I'm trying hard to add a regression line on a ggplot. I first tried with abline but I didn't manage to make it work. Then I tried this...
data = data.frame(x.plot=rep(seq(1,5),10),y.plot=rnorm(50)) ggplot(data,aes(x.plot,y.plot))+stat_summary(fun.data=mean_cl_normal) + geom_smooth(method='lm',formula=data$y.plot~data$x.plot) But it is not working either.
In general, to provide your own formula you should use arguments x and y that will correspond to values you provided in ggplot() - in this case x will be interpreted as x.plot and y as y.plot. You can find more information about smoothing methods and formula via the help page of function stat_smooth() as it is the default stat used by geom_smooth().
ggplot(data,aes(x.plot, y.plot)) + stat_summary(fun.data=mean_cl_normal) + geom_smooth(method='lm', formula= y~x) If you are using the same x and y values that you supplied in the ggplot() call and need to plot the linear regression line then you don't need to use the formula inside geom_smooth(), just supply the method="lm".
ggplot(data,aes(x.plot, y.plot)) + stat_summary(fun.data= mean_cl_normal) + geom_smooth(method='lm') 
As I just figured, in case you have a model fitted on multiple linear regression, the above mentioned solution won't work.
You have to create your line manually as a dataframe that contains predicted values for your original dataframe (in your case data).
It would look like this:
# read dataset df = mtcars # create multiple linear model lm_fit <- lm(mpg ~ cyl + hp, data=df) summary(lm_fit) # save predictions of the model in the new data frame # together with variable you want to plot against predicted_df <- data.frame(mpg_pred = predict(lm_fit, df), hp=df$hp) # this is the predicted line of multiple linear regression ggplot(data = df, aes(x = mpg, y = hp)) + geom_point(color='blue') + geom_line(color='red',data = predicted_df, aes(x=mpg_pred, y=hp)) 
# this is predicted line comparing only chosen variables ggplot(data = df, aes(x = mpg, y = hp)) + geom_point(color='blue') + geom_smooth(method = "lm", se = FALSE) 
The simple and versatile solution is to draw a line using slope and intercept from geom_abline. Example usage with a scatterplot and lm object:
library(tidyverse) petal.lm <- lm(Petal.Length ~ Petal.Width, iris) ggplot(iris, aes(x = Petal.Width, y = Petal.Length)) + geom_point() + geom_abline(slope = coef(petal.lm)[["Petal.Width"]], intercept = coef(petal.lm)[["(Intercept)"]]) 
coef is used to extract the coefficients of the formula provided to lm. If you have some other linear model object or line to plot, just plug in the slope and intercept values similarly.
+0 you can use names. data.lm$coefficients[['(Intercept)']] and data.lm$coefficients[['DepDelay']].
(Intercept) will be listed first. The names do make the code clearer.
I found this function on a blog
ggplotRegression <- function (fit) { `require(ggplot2) ggplot(fit$model, aes_string(x = names(fit$model)[2], y = names(fit$model)[1])) + geom_point() + stat_smooth(method = "lm", col = "red") + labs(title = paste("Adj R2 = ",signif(summary(fit)$adj.r.squared, 5), "Intercept =",signif(fit$coef[[1]],5 ), " Slope =",signif(fit$coef[[2]], 5), " P =",signif(summary(fit)$coef[2,4], 5))) }` once you loaded the function you could simply
ggplotRegression(fit) you can also go for ggplotregression( y ~ x + z + Q, data)
Hope this helps.
coef(fit) instead of accessing coefficients directly stackoverflow.com/questions/17824461/…I found a more simple (to me) answer from this YouTube video which worked really well.
library(ggpubr) ggplot(data,aes(x.plot, y.plot)) + geom_smooth(method = 'lm', se = FALSE, formula = y ~ x) + stat_cor(label.x = 30, label.y = 130, size = 4) + stat_regline_equation(label.x = 30, label.y = 150, size = 4) The label.x and label.y just indicate the positions on the x-axis and y-axis where the equation and coefficient should be placed. You can play around with these to fit your graph.
If you want to fit other type of models, like a dose-response curve using logistic models you would also need to create more data points with the function predict if you want to have a smoother regression line:
fit: your fit of a logistic regression curve
#Create a range of doses: mm <- data.frame(DOSE = seq(0, max(data$DOSE), length.out = 100)) #Create a new data frame for ggplot using predict and your range of new #doses: fit.ggplot=data.frame(y=predict(fit, newdata=mm),x=mm$DOSE) ggplot(data=data,aes(x=log10(DOSE),y=log(viability)))+geom_point()+ geom_line(data=fit.ggplot,aes(x=log10(x),y=log(y))) Another way to use geom_line() to add regression line is to use broom package to get fitted values and use it as shown here https://cmdlinetips.com/2022/06/add-regression-line-to-scatterplot-ggplot2/
