Revisits

This number is how many trees they pointlessly looked at again after already getting the fruit - it corresponds with memory errors.

E1 Revisits: Prepare the data

Read in the data and pre-process it.

Show the code

e1 <- readRDS("001-00-e1-data.RDS")

# remove things from the raw data to make it 
# suitable for this particular analysis

# remove samples that did not look at a tree
e1 <- e1 %>% filter(fl>0)

# remove the second (and any subsequent) *consecutive* duplicates
e1 <- e1 %>% 
  group_by(pp, rr, tb) %>% 
  filter(is.na(tl != lag(tl)) | tl != lag(tl)) %>% 
  ungroup()

# remove trials where they failed to get 10 fruit
e1 <- e1 %>%
  group_by(pp, rr, tb) %>%
  mutate(max_fr = max(fr)) %>%
  ungroup() %>%
  filter(max_fr==10) %>% 
  select(-c(ex, max_fr, te, tt, st, xx, yy, ll))

# currently some trials don't have entries for fruit of zero
# these trials are where they found a fruit on the first tree
# We want to say that these were:
# number of revisits = 0 
# (not number of revisits = "a structural missing")
design <-
  tibble(
    expand.grid(
      pp=unique(e1$pp),
      rr=unique(e1$rr),
      tb=unique(e1$tb),
      fr=0
    )
  )
e1 <- 
  full_join(x=design, y=e1, by = join_by(pp, rr, tb, fr)) %>% 
  group_by(pp, rr, tb) %>% 
  arrange(pp, rr, tb, fr, tm, .by_group = TRUE) %>% 
  replace_na(list(ix=0, tm=0, tl=0, fl=-1)) %>% 
  ungroup()

# annotate with revisit
e1 <- e1 %>% 
  group_by(pp, rr, tb) %>% 
  mutate(isrv = duplicated(tl)) %>% 
  ungroup()  

# get number of revisits per fruit
# (is how many times they looked at a tree that 
#  they saw before on this trial on the way to 
#  getting this particular fruit)
e1 <- e1 %>% 
  select(pp, rr, tb, fr, isrv) %>% 
  group_by(pp, rr, tb, fr) %>% 
  summarise(nrv=sum(isrv)) %>% 
  ungroup()

# add the stage IV
e1 <- e1 %>% 
  mutate(
    st = ifelse(tb<=5, "early", "late")
  ) %>% 
  select(pp,rr, st, tb, fr, nrv)

# factors
e1 <- e1 %>% 
  mutate(pp=as_factor(pp), rr=as_factor(rr), st=as_factor(st)) %>% 
  ungroup()

# collapse over trials - what was the average number of revisits for this fruit,
# now that the absence of visits to trees while fr was zero contributes a zero
# not a structural missing. Also prune entries for fr == 10 which are all 
# constrained to be zero
nrev_data_for_aov <- e1 %>% 
  group_by(pp, rr, st, fr) %>% 
  summarise(nrv=mean(nrv)) %>% 
  filter(fr!=10) %>% 
  mutate(fr=as_factor(fr)) %>% 
  ungroup()

# collapse over trials to stages
nrev_data_for_ggplot <- nrev_data_for_aov %>% 
  group_by(pp, rr, st) %>% 
  summarise(nrv=mean(nrv)) %>% 
  ungroup()

saveRDS(nrev_data_for_ggplot, "e1_nrevisits_plot_data.rds")

E1 Revisits: ANOVA

2 * 2 * 10

A 2x2x10 ANOVA with the within factors resource distribution (patchy, dispersed) and trial (early [mean trials 1-5], late [mean trials 6-10]) and number of fruit consumed (1-10)

Run the ANOVA

Show the code

options(contrasts=c("contr.sum","contr.poly"))
e1_revisits_ANOVA <- 
  ezANOVA(data=nrev_data_for_aov,
          dv=nrv,
          wid=pp,
          within=c(rr, st, fr),
          type=3, 
          return_aov = TRUE)
if("Sphericity Corrections" %in% names(e1_revisits_ANOVA)){
  corr=aovSphericityAdjustment(e1_revisits_ANOVA)$ANOVA %>% tibble()
} else {
  corr=e1_revisits_ANOVA$ANOVA %>% tibble()  
  }

Table the ANOVA.

Show the code

prettify_sphericity_3_way(
  aovSphericityAdjustment(e1_revisits_ANOVA), 
  "E1 NRevisits ANOVA")

E1 NRevisits ANOVA
Effect	DFn	DFd	F	p	sig
rr	1	41	3.84	0.057
st	1	41	17.83	0.000	***
fr	4.3	177.5	6.39	0.000	***
rr:st	1	41	0.12	0.730
rr:fr	4.5	182.6	4.41	0.001	**
st:fr	5.8	238.8	0.50	0.799
rr:st:fr	4.7	193.0	1.19	0.315

Report the ANOVA

The effect of resources was F(1, 41) = 3.84, p=0.057.

The effect of stage was F(1, 41) = 17.83, p<.001.

The effect of fruit was F(4.3, 177.5) = 6.39, p<.001.

The fruit x resources interaction was F(4.5, 182.6) = 4.41, p<.01.

E1 NRevisits: Plot

Two points along the x axis, each participant contributes one point per cell

Show the code

ggplot(data=nrev_data_for_ggplot, aes(y=nrv, x=st, group=rr, shape=rr, fill=rr)
) +
  my_fgms_theme+
  ggtitle("(b): Revisits (memory errors)")+
  ylab("Revisits")+
  xlab("Trials")+
  scale_fill_manual(name="Resource\ndistribution",values=c("white", "black")) +
  scale_shape_manual(name="Resource\ndistribution",values=c(24,19)) +
  stat_summary(fun.data = mean_cl_normal, geom = "errorbar", width=0.1, position=pd) +
  stat_summary(fun = mean, geom = "line", position=pd) + 
  stat_summary(fun = mean, geom = "point", size=4, position=pd)+
  scale_x_discrete(labels=c("early\ntrials 1 to 5", "late\ntrials 6 to 10"))

E1 NRevisits: Stage means

Table means for stage

Show the code

frpremeans = nrev_data_for_ggplot %>% group_by(st, pp, rr) %>%
  summarise(mu=mean(nrv)) %>% 
  summarise(mu=mean(mu))  
frmeans <- frpremeans %>% 
  summarise(mean=mean(mu), sd=sd(mu))
prettify_means(frmeans, "E1 NRevisits stage means")

E1 NRevisits stage means
st	mean	sd
early	0.11	0.08
late	0.08	0.07

E1 NRevisits: Fruit means

Show the code

pre.means = nrev_data_for_aov %>% 
  group_by(fr, pp, st) %>% 
  # average over resources
  summarise(mean.nrv=mean(nrv)) %>% 
  group_by(fr, pp) %>% 
  # average over stage
  summarise(mean.nrv=mean(mean.nrv))
ok.means = pre.means %>% 
  group_by(fr) %>% 
  summarise(mean=mean(mean.nrv), sd=sd(mean.nrv))
prettify_means(ok.means, "E1 Nrevisits fruit means")

E1 Nrevisits fruit means
fr	mean	sd
0	0.06	0.06
1	0.11	0.14
2	0.08	0.10
3	0.06	0.10
4	0.08	0.10
5	0.09	0.11
6	0.08	0.10
7	0.08	0.08
8	0.12	0.15
9	0.19	0.21