查找选定列中多个点的斜率 [英] Finding the slope for multiple points in selected columns
本文介绍了查找选定列中多个点的斜率的处理方法,对大家解决问题具有一定的参考价值,需要的朋友们下面随着小编来一起学习吧!
问题描述
给出以下数据框:
structure(list(`-5` = c(0, 1, 0, 0, 9, 22), `-4` = c(1, 3, 0,
0, 1, 17), `-3` = c(1, 3, 0, 0, 0, 12), `-2` = c(1, 3, 0, 0,
2, 10), `-1` = c(0, 0, 0, 4, 3, 9), `0` = c(0, 1, 0, 2, 2, 21
), `1` = c(0, 1, 1, 7, 1, 21), `2` = c(1, 0, 1, 2, 1, 10), `3` = c(0,
9, 0, 6, 1, 12), `4` = c(0, 2, 0, 5, 0, 18), `5` = c(0, 0, 0,
3, 0, 23)), .Names = c("-5", "-4", "-3", "-2", "-1", "0", "1",
"2", "3", "4", "5"), row.names = c(NA, 6L), class = "data.frame")
# -5 -4 -3 -2 -1 0 1 2 3 4 5
#1 0 1 1 1 0 0 0 1 0 0 0
#2 1 3 3 3 0 1 1 0 9 2 0
#3 0 0 0 0 0 0 1 1 0 0 0
#4 0 0 0 0 4 2 7 2 6 5 3
#5 9 1 0 2 3 2 1 1 1 0 0
#6 22 17 12 10 9 21 21 10 12 18 23
我希望R给我列-5:-1的每一行中所有数据点的斜率.基本上,基于这5个数据点的线性回归趋势线的斜率.然后是列1:5的所有数据点的第二个斜率.年份0被忽略.
I would like R to give me the slope for all the data points in each row for columns -5:-1. Basically the slope for a linear regression trendline based on those 5 data points. Then a second slope for all the data points for the columns 1:5. The year 0 is ignored.
基本上就是这样(使用Excel计算的最后两列):
Basically this is what it would look like (the two last columns computed using Excel):
structure(list(`-5` = c(0, 1, 0, 0, 9, 22), `-4` = c(1, 3, 0,
0, 1, 17), `-3` = c(1, 3, 0, 0, 0, 12), `-2` = c(1, 3, 0, 0,
2, 10), `-1` = c(0, 0, 0, 4, 3, 9), `0` = c(0, 1, 0, 2, 2, 21
), `1` = c(0, 1, 1, 7, 1, 21), `2` = c(1, 0, 1, 2, 1, 10), `3` = c(0,
9, 0, 6, 1, 12), `4` = c(0, 2, 0, 5, 0, 18), `5` = c(0, 0, 0,
3, 0, 23), `Negative Years` = c(0, -2, 0, 0.8, -1.1, -3.3), `Positive Years` = c(-0.1,
0, -0.3, -0.5, -0.3, 1.2)), .Names = c("-5", "-4", "-3", "-2",
"-1", "0", "1", "2", "3", "4", "5", "Negative Years", "Positive Years"
), row.names = c(NA, 6L), class = "data.frame")
# -5 -4 -3 -2 -1 0 1 2 3 4 5 Negative Years Positive Years
#1 0 1 1 1 0 0 0 1 0 0 0 0.0 -0.1
#2 1 3 3 3 0 1 1 0 9 2 0 -2.0 0.0
#3 0 0 0 0 0 0 1 1 0 0 0 0.0 -0.3
#4 0 0 0 0 4 2 7 2 6 5 3 0.8 -0.5
#5 9 1 0 2 3 2 1 1 1 0 0 -1.1 -0.3
#6 22 17 12 10 9 21 21 10 12 18 23 -3.3 1.2
推荐答案
这就是统计学家(而非数据科学家)会做的事情.
This is what a statistician (not a data scientist) would do.
让您的数据框为dat
.
Y <- t(dat) ## response matrix
t <- -5:5 ## time stamps
id <- c(rep("-", 5), NA, rep("+", 5)) ## group index (factor)
fit <- lm(Y ~ t * id) ## mlm
m <- coef(fit)[c(2, 4), ] ## coefficient matrix
m[2, ] <- m[2, ] + m[1, ] ## reverse contrast
round(t(m), 2)
# t t:id+
#1 0.0 -0.1
#2 -0.2 0.0
#3 0.0 -0.3
#4 0.8 -0.5
#5 -1.1 -0.3
#6 -3.3 1.2
将列名称更改为所需的名称.
Change column names to what you desire.
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