如何基于R包增长率的非线性回归在ggplot中重现绘图？

我正在使用Growthrates包为我的数据生成增长率曲线的参数估计。我已经进行了回归并查看了生成的图，我对数据感到满意，但我想在ggplot2中重现以下图

图1：

我希望每组都有多条回归线：治疗组合，但我在（（即logistic、gompertz、gompertz2等）中对其进行了所有回归。到目前为止，我有：

library(growthrates)

####Using logistic regression to fit the data across mutliple groups
p     <- c(y0 = 1, mumax = 0.5, K = 200)
lower <- c(y0 = 0, mumax = 0,   K = 20)
upper <- c(y0 = 100, mumax = 5,   K = 400)

many_logistics <- all_growthmodels(y_data ~ 
                                     grow_logistic(total_time_days, parms) | sample + treatment,
                                   data = Alldata, 
                                   p = p,
                                   lower = lower, 
                                   upper = upper, 
                                   log = "y")
pp   <- coef(many_logistics)

par(mfrow = c(5, 3))
par(mar = c(2.5, 4, 2, 1))
plot(many_logistics)

many_logistics_results <- results(many_logistics)
xyplot(mumax ~ treatment | sample, data = many_logistics_results, layout = c(3, 1))
xyplot(r2 ~ treatment | sample, data = many_logistics_results, layout = c(3, 1))
xyplot(K ~ treatment | sample, data = many_logistics_results, layout = c(3, 1))

curve_logistics <- predict(many_logistics) #Prediction for given data (data for curve)
est_logistics <- predict(many_logistics, newdata=data.frame(time=seq(0, 1, 0.1))) #Extrapolation/Interpolation from curve


####Using Gompertz regression to fit the data across mutliple groups
p     <- c(y0 = 1, mumax = 0.5, K = 200)
lower <- c(y0 = 0, mumax = 0,   K = 20)
upper <- c(y0 = 100, mumax = 5,   K = 400)

many_gompertz <- all_growthmodels(y_datay_data ~ 
                                    grow_gompertz(total_time_days, parms) | sample + treatment,
                                   data = Alldata, 
                                   p = p,
                                   lower = lower, 
                                   upper = upper)
pp   <- coef(many_gompertz)

par(mfrow = c(5, 3))
par(mar = c(2.5, 4, 2, 1))
plot(many_gompertz)

many_gompertz_results <- results(many_gompertz)
xyplot(mumax ~ treatment | sample, data = many_gompertz_results, layout = c(3, 1))
xyplot(r2 ~ treatment | sample, data = many_gompertz_results, layout = c(3, 1))
xyplot(K ~ treatment | sample, data = many_gompertz_results, layout = c(3, 1))

curve_gompertz <- predict(many_gompertz) #Prediction for given data (data for curve)
est_gompertz <- predict(many_gompertz, newdata=data.frame(time=seq(0, 1, 0.1))) #Extrapolation/Interpolation from curve

#Prepare the data frames

curve_logistics2 <- curve_logistics %>% 
  map_df(as_tibble, .id = "src") %>%
  separate(src, c("sample", "treatment"), ":") %>%
  mutate(regression = "logistic")

curve_gompertz2 <- curve_gompertz %>% 
  map_df(as_tibble, .id = "src") %>%
  separate(src, c("sample", "treatment"), ":") %>%
  mutate(regression = "gompertz")

alldata2<- Alldata %>%
  select("sample", "treatment","total_time_days", "y_data") %>%
  rename(time = "total_time_days") %>%
  rename(y = "y_data") %>%
  mutate(regression = "none") 

comp_reg <- bind_rows(curve_logistics2, curve_gompertz2, alldata2)

#define the function to automatically generate plots#define the function to automatically generate plots

REGRESSION_LINE_PLOT <-function(x) {ggplot(data = x, aes(x=time, y=y, colour = regression, linetype = regression)) + 
    geom_point(size = 2.5, data = subset(x, regression %in% c("none"))) +
    stat_smooth(data = subset(x, regression %in% c("gompertz", "logistic"))) +
    theme_bw() + 
    theme(panel.grid.major=element_blank(),
          panel.grid.minor=element_blank(),
          panel.spacing = unit(0,"cm"),
          axis.line=element_line(colour="black"),
          # axis.title.x = element_text(size=14, colour = "black"),
          axis.title.x = element_blank(),
          # axis.title.y = element_text(size=14, colour = "black"),
          axis.title.y = element_blank(),
          # axis.text.y = element_text(size=14, colour = "black"),
          # axis.text.x = element_text(size=14, colour = "black"),
          strip.background = element_blank(),
          strip.text = element_text(size = 12, colour="black", face = "bold"),
          legend.text= element_text(size = 12, colour = "black"),
          legend.title=element_blank(), 
          text = element_text(size=12,  family="Arial")) +
    # plot.margin=unit(c(0.1,0.1,0.1,0.1),"cm")) +
    #scale_colour_manual(values = cbbPalette) + ### here I tell R to use my custom colour palette
    #scale_x_continuous(limits = c(-1,14)) + # set time range from -1 to 70 since we started sampling on day -1
    #scale_y_continuous(limits = c(-1,350), breaks = seq(0, 360, 90)) + # For comparison purposes, i want all my panels to have the same y axis scale
    ylab("") + 
    xlab("")
}

comp_reg_nested<- comp_reg %>%
  group_by(sample, treatment) %>%
  nest() %>%
  mutate(plots=map(.x=data, ~REGRESSION_LINE_PLOT(.x)))

fo_ad_line <- comp_reg_nested[[1,"plots"]]

库（growthrates）
####使用logistic回归对多组数据进行拟合
p我从软件包的内置数据创建了一个或多或少类似于您的数据结构的数据示例，并稍微简化了代码，省略了默认的绘图函数。我非常喜欢您使用
map\u df
构建数据框的方法，谢谢。然后我添加了一个简单的ggplot，当然可以扩展，并且适合你的需要

library(growthrates)
library(dplyr)
library(purrr)
library(tidyr)
library(ggplot2)

## use subset of built-in example data of the package
## and adapt it to the existing part of the script
data(bactgrowth)
Alldata <- bactgrowth[(bactgrowth$conc < 1) & bactgrowth$replicate == 1, ]
names(Alldata)    <- c("sample", "replicate", "treatment", "total_time_days", "y_data")
Alldata$y_data    <- Alldata$y_data * 1000
Alldata$treatment <- as.character(Alldata$treatment)

####Using logistic regression to fit the data across mutliple groups
p     <- c(y0 = 1, mumax = 0.5, K = 200)
lower <- c(y0 = 0, mumax = 0,   K = 20)
upper <- c(y0 = 100, mumax = 5,   K = 400)

many_logistics <- all_growthmodels(y_data ~
  grow_logistic(total_time_days, parms) | sample + treatment,
  data = Alldata,
  p = p,
  lower = lower,
  upper = upper)

many_logistics_results <- results(many_logistics)
curve_logistics <- predict(many_logistics)

####Using Gompertz regression to fit the data across mutliple groups
many_gompertz <- all_growthmodels(y_data ~
  grow_gompertz(total_time_days, parms) | sample + treatment,
  data = Alldata,
  p = p,
  lower = lower,
  upper = upper)

many_gompertz_results <- results(many_gompertz)
curve_gompertz <- predict(many_gompertz)

#Prepare the data frames
curve_logistics2 <- curve_logistics %>%
  map_df(as_tibble, .id = "src") %>%
  separate(src, c("sample", "treatment"), ":") %>%
  mutate(regression = "logistic")

curve_gompertz2 <- curve_gompertz %>%
  map_df(as_tibble, .id = "src") %>%
  separate(src, c("sample", "treatment"), ":") %>%
  mutate(regression = "gompertz")

alldata2<- Alldata %>%
  rename(time = "total_time_days", y = "y_data")

## combine the two curves to a joint data frame
comp_reg <- bind_rows(curve_logistics2, curve_gompertz2)

## plot it
ggplot(comp_reg, aes(time, y)) +
  geom_point(data = alldata2) +
  geom_line(aes(color = regression)) +
  facet_grid(treatment ~ sample)

库（growthrates）
图书馆（dplyr）
图书馆（purrr）
图书馆（tidyr）
图书馆（GG2）
##使用包的内置示例数据的子集
##并使其适应脚本的现有部分
数据（bactgrowth）
Alldata我从软件包的内置数据创建了一个或多或少类似于您的数据结构的数据示例，并稍微简化了代码，省略了默认的绘图函数。我非常喜欢您使用
map_df
构建数据框的方法，谢谢。然后我添加了一个简单的ggplot，当然可以扩展和修改适合你的需要

library(growthrates)
library(dplyr)
library(purrr)
library(tidyr)
library(ggplot2)

## use subset of built-in example data of the package
## and adapt it to the existing part of the script
data(bactgrowth)
Alldata <- bactgrowth[(bactgrowth$conc < 1) & bactgrowth$replicate == 1, ]
names(Alldata)    <- c("sample", "replicate", "treatment", "total_time_days", "y_data")
Alldata$y_data    <- Alldata$y_data * 1000
Alldata$treatment <- as.character(Alldata$treatment)

####Using logistic regression to fit the data across mutliple groups
p     <- c(y0 = 1, mumax = 0.5, K = 200)
lower <- c(y0 = 0, mumax = 0,   K = 20)
upper <- c(y0 = 100, mumax = 5,   K = 400)

many_logistics <- all_growthmodels(y_data ~
  grow_logistic(total_time_days, parms) | sample + treatment,
  data = Alldata,
  p = p,
  lower = lower,
  upper = upper)

many_logistics_results <- results(many_logistics)
curve_logistics <- predict(many_logistics)

####Using Gompertz regression to fit the data across mutliple groups
many_gompertz <- all_growthmodels(y_data ~
  grow_gompertz(total_time_days, parms) | sample + treatment,
  data = Alldata,
  p = p,
  lower = lower,
  upper = upper)

many_gompertz_results <- results(many_gompertz)
curve_gompertz <- predict(many_gompertz)

#Prepare the data frames
curve_logistics2 <- curve_logistics %>%
  map_df(as_tibble, .id = "src") %>%
  separate(src, c("sample", "treatment"), ":") %>%
  mutate(regression = "logistic")

curve_gompertz2 <- curve_gompertz %>%
  map_df(as_tibble, .id = "src") %>%
  separate(src, c("sample", "treatment"), ":") %>%
  mutate(regression = "gompertz")

alldata2<- Alldata %>%
  rename(time = "total_time_days", y = "y_data")

## combine the two curves to a joint data frame
comp_reg <- bind_rows(curve_logistics2, curve_gompertz2)

## plot it
ggplot(comp_reg, aes(time, y)) +
  geom_point(data = alldata2) +
  geom_line(aes(color = regression)) +
  facet_grid(treatment ~ sample)

库（growthrates）
图书馆（dplyr）
图书馆（purrr）
图书馆（tidyr）
图书馆（GG2）
##使用包的内置示例数据的子集
##并使其适应脚本的现有部分
数据（bactgrowth）
Alldata嗨，你的代码遗漏了一个可复制的数据示例。嗨，你的代码遗漏了一个可复制的数据示例。谢谢你提供了一个示例！我唯一剩下的问题是，当你在ggplot中使用geom_线时，它只会在预测函数的点之间绘制线。这不是一条平滑曲线，所以我想知道stat_平滑是否会更好e适当并更好地表示实际建模曲线？或者使用geom_smooth（）但通过所有处理和样本的迭代？我找到了它。我只有几个实际时间点（不幸的是）和预测（）只预测我拥有的那些真实点的点。所以我修改了曲线_gompertz是的，没错。最简单的方法就是使用“新数据”predict的参数。作为替代，您也可以直接使用增长函数及其参数。感谢您提供一个示例！我唯一剩下的问题是，当您在ggplot中使用geom_线时，它只在predict函数的点之间绘制线。这不是一条平滑曲线，所以我想知道stat_smooth是否为mo重新调整并更好地表示实际建模曲线？或者使用geom_smooth（）但通过所有处理和样本的迭代？我找到了它。我只有几个实际时间点（不幸的是）和预测（）只有我拥有的那些真实点的预测点。所以我修改了曲线_gompertz是的，准确地说。最简单的方法就是使用predict的“newdata”参数。另外，你也可以直接使用增长函数及其参数。