Foraging manuscript data analysis

E2 Distance - Prepare the data

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e2 <- readRDS("002-00-e2-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
e2 <- e2 %>% filter(fl>0)

# remove the second (and any subsequent) *consecutive* duplicates
e2 <- e2 %>% 
  group_by(pp, rr, tb) %>% 
  filter(is.na(tl != lag(tl)) | tl != lag(tl)) %>% 
  ungroup()

# remove trials where they failed to get 14 fruit
e2 <- e2 %>%
  group_by(pp, rr, tb) %>%
  mutate(max_fr = max(fr)) %>%
  ungroup() %>%
  filter(max_fr==14) 

# Euclid
e2 <- e2 %>% 
  group_by(pp, rr, tb) %>% 
  mutate(dist = round(sqrt((lead(xx)-xx)^2 + (lead(yy)-yy)^2), 2)) %>% 
  ungroup()

# time
e2 <- e2 %>% 
  group_by(pp, rr, tb) %>% 
  mutate(tm=tm-first(tm)) %>%
  ungroup()

e2 <- e2 %>% rename(ll=ln)

# fewer columns
e2 <- e2 %>% 
  select(ff, pp, rr, st, tb, tm, ll, tl, dist)

e2 <- e2 %>% 
  mutate(ff=as_factor(ff),
         pp=as_factor(pp), 
         st=as_factor(st), 
         tb=as_factor(tb),
         ll=factor(ll, levels=c("fruit", "not"), labels=c(
           "Launched from fruit", "Launched from tree without fruit"
         )),
         rr=factor(rr, levels=c("dispersed", "patchy")), 
         )

dst <- e2 %>% 
  group_by(ff, pp, rr, st, ll, tb) %>% 
  # average over tree-visits yielding
  # two values for each trial, 
  # one for launch from fruit, 
  # one for launch from empty
  summarise(dist=mean(dist, na.rm=TRUE)) %>% 
  arrange(ff, pp, rr, st, ll, tb, .by_group = TRUE) %>% 
  ungroup()

# average over trials in each of the two stages,
# to yield one row for each launch type, per stage
# 8 rows per subject, 2 x 2 x 2
# 2 x 2 x 2 x 42=336 rows
dst <- dst %>% 
  group_by(ff, pp, rr, st, ll) %>% 
  summarise(mu.dist=mean(dist, na.rm=TRUE)) %>% 
  ungroup()

dst <- dst %>% 
  select(ff, rr, st, ll, pp, mu.dist) %>% 
  arrange(ff, rr, st, ll, pp, mu.dist)

saveRDS(dst, "e2_distance_data.rds")

e2 Distance ANOVA

Run the ANOVA

2 x 2 x 2 x 2 fading, resources, stage, launch

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options(contrasts=c("contr.sum","contr.poly"))
e2_distance_ANOVA <- 
  ezANOVA(data=dst,
          dv=mu.dist,
          wid=pp,
          within=c(rr, st, ll),
          between=ff,
          type=3, 
          return_aov = TRUE)

Warning: Data is unbalanced (unequal N per group). Make sure you specified a
well-considered value for the type argument to ezANOVA().

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if("Sphericity Corrections" %in% names(e2_distance_ANOVA)){
  corr=aovSphericityAdjustment(e2_distance_ANOVA)$ANOVA %>% tibble()
} else {
  corr=e2_distance_ANOVA$ANOVA %>% tibble()  
  }

Table the ANOVA

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prettify_anova(e2_distance_ANOVA, "e2 Distance ANOVA")

Effect	DFn	DFd	F	p	sig
e2 Distance ANOVA
ff	1	40	0.80	0.375
rr	1	40	35.46	0.000	***
st	1	40	3.64	0.064
ll	1	40	28.98	0.000	***
ff:rr	1	40	3.00	0.091
ff:st	1	40	3.24	0.079
ff:ll	1	40	2.53	0.120
rr:st	1	40	0.00	0.954
rr:ll	1	40	23.61	0.000	***
st:ll	1	40	4.20	0.047	*
ff:rr:st	1	40	0.81	0.375
ff:rr:ll	1	40	0.32	0.575
ff:st:ll	1	40	0.11	0.746
rr:st:ll	1	40	1.53	0.223
ff:rr:st:ll	1	40	3.74	0.060

Report the ANOVA

The effect of fading was F(1, 40) = 0.80, p=0.375.

The effect of resources was F(1, 40) = 35.46, p<.001.

The effect of stage was F(1, 40) = 3.64, p=0.064.

The effect of stage was F(1, 40) = 28.98, p<.001.

2-way interactions

The resources x launch interaction was F(1, 40) = 23.61, p<.001.

The stage x launch interaction was F(1, 40) = 4.20, p<.05.

e2 Distance: Plot

x axis is stage; y axis is distance; group is resources; panel is launch site type

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ggplot(data=dst, aes(y=mu.dist, x=st, group=rr, fill=rr, shape=rr)) +
  facet_wrap(~ll) +
  labs(title="(d): Distance moved between trees", subtitle = "The eyes move further to the next tree if the current tree has no fruit")+
  ylab("Pixels")+
  xlab("Trials")+
  my_fgms_theme+
  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=3, position=pd)+
  scale_x_discrete(labels=c("early trials\n1 to 10", "late trials\n11 to 20"))

E2 Distance means for resources

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dst %>% 
  group_by(rr, pp) %>%
  summarise(mu=mean(mu.dist)) %>% 
  summarise(mean=mean(mu), sd=sd(mu)) %>% 
  gt() %>% 
  fmt_number(decimals=0) %>% 
  tab_header("Pixels ")

rr	mean	sd
Pixels
dispersed	275	35
patchy	301	47

E2 Distance means for launch-site

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dst %>% 
  group_by(ll, pp) %>%
  summarise(mu=mean(mu.dist)) %>% 
  summarise(mean=mean(mu), sd=sd(mu)) %>% 
  gt() %>% 
  fmt_number(decimals=0) %>% 
  tab_header("Pixels ")

ll	mean	sd
Pixels
Launched from fruit	276	36
Launched from tree without fruit	299	45

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dst %>%
  filter(rr=="dispersed") %>% 
  group_by(ll, pp) %>% 
  summarise(mu=mean(mu.dist))%>% 
  summarise(mean=mean(mu), sd=sd(mu)) %>% 
  gt() %>% 
  fmt_number(decimals=0) %>% 
  tab_header("Pixels dispersed condition")

ll	mean	sd
Pixels dispersed condition
Launched from fruit	272	35
Launched from tree without fruit	277	36

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dst %>%
  filter(rr=="patchy") %>% 
  group_by(ll, pp) %>% 
  summarise(mu=mean(mu.dist))%>% 
  summarise(mean=mean(mu), sd=sd(mu)) %>% 
  gt() %>% 
  fmt_number(decimals=0) %>% 
  tab_header("Pixels patchy condition")

ll	mean	sd
Pixels patchy condition
Launched from fruit	280	41
Launched from tree without fruit	321	63