Adding a regression line on a ggplot

Question

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

User · Answer

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.

User · Answer

The simple solution using geom abline  geom abline slope   coef data lm   2    intercept   coef data lm   1     Where data lm is an lm object  and coef data lm  looks something like this   gt  coef data lm   Intercept     DepDelay    -2 006045    1 025109   The numeric indexing  assumes that  Intercept  is listed first  which is the case if the model includes an intercept  If you have some other linear model object  just plug in the slope and intercept values similarly

User · Answer

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  lt - 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

User · Answer

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  lt - 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  lt - 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

User · Answer

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  quot lm quot   ggplot data aes x plot  y plot       stat summary fun data  mean cl normal       geom smooth method  lm

[r] Adding a regression line on a ggplot

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