How can I fit a line binomial data set for an LC50, using GGPlot?-CodePudding

I need to graph a dose response curve for some bad data. How can I fit a line to this for an LC50?

lc50<- data.table(Concentration = c(0,0.5,1.5,2.5), Mortality= c(0,0,100,100))  

ggplot(lc50,aes(Concentration, Mortality))  
      stat_summary(fun.data=mean_cl_normal)   
      scale_x_continuous(name="Concentration (mg/L)", limits=c(0, 2.5))  
      scale_y_continuous(name="% Mortality", limits=c(0, 100))  
      theme(panel.grid.major = element_blank(), panel.grid.minor = 
              element_blank(),panel.background = 
              element_blank(), axis.line = 
              element_line(colour = "black"))

CodePudding user response：

You can use this code:

lc50<- data.table(Concentration = c(0,0.5,1.5,2.5), Mortality= c(0,0,100,100))  
         
         ggplot(lc50,aes(Concentration, Mortality))  
           geom_line()  
           geom_smooth(method = "auto")  
           stat_summary(fun.data=mean_cl_normal)   
           scale_x_continuous(name="Concentration (mg/L)", limits=c(0, 2.5))  
           scale_y_continuous(name="% Mortality", limits=c(0, 100))  
           theme(panel.grid.major = element_blank(), panel.grid.minor = 
                   element_blank(),panel.background = 
                   element_blank(), axis.line = 
                   element_line(colour = "black"))

Output:

CodePudding user response：

geom_line makes exact lines through all points, whereas geom_smooth(method="lm") will fit a single line with the smallest average distance to all points:

library(tidyverse)
library(data.table)
#> 
#> Attaching package: 'data.table'
#> The following objects are masked from 'package:dplyr':
#> 
#>     between, first, last
#> The following object is masked from 'package:purrr':
#> 
#>     transpose

lc50 <- data.table(Concentration = c(0, 0.5, 1.5, 2.5), Mortality = c(0, 0, 100, 100))

ggplot(lc50, aes(Concentration, Mortality))  
  geom_line(color = "blue")  
  geom_smooth(method = "lm", color = "red")  
  stat_summary(fun.data = mean_cl_normal)  
  scale_x_continuous(name = "Concentration (mg/L)", limits = c(0, 2.5))  
  scale_y_continuous(name = "% Mortality", limits = c(0, 100))  
  theme(
    panel.grid.major = element_blank(), panel.grid.minor =
      element_blank(), panel.background =
      element_blank(), axis.line =
      element_line(colour = "black")
  )
#> `geom_smooth()` using formula 'y ~ x'
#> Warning: Removed 14 rows containing missing values (geom_smooth).
#> Warning in max(ids, na.rm = TRUE): no non-missing arguments to max; returning
#> -Inf
#> Warning: Removed 4 rows containing missing values (geom_segment).

^{Created on 2022-03-03 by the reprex package (v2.0.0)}