在基本 R 代码（无 GGPlot）中添加趋势线，用于指数和 2 因子多项式曲线的多重绘图

Question

我正在尝试使用基本 R 代码将趋势线添加到多图图表中。可以使用一些帮助来添加非线性函数的趋势线，并且不确定这对于基本 R 是否可行。我可能需要将我的技能升级到 ggplot，并认为我会先在这里检查。

我有两个地点的土壤数据，一个参考地点和一个牲畜放牧地点。我在每个地点跟踪了四个垃圾分解指标：S、k、Bg 和 Br。该实验的目的是确定每个指标的分解曲线的形状。 R2 分析发现，S 最适合指数衰减模型，而 k、Bg 和 Br 最适合 2 因子多项式曲线。

我正在尝试可视化随着时间的推移每个指标的每条最佳拟合曲线的形状。我尝试添加趋势线，但无法使任何非线性工作。我尝试过不同的 abline 函数，但不确定这是正确的方法。感谢艾伦·卡梅伦的回复。我编辑了这个问题以提供每个模型的完整代码。我不知道如何上传我的数据框，但也提供了我的 .csv 中的一些标题数据。

这是我迄今为止一直在使用的 R 代码：

# Load the farm data file
soil_data <- read.csv("C1Sandown020223.csv", header = TRUE)

# Check the header of my data
head(soil_data)

# Look at the whole dataset
soil_data

# Create a new plot
plot.new()

# Set up multiple plots to visualize data (4 rows, 2 columns)
par(mfrow = c(1, 1))

# Plot S for each site

# Graph linear model of S for Reference against the number of 
# days in the ground
plot(soil_data$Days1, soil_data$S1, col = "darkgreen", xlab = "Days",
    ylab = "S", ylim = c(0, 0.7), las = 1,
   main = "Reference")

modelS1 <- lm(S1 ~ Days1, data = soil_data)
abline(modelS, col = "green")
legend(100, 0.70, bty="n", legend=c("R2 = 0.873"))
summary(modelS1)

# Graph linear model of S for Grazed against the number of 
# days in the ground
plot(soil_data$Days2, soil_data$S2, col = "darkgreen", xlab = "Days",
    ylab = "S", ylim = c(0, 0.7), las = 1,
     main = "Grazed")

modelS2 <- lm(S2 ~ Days2, data = soil_data)
abline(modelS2, col = "green")
legend(100, 0.70, bty="n", legend=c("R2 = 0.839"))
summary(modelS2)

# Graph linear model of S for Combined Sites against the 
# number of days in the ground
plot(soil_data$Days, soil_data$S, col = "darkblue", xlab = "Days",
    ylab = "S", ylim = c(0, 0.7), las = 1,
 main = "Combined")

modelS <- lm(S ~ Days, data = soil_data)
abline(modelS, col = "darkblue")
legend(100, 0.70, bty="n", legend=c("R2 = 0.857"))
summary(modelS)

# Plot linear model of k for each site 

# Graph linear model of k for Reference against the number of 
# days in the ground
plot(soil_data$Days1, soil_data$k1, col = "darkred", xlab = "Days",
 ylab = "k", ylim = c(0, 0.05), las = 1,
 main = "Reference")

modelk1 <- lm(k1 ~ Days1, data = soil_data)
abline(modelk1, col = "darkred")
legend("topright", bty="n", legend=c("R2 = 0.523"))
summary(modelk1)

# Graph linear model of k for Grazed against the # days in the ground
plot(soil_data$Days2, soil_data$k2, col = "darkred", xlab = "Days",
 ylab = "k", ylim = c(0, 0.05), las = 1,
 main = "Grazed")

modelk2 <- lm(k2 ~ Days2, data = soil_data)
abline(modelk2, col = "darkred")
legend("topright", bty="n", legend=c("R2 = 0.459"))
summary(modelk2)

# Graph linear model of k for Combined Sites against the # days 
plot(soil_data$Days, soil_data$k, col = "darkblue", xlab = "Days",
 ylab = "k", ylim = c(0, 0.05), las = 1,
 main = "Combined")

modelk <- lm(k ~ Days, data = soil_data)
abline(modelk, col = "darkblue")
legend("topright", bty="n", legend=c("R2 = 0.465"))
summary(modelk)

# Plot linear model of Bg for each site 

# Graph linear model of Bg for Reference against the number of days 
plot(soil_data$Days1, soil_data$BG1, col = "green", xlab = "Days",
 ylab = "Bg", ylim = c(0.3, 0.8), las = 1,
 main = "Reference")

modelBG1 <- lm(BG1 ~ Days1, data = soil_data)
abline(modelBG1, col = "green")
legend(100, 0.40, bty="n", legend=c("R2 = 0.873"))
summary(modelBG1)

# Graph linear model of Bg for Grazed against the number of days 
plot(soil_data$Days2, soil_data$BG2, col = "green", xlab = "Days",
 ylab = "Bg", ylim = c(0.3, 0.8), las = 1,
 main = "Grazed")

modelBG2 <- lm(BG2 ~ Days2, data = soil_data)
abline(modelBG2, col = "green")
legend(100, 0.40, bty="n", legend=c("R2 = 0.839"))
summary(modelBG2)

# Graph linear model of Bg for Combined Sites against # of days 
plot(soil_data$Days, soil_data$BG, col = "darkblue", xlab = "Days",
 ylab = "Bg", ylim = c(0.3, 0.8), las = 1,
 main = "Combined")

modelBG <- lm(BG ~ Days, data = soil_data)
abline(modelBG, col = "darkblue")
legend(100, 0.40, bty="n", legend=c("R2 = 0.857"))
summary(modelBG)

# Plot linear model of Br for each site

# Graph linear model of Br for reference against the # days 
plot(soil_data$Days1, soil_data$BR1, col = "darkred", xlab = "Days",
 ylab = "Br", ylim = c(0.2, 0.5), las = 1,
 main = "Reference")

modelBR1 <- lm(BR1 ~ Days1, data = soil_data)
abline(modelBR1, col = "darkred")
legend("bottomright", bty="n", legend=c("R2 = 0.921"))
summary(modelBR1)

# Graph linear model of Br for Grazed against the # of days 
plot(soil_data$Days2, soil_data$BR2, col = "darkred", xlab = "Days",
 ylab = "Br", ylim = c(0.2, 0.5), las = 1,
 main = "Grazed")

modelBR2 <- lm(BR2 ~ Days2, data = soil_data)
abline(modelBR2, col = "darkred")
legend("bottomright", bty="n", legend=c("R2 = 0.833"))
summary(modelBR2)

# Graph linear model of Br for Combined against the number of days 
plot(soil_data$Days, soil_data$BR, col = "darkblue", xlab = "Days",
 ylab = "Br", ylim = c(0.2, 0.5), las = 1,
 main = "Combined")

modelBR <- lm(BR ~ Days, data = soil_data)
abline(modelBR, col = "darkblue")
legend("bottomright", bty="n", legend=c("R2 = 0.885"))
summary(modelBR)

# EXPONENTIAL MODELS FOR EACH MATERIAL AND MEASURE 
# (log value of y axis)
# take the log of response variable, plot versus predictor 
# variable (days)
# method eg. https://www.statology.org/exponential-regression-in-r/

# Exponential model of S

# S Reference Exp
expSRef = lm(log(soil_data$S1) ~ soil_data$Days1)
summary(expSRef)

# S Grazed Exp
expSGraz = lm(log(soil_data$S2) ~ soil_data$Days2)
summary(expSGraz)

# S Combined Exp
expS = lm(log(soil_data$S) ~ soil_data$Days)
summary(expS)

# Exponential model of k for each site 

# k Reference Exp
expkRef = lm(log(soil_data$k1) ~ soil_data$Days1)
summary(expkRef)

# k Grazed Exp
expkGraz = lm(log(soil_data$k2) ~ soil_data$Days2)
summary(expkGraz)

# k Combined Exp
expk = lm(log(soil_data$k) ~ soil_data$Days)
summary(expk)

# Exponential model of Bg for each site 

# Bg Reference Exp
expBgRef = lm(log(soil_data$BG1) ~ soil_data$Days1)
summary(expBgRef)

# Bg Grazed Exp
expBgGraz = lm(log(soil_data$BG2) ~ soil_data$Days2)
summary(expBgGraz)

# Bg Combined Exp
expBg = lm(log(soil_data$BG) ~ soil_data$Days)
summary(expBg)

# Exponential model of Br for each site

# Br Reference Exp
expBrRef = lm(log(soil_data$BR1) ~ soil_data$Days1)
summary(expBrRef)

# Br Grazed Exp
expBrGraz = lm(log(soil_data$BR2) ~ soil_data$Days2)
summary(expBrGraz)

# Br Combined Exp
expBr = lm(log(soil_data$BR) ~ soil_data$Days)
summary(expBr)


# LOGARITHMIC MODELS FOR EACH MATERIAL AND MEASURE  
# (log value of x axis)
# https://www.statology.org/logarithmic-regression-in-r/

# Logarithmic model of S

# S Reference Log
logSRef = lm(log(soil_data$Days1) ~ soil_data$S1)
summary(logSRef)

# S Grazed Log
logSGraz = lm(log(soil_data$Days2) ~ soil_data$S2)
summary(logSGraz)

# S Combined Log
logS = lm(log(soil_data$Days) ~ soil_data$S)
summary(logS)


# Logarithmic model of k for each site 

# k Reference Log
logkRef = lm(log(soil_data$Days1) ~ soil_data$k1)
summary(logkRef)

# k Grazed Log
logkGraz = lm(log(soil_data$Days2) ~ soil_data$k2)
summary(logkGraz)

# k Combined Log
logk = lm(log(soil_data$Days) ~ soil_data$k)
summary(logk)

# Logarithmic model of Bg for each site 

# Bg Reference Log
logBgRef = lm(log(soil_data$Days1) ~ soil_data$BG1)
summary(logBgRef)

# Bg Grazed Log
logBgGraz = lm(log(soil_data$Days2) ~ soil_data$BG2)
summary(logBgGraz)

# Bg Combined Log
logBg = lm(log(soil_data$Days) ~ soil_data$BG)
summary(logBg)

# Logarithmic model of Br for each site

# Br Reference Log
logBrRef = lm(log(soil_data$Days1) ~ soil_data$BR1)
summary(logBrRef)

# Br Grazed Log
logBrGraz = lm(log(soil_data$Days2) ~ soil_data$BR2)
summary(logBrGraz)

# Br Combined Log
logBr = lm(log(soil_data$Days) ~ soil_data$BR)
summary(logBr)


# POLYNOMIAL MODEL OF EACH METRIC AND MEASURE - 2 dimensions

# Polynomial model of S

# S Reference Poly 2 dimensions
polySRef = lm(S1~poly(Days1, 2, raw = TRUE), data=soil_data)
summary(polySRef)

# S Grazed Poly 2 dimensions
polySGraz = lm(S2~poly(Days2, 2, raw = TRUE), data=soil_data)
summary(polySGraz)

# S Combined Poly 2 dimensions
polyS = lm(S~poly(Days, 2, raw = TRUE), data=soil_data)
summary(polyS)


# Polynomial model of k for each site 

# k Reference Poly 2 dimensions
polykRef = lm(k1~poly(Days1, 2, raw = TRUE), data=soil_data)
summary(polykRef)

# k Grazed Poly 2 dimensions
polykGraz = lm(k2~poly(Days2, 2, raw = TRUE), data=soil_data)
summary(polykGraz)

# k Combined Poly 2 dimensions
polyk = lm(k~poly(Days, 2, raw = TRUE), data=soil_data)
summary(polyk)

# Polynomial model of Bg for each site 

# Bg Reference Poly 2 dimensions
polyBgRef = lm(BG1~poly(Days1, 2, raw = TRUE), data=soil_data)
summary(polyBgRef)

# Bg Grazed Poly 2 dimensions
polyBgGraz = lm(BG2~poly(Days2, 2, raw = TRUE), data=soil_data)
summary(polyBgGraz)

# Bg Combined Poly 2 dimensions
polyBg = lm(BG~poly(Days, 2, raw = TRUE), data=soil_data)
summary(polyBg)

# Polynomial model of Br for each site

# Br Reference Poly 2 dimensions
polyBrRef = lm(BR1~poly(Days1, 2, raw = TRUE), data=soil_data)
summary(polyBrRef)

# Br Grazed Poly 2 dimensions
polyBrGraz = lm(BR2~poly(Days2, 2, raw = TRUE), data=soil_data)
summary(polyBrGraz)

# Br Combined Poly 2 dimensions
polyBr = lm(BR~poly(Days, 2, raw = TRUE), data=soil_data)
summary(polyBr)


# Summary R squared data for each model

# LINEAR
summary(modelS1)$r.squared
summary(modelS2)$r.squared
summary(modelS)$r.squared
summary(modelk1)$r.squared
summary(modelk2)$r.squared
summary(modelk)$r.squared
summary(modelBG1)$r.squared
summary(modelBG2)$r.squared
summary(modelBG)$r.squared
summary(modelBR1)$r.squared
summary(modelBR2)$r.squared
summary(modelBR)$r.squared

# EXPONENTIAL
summary(expSRef)$r.squared
summary(expSGraz)$r.squared
summary(expS)$r.squared
summary(expkRef)$r.squared
summary(expkGraz)$r.squared
summary(expk)$r.squared
summary(expBgRef)$r.squared
summary(expBgGraz)$r.squared
summary(expBg)$r.squared
summary(expBrRef)$r.squared
summary(expBrGraz)$r.squared
summary(expBr)$r.squared

# LOGARITHMIC 
summary(logSRef)$r.squared
summary(logSGraz)$r.squared
summary(logS)$r.squared
summary(logkRef)$r.squared
summary(logkGraz)$r.squared
summary(logk)$r.squared
summary(logBgRef)$r.squared
summary(logBgGraz)$r.squared
summary(logBg)$r.squared
summary(logBrRef)$r.squared
summary(logBrGraz)$r.squared
summary(logBr)$r.squared

# POLYNOMIAL - 2 dimensions
summary(polySRef)$r.squared
summary(polySGraz)$r.squared
summary(polyS)$r.squared
summary(polykRef)$r.squared
summary(polykGraz)$r.squared
summary(polyk)$r.squared
summary(polyBgRef)$r.squared
summary(polyBgGraz)$r.squared
summary(polyBg)$r.squared
summary(polyBrRef)$r.squared
summary(polyBrGraz)$r.squared
summary(polyBr)$r.squared

# Create combined dataset of S, k, Br, Bg, with 
# different shapes for each site

# Create a new plot
plot.new()

# Set up multiple plots to visualize data (4 rows, 2 columns)
par(mfrow = c(2, 2))

# Graph linear model of S for Combined Sites against # days 

plot(soil_data$Days[soil_data$Type == "Grazed"], 
soil_data$S[soil_data$Type == "Grazed"], 
 pch = 16, cex = 1, col = "darkblue", ylim = c(0, 0.7), 
xlab = "Days", ylab = "S", main = "S")
points(soil_data$Days[soil_data$Type == "Reference"], 
soil_data$S[soil_data$Type == "Reference"], 
   pch = 17, cex = 1, col = "darkred")
# this abline function is giving a straight line and 
# not the actual model, does the exp not make 
# an exponential trendline? 
abline(expSGraz, col = "darkblue")
abline(expSRef, col = "darkred")
legend("topright", legend = unique(soil_data$Type), pch = c(16, 17),
   col = c("darkblue", "darkred"), cex = 0.8, title = "Data Type")


# Now for k

plot(soil_data$Days[soil_data$Type == "Grazed"], 
soil_data$k[soil_data$Type == "Grazed"], 
 pch = 16, cex = 1, col = "darkblue", xlab = "Days", 
ylab = "k", main = "k")
points(soil_data$Days[soil_data$Type == "Reference"],
soil_data$k[soil_data$Type == "Reference"], 
   pch = 17, cex = 1, col = "darkred")
# this abline function is giving a straight line and 
# not the actual model 
abline(polykGraz, col = "darkblue")
abline(polykRef, col = "darkred")
legend("topright", legend = unique(soil_data$Type), pch = c(16, 17),
   col = c("darkblue", "darkred"), cex = 0.8, title = "Data Type")

# Bg

plot(soil_data$Days[soil_data$Type == "Grazed"],
soil_data$BG[soil_data$Type == "Grazed"], 
 pch = 16, cex = 1, col = "darkblue", xlab = "Days", 
ylab = "Bg", main = "Bg")
points(soil_data$Days[soil_data$Type == "Reference"],
soil_data$BG[soil_data$Type == "Reference"], 
   pch = 17, cex = 1, col = "darkred")
# this abline function is giving a straight line and 
# not the actual model 
abline(polyBgGraz, col = "darkblue")
abline(polyBgRef, col = "darkred")
legend("topleft", legend = unique(soil_data$Type), pch = c(16, 17),
   col = c("darkblue", "darkred"), cex = 0.8, title = "Data Type")

#Br

plot(soil_data$Days[soil_data$Type == "Grazed"],
soil_data$BR[soil_data$Type == "Grazed"], 
 pch = 16, cex = 1, col = "darkblue", xlab = "Days", 
ylab = "Br", main = "Br")
points(soil_data$Days[soil_data$Type == "Reference"],
soil_data$BR[soil_data$Type == "Reference"], 
   pch = 17, cex = 1, col = "darkred")
# this abline function is giving a straight line 
# and not the actual model 
abline(polyBrGraz, col = "darkblue")
abline(polyBrRef, col = "darkred")

legend("topleft", legend = unique(soil_data$Type), pch = c(16, 17),
   col = c("darkblue", "darkred"), cex = 0.8, title = "Data Type")

dput(土壤数据) 结构（列表（站点= c（23L，19L，18L，16L，20L，17L，21L，22L， 24L、6L、7L、2L、5L、3L、12L、13L、8L、14L、9L、15L、4L、1L、 10L，11L)，类型 = c("放牧"，"放牧"，"放牧"，"放牧"，"放牧"， “擦过”、“擦过”、“擦过”、“擦过”、“参考”、“参考”、 “参考”，“参考”，“参考”，“参考”，“参考”， “参考”，“参考”，“参考”，“参考”，“参考”， "参考", "参考", "参考"), 天数 = c(35L, 49L, 63L, 77L、91L、98L、105L、119L、133L、35L、42L、49L、56L、63L、70L、 77L、84L、91L、98L、105L、112L、119L、126L、133L)，S = c(0.566， 0.593、0.57、0.285、0.225、0.197、0.269、0.205、0.119、0.573、 0.566、0.42、0.628、0.507、0.455、0.268、0.306、0.231、0.196、 0.198, 0.164, 0.101, 0.127, 0.114), k = c(0.0591, 0.0089, 0.0281, 0.0113、0.0061、0.0099、0.0052、0.0106、0.0079、0.0484、0.0227、 0.0216、0.0116、0.0237、0.0186、0.0136、0.0227、0.01、0.0105、 0.0083, 0.0082, 0.0125, 0.0124, 0.0072), BG = c(0.365, 0.343, 0.362、0.602、0.653、0.676、0.615、0.669、0.742、0.359、0.365、 0.488、0.313、0.415、0.459、0.616、0.584、0.647、0.677、0.676、 0.704, 0.757, 0.735, 0.746), BR = c(0.239, 0.225, 0.366, 0.395, 0.428、0.443、0.403、0.439、0.486、0.235、0.239、0.32、0.325、 0.327、0.301、0.404、0.383、0.433、0.444、0.443、0.461、0.496、 0.482, 0.489), 第 2 天 = c(35L, 49L, 63L, 77L, 91L, 98L, 105L, 119L、133L、不适用、不适用、不适用、不适用、不适用、不适用、不适用、不适用、不适用、不适用、不适用、不适用、不适用、 不适用, 不适用), S2 = c(0.566, 0.593, 0.57, 0.285, 0.225, 0.197, 0.269, 0.205、0.119、不适用、不适用、不适用、不适用、不适用、不适用、不适用、不适用、不适用、不适用、不适用、不适用、 NA, NA, NA), k2 = c(0.0591, 0.0089, 0.0281, 0.0113, 0.0061, 0.0099, 0.0052、0.0106、0.0079、不适用、不适用、不适用、不适用、不适用、不适用、不适用、不适用、不适用、不适用、 不适用，不适用，不适用，不适用，不适用），BG2 = c（0.365，0.343，0.362，0.602，0.653， 0.676、0.615、0.669、0.742、不适用、不适用、不适用、不适用、不适用、不适用、不适用、不适用、不适用、 NA, NA, NA, NA, NA, NA), BR2 = c(0.239, 0.225, 0.366, 0.395, 0.428、0.443、0.403、0.439、0.486、不适用、不适用、不适用、不适用、不适用、不适用、不适用、 NA, NA, NA, NA, NA, NA, NA, NA), 第 1 天 = c(NA, NA, NA, NA, NA, 不适用、不适用、不适用、不适用、35L、42L、49L、56L、63L、70L、77L、84L、91L、 98L, 105L, 112L, 119L, 126L, 133L), S1 = c(NA, NA, NA, NA, NA, 不适用、不适用、不适用、不适用、0.573、0.566、0.42、0.628、0.507、0.455、0.268、 0.306, 0.231, 0.196, 0.198, 0.164, 0.101, 0.127, 0.114), k1 = c(NA, 不适用，不适用，不适用，不适用，不适用，不适用，不适用，不适用，0.0484，0.0227，0.0216，0.0116， 0.0237、0.0186、0.0136、0.0227、0.01、0.0105、0.0083、0.0082、 0.0125, 0.0124, 0.0072), BG1 = c(NA, NA, NA, NA, NA, NA, NA, 不适用、不适用、0.359、0.365、0.488、0.313、0.415、0.459、0.616、0.584、 0.647, 0.677, 0.676, 0.704, 0.757, 0.735, 0.746), BR1 = c(NA, 不适用，不适用，不适用，不适用，不适用，不适用，不适用，不适用，0.235，0.239，0.32，0.325，0.327， 0.301、0.404、0.383、0.433、0.444、0.443、0.461、0.496、0.482、 0.489)), 类 = "data.frame", row.names = c(NA, -24L))

Answer 1

能够在图表上绘制趋势线应该不会太困难。

您已经创建了线性模型。

用途：

 coef(modelBG1)[1] for the intercept
 coef(modelBG1)[2] for the slope


 modelBG1 <- lm(BG1 ~ Days1, data = soil_data) # First the linear model

 plot(soil_data$Days1, soil_data$BG1, col = "green", xlab = "Days",
 ylab = "Bg", ylim = c(0.3, 0.8), las = 1, main = "Reference")
 # then the plot

 # then the abline or trend line 
 abline(coef(modelBG1)[1], coef(modelBG1)[2] , col = "green")

上面应该为您提供趋势线，您将从线性模型中获得系数。

希望这有帮助