] <= x[i + 1])) return(i + 1) else return(numeric(0))
})
pks <- unlist(pks)
pks
}
所以对于你的情况 m = 1:
find_peaks(x, m = 1)
#[1] 5 7 10 12 14 16 20 24 27 31 33 35
对于最小值：
find_peaks(-x, m = 1)
#[1] 3 6 8 11 13 15 19 23 26 28 32 34
我正在尝试创建一个函数来查找 "maxima" 和 "minima"。我有以下数据：
y
157
144
80
106
124
46
207
188
190
208
143
170
162
178
155
163
162
149
135
160
149
147
133
146
126
120
151
74
122
145
160
155
173
126
172
93
我试过这个函数来寻找"maxima"
localMaxima <- function(x) {
# Use -Inf instead if x is numeric (non-integer)
y <- diff(c(-.Machine$integer.max, x)) > 0L
rle(y)$lengths
y <- cumsum(rle(y)$lengths)
y <- y[seq.int(1L, length(y), 2L)]
if (x[[1]] == x[[2]]) {
y <- y[-1]
}
y
}
maks <- localMaxima(x)
和查找函数"minima"
localMinima <- function(x) {
# Use -Inf instead if x is numeric (non-integer)
y <- diff(c(.Machine$integer.max, x)) > 0L
rle(y)$lengths
y <- cumsum(rle(y)$lengths)
y <- y[seq.int(1L, length(y), 2L)]
if (x[[1]] == x[[2]]) {
y <- y[-1]
}
y
}
mins <- localMinima(x)
结果并非 100% 正确
maks = 1 5 7 10 12 14 16 20 24 27 31 33 35
mins = 3 6 8 11 13 15 19 23 26 28 32 34 36
结果应该
maks = 5 7 10 12 14 16 20 24 27 31 33 35
mins = 3 6 8 11 13 15 19 23 26 28 32 34
Finding local maxima and minima in R 接近，但不太合适。
我该如何解决这个问题？
非常感谢
您可以定义如下两个函数来生成您需要的向量：
library(data.table)
#shift lags or leads a vector by a certain amount defined as the second argument
#the default is to lag a vector.
#The rationale behind the below code is that each local minimum's adjucent
#values will be greater than itself. The opposite is true for a local
#maximum. I think this is what you are trying to achieve and one way to do
#it is the following code
maximums <- function(x) which(x - shift(x, 1) > 0 & x - shift(x, 1, type='lead') > 0)
minimums <- function(x) which(x - shift(x, 1) < 0 & x - shift(x, 1, type='lead') < 0)
输出：
> maximums(y)
[1] 5 7 10 12 14 16 20 24 27 31 33 35
> minimums(y)
[1] 3 6 8 11 13 15 19 23 26 28 32 34
这是我不久前写的一个函数（它比您需要的更通用）。它在顺序数据 x 中找到峰值，我将峰值定义为局部最大值，其两侧的 m 点的值都低于它（因此更大的 m 导致更严格的标准寻找峰值）：
find_peaks <- function (x, m = 3){
shape <- diff(sign(diff(x, na.pad = FALSE)))
pks <- sapply(which(shape < 0), FUN = function(i){
z <- i - m + 1
z <- ifelse(z > 0, z, 1)
w <- i + m + 1
w <- ifelse(w < length(x), w, length(x))
if(all(x[c(z : i, (i + 2) : w)] <= x[i + 1])) return(i + 1) else return(numeric(0))
})
pks <- unlist(pks)
pks
}
所以对于你的情况 m = 1:
find_peaks(x, m = 1)
#[1] 5 7 10 12 14 16 20 24 27 31 33 35
对于最小值：
find_peaks(-x, m = 1)
#[1] 3 6 8 11 13 15 19 23 26 28 32 34