代码:
library(ggplot2)
df <- structure(list(concentration = c(0, 0.5, 1.5, 4, 12, 35, 100),
response = c(0.015, 0.03673, 0.07212, 0.1027, 0.1286, 0.1858, 0.1812)),
class = "data.frame", row.names = c(NA, -7L))
df.fit <- nls(response ~ k0 + (ki*concentration/(KI + concentration)), df, start = list(k0 = 0.001, ki = 0.18, KI = 1))
coef(df.fit)
plot <- ggplot(df, aes(concentration, response))+
geom_point()+
geom_smooth(method = "nls", se = F, method.args = list(formula = response ~ k0 + (ki*concentration/(KI + concentration)),
start = list(k0 = 0.001, ki = 0.18, KI = 1)))
plot
我想念什么?
nls
适合geom_smooth
。从帮助文件: method: Smoothing method (function) to use, accepts either a
character vector, e.g. ‘"auto"’, ‘"lm"’, ‘"glm"’, ‘"gam"’,
‘"loess"’ or a function, e.g. ‘MASS::rlm’ or ‘mgcv::gam’,
‘stats::lm’, or ‘stats::loess’.
您可以指定一个新的预测向量,并使用geom_line
进行绘制:
library(ggplot2) df <- structure(list(concentration = c(0, 0.5, 1.5, 4, 12, 35, 100), response = c(0.015, 0.03673, 0.07212, 0.1027, 0.1286, 0.1858, 0.1812)), class = "data.frame", row.names = c(NA, -7L)) df.fit <- nls(response ~ k0 + (ki*concentration/(KI + concentration)), df, start = list(k0 = 0.001, ki = 0.18, KI = 1)) coef(df.fit) df$pred <- predict(df.fit) plot <- ggplot(df, aes(concentration, response))+ geom_point()+ geom_line(aes(x = concentration, y = pred)) plot
library(ggplot2)
df <- structure(list(concentration = c(0, 0.5, 1.5, 4, 12, 35, 100),
response = c(0.015, 0.03673, 0.07212, 0.1027, 0.1286, 0.1858, 0.1812)),
class = "data.frame", row.names = c(NA, -7L))
df.fit <- nls(response ~ k0 + (ki*concentration/(KI + concentration)), df, start = list(k0 = 0.001, ki = 0.18, KI = 1))
coef(df.fit)
plot <- ggplot(df, aes(concentration, response))+
geom_point()+
geom_smooth(method = "nls", se = F, method.args = list(formula = y ~ k0 + (ki*x/(KI + x)),
start = list(k0 = 0.001, ki = 0.18, KI = 1)))
plot