How to use results from different regression models in a scatterplot built using group_by in R?

2
votes

I would like to add 2 different regression curves, coming from different models, in a scatter plot. Let's use the example below:

Weight=c(12.6,12.6,16.01,17.3,17.7,10.7,17,10.9,15,14,13.8,14.5,17.3,10.3,12.8,14.5,13.5,14.5,17,14.3,14.8,17.5,2.9,21.4,15.8,40.2,27.3,18.3,10.7,0.7,42.5,1.55,46.7,45.3,15.4,25.6,18.6,11.7,28,35,17,21,41,42,18,33,35,19,30,42,23,44,22)
Increment=c(0.55,0.53,16.53,55.47,80,0.08,41,0.1,6.7,2.2,1.73,3.53,64,0.05,0.71,3.88,1.37,3.8,40,3,26.3,29.7,10.7,35,27.5,60,43,31,21,7.85,63,9.01,67.8,65.8,27,40.1,31.2,22.3,35,21,74,75,12,19,4,20,65,46,9,68,74,57,57)
Id=c(rep("Aa",20),rep("Ga",18),rep("Za",15))
df=data.frame(Id,Weight,Increment)

The scatter plot looks like this:

plot_df <- ggplot(df, aes(x = Weight, y = Increment, color=Id)) + geom_point()

enter image description here

I tested a linear and an exponential regression model and could extract the results following loki's answer there:

linear_df <- df %>% group_by(Id) %>% do(model = glance(lm(Increment ~ Weight,data = .))) %>% unnest(model)
exp_df <- df %>% group_by(Id) %>% do(model = glance(lm(log(Increment) ~ Weight,data = .))) %>% unnest(model)

The linear model fits better for the Ga group, the exponential one for the Aa group, and nothing for the Za one:

> linear_df
# A tibble: 3 x 13
  Id    r.squared adj.r.squared  sigma  statistic  p.value    df logLik    AIC    BIC deviance df.residual  nobs
  <chr>     <dbl>         <dbl>  <dbl>      <dbl>    <dbl> <dbl>  <dbl>  <dbl>  <dbl>    <dbl>       <int> <int>
1 Aa       0.656         0.637  15.1       34.4   1.50e- 5     1 -81.6  169.   172.   4106.             18    20
2 Ga       1.00          1.00    0.243 104113.    6.10e-32     1   1.01   3.98   6.65    0.942          16    18
3 Za       0.0471       -0.0262 26.7        0.642 4.37e- 1     1 -69.5  145.   147.   9283.             13    15

> exp_df
# A tibble: 3 x 13
  Id    r.squared adj.r.squared  sigma  statistic  p.value    df logLik     AIC    BIC deviance df.residual  nobs
  <chr>     <dbl>         <dbl>  <dbl>      <dbl>    <dbl> <dbl>  <dbl>   <dbl>  <dbl>    <dbl>       <int> <int>
1 Aa      0.999          0.999  0.0624 24757.     1.05e-29     1  28.2  -50.3   -47.4    0.0700          18    20
2 Ga      0.892          0.885  0.219    132.     3.86e- 9     1   2.87   0.264   2.94   0.766           16    18
3 Za      0.00444       -0.0721 0.941      0.0580 8.14e- 1     1 -19.3   44.6    46.7   11.5             13    15

Now, how can I draw the linear regression line for the Aa group, the exponential regression curve for the Ga group, and no curve for the Za group? There is this, but it applies for different regressions built inside the same model type. How can I combine my different objects?

1
rggplot2dplyrregressionscatter-plot

1 Answers

0
votes

Create the lm objects for each model and then plot each.

library(ggplot2)

fm.lin <- lm(Increment ~ Id/Weight, df)
fm.exp <- lm(log(Increment) ~ Id/Weight, df)

df %>%
  ggplot(aes(Weight, Increment, color=Id)) +
  geom_point() +
  geom_line(aes(y = fitted(fm.Lin)), alpha = 0.1, lwd = 5) +
  geom_line(aes(y = exp(fitted(fm.Exp)))) +
  ylim(range(Increment))

screenshot