In GRousselet/rogme: Robust Graphical Methods For Group Comparisons

knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
)

In the README file, we considered the case of 2 independent groups that differ in spread. To get a better understanding of the shift function, here we consider other situations in which there is no clear difference, a difference in mean, a difference in skewness. We also look at how the shift function can quantify different patterns that can be observed in skewed observations such as reaction times.

# library(rogme)
devtools::load_all()

suppressMessages(library(cowplot))
n = 300 #> number of observations per group

No clear difference

set.seed(6)
g1 <- rnorm(n)
g2 <- rnorm(n)

ks(g1,g2) #> Kolmogorov-Smirnov test
t.test(g1,g2) #> regular Welsh t-test

#> make tibble
df <- mkt2(g1,g2)

#> -------------------------------------------------------
#> scatterplots alone
ps <- plot_scat2(df,
                 xlabel = "",
                 ylabel = "Scores (a.u.)",
                 alpha = 1,
                 shape = 21,
                 colour = "grey10",
                 fill = "grey90")
ps <- ps + coord_flip()
ps <- ps + labs(title = "No clear difference") +
  theme(plot.title = element_text(face = "bold", size = 20))
#> ps

#> compute shift function
sf <- shifthd(data = df, formula = obs ~ gr, nboot = 200)

#> plot shift function
psf <- plot_sf(sf, plot_theme = 2)

#> change axis labels
psf[[1]] <- psf[[1]] +
            labs(x = "Group 1 quantiles of scores (a.u.)",
                 y = "Group 1 - group 2 \nquantile differences (a.u.)")

#> add labels for deciles 1 & 9
psf <- add_sf_lab(psf, sf, y_lab_nudge = .1, labres = 1, text_size = 4)
#> psf

#> -------------------------------------------------------
#> scatterplot + deciles + colour coded decile differences
p <- plot_scat2(df,
                xlabel = "",
                ylabel = "Scores (a.u.)",
                alpha = .3,
                shape = 21,
                colour = "grey10",
                fill = "grey90")
p <- plot_hd_links(p, sf[[1]],
                    q_size = 1,
                    md_size = 1.5,
                    add_rect = TRUE,
                    rect_alpha = 0.1,
                    rect_col = "grey50",
                    add_lab = TRUE) #> superimposed deciles + rectangle
p <- p + coord_flip() #> flip axes
#> p

cowplot::plot_grid(ps, p, psf[[1]], labels=c("A", "B", "C"), ncol = 1, nrow = 3,
                             rel_heights = c(1, 1, 1), 
                             label_size = 20, 
                             hjust = -0.5, 
                             scale=.95,
                             align = "v")

The figure above illustrates two large samples drawn from a standard normal population. In that case, a t-test on means in inconclusive (t = -0.45, P = 0.65), and so is Kolmogorov-Smirnov test (KS statistic = 0.05, P = 0.82). As expected, the shift function shows only weak differences at all the deciles. This allows us to suggest more comfortably that the two distributions are similar, which cannot be done with a t-test because it considers only a very limited aspect of the data.

The shift function is not perfectly flat, as expected from random sampling of a limited sample size. The samples are both n = 300, so for smaller samples even more uneven shift functions can be expected by chance.

Mean difference

set.seed(21)
g1 <- rnorm(n) + 6
g2 <- rnorm(n) + 6.5

ks(g1,g2) #> Kolmogorov-Smirnov test
t.test(g1,g2) #> regular Welsh t-test

#> make tibble
df <- mkt2(g1,g2)

#> -------------------------------------------------------
#> scatterplots alone
ps <- plot_scat2(df,
                 xlabel = "",
                 ylabel = "Scores (a.u.)",
                 alpha = 1,
                 shape = 21,
                 colour = "grey10",
                 fill = "grey90")
ps <- ps + coord_flip()
ps <- ps + labs(title = "Mean difference") +
  theme(plot.title = element_text(face = "bold", size = 20),
        axis.text.y = element_blank(),
        axis.title.y = element_blank())
#> ps

#> compute shift function
sf <- shifthd(data = df, formula = obs ~ gr, nboot = 200)

#> plot shift function
psf <- plot_sf(sf, plot_theme = 2)

#> change axis labels
psf[[1]] <- psf[[1]] +
            labs(x = "Group 1 quantiles of scores (a.u.)",
                 y = "Group 1 - group 2 \nquantile differences (a.u.)")
  #> theme(axis.title.y = element_blank())

#> add labels for deciles 1 & 9
psf <- add_sf_lab(psf, sf, y_lab_nudge = .2, labres = 1, text_size = 4)
#> psf

#> -------------------------------------------------------
#> scatterplot + deciles + colour coded decile differences
p <- plot_scat2(df,
                xlabel = "",
                ylabel = "Scores (a.u.)",
                alpha = .3,
                shape = 21,
                colour = "grey10",
                fill = "grey90") +
     theme(axis.text.y = element_blank(),
           axis.title.y = element_blank())
p <- plot_hd_links(p, sf[[1]],
                    q_size = 1,
                    md_size = 1.5,
                    add_rect = TRUE,
                    rect_alpha = 0.1,
                    rect_col = "grey50",
                    add_lab = TRUE) #> superimposed deciles + rectangle
p <- p + coord_flip() #> flip axes
#> p

#> combine plots into one figure
#> library(cowplot)
cowplot::plot_grid(ps, p, psf[[1]], labels=c("A", "B", "C"), ncol = 1, nrow = 3,
                   rel_heights = c(1, 1, 1), 
                   label_size = 20, 
                   hjust = -0.5, 
                   scale=.95,
                   align = "v")

In the figure above, the two distributions differ in central tendency: in that case, a t-test on means returns a large negative t value (t = -7.56, P < 0.0001), but this is not the full story. The shift function shows that all the differences between deciles are negative and around 0.6. That all the deciles show an effect in the same direction is the hallmark of a completely effective method or experimental intervention. This consistent shift can also be described as first-order stochastic ordering, in which one distribution stochastically dominates another (Speckman et al., 2008).

Skewness difference

set.seed(4)
g1 <- rgamma(n, shape = 7, scale = 2)
g2 <- rgamma(n, shape = 7, scale = 2.1)
md.g2 <- hd(g2) #> median
#> g2plot(g1,g2,op=4);abline(v=md.g2)
g2[g2>md.g2] <- sort(g2[g2>md.g2]) * seq(1,1.3,length.out=sum(g2>md.g2))
g1 <- g1*4 + 300
g2 <- g2*4 + 300

ks(g1,g2) #> Kolmogorov-Smirnov test
t.test(g1,g2) #> regular Welsh t-test

#> make tibble
df <- mkt2(g1,g2)

#> -------------------------------------------------------
#> scatterplots alone
ps <- plot_scat2(df,
                 xlabel = "",
                 ylabel = "Scores (a.u.)",
                 alpha = 1,
                 shape = 21,
                 colour = "grey10",
                 fill = "grey90")
ps <- ps + coord_flip()
ps <- ps + labs(title = "Skewness difference") +
  theme(plot.title = element_text(face = "bold", size = 20),
          axis.text.y = element_blank(),
        axis.title.y = element_blank())
#> ps

#> compute shift function
sf <- shifthd(data = df, formula = obs ~ gr, nboot = 200)

#> plot shift function
psf <- plot_sf(sf, plot_theme = 2)

#> change axis labels
psf[[1]] <- psf[[1]] +
            labs(x = "Group 1 quantiles of scores (a.u.)",
                 y = "Group 1 - group 2 \nquantile differences (a.u.)")
  #> theme(axis.title.y = element_blank())

#> add labels for deciles 1 & 9
psf <- add_sf_lab(psf, sf, y_lab_nudge = 4, labres = 1, text_size = 4)
#> psf

#> -------------------------------------------------------
#> scatterplot + deciles + colour coded decile differences
p <- plot_scat2(df,
                xlabel = "",
                ylabel = "Scores (a.u.)",
                alpha = .3,
                shape = 21,
                colour = "grey10",
                fill = "grey90") +
      theme(axis.text.y = element_blank(),
            axis.title.y = element_blank())
p <- plot_hd_links(p, sf[[1]],
                    q_size = 1,
                    md_size = 1.5,
                    add_rect = TRUE,
                    rect_alpha = 0.1,
                    rect_col = "grey50",
                    add_lab = TRUE) #> superimposed deciles + rectangle
p <- p + coord_flip() #> flip axes
# p

cowplot::plot_grid(ps, p, psf[[1]], labels=c("A", "B", "C"), ncol = 1, nrow = 3,
                             rel_heights = c(1, 1, 1), 
                             label_size = 20, 
                             hjust = -0.5, 
                             scale=.95,
                             align = "v")

In this third figure, similarly to our second example, a t-test on means also returns a large t value (t = -3.74, P = 0.0002). However, the way the two distributions differ is very different from our previous example: the first five deciles are near zero and follow almost a horizontal line, and from deciles 5 to 9, differences increase linearly. The confidence intervals also increase as we move from the left to the right of the distributions: there is growing uncertainty about the size of the group difference in the right tails of the distributions.

More generally, the shift function is well suited to investigate how skewed distributions differ. The figures in the next section illustrate reaction time data in which a manipulation:

• affects most strongly slow behavioural responses, but with limited effects on fast responses;

• affects all responses, fast and slow, similarly;

• has stronger effects on fast responses, and weaker ones for slow responses.

Reaction time differences

The detailed dissociations presented below have been reported in the literature, and provide much stronger constraints on theories than comparisons limited to say the median reaction times across participants (Ridderinkhof et al., 2005; Pratte et al., 2010).

library(retimes)
Nb = 1000 #> number of bootstrap samples
nobs <- 1000 #> number of observations per group

Weak early differences, then increasing differences

Panel A = violin plots Panel B = shift function

set.seed(3)
g1 <- rexgauss(nobs, mu=300, sigma=10, tau=30)
g2 <- rexgauss(nobs, mu=300, sigma=17, tau=70)

#> make tibble
df <- mkt2(g1,g2)

#> -------------------------------------------------------
#> violinplots
ps <- ggplot(df, aes(x = gr, y = obs, fill = gr, colour = gr, shape = gr)) + 
  geom_violin(fill = "grey95", colour = "black", size = 1.25) +
  theme_bw() + 
  theme(legend.position = "none") + 
  theme(axis.title.x = element_text(size = 16, face = "bold"), 
        axis.text.x = element_text(size = 16),
        axis.text.y = element_text(size = 16)) + 
  scale_y_continuous(limits=c(250, 700),breaks=seq(200,800,100)) +
  scale_x_discrete(labels = c("g1", "g2"), expand = c(0.02, 0.02)) +
  labs(title = "Increasing differences", x = "", y = "Response latencies in ms") +
  theme(plot.title = element_text(face = "bold", size = 20)) + 
  coord_flip()

#> -------------------------------------------------------
#> compute shift function - deciles
#> sparse: q=c(.25,.5,.75)
#> dense: q=seq(.05,.95,0.05)
set.seed(4)
sf <- shifthd_pbci(data = df, formula = obs ~ gr, q=seq(.1,.9,.1), nboot = Nb)

#> plot shift function
psf <- plot_sf(sf, plot_theme = 2, symb_size = 4)[[1]] + 
  scale_y_continuous(breaks = seq(-160, 20, 20), limits = c(-160, 20)) +
  labs(y = "Decile differences")
#> psf

#> combine kernel density plots + shift functions
cowplot::plot_grid(ps, psf,
                    labels=c("A", "B"),
                    ncol = 1,
                    nrow = 2,
                    rel_heights = c(1, 1),
                    label_size = 20,
                    hjust = -0.5,
                    scale=.95,
                    align = "v")

Complete shift

set.seed(3)
g1<-rexgauss(nobs, mu=300, sigma=10, tau=50)
g2<-rexgauss(nobs, mu=300, sigma=10, tau=50) + 50

#> make tibble
df <- mkt2(g1,g2)

#> -------------------------------------------------------
#> violinplots
ps <- ggplot(df, aes(x = gr, y = obs, fill = gr, colour = gr, shape = gr)) + 
  geom_violin(fill = "grey95", colour = "black", size = 1.25) +
  theme_bw() + 
  theme(legend.position = "none") + 
  theme(axis.title.x = element_text(size = 16, face = "bold"), 
        axis.text.x = element_text(size = 16),
        axis.text.y = element_text(size = 16)) + 
  scale_y_continuous(limits=c(250, 700),breaks=seq(200,800,100)) +
  scale_x_discrete(labels = c("g1", "g2"), expand = c(0.02, 0.02)) +
  labs(title = "Complete shift", x = "", y = "Response latencies in ms") +
  theme(plot.title = element_text(face = "bold", size = 20)) + 
  coord_flip()

#> -------------------------------------------------------
#> compute shift function - deciles
set.seed(4)
sf <- shifthd_pbci(data = df, formula = obs ~ gr, q=seq(.1,.9,.1), nboot = Nb)

#> plot shift function
psf <- plot_sf(sf, plot_theme = 2, symb_size = 4)[[1]] +
                  scale_y_continuous(breaks = seq(-100, 20, 20), limits = c(-100, 20)) +
                  labs(y = "Decile differences")

#> combine kernel density plots + shift functions
cowplot::plot_grid(ps, psf,
                   labels=c("A", "B"),
                   ncol = 1,
                   nrow = 2,
                   rel_heights = c(1, 1),
                   label_size = 20,
                   hjust = -0.5,
                   scale=.95,
                   align = "v")

Early differences, then decreasing differences

set.seed(1)
g1<-rexgauss(nobs, mu=400, sigma=20, tau=50)
g2<-rexgauss(nobs, mu=370, sigma=20, tau=70)

#> make tibble
df <- mkt2(g1,g2)

#> -------------------------------------------------------
#> violinplots
ps <- ggplot(df, aes(x = gr, y = obs, fill = gr, colour = gr, shape = gr)) + 
  geom_violin(fill = "grey95", colour = "black", size = 1.25) +
  theme_bw() + 
  theme(legend.position = "none") + 
  theme(axis.title.x = element_text(size = 16, face = "bold"), 
        axis.text.x = element_text(size = 16),
        axis.text.y = element_text(size = 16)) + 
  scale_y_continuous(limits=c(200, 700),breaks=seq(200,800,100)) +
  scale_x_discrete(labels = c("g1", "g2"), expand = c(0.02, 0.02)) +
  labs(title = "Early differences", x = "", y = "Response latencies in ms") +
  theme(plot.title = element_text(face = "bold", size = 20)) + 
  coord_flip()

#> -------------------------------------------------------
#> compute shift function - deciles
set.seed(4)
sf <- shifthd_pbci(data = df, formula = obs ~ gr, q=seq(.1,.9,.1), nboot = Nb)

#> plot shift function
psf <- plot_sf(sf, plot_theme = 2, symb_size = 4)[[1]] +
                scale_y_continuous(breaks = seq(-70, 30, 10), limits = c(-70, 30)) +
                labs(y = "Decile differences")

#> combine kernel density plots + shift functions
cowplot::plot_grid(ps, psf,
                    labels=c("A", "B"),
                    ncol = 1,
                    nrow = 2,
                    rel_heights = c(1, 1),
                    label_size = 20,
                    hjust = -0.5,
                    scale=.95,
                    align = "v")

References

Pratte, M.S., Rouder, J.N., Morey, R.D. & Feng, C.N. (2010) Exploring the differences in distributional properties between Stroop and Simon effects using delta plots. Atten Percept Psycho, 72, 2013-2025.

Ridderinkhof, K.R., Scheres, A., Oosterlaan, J. & Sergeant, J.A. (2005) Delta plots in the study of individual differences: New tools reveal response inhibition deficits in AD/HD that are eliminated by methylphenidate treatment. J Abnorm Psychol, 114, 197-215.

Rousselet, G.A., Pernet, C.R. & Wilcox, R.R. (2017) Beyond differences in means: robust graphical methods to compare two groups in neuroscience. The European journal of neuroscience, 46, 1738-1748. [article] [preprint] [reproducibility package]

Speckman, P.L., Rouder, J.N., Morey, R.D. & Pratte, M.S. (2008) Delta plots and coherent distribution ordering. Am Stat, 62, 262-266.

GRousselet/rogme documentation built on Nov. 12, 2022, 4:38 a.m.