How to fix jagged line from predict_gam in ggplot2?-CodePudding

Data:

structure(list(ID = c(19903L, 28185L, 28207L, 28429L, 28522L, 
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71L, 33L, 38L, 56L, 17L, 29L, 28L, 6L, 8L), Sex = structure(c(1L, 
1L, 2L, 2L, 1L, 2L, 1L, 1L, 1L, 2L, 1L, 2L, 2L, 1L, 1L, 1L, 1L, 
2L, 2L, 1L, 2L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 1L, 2L, 
1L, 2L, 2L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 2L, 1L, 2L, 2L, 2L, 
1L, 2L, 1L, 2L, 1L, 2L, 2L, 2L, 2L, 1L, 1L, 2L, 2L, 1L, 1L, 1L, 
1L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 2L, 1L, 2L, 2L, 1L, 1L, 2L, 2L, 
1L, 2L, 2L, 2L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 1L, 
2L, 2L, 1L, 1L, 1L, 2L, 2L, 2L, 1L, 1L, 1L, 2L, 1L, 2L, 1L, 1L, 
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1L, 1L, 1L, 2L, 2L, 1L, 2L, 2L, 2L, 1L, 1L, 2L, 1L, 2L, 2L, 1L, 
2L, 2L, 2L, 2L, 1L, 2L, 1L, 1L, 2L, 2L, 2L, 1L, 1L, 2L, 2L, 1L, 
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2L, 1L, 2L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 2L, 2L, 1L, 
1L, 2L, 2L), .Label = c("Male", "Female"), class = "factor"), 
    mean_FA_scaled = c(-1.52160414281774, -1.30073487609629, 
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    1.05460061968224, 1.61507104537468, -1.62830066556388, 0.0799550676933195
    ), RAVLT_DELAY = c(NA, 12L, NA, NA, NA, NA, NA, NA, NA, NA, 
    NA, NA, NA, NA, NA, NA, NA, 5L, NA, NA, NA, NA, NA, NA, NA, 
    7L, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 5L, 12L, 
    NA, NA, NA, NA, 14L, NA, NA, NA, NA, NA, 6L, 7L, NA, NA, 
    NA, NA, 7L, 1L, 1L, 11L, 4L, 12L, 7L, 9L, 9L, 8L, 14L, 12L, 
    7L, 12L, 7L, 6L, 13L, 10L, 13L, NA, 11L, 14L, 8L, 0L, 11L, 
    15L, 13L, 6L, 9L, 9L, 12L, 5L, 14L, 15L, 12L, 4L, 15L, 8L, 
    15L, 14L, 5L, 12L, 8L, 9L, 9L, 13L, 6L, 4L, 10L, NA, 4L, 
    13L, 9L, 14L, 8L, 15L, 14L, 9L, 15L, 14L, 11L, 11L, 15L, 
    12L, 9L, 13L, 14L, 7L, 13L, 9L, 12L, 10L, 6L, 9L, 10L, 11L, 
    15L, 11L, 11L, NA, 9L, 12L, 10L, 9L, 11L, 2L, 12L, NA, 6L, 
    12L, 12L, 10L, 11L, 4L, 13L, 4L, 5L, 6L, 12L, 15L, 11L, 11L, 
    14L, 2L, 11L, 5L, 10L, 12L, 10L, NA, 12L, 8L, 12L, 12L, 8L, 
    7L, 14L, 14L, 7L, 8L, NA, 9L, 6L, 15L, 7L, 14L, 8L, 14L, 
    11L, 13L, 6L, 12L, 11L, 14L, 15L, 10L, 6L, 13L, 7L, 4L, 12L, 
    14L, 7L, 13L, 3L, 13L, 7L, 10L, 6L, 8L, 3L, 15L, 11L, 15L, 
    11L, 11L, 8L, 4L, 7L, 10L, 5L, 7L, 8L, 9L, 14L, 12L, 14L, 
    12L, NA, NA, 11L, 10L, 13L, 7L, 12L, 12L, 14L, 8L, 13L, 2L, 
    11L, 8L, 7L, 4L, 7L, 9L, 4L, 12L, 14L, 15L, 12L, 13L, 9L, 
    7L, 11L, 10L, 14L, 6L, 5L, 5L, 10L, 8L, 5L, 12L, 2L, 11L, 
    8L, NA, 9L, 7L, 8L, 12L, 10L, 7L, 13L, 15L, 9L, 6L, 4L, 10L, 
    8L, 13L, 10L, 9L, 7L, 7L, 15L, 8L, 12L, 9L, 10L, 12L, 6L, 
    13L, 8L, 11L, 9L, 1L, 13L, 12L, NA, 8L, 2L, 11L, 9L, 7L, 
    6L, 10L, 13L, 15L, 6L, 5L, 7L, 5L, 5L, 11L, 11L, 13L, 9L, 
    4L, 10L, 2L, NA, 12L, 10L, 15L, NA, 6L)), row.names = c(NA, 
-324L), class = c("tbl_df", "tbl", "data.frame"))

I am using the following model in mgcv::gam:

m1 <- gam(mean_FA_scaled ~ s(Age, bs = 'ad', k = -1)   Sex  
            te(Age, by = Sex, bs ='fs')   
            te(RAVLT_DELAY, by = Sex, bs = 'fs')   s(RAVLT_DELAY), 
            data = DF,
            method = 'REML', family = gaussian)

I would like to reproduce the gam plot:

But in ggplot. However, When I use predict_gam my plot is very jagged. This doesn't happen when I try to plot the smooth term effect on age.

# Plot
m1_p <- predict_gam(m1)

m1_p %>% 
  ggplot(aes(x = RAVLT_DELAY, y = fit))  
  geom_line(aes(color = Sex))
  geom_smooth_ci(Sex, size = 1, alpha = 1)  
  theme_classic(base_size = 24)

CodePudding user response：

Your fit object has predictions for each age and each sex along the length of RAVLY_DELAY. With your existing code, each series tries to plot all the values from these various lines as one series, hence the jaggies.

If we tell ggplot to treat each Age,Sex combination as a different series (aka group), we get:

m1_p %>% 
  ggplot(aes(x = RAVLT_DELAY, y = fit))  
  geom_line(aes(color = Sex, group = interaction(Age,Sex)))

There are a lot of age groups here, which we could see separately with:

m1_p %>% 
  mutate(Age = round(Age, 1)) %>%
  ggplot(aes(x = RAVLT_DELAY, y = fit))  
  geom_line(aes(color = Sex))  
  facet_wrap(~Age, ncol = 10)

While wrong, I liked the aesthetic qualities that arose when I grouped by Age only: