-
Notifications
You must be signed in to change notification settings - Fork 0
/
Copy pathstevens_etal_2022_rcode.R
744 lines (633 loc) · 47.2 KB
/
stevens_etal_2022_rcode.R
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480
481
482
483
484
485
486
487
488
489
490
491
492
493
494
495
496
497
498
499
500
501
502
503
504
505
506
507
508
509
510
511
512
513
514
515
516
517
518
519
520
521
522
523
524
525
526
527
528
529
530
531
532
533
534
535
536
537
538
539
540
541
542
543
544
545
546
547
548
549
550
551
552
553
554
555
556
557
558
559
560
561
562
563
564
565
566
567
568
569
570
571
572
573
574
575
576
577
578
579
580
581
582
583
584
585
586
587
588
589
590
591
592
593
594
595
596
597
598
599
600
601
602
603
604
605
606
607
608
609
610
611
612
613
614
615
616
617
618
619
620
621
622
623
624
625
626
627
628
629
630
631
632
633
634
635
636
637
638
639
640
641
642
643
644
645
646
647
648
649
650
651
652
653
654
655
656
657
658
659
660
661
662
663
664
665
666
667
668
669
670
671
672
673
674
675
676
677
678
679
680
681
682
683
684
685
686
687
688
689
690
691
692
693
694
695
696
697
698
699
700
701
702
703
704
705
706
707
708
709
710
711
712
713
714
715
716
717
718
719
720
721
722
723
724
725
726
727
728
729
730
731
732
733
734
735
736
737
738
739
740
741
742
743
744
## ---
##
## Script name: stevens_etal_2022_rcode.R
##
## Purpose of script: Analyze dog spatial choice data
##
## Author: Dr. Jeffrey R. Stevens ([email protected])
##
## Date Created: 2021-03-08
##
## Date Finalized: 2022-05-09
##
## License: All materials presented here are released under the Creative Commons Attribution 4.0 International Public License (CC BY 4.0).
## You are free to:
## Share — copy and redistribute the material in any medium or format
## Adapt — remix, transform, and build upon the material for any purpose, even commercially.
## Under the following terms:
## Attribution — You must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.
## No additional restrictions — You may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.
##
##
## ---
# Load libraries ---------------------------------------------------------------
library(BayesFactor)
library(psych)
library(tidyverse)
library(metaBMA)
library(ggbeeswarm)
library(patchwork)
library(ggdist)
library(papaja)
library(conflicted)
conflict_prefer("filter", "dplyr")
# Define functions ---------------------------------------------------------------
# Run apa_print but include initial zeros
apa_print2 <- function(x) {
add0 <- function(y) {
z <- gsub(" \\.", " 0.", y)
gsub("-\\.", "-0.", z)
}
apa <- apa_print(x)
apa %>% map(add0)
}
# Calculate correlation coefficient and Bayes factor
print_correlation <- function(df, x, y, freq = TRUE) {
df2 <- filter(df, !is.na(df[, x]) & !is.na(df[, y])) %>% # remove rows with NAs
select(x = x, y = y) # select only x and y columns and rename
corr_test <- cor.test(df2$y, df2$x) # conduct correlation
r <- round(corr_test$estimate, 2) # extract correlation coefficient
p <- round(corr_test$p.value, 2) # extract p-value
bfmodel <- correlationBF(df2$y, df2$x) # conduct Bayesian correlation
bf <- format(extractBF(bfmodel)$bf, digits = 2) # extract Bayes factor
if (freq) {
paste("N = ", nrow(df2), ", r = ", r, ", p = ", p, ", BF = ", bf, sep = "") # create text for r and BF
} else {
paste("N = ", nrow(df2), ", BF = ", bf, sep = "") # create text for r and BF
}
}
# Plot correlations
plot_correlation <- function(df, x, y, xlab, ylab, title = TRUE, stats = FALSE, freq = freq) {
df %>%
select(xvar = x, yvar = y) %>%
ggplot(aes(x = xvar, y = yvar)) +
geom_point(position = position_jitter(width = 0.005, height = 0.05), alpha = 0.7) + # plot individual points
geom_smooth(method = "lm", formula = y ~ x, color = cb_palette_black[5]) + # plot regression line
ylim(0, 4.8) +
{if (title) labs(x = xlab, y = ylab, subtitle = print_correlation(df, x, y, freq))} +
{if (!title) labs(x = xlab, y = ylab)} +
{if (stats) annotation_custom(grid::textGrob(print_correlation(df, x, y, freq)), xmin = -Inf, xmax = Inf, ymin = 4.7, ymax = 4.9)} +
scale_color_manual(values = cb_palette_black[5]) + # set group color
theme_bw() +
theme(text=element_text(family = "arial"),
legend.position = "none",
axis.title = element_text(size = 15),
axis.text = element_text(size = 12)) +
NULL
}
# Print ANOVA output
print_anova <- function(df, x, y, freq = TRUE) {
df2 <- df %>%
select(x = x, y = y) %>% # select only x and y columns and rename
filter(!is.na(x) & !is.na(y)) %>% # remove rows with NAs
mutate(x = as.factor(x))
if (length(levels(df2$x)) == 2) {
anova_test <- t.test(y ~ x, data = df2) # conduct t-test for 2 levels
} else if (length(levels(df2$x)) > 2) {
anova_test <- aov(y ~ x, data = df2) # conduct ANOVA for > 2 levels
} else {
stop("The independent variable requires at least two levels for 'print_anova()`.")
}
anova_results <- apa_print2(anova_test)$statistic
anova_results <- gsub("\\$", "", anova_results)
bfmodel <- anovaBF(y ~ x, data = df2, progress = FALSE) # conduct Bayesian correlation
bf <- format(extractBF(bfmodel)$bf, digits = 2) # extract Bayes factor
if (freq) {
paste0("N = ", nrow(df2), ", ", anova_results, ", BF = ", bf) # create text for r and BF
} else {
paste0("N = ", nrow(df2), ", BF = ", bf) # create text for r and BF
}
}
# Plot raincloud plots
plot_raincloud <- function(df, x, y, xlab, ylab, binwidth = 0.05, ymin = 0.2, ymax = 4.8, xmax = 6, title = TRUE, stats = FALSE, rotate = FALSE, freq = freq, rev = FALSE) {
df %>%
select(xvar = x, yvar = y) %>%
filter(!is.na(xvar) & !is.na(yvar)) %>% # remove rows with NAs
{if (rev) mutate(., xvar = fct_rev(xvar)) else .} %>%
ggplot(
aes(x = xvar, y = yvar, color = xvar, fill = xvar)
) +
stat_dots(side = "left", alpha = 0.5, binwidth = binwidth, justification = 1.05) + # plot individual points
stat_slab(alpha = 0.5, scale = 0.6) +
geom_boxplot(position = position_nudge(x = 0.1, y = 0), width = 0.2, outlier.shape = NA, alpha = 0.5, color = "black") +
stat_summary(fun.data = "mean_cl_boot", position = position_nudge(x = -0.1, y = 0), color = "black") +
{if (title) labs(x = xlab, y = ylab, subtitle = print_anova(df, x, y, freq))} +
{if (!title) labs(x = xlab, y = ylab)} +
{if (stats & !rotate) annotation_custom(grid::textGrob(print_anova(df, x, y, freq)), xmin = -Inf, xmax = Inf, ymin = ymax - 0.1, ymax = ymax + 0.1)} +
{if (stats & rotate) annotation_custom(grid::textGrob(print_anova(df, x, y, freq)), ymin = -Inf, ymax = Inf, xmin = xmax + 0.5, xmax = xmax + 0.6)} +
ylim(ymin, ymax) +
scale_color_manual(values = cb_palette_black[c(5, 3, 1, 6, 7, 2)]) + # set group color
scale_fill_manual(values = cb_palette_black[c(5, 3, 1, 6, 7, 2)]) + # set group color
# labs(x = xlab, y = ylab, subtitle = print_anova(df, x, y)) +
{if (rotate) coord_flip()} +
theme_bw() +
theme(text = element_text(family = "arial"),
legend.position = "none",
axis.title = element_text(size = 15),
axis.text = element_text(size = 12)) +
NULL
}
# Calculate reliability (omega) for scales
calculate_reliability <- function(df, scale, type = "omega") {
clean_data <- df %>%
filter(grepl(scale, survey)) %>% # filter this scale
select(-experiment, -survey) %>% # remove naming column
select_if(function(x) {!all(is.na(x))}) # remove empty columns
if (type == "omega") {
reliability <- psych::omega(clean_data, warnings = FALSE, plot = FALSE)
} else if (type == "alpha") {
reliability <- psych::alpha(clean_data)
} else {
stop("Incorrect reliability measure specified. Use 'omega' or 'alpha'")
}
return(reliability)
}
# Print Bayes factors
printbf <- function(x, digits = 2, cutoff = NULL) {
# Check if object is numeric, BFBayesFactor, or other
if (is.numeric(x)) {
bf <- x
} else if (class(x) == "BFBayesFactor") {
bf <- extractBF(x)$bf
} else {
stop("Object is not numeric or of class BFBayesFactor.")
}
# Format Bayes factor
if (is.null(cutoff)) {
if (bf > 1000 | bf < 0.001) {
bf <- typeset_scientific(sprintf("%.2e", bf))
}
p <- paste0("$\\mathrm{BF}_{\\textrm{10}} = ", printnum(bf), "$")
} else {
if (bf > cutoff) {
p <- paste0("$\\mathrm{BF}_{\\textrm{10}} > ", cutoff, "$")
} else if (bf < 1 / cutoff) {
p <- paste0("$\\mathrm{BF}_{\\textrm{10}} < ", printnum(1 / cutoff, digits = digits), "$")
} else {
p <- paste0("$\\mathrm{BF}_{\\textrm{10}} = ", printnum(bf), "$")
}
}
return(p)
}
# Print reliability data
print_reliability <- function(x, type = "omega") {
if (type == "omega") {
round(x$omega.tot, 2)
} else if (type == "alpha") {
round(x$total$raw_alpha, 2)
} else {
stop("Enter reliability type of 'omega' or 'delta'.")
}
}
# Define color-blind safe colors
cb_palette_black <- c("#E69F00", "#56B4E9", "#009E73", "#F0E442", "#0072B2", "#D55E00", "#CC79A7", "#000000")
# Import data ---------------------------------------------------
all_data <- read_csv("stevens_etal_2022_data1.csv") %>%
mutate(experiment = as.character(experiment),
cognitive_ability = crt_score + numeracy_score,
household_income = fct_relevel(household_income, "Less than $25,000", "$25,000-$49,999", "$50,000-$74,999", "$75,000-$99,999", "More than $100,000", "I would rather not say"))
item_data <- read_csv("stevens_etal_2022_data2.csv")
# CCHIL data ---------------------------------------------------
## Extract CCHIL data ---------------------------------------------------
clean_data_cchil <- all_data %>%
filter(experiment == 1)
item_data_cchil <- item_data %>%
filter(experiment == 1)
## Demographics ---------------------------------------------------
owner_gender_cchil <- table(clean_data_cchil$owner_gender)
owner_marital_cchil <- table(clean_data_cchil$owner_marital_status)
owner_otherdogs_cchil <- table(clean_data_cchil$other_dogs)
owner_income_cchil <- table(clean_data_cchil$household_income)
dog_age_cchil <- clean_data_cchil %>%
summarise(mean = mean(dog_age),
sd = sd(dog_age),
min = min(dog_age),
max = max(dog_age))
dog_sex_cchil <- clean_data_cchil %>%
select(dog_sex, dog_neutered) %>%
unite(sex_neuter, everything())
dog_sex_table_cchil <- table(dog_sex_cchil)
## Reliability for survey measures ---------------------------------------------------
### Dog behavior (Bennett & Rolf 2007 ---------------------------------------------------
dog_behavior_disobedient_reliability_cchil <- calculate_reliability(item_data_cchil, "dog_behavior_disobedient")
dog_behavior_aggressive_reliability_cchil <- calculate_reliability(item_data_cchil, "dog_behavior_aggressive")
dog_behavior_nervous_reliability_cchil <- calculate_reliability(item_data_cchil, "dog_behavior_nervous")
dog_behavior_destructive_reliability_cchil <- calculate_reliability(item_data_cchil, "dog_behavior_destructive")
dog_behavior_excitable_reliability_cchil <- calculate_reliability(item_data_cchil, "dog_behavior_excitable")
### Dog obedience (Hiby et al. 2004 ---------------------------------------------------
dog_obedience_reliability_cchil <- calculate_reliability(item_data_cchil, "dog_obedience")
### Dog problematic behaviors (Hiby et al. 2004 ---------------------------------------------------
dog_problem_behaviors_reliability_cchil <- calculate_reliability(item_data_cchil, "dog_problem_behaviors")
### DIAS (Wright et al. 2011 ---------------------------------------------------
dias_reliability_cchil <- calculate_reliability(item_data_cchil, "dias_")
dias_behavioral_regulation_reliability_cchil <- calculate_reliability(item_data_cchil, "dias_behavioral_regulation")
dias_aggression_reliability_cchil <- calculate_reliability(item_data_cchil, "dias_aggression")
dias_responsiveness_reliability_cchil <- calculate_reliability(item_data_cchil, "dias_responsiveness")
### MDORS (Dwyer et al. 2006 ---------------------------------------------------
mdors_reliability_cchil <- calculate_reliability(item_data_cchil, "mdors")
### Owner personality (Gosling et al., 2003 ---------------------------------------------------
# Note: must use alpha because only two items per scale
owner_personality_extraversion_reliability_cchil <- calculate_reliability(item_data_cchil, "owner_personality_extraversion", type = "alpha")
owner_personality_agreeableness_reliability_cchil <- calculate_reliability(item_data_cchil, "owner_personality_agreeableness", type = "alpha")
owner_personality_conscientiousness_reliability_cchil <- calculate_reliability(item_data_cchil, "owner_personality_conscientiousness", type = "alpha")
owner_personality_stability_reliability_cchil <- calculate_reliability(item_data_cchil, "owner_personality_stability", type = "alpha")
owner_personality_openness_reliability_cchil <- calculate_reliability(item_data_cchil, "owner_personality_openness", type = "alpha")
### Cognitive Reflection Test (Frederick 2005 ---------------------------------------------------
crt_reliability_cchil <- calculate_reliability(item_data_cchil, "crt")
### Berlin Numeracy Test (Cokely et al., 2012 ---------------------------------------------------
numeracy_reliability_cchil <- calculate_reliability(item_data_cchil, "numeracy")
## DIAS ------------------------------------------------------------------
### Correlations ------------------------------------------------------------------
dias_corr_cchil <- cor.test(clean_data_cchil$max_distance, clean_data_cchil$dias_overall_score)
dias_corr_bf_cchil <- correlationBF(clean_data_cchil$max_distance, clean_data_cchil$dias_overall_score)
dias_reg_corr_cchil <- cor.test(clean_data_cchil$max_distance, clean_data_cchil$dias_behavioral_regulation_score)
dias_reg_corr_bf_cchil <- correlationBF(clean_data_cchil$max_distance, clean_data_cchil$dias_behavioral_regulation_score)
dias_agg_corr_cchil <- cor.test(clean_data_cchil$max_distance, clean_data_cchil$dias_aggression_score)
dias_agg_corr_bf_cchil <- correlationBF(clean_data_cchil$max_distance, clean_data_cchil$dias_aggression_score)
dias_resp_corr_cchil <- cor.test(clean_data_cchil$max_distance, clean_data_cchil$dias_responsiveness_score)
dias_resp_corr_bf_cchil <- correlationBF(clean_data_cchil$max_distance, clean_data_cchil$dias_responsiveness_score)
### Plots ------------------------------------------------------------------
dias_plot_cchil <- plot_correlation(clean_data_cchil, "dias_overall_score", "max_distance", "DIAS overall score", "Distance traveled (m)", title = FALSE, freq = TRUE)
dias_reg_plot_cchil <- plot_correlation(clean_data_cchil, "dias_behavioral_regulation_score", "max_distance", "DIAS behavioral regulation score", "Distance traveled (m)", title = FALSE, stats = TRUE, freq = TRUE)
dias_agg_plot_cchil <- plot_correlation(clean_data_cchil, "dias_aggression_score", "max_distance", "DIAS aggression score", "Distance traveled (m)", title = FALSE, stats = TRUE, freq = TRUE)
dias_resp_plot_cchil <- plot_correlation(clean_data_cchil, "dias_responsiveness_score", "max_distance", "DIAS responsiveness score", "Distance traveled (m)", title = FALSE, stats = TRUE, freq = TRUE)
# Kenl Inn data ---------------------------------------------------
## Extract CCHIL data ---------------------------------------------------
clean_data_kenlinn <- all_data %>%
filter(experiment == 2)
item_data_kenlinn <- item_data %>%
filter(experiment == 2)
## Demographics ---------------------------------------------------
owner_gender_kenlinn <- table(clean_data_kenlinn$owner_gender)
owner_marital_kenlinn <- table(clean_data_kenlinn$owner_marital_status)
owner_otherdogs_kenlinn <- table(clean_data_kenlinn$other_dogs)
owner_income_kenlinn <- table(clean_data_kenlinn$household_income)
dog_age_kenlinn <- clean_data_kenlinn %>%
summarise(mean = mean(dog_age),
sd = sd(dog_age),
min = min(dog_age),
max = max(dog_age))
dog_sex_kenlinn <- clean_data_kenlinn %>%
select(dog_sex, dog_neutered) %>%
unite(sex_neuter, everything())
dog_sex_table_kenlinn <- table(dog_sex_kenlinn)
source <- table(clean_data_kenlinn$source)
## Reliability for survey measures ---------------------------------------------------
### C-BARQ training ---------------------------------------------------
dog_obedience_reliability_kenlinn <- calculate_reliability(item_data_kenlinn, "dog_obedience")
### Dog obedience (Hiby et al. 2004) ---------------------------------------------------
dog_obedience_reliability_kenlinn <- calculate_reliability(item_data_kenlinn, "dog_obedience")
### DIAS (Wright et al. 2011 ---------------------------------------------------
dias_reliability_kenlinn <- calculate_reliability(item_data_kenlinn, "dias_")
dias_behavioral_regulation_reliability_kenlinn <- calculate_reliability(item_data_kenlinn, "dias_behavioral_regulation")
dias_aggression_reliability_kenlinn <- calculate_reliability(item_data_kenlinn, "dias_aggression")
dias_responsiveness_reliability_kenlinn <- calculate_reliability(item_data_kenlinn, "dias_responsiveness")
### Owner personality (Gosling et al., 2003) ---------------------------------------------------
# Note: must use alpha because only two items per scale
owner_personality_extraversion_reliability_kenlinn <- calculate_reliability(item_data_kenlinn, "owner_personality_extraversion", type = "alpha")
owner_personality_agreeableness_reliability_kenlinn <- calculate_reliability(item_data_kenlinn, "owner_personality_agreeableness", type = "alpha")
owner_personality_conscientiousness_reliability_kenlinn <- calculate_reliability(item_data_kenlinn, "owner_personality_conscientiousness", type = "alpha")
owner_personality_stability_reliability_kenlinn <- calculate_reliability(item_data_kenlinn, "owner_personality_stability", type = "alpha")
owner_personality_openness_reliability_kenlinn <- calculate_reliability(item_data_kenlinn, "owner_personality_openness", type = "alpha")
## DIAS ---------------------------------------------------
max_distance_source_ttest <- t.test(max_distance ~ source, data = clean_data_kenlinn)
max_distance_source_ttest_bf <- ttestBF(formula = max_distance ~ source, data = clean_data_kenlinn)
dias_source_ttest <- t.test(dias_overall_score ~ source, data = clean_data_kenlinn)
dias_source_ttest_bf <- ttestBF(formula = dias_overall_score ~ source, data = clean_data_kenlinn)
### Correlations ------------------------------------------------------------------
dias_corr_kenlinn <- cor.test(clean_data_kenlinn$max_distance, clean_data_kenlinn$dias_overall_score)
dias_corr_bf_kenlinn <- correlationBF(clean_data_kenlinn$max_distance, clean_data_kenlinn$dias_overall_score)
dias_reg_corr_kenlinn <- cor.test(clean_data_kenlinn$max_distance, clean_data_kenlinn$dias_behavioral_regulation_score)
dias_reg_corr_bf_kenlinn <- correlationBF(clean_data_kenlinn$max_distance, clean_data_kenlinn$dias_behavioral_regulation_score)
dias_agg_corr_kenlinn <- cor.test(clean_data_kenlinn$max_distance, clean_data_kenlinn$dias_aggression_score)
dias_agg_corr_bf_kenlinn <- correlationBF(clean_data_kenlinn$max_distance, clean_data_kenlinn$dias_aggression_score)
dias_resp_corr_kenlinn <- cor.test(clean_data_kenlinn$max_distance, clean_data_kenlinn$dias_responsiveness_score)
dias_resp_corr_bf_kenlinn <- correlationBF(clean_data_kenlinn$max_distance, clean_data_kenlinn$dias_responsiveness_score)
### Plots ------------------------------------------------------------------
dias_plot_kenlinn <- plot_correlation(clean_data_kenlinn, "dias_overall_score", "max_distance", "DIAS overall score", "Distance traveled (m)", title = FALSE, freq = TRUE)
dias_reg_plot_kenlinn <- plot_correlation(clean_data_kenlinn, "dias_behavioral_regulation_score", "max_distance", "DIAS behavioral regulation score", "Distance traveled (m)", title = FALSE, stats = TRUE, freq = TRUE)
dias_agg_plot_kenlinn <- plot_correlation(clean_data_kenlinn, "dias_aggression_score", "max_distance", "DIAS aggression score", "Distance traveled (m)", title = FALSE, stats = TRUE, freq = TRUE)
dias_resp_plot_kenlinn <- plot_correlation(clean_data_kenlinn, "dias_responsiveness_score", "max_distance", "DIAS responsiveness score", "Distance traveled (m)", title = FALSE, stats = TRUE, freq = TRUE)
# dias_plot_kenlinn_poster <- plot_correlation(clean_data_kenlinn, "dias_overall_score", "max_distance", "Owner reported impulsivity", "Distance dog traveled (m)", title = FALSE, freq = TRUE)
# ggsave("figures/distance_dias_poster.png", width = 10, height = 6, scale = 0.5)
### Combined plots ------------------------------------------------------------------
dias_plot_cchil + dias_plot_kenlinn + plot_annotation(tag_levels = "a", tag_suffix = ")", tag_prefix = "(")
ggsave("figures/distance_dias_overall.png", width = 10, height = 5)
dias_reg_plot_cchil + dias_reg_plot_kenlinn +
dias_agg_plot_cchil + dias_agg_plot_kenlinn +
dias_resp_plot_cchil + dias_resp_plot_kenlinn +
plot_layout(ncol = 2) +
plot_annotation(tag_levels = "a", tag_suffix = ")", tag_prefix = "(")
ggsave("figures/distance_dias_subscales.png", width = 10, height = 15)
# All data ---------------------------------------------------
## Compare data sets ---------------------------------------------------
max_distance_ttest <- t.test(max_distance ~ experiment, data = all_data)
max_distance_ttest_bf <- ttestBF(formula = max_distance ~ experiment, data = all_data)
distance_study_plot <- plot_raincloud(all_data, "experiment", "max_distance", "Study", "Distance traveled (m)", freq = TRUE, title = FALSE, stats = TRUE)
dias_ttest <- t.test(dias_overall_score ~ experiment, data = all_data)
dias_ttest_bf <- ttestBF(formula = dias_overall_score ~ experiment, data = all_data)
dias_study_plot <- plot_raincloud(all_data, "experiment", "dias_overall_score", "Study", "DIAS overall score", binwidth = 0.005, ymin = 0.31, ymax = 0.72, freq = TRUE, title = FALSE, stats = TRUE)
# plot_raincloud(all_data, "experiment", "dog_weight", "Study", "Dog weight (kg)", binwidth = 0.2, ymin = 0, ymax = 80, freq = FALSE)
age_ttest <- t.test(dog_age ~ experiment, data = all_data)
age_ttest_bf <- ttestBF(formula = dog_age ~ experiment, data = all_data)
age_study_plot <- plot_raincloud(all_data, "experiment", "dog_age", "Study", "Dog age (years)", binwidth = 0.2, ymin = 0.75, ymax = 16.8, freq = TRUE, title = FALSE, stats = TRUE)
training_ttest <- t.test(training_sessions ~ experiment, data = all_data)
training_ttest_bf <- ttestBF(formula = training_sessions ~ experiment, data = filter(all_data, !is.na(training_sessions)))
training_summary <- all_data %>%
group_by(experiment) %>%
summarize(mean = mean(training_sessions, na.rm = TRUE), sd = sd(training_sessions, na.rm = TRUE))
## Correlates ---------------------------------------------------
### DIAS ---------------------------------------------------
dias_corr_r <- cor(all_data$max_distance, all_data$dias_overall_score)
dias_corr_bf <- correlationBF(all_data$max_distance, all_data$dias_overall_score)
dias_reg_corr_r <- cor(all_data$max_distance, all_data$dias_behavioral_regulation_score)
dias_reg_corr_bf <- correlationBF(all_data$max_distance, all_data$dias_behavioral_regulation_score)
dias_agg_corr_r <- cor(all_data$max_distance, all_data$dias_aggression_score)
dias_agg_corr_bf <- correlationBF(all_data$max_distance, all_data$dias_aggression_score)
dias_resp_corr_r <- cor(all_data$max_distance, all_data$dias_responsiveness_score)
dias_resp_corr_bf <- correlationBF(all_data$max_distance, all_data$dias_responsiveness_score)
# Remove young dogs
age_data <- all_data %>%
filter(dog_age >= 2 & dog_age <= 10)
dias_age_corr_r <- cor(age_data$max_distance, age_data$dias_overall_score)
dias_age_corr_bf <- correlationBF(age_data$max_distance, age_data$dias_overall_score)
### Dog characteristics ---------------------------------------------------
dog_sex_plot <- plot_raincloud(all_data, "dog_sex", "max_distance", "Dog sex", "Distance traveled (m)", freq = FALSE, title = FALSE, stats = TRUE)
# plot_raincloud(all_data, "dog_neutered", "max_distance", "Dog neuter status", "Distance traveled (m)", freq = FALSE, title = FALSE, stats = TRUE)
dog_weight_plot <- plot_correlation(all_data, "dog_weight", "max_distance", "Dog weight (kg)", "Distance traveled (m)", freq = FALSE, title = FALSE, stats = TRUE)
dog_age_plot <- plot_correlation(all_data, "dog_age", "max_distance", "Dog age (yrs)", "Distance traveled (m)", freq = FALSE, title = FALSE, stats = TRUE)
dog_akc_plot <- plot_raincloud(all_data, "akccgc", "max_distance", "Dog CGC certified", "Distance traveled (m)", freq = FALSE, title = FALSE, stats = TRUE)
dog_sex_plot + dog_weight_plot + dog_age_plot + dog_akc_plot +
plot_layout(ncol = 2) +
plot_annotation(tag_levels = "a", tag_suffix = ")", tag_prefix = "(")
ggsave("figures/dog_characteristics.png", width = 10, height = 10)
### Dog behavior ---------------------------------------------------
disobedience_plot <- plot_correlation(all_data, "dog_behavior_bennett_disobedient_score", "max_distance", "Bennett disobedience score", "Distance traveled (m)", freq = FALSE, title = FALSE, stats = TRUE)
aggression_plot <- plot_correlation(all_data, "dog_behavior_bennett_aggressive_score", "max_distance", "Bennett aggression score", "Distance traveled (m)", freq = FALSE, title = FALSE, stats = TRUE)
destructive_plot <- plot_correlation(all_data, "dog_behavior_bennett_destructive_score", "max_distance", "Bennett destructiveness score", "Distance traveled (m)", freq = FALSE, title = FALSE, stats = TRUE)
nervousness_plot <- plot_correlation(all_data, "dog_behavior_bennett_nervous_score", "max_distance", "Bennett nervousness score", "Distance traveled (m)", freq = FALSE, title = FALSE, stats = TRUE)
excitable_plot <- plot_correlation(all_data, "dog_behavior_bennett_excitable_score", "max_distance", "Bennett excitability score", "Distance traveled (m)", freq = FALSE, title = FALSE, stats = TRUE)
hiby_obedience_plot <- plot_correlation(all_data, "dog_obedience_hiby_score", "max_distance", "Hiby obedience score", "Distance traveled (m)", freq = FALSE, title = FALSE, stats = TRUE)
training_plot <- plot_correlation(all_data, "rate_dog_trained", "max_distance", "Dog training rating", "Distance traveled (m)", freq = FALSE, title = FALSE, stats = TRUE)
cbarq_training_plot <- plot_correlation(all_data, "cbarq_training_score", "max_distance", "CBARQ training score", "Distance traveled (m)", freq = FALSE, title = FALSE, stats = TRUE)
sep_anxiety_plot <- plot_raincloud(all_data, "separation_anxiety_yesno", "max_distance", "Separation anxiety", "Distance traveled (m)", binwidth = 0.1, freq = FALSE, title = FALSE, stats = TRUE)
disobedience_plot + aggression_plot + destructive_plot +
nervousness_plot + excitable_plot + hiby_obedience_plot +
training_plot + cbarq_training_plot + sep_anxiety_plot +
plot_layout(ncol = 3) +
plot_annotation(tag_levels = "a", tag_suffix = ")", tag_prefix = "(")
ggsave("figures/dog_behavior.png", width = 10, height = 10)
### Owner characteristics ---------------------------------------------------
mdors_plot <- plot_correlation(all_data, "mdors_score", "max_distance", "MDORS score", "Distance traveled (m)", freq = FALSE, title = FALSE, stats = TRUE)
extraversion_plot <- plot_correlation(all_data, "personality_extraversion_score", "max_distance", "Owner extraversion", "Distance traveled (m)", freq = FALSE, title = FALSE, stats = TRUE)
aggreeable_plot <- plot_correlation(all_data, "personality_agreeableness_score", "max_distance", "Owner agreeableness", "Distance traveled (m)", freq = FALSE, title = FALSE, stats = TRUE)
conscientious_plot <- plot_correlation(all_data, "personality_conscientiousness_score", "max_distance", "Owner conscientiousness", "Distance traveled (m)", freq = FALSE, title = FALSE, stats = TRUE)
stability_plot <- plot_correlation(all_data, "personality_stability_score", "max_distance", "Owner stability", "Distance traveled (m)", freq = FALSE, title = FALSE, stats = TRUE)
openness_plot <- plot_correlation(all_data, "personality_openness_score", "max_distance", "Owner openness", "Distance traveled (m)", freq = FALSE, title = FALSE, stats = TRUE)
cognition_plot <- plot_correlation(all_data, "cognitive_ability", "max_distance", "Owner cognitive ability", "Distance traveled (m)", freq = FALSE, title = FALSE, stats = TRUE)
otherdogs_plot <- plot_raincloud(all_data, "other_dogs", "max_distance", "Other dogs in household", "Distance traveled (m)", freq = FALSE, title = FALSE, stats = TRUE)
income_plot <- plot_raincloud(all_data, "household_income", "max_distance", "Household income", "Distance traveled (m)", rotate = TRUE, freq = FALSE, title = FALSE, stats = TRUE, rev = TRUE)
(mdors_plot + extraversion_plot + aggreeable_plot +
conscientious_plot + stability_plot + openness_plot +
cognition_plot + otherdogs_plot) + #income_plot +
# plot_spacer() + income_plot + plot_spacer() +
plot_layout(ncol = 3) +
plot_annotation(tag_levels = "a", tag_suffix = ")", tag_prefix = "(")
ggsave("figures/owner_characteristics.png", width = 10, height = 10)
## Study comparison --------------------------------------------------------
# Adult 1
brady_adult1_max_dist <- c(0.25, 1.5, 1.75, 1.75, 2, 2, 2.25, 2.5, 2.75, 3, 3, 3.25, 3.5, 3.5, 3.75, 4, 4.5, 4.5, 3.5, 5.75, 6.75, 7.5, 3.25, 1)
brady_adult1_dias_overall <- c(0.74, 0.53, 0.39, 0.67, 0.74, 0.42, 0.34, 0.52, 0.49, 0.6, 0.65, 0.63, 0.5, 0.49, 0.59, 0.47, 0.53, 0.49, 0.64, 0.41, 0.46, 0.45, 0.61, 0.54)
# Brady et al. ages
brady_adult1_ages <- c(24, 36, 96, 48, 36, 42, 76, 28, 96, 61, 48, 86, 42, 50, 52, 102, 26, 38, 41, 118, 108, 62, 71, 84)
mean(brady_adult1_max_dist)
sd(brady_adult1_max_dist)
mean(brady_adult1_dias_overall)
sd(brady_adult1_dias_overall)
brady_full_corr <- cor.test(brady_adult1_max_dist, brady_adult1_dias_overall)
brady_adult1_max_dist_trimmed <- brady_adult1_max_dist[-7]
brady_adult1_dias_overall_trimmed <- brady_adult1_dias_overall[-7]
brady_adult1_ages_trimmed <- brady_adult1_ages[-7]
mean(brady_adult1_max_dist_trimmed)
sd(brady_adult1_max_dist_trimmed)
mean(brady_adult1_dias_overall_trimmed)
sd(brady_adult1_dias_overall_trimmed)
brady_trimmed_corr <- cor.test(brady_adult1_max_dist_trimmed, brady_adult1_dias_overall_trimmed)
# Adult 2
brady_adult2_max_dist <- c(1, 0.75, 5, 1.5, 1.5, 0.75, 0.75, 1, 0.25, 3.75, 3.25, 0.5, 4.5)
brady_adult2_dias_overall <- c(0.56, 0.62, 0.43, 0.59, 0.49, 0.64, 0.49, 0.62, 0.49, 0.5, 0.5, 0.58, 0.43)
brady_adult2_ages <- c(108, 108, 84, 48, 36, 48, 72, 84, 60, 72, 96, 36, 48)
mean(brady_adult2_max_dist)
sd(brady_adult2_max_dist)
mean(brady_adult2_dias_overall)
sd(brady_adult2_dias_overall)
cor.test(brady_adult2_max_dist, brady_adult2_dias_overall)
brady_adult_ages <- c(brady_adult1_ages, brady_adult2_ages)
# Pup
brady_pup_max_dist <- c(2, 1.25, 0.25, 2.5, 5.25, 2.75, 7, 1.7, 3, 0, 2, 2.75, 1.5, 3.25, 5.5, 3.25, 4.75, 0, 0.25, 1.25, 0, 3, 5)
brady_pup_dias_overall <- c(0.59, 0.56, 0.59, 0.56, 0.52, 0.47, 0.61, 0.66, 0.44, 0.58, 0.46, 0.69, 0.46, 0.54, 0.53, 0.59, 0.57, 0.53, 0.59, 0.46, 0.55, 0.47, 0.52)
brady_pup_ages <- c(3,2,2,3,5,4,4,3,4,3,4,3,2,3,3,3,3,2,4,4,4,3,4)
# Compare our results to Brady et al.
brady_adult1_distance_ttest <- t.test(all_data$max_distance, brady_adult1_max_dist_trimmed)
brady_adult1_distance_ttest_bf <- ttestBF(all_data$max_distance, brady_adult1_max_dist_trimmed)
brady_adult2_distance_ttest <- t.test(all_data$max_distance, brady_adult2_max_dist)
brady_adult2_distance_ttest_bf <- ttestBF(all_data$max_distance, brady_adult2_max_dist)
brady_pup_distance_ttest <- t.test(all_data$max_distance, brady_pup_max_dist)
brady_pup_distance_ttest_bf <- ttestBF(all_data$max_distance, brady_pup_max_dist)
brady_adult1_dias_ttest <- t.test(all_data$dias_overall_score, brady_adult1_dias_overall_trimmed)
brady_adult1_dias_ttest_bf <- ttestBF(all_data$dias_overall_score, brady_adult1_dias_overall_trimmed)
brady_adult2_dias_ttest <- t.test(all_data$dias_overall_score, brady_adult2_dias_overall)
brady_adult2_dias_ttest_bf <- ttestBF(all_data$dias_overall_score, brady_adult2_dias_overall)
brady_pup_dias_ttest <- t.test(all_data$dias_overall_score, brady_pup_dias_overall)
brady_pup_dias_ttest_bf <- ttestBF(all_data$dias_overall_score, brady_pup_dias_overall)
all_max_distance <- c(clean_data_cchil$max_distance, clean_data_kenlinn$max_distance, brady_adult1_max_dist_trimmed, brady_adult2_max_dist, brady_pup_max_dist)
all_dias_overall <- c(clean_data_cchil$dias_overall_score, clean_data_kenlinn$dias_overall_score, brady_adult1_dias_overall_trimmed, brady_adult2_dias_overall, brady_pup_dias_overall)
all_age <- c(clean_data_cchil$dog_age, clean_data_kenlinn$dog_age, brady_adult1_ages_trimmed / 12, brady_adult2_ages / 12, brady_pup_ages / 12)
study_compare <- bind_cols(max_distance = all_max_distance, dias = all_dias_overall, age = all_age) %>%
mutate(study = c(rep(paste0("Current Study 1 (N = ", length(clean_data_cchil$max_distance), ")"), length(clean_data_cchil$max_distance)),
rep(paste0("Current Study 2 (N = ", length(clean_data_kenlinn$max_distance), ")"), length(clean_data_kenlinn$max_distance)),
rep(paste0("Brady et al. (2018) Lab Study 1 (N = ", length(brady_adult1_max_dist_trimmed), ")"), length(brady_adult1_max_dist_trimmed)),
rep(paste0("Brady et al. (2018) Field Study 1 (N = ", length(brady_adult2_max_dist), ")"), length(brady_adult2_max_dist)),
rep(paste0("Brady et al. (2018) Lab Study 2 (N = ", length(brady_pup_max_dist), ")"), length(brady_pup_max_dist))),
.before = 1) %>%
mutate(study = fct_relevel(study, paste0("Current Study 2 (N = ", length(clean_data_kenlinn$max_distance), ")"), paste0("Current Study 1 (N = ", length(clean_data_cchil$max_distance), ")"), paste0("Brady et al. (2018) Lab Study 2 (N = ", length(brady_pup_max_dist), ")"), paste0("Brady et al. (2018) Field Study 1 (N = ", length(brady_adult2_max_dist), ")"), paste0("Brady et al. (2018) Lab Study 1 (N = ", length(brady_adult1_max_dist_trimmed), ")")))
max_dist_plot <- study_compare %>%
ggplot(aes(x = study, y = max_distance, color = study, fill = study)) +
stat_dots(side = "left", alpha = 0.5, binwidth = 0.09, justification = 1.05) + # plot individual points
stat_slab(alpha = 0.5, scale = 0.6) +
geom_boxplot(position = position_nudge(x = 0.1, y = 0), width = 0.2, outlier.shape = NA, alpha = 0.5, color = "black") +
stat_summary(fun.data = "mean_cl_boot", position = position_nudge(x = -0.1, y = 0), color = "black") +
scale_color_manual(values = cb_palette_black[c(5, 3, 1, 6, 7)]) + # set group color
scale_fill_manual(values = cb_palette_black[c(5, 3, 1, 6, 7)]) + # set group color
labs(x = "", y = "Distanced traveled (m)") +
coord_flip() +
theme_bw() +
theme(text = element_text(family = "arial"),
legend.position = "none",
axis.title = element_text(size = 15),
axis.text.x = element_text(size = 12),
axis.text.y = element_text(size = 15)) +
ggtitle("(a)") +
NULL
dias_plot <- study_compare %>%
ggplot(aes(x = study, y = dias, color = study, fill = study)) +
stat_dots(side = "left", alpha = 0.5, binwidth = 0.005, justification = 1.05) + # plot individual points
stat_slab(alpha = 0.5, scale = 0.6) +
geom_boxplot(position = position_nudge(x = 0.1, y = 0), width = 0.2, outlier.shape = NA, alpha = 0.5, color = "black") +
stat_summary(fun.data = "mean_cl_boot", position = position_nudge(x = -0.1, y = 0), color = "black") +
scale_color_manual(values = cb_palette_black[c(5, 3, 1, 6, 7)]) + # set group color
scale_fill_manual(values = cb_palette_black[c(5, 3, 1, 6, 7)]) + # set group color
labs(x = "", y = "DIAS overall score") +
coord_flip() +
theme_bw() +
theme(text = element_text(family = "arial"),
legend.position = "none",
axis.title = element_text(size = 15),
axis.text.x = element_text(size = 12),
axis.text.y = element_blank()) +
ggtitle("(b)") +
NULL
age_plot <- study_compare %>%
ggplot(aes(x = study, y = age, color = study, fill = study)) +
stat_dots(side = "left", alpha = 0.5, binwidth = 0.2, justification = 1.05) + # plot individual points
stat_slab(alpha = 0.5, scale = 0.6) +
geom_boxplot(position = position_nudge(x = 0.1, y = 0), width = 0.2, outlier.shape = NA, alpha = 0.5, color = "black") +
stat_summary(fun.data = "mean_cl_boot", position = position_nudge(x = -0.1, y = 0), color = "black") +
scale_color_manual(values = cb_palette_black[c(5, 3, 1, 6, 7)]) + # set group color
scale_fill_manual(values = cb_palette_black[c(5, 3, 1, 6, 7)]) + # set group color
labs(x = "", y = "Dog age (years)") +
coord_flip() +
theme_bw() +
theme(text = element_text(family = "arial"),
legend.position = "none",
axis.title = element_text(size = 15),
axis.text.x = element_text(size = 12),
axis.text.y = element_blank()) +
ggtitle("(c)") +
NULL
max_dist_plot + dias_plot + age_plot
ggsave("figures/study_comparison.png", width = 10, height = 4, scale = 1.2)
## Meta-analysis -----------------------------------------------------------
meta_data <- tibble(study = c("Brady et al. (2018) Lab Study 1", "Brady et al. (2018) Field Study 1", "Brady et al. (2018) Lab Study 2", "Mongillo et al. (2019)", "Current Study 1", "Current Study 2"), r = c(-0.46, -0.61, -0.053, 0.01, -0.10, 0.04), n = c(23, 13, 24, 48, nrow(clean_data_cchil), nrow(clean_data_kenlinn))) %>%
mutate(z = transform_es(.$r, from = "r", to = "z"),
SE = 1 / sqrt(n - 3),
z_lower = z - qnorm(1.95 / 2) * SE,
z_upper = z + qnorm(1.95 / 2) * SE) %>%
mutate(r_lower = transform_es(.$z_lower, from = "z", to = "r"),
r_upper = transform_es(.$z_upper, from = "z", to = "r"))
(meta_z_avg <- meta_bma(y = z, SE = SE, labels = study, data = meta_data, iter = 2000,
d = metaBMA::prior("cauchy", c(location = 0, scale = 0.354)),
tau = metaBMA::prior("invgamma", c(shape = 1, scale = 0.075))))
z_bf <- meta_z_avg$inclusion$incl.BF
tau_bf <- (meta_z_avg$inclusion$posterior[3] + meta_z_avg$inclusion$posterior[4]) / (meta_z_avg$inclusion$posterior[1] + meta_z_avg$inclusion$posterior[2])
meta_data <- meta_data %>%
bind_rows(tibble(study = c("", "Averaged"),
r = c(NA, as.numeric(transform_es(meta_z_avg$estimates[1], from = "z", to = "r"))),
n = c(NA, sum(meta_z_avg$data$data$n)),
z = c(NA, meta_z_avg$estimates[1]),
SE = c(NA, NA),
z_lower = c(NA, meta_z_avg$estimates[7]),
z_upper = c(NA, meta_z_avg$estimates[13]),
r_lower = c(NA, as.numeric(transform_es(meta_z_avg$estimates[7], from = "z", to = "r"))),
r_upper = c(NA, as.numeric(transform_es(meta_z_avg$estimates[13], from = "z", to = "r")))))
forestplot_bf <- meta_data %>%
mutate(study = fct_relevel(study, "Averaged", "", "Current Study 2", "Current Study 1", "Mongillo et al. (2019)", "Brady et al. (2018) Lab Study 2", "Brady et al. (2018) Field Study 1", "Brady et al. (2018) Lab Study 1")) %>%
ggplot(aes(x = study, y = r)) +
geom_hline(yintercept = 0, color = "grey20", linetype = 2) +
geom_pointrange(aes(ymin = r_lower, ymax = r_upper), size = c(rep(1, 7),2), shape = c(rep(21, 7), 23), fill = c("black", "black", rep("grey", 5), "black")) +
coord_flip() +
labs(x = "", y = "Effect") +
ylim(-0.95, 0.45) +
theme_minimal() +
theme(panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
axis.line.x = element_line(),
text = element_text(size = 20, family = "Arial"))
ggsave("figures/forestplot_bf.png", width = 9, height = 4)
## Reliability scores ------------------------------------------------------
scales <- c("Bennett and Rohlf disobedience", "Bennett and Rohlf aggression", "Bennett and Rohlf nervousness", "Bennett and Rohlf destructiveness", "Bennett and Rohlf excitability", "Hiby et al. obedience", "Hiby et al. problem behaviors", "DIAS overall", "DIAS behavioral regulation", "DIAS aggression", "DIAS responsiveness", "MDORS", "Owner extraversion*", "Owner agreeableness*", "Owner conscientiousness*", "Owner stability*", "Owner openness*", "Cognitive reflection task", "Berlin numeracy test", "CBARQ training")
study1_reliability <- c(print_reliability(dog_behavior_disobedient_reliability_cchil),
print_reliability(dog_behavior_aggressive_reliability_cchil),
print_reliability(dog_behavior_nervous_reliability_cchil),
print_reliability(dog_behavior_destructive_reliability_cchil),
print_reliability(dog_behavior_excitable_reliability_cchil),
print_reliability(dog_obedience_reliability_cchil),
print_reliability(dog_problem_behaviors_reliability_cchil),
print_reliability(dias_reliability_cchil),
print_reliability(dias_behavioral_regulation_reliability_cchil),
print_reliability(dias_aggression_reliability_cchil),
print_reliability(dias_responsiveness_reliability_cchil),
print_reliability(mdors_reliability_cchil),
print_reliability(owner_personality_extraversion_reliability_cchil, type = "alpha"),
print_reliability(owner_personality_agreeableness_reliability_cchil, type = "alpha"),
print_reliability(owner_personality_conscientiousness_reliability_cchil, type = "alpha"),
print_reliability(owner_personality_stability_reliability_cchil, type = "alpha"),
print_reliability(owner_personality_openness_reliability_cchil, type = "alpha"),
print_reliability(crt_reliability_cchil),
print_reliability(numeracy_reliability_cchil),
NA
)
study2_reliability <- c(NA,
NA,
NA,
NA,
NA,
print_reliability(dog_obedience_reliability_kenlinn),
NA,
print_reliability(dias_reliability_kenlinn),
print_reliability(dias_behavioral_regulation_reliability_kenlinn),
print_reliability(dias_aggression_reliability_kenlinn),
print_reliability(dias_responsiveness_reliability_kenlinn),
NA,
print_reliability(owner_personality_extraversion_reliability_kenlinn, type = "alpha"),
print_reliability(owner_personality_agreeableness_reliability_kenlinn, type = "alpha"),
print_reliability(owner_personality_conscientiousness_reliability_kenlinn, type = "alpha"),
print_reliability(owner_personality_stability_reliability_kenlinn, type = "alpha"),
print_reliability(owner_personality_openness_reliability_kenlinn, type = "alpha"),
NA,
NA,
print_reliability(dog_obedience_reliability_kenlinn)
)
reliability_table <- data.frame(scales, study1_reliability, study2_reliability)
## Demographics ------------------------------------------------------------
demo_labels <- c("", "Female", "Male", "Nonbinary", "Single", "Married", "Separated/divorced", "Widowed", "Yes", "No", "<$25,000", "$25,001-$50,000", "$50,001-$75,000", "$75,001-$100,000", ">$100,000", "Prefer not to answer")
demo_cchil <- c(
paste("Study 1 (N=", nrow(clean_data_cchil), ")", sep = ""),
paste(unname(owner_gender_cchil["Female"]), " (", printnum(owner_gender_cchil["Female"] / sum(owner_gender_cchil) * 100, digits = 1), "%)", sep = ""),
paste(unname(owner_gender_cchil["Male"]), " (", printnum(owner_gender_cchil["Male"] / sum(owner_gender_cchil) * 100, digits = 1), "%)", sep = ""),
paste(unname(owner_gender_cchil["Other"]), " (", printnum(owner_gender_cchil["Other"] / sum(owner_gender_cchil) * 100, digits = 1), "%)", sep = ""),
paste(unname(owner_marital_cchil["Single"]), " (", printnum(owner_marital_cchil["Single"] / sum(owner_marital_cchil) * 100, digits = 1), "%)", sep = ""),
paste(unname(owner_marital_cchil["Married"]), " (", printnum(owner_marital_cchil["Married"] / sum(owner_marital_cchil) * 100, digits = 1), "%)", sep = ""),
paste(unname(owner_marital_cchil["Separated/ divorced"]), " (", printnum(owner_marital_cchil["Separated/ divorced"] / sum(owner_marital_cchil) * 100, digits = 1), "%)", sep = ""),
paste(unname(owner_marital_cchil["Widowed"]), " (", printnum(owner_marital_cchil["Widowed"] / sum(owner_marital_cchil) * 100, digits = 1), "%)", sep = ""),
paste(unname(owner_otherdogs_cchil["Yes"]), " (", printnum(owner_otherdogs_cchil["Yes"] / sum(owner_otherdogs_cchil) * 100, digits = 1), "%)", sep = ""),
paste(unname(owner_otherdogs_cchil["No"]), " (", printnum(owner_otherdogs_cchil["No"] / sum(owner_otherdogs_cchil) * 100, digits = 1), "%)", sep = ""),
paste(unname(owner_income_cchil["Less than $25,000"]), " (", printnum(owner_income_cchil["Less than $25,000"] / sum(owner_income_cchil) * 100, digits = 1), "%)", sep = ""),
paste(unname(owner_income_cchil["$25,000-$49,999"]), " (", printnum(owner_income_cchil["$25,000-$49,999"] / sum(owner_income_cchil) * 100, digits = 1), "%)", sep = ""),
paste(unname(owner_income_cchil["$50,000-$74,999"]), " (", printnum(owner_income_cchil["$50,000-$74,999"] / sum(owner_income_cchil) * 100, digits = 1), "%)", sep = ""),
paste(unname(owner_income_cchil["$75,000-$99,999"]), " (", printnum(owner_income_cchil["$75,000-$99,999"] / sum(owner_income_cchil) * 100, digits = 1), "%)", sep = ""),
paste(unname(owner_income_cchil["More than $100,000"]), " (", printnum(owner_income_cchil["More than $100,000"] / sum(owner_income_cchil) * 100, digits = 1), "%)", sep = ""),
paste(unname(owner_income_cchil["I would rather not say"]), " (", printnum(owner_income_cchil["I would rather not say"] / sum(owner_income_cchil) * 100, digits = 1), "%)", sep = "")
)
demo_kenlinn <- c(
paste("Study 2 (N=", nrow(clean_data_kenlinn), ")", sep = ""),
paste(unname(owner_gender_kenlinn["Female"]), " (", printnum(owner_gender_kenlinn["Female"] / sum(owner_gender_kenlinn) * 100, digits = 1), "%)", sep = ""),
paste(unname(owner_gender_kenlinn["Male"]), " (", printnum(owner_gender_kenlinn["Male"] / sum(owner_gender_kenlinn) * 100, digits = 1), "%)", sep = ""),
paste(unname(owner_gender_kenlinn["Other"]), " (", printnum(owner_gender_kenlinn["Other"] / sum(owner_gender_kenlinn) * 100, digits = 1), "%)", sep = ""),
paste(unname(owner_marital_kenlinn["Single"]), " (", printnum(owner_marital_kenlinn["Single"] / sum(owner_marital_kenlinn) * 100, digits = 1), "%)", sep = ""),
paste(unname(owner_marital_kenlinn["Married"]), " (", printnum(owner_marital_kenlinn["Married"] / sum(owner_marital_kenlinn) * 100, digits = 1), "%)", sep = ""),
paste(unname(owner_marital_kenlinn["Separated/ divorced"]), " (", printnum(owner_marital_kenlinn["Separated/ divorced"] / sum(owner_marital_kenlinn) * 100, digits = 1), "%)", sep = ""),
paste(unname(owner_marital_kenlinn["Widowed"]), " (", printnum(owner_marital_kenlinn["Widowed"] / sum(owner_marital_kenlinn) * 100, digits = 1), "%)", sep = ""),
paste(unname(owner_otherdogs_kenlinn["Yes"]), " (", printnum(owner_otherdogs_kenlinn["Yes"] / sum(owner_otherdogs_kenlinn) * 100, digits = 1), "%)", sep = ""),
paste(unname(owner_otherdogs_kenlinn["No"]), " (", printnum(owner_otherdogs_kenlinn["No"] / sum(owner_otherdogs_kenlinn) * 100, digits = 1), "%)", sep = ""),
paste(unname(owner_income_kenlinn["Less than $25,000"]), " (", printnum(owner_income_kenlinn["Less than $25,000"] / sum(owner_income_kenlinn) * 100, digits = 1), "%)", sep = ""),
paste(unname(owner_income_kenlinn["$25,000-$49,999"]), " (", printnum(owner_income_kenlinn["$25,000-$49,999"] / sum(owner_income_kenlinn) * 100, digits = 1), "%)", sep = ""),
paste(unname(owner_income_kenlinn["$50,000-$74,999"]), " (", printnum(owner_income_kenlinn["$50,000-$74,999"] / sum(owner_income_kenlinn) * 100, digits = 1), "%)", sep = ""),
paste(unname(owner_income_kenlinn["$75,000-$99,999"]), " (", printnum(owner_income_kenlinn["$75,000-$99,999"] / sum(owner_income_kenlinn) * 100, digits = 1), "%)", sep = ""),
paste(unname(owner_income_kenlinn["More than $100,000"]), " (", printnum(owner_income_kenlinn["More than $100,000"] / sum(owner_income_kenlinn) * 100, digits = 1), "%)", sep = ""),
paste(unname(owner_income_kenlinn["I would rather not say"]), " (", printnum(owner_income_kenlinn["I would rather not say"] / sum(owner_income_kenlinn) * 100, digits = 1), "%)", sep = "")
)
demo_table <- bind_cols(labels = demo_labels, cchil = demo_cchil, kenlinn = demo_kenlinn) %>%
mutate(cchil = str_replace_all(cchil, "NA", "0"),
kenlinn = str_replace_all(kenlinn, "NA", "0"))
# save.image("dog_spatial_workspace.RData")