This supplement adds exploratory regression models by creating tables in {gt}.
For each study, we should see a row in the table per outcome and per predictor. The t and p-values are included. A column shows \(p < .05\). Where the primary predictor other than the MASQ is significant according to the alpha level of that study, the row is highlighted in blue and bold.
ear1 <- read_csv(here::here("data and codebooks", "study1a_processed.csv"))## Rows: 214 Columns: 282
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (3): ResponseId, date, Gender
## dbl (277): ID, filter, Friend1_LikelyEnd, Friend1_LikelyExplicit, Friend1_Gh...
## lgl (2): Race_Other, RomanticInterest_Other
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.ear1 <- ear1 %>% filter(filter == 1)outcomes <- list("OverallDifficulty_Avg",
"OverallLikelyEnd_Avg",
"PercentRelationshipsYouEnded",
"OverallNegEmo_Avg"
#"OverallLikelyGhost_Avg",
#"LikelihoodEndingRomantic_Avg",
#"LikelihoodGhostRomantic_Avg",
#"LikelihoodExplicitRejectRomantic_Avg"
)
predictors <- list("RosenbergSelfEsteem_Sum",
"RSQ_Score")
table1 <- map_dfr(outcomes, function(outcome) {
map_dfr(predictors, function(predictor) {
ear1 %>%
select(x = {{predictor}},
y = {{outcome}},
MASQ_GD, MASQ_AD, MASQ_AA) %>%
lm(data = .,
y ~ x + MASQ_GD + MASQ_AD + MASQ_AA) %>%
broom::tidy(conf.int = TRUE) %>%
mutate(term = case_when(term == "x" ~ predictor,
TRUE ~ term)) %>%
select(-std.error) %>%
mutate(predictor = predictor)
}) %>%
mutate(outcome = outcome) %>%
relocate(outcome)
})
table1 <- table1 %>%
mutate(sig = ifelse(p.value < .05,
"*",
NA_character_),
matters = case_when(
p.value < .05 &
term == "RosenbergSelfEsteem_Sum" ~
"highlight",
p.value < .05 &
term == "RSQ_Score" ~ "highlight",
TRUE ~ NA_character_
))
outcome_names <- c(
"OverallDifficulty_Avg" = "Overall Difficulty",
"OverallLikelyEnd_Avg" = "Likelihood Ending Overall",
"PercentRelationshipsYouEnded" = "Percent Relationships Participant Ended",
"OverallNegEmo_Avg" = "Overall Negative Emotions",
"LikelihoodExplicitRejectRomantic_Avg" = "Explicit Romantic Likelihood",
"OverallLikelyGhost_Avg" = "Ghosting Likelihood",
"LikelihoodEndingRomantic_Avg" = "Romantic Likelihood",
"LikelihoodGhostRomantic_Avg" = "Ghosting Romantic Likelihood")
predictor_names <- c(
"RosenbergSelfEsteem_Sum" = "Self-Esteem (Rosenberg)",
"RSQ_Score" = "Rejection Sensitivity (RSQ)",
"MASQ_GD" = "General Distress (MASQ)",
"MASQ_AD" = "Anhedonic Depression (MASQ)",
"MASQ_AA" = "Anxious Arousal (MASQ)",
"(Intercept)" = "(Intercept)"
)
table1 %>%
mutate(outcome = outcome_names[outcome],
term = predictor_names[term],
predictor = predictor_names[predictor]) %>%
group_by(outcome, predictor) %>%
gt::gt(rowname_col = "term") %>%
gt::cols_merge(columns = c(estimate, conf.low,
conf.high),
pattern = "{1} [{2}–{3}]") %>%
gt::cols_label(
estimate = md("*b* [95% CI]"),
statistic = md("*t*"),
p.value = md("*p*"),
sig = md("*p* < .05")
) %>%
gt::sub_missing(sig, missing_text = "") %>%
gt::sub_small_vals(columns = c(estimate,
statistic,
conf.low,
conf.high),
threshold = .01) %>%
gt::sub_small_vals(columns = p.value,
threshold = .001,
small_pattern = "<.001") %>%
gt::fmt_number(p.value, decimals = 3) %>%
gt::fmt_number(c(estimate, statistic,
conf.low, conf.high),
decimals = 2) %>%
gt::tab_style(
style = list(cell_fill(color = "lightblue"),
cell_text(weight = "bold")),
locations = cells_body(
rows = matters == "highlight"
)
) %>%
gt::cols_hide(matters)# %>%| b [95% CI] | t | p | p < .05 | |
|---|---|---|---|---|
| Overall Difficulty - Self-Esteem (Rosenberg) | ||||
| (Intercept) | 5.91 [4.38–7.45] | 7.59 | <.001 | * |
| Self-Esteem (Rosenberg) | −0.04 [−0.08–−0.01] | −2.81 | 0.005 | * |
| General Distress (MASQ) | 0.03 [<0.01–0.05] | 2.76 | 0.006 | * |
| Anhedonic Depression (MASQ) | −0.01 [−0.03–0.01] | −0.93 | 0.355 | |
| Anxious Arousal (MASQ) | −0.03 [−0.05–−0.01] | −2.69 | 0.008 | * |
| Overall Difficulty - Rejection Sensitivity (RSQ) | ||||
| (Intercept) | 3.82 [3.20–4.44] | 12.15 | <.001 | * |
| Rejection Sensitivity (RSQ) | <0.01 [−0.04–0.05] | 0.30 | 0.764 | |
| General Distress (MASQ) | 0.04 [0.02–0.06] | 4.29 | <.001 | * |
| Anhedonic Depression (MASQ) | <0.01 [−0.02–0.02] | 0.17 | 0.869 | |
| Anxious Arousal (MASQ) | −0.02 [−0.04–<0.01] | −1.95 | 0.053 | |
| Likelihood Ending Overall - Self-Esteem (Rosenberg) | ||||
| (Intercept) | 3.06 [2.23–3.89] | 7.24 | <.001 | * |
| Self-Esteem (Rosenberg) | 0.01 [−0.01–0.03] | 1.37 | 0.173 | |
| General Distress (MASQ) | <0.01 [−0.01–0.02] | 1.07 | 0.287 | |
| Anhedonic Depression (MASQ) | 0.00 [−0.01–0.01] | −0.01 | 0.992 | |
| Anxious Arousal (MASQ) | <0.01 [−0.01–0.01] | 0.31 | 0.753 | |
| Likelihood Ending Overall - Rejection Sensitivity (RSQ) | ||||
| (Intercept) | 3.71 [3.37–4.04] | 21.64 | <.001 | * |
| Rejection Sensitivity (RSQ) | −0.01 [−0.03–0.01] | −0.89 | 0.376 | |
| General Distress (MASQ) | <0.01 [−0.01–0.01] | 0.41 | 0.680 | |
| Anhedonic Depression (MASQ) | 0.00 [−0.01–<0.01] | −0.63 | 0.531 | |
| Anxious Arousal (MASQ) | <0.01 [−0.01–0.01] | 0.07 | 0.946 | |
| Percent Relationships Participant Ended - Self-Esteem (Rosenberg) | ||||
| (Intercept) | 0.71 [0.06–1.37] | 2.14 | 0.034 | * |
| Self-Esteem (Rosenberg) | −0.01 [−0.02–<0.01] | −1.10 | 0.274 | |
| General Distress (MASQ) | <0.01 [−0.01–0.01] | 0.31 | 0.758 | |
| Anhedonic Depression (MASQ) | 0.00 [−0.01–<0.01] | −0.47 | 0.636 | |
| Anxious Arousal (MASQ) | 0.00 [−0.01–<0.01] | −0.02 | 0.984 | |
| Percent Relationships Participant Ended - Rejection Sensitivity (RSQ) | ||||
| (Intercept) | 0.41 [0.15–0.67] | 3.15 | 0.002 | * |
| Rejection Sensitivity (RSQ) | −0.01 [−0.02–0.01] | −0.65 | 0.515 | |
| General Distress (MASQ) | <0.01 [0.00–0.01] | 1.00 | 0.317 | |
| Anhedonic Depression (MASQ) | 0.00 [−0.01–<0.01] | −0.06 | 0.955 | |
| Anxious Arousal (MASQ) | <0.01 [−0.01–<0.01] | 0.23 | 0.818 | |
| Overall Negative Emotions - Self-Esteem (Rosenberg) | ||||
| (Intercept) | 4.65 [3.48–5.82] | 7.87 | <.001 | * |
| Self-Esteem (Rosenberg) | −0.03 [−0.05–0.00] | −2.34 | 0.020 | * |
| General Distress (MASQ) | 0.02 [<0.01–0.03] | 2.18 | 0.031 | * |
| Anhedonic Depression (MASQ) | <0.01 [−0.01–0.02] | 0.67 | 0.504 | |
| Anxious Arousal (MASQ) | 0.00 [−0.02–0.01] | −0.40 | 0.686 | |
| Overall Negative Emotions - Rejection Sensitivity (RSQ) | ||||
| (Intercept) | 3.36 [2.88–3.83] | 14.03 | <.001 | * |
| Rejection Sensitivity (RSQ) | <0.01 [−0.03–0.04] | 0.28 | 0.778 | |
| General Distress (MASQ) | 0.03 [0.01–0.04] | 3.47 | <.001 | * |
| Anhedonic Depression (MASQ) | 0.01 [0.00–0.02] | 1.48 | 0.140 | |
| Anxious Arousal (MASQ) | <0.01 [−0.01–0.02] | 0.18 | 0.859 | |
# gt::as_latex() %>% as.character()ear2 <- read_csv(here::here("data and codebooks", "study1b_processed.csv"))## Rows: 264 Columns: 259
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (5): ID, date, ResponseId, Gender, Race
## dbl (252): filter, Romantic1_LikelyEnd, Romantic1_LikelyExplicit, Romantic1_...
## lgl (2): Race_Other, RomanticInterest_Other
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.ear2 <- ear2 %>% filter(filter == 1)outcomes2 <- list("OverallLikelyEnd_Avg",
"Explicit_OverallDifficulty_Avg",
"Explicit_OverallLikelyEnd_Avg",
"Explicit_OverallNegEmo_Avg",
"Ghost_OverallDifficulty_Avg",
"Ghost_OverallLikelyEnd_Avg",
"Ghost_OverallNegEmo_Avg")
predictors2 <- list("RosenbergSelfEsteem_Sum",
"RSQ_Score_Fixed")
table2 <- map_dfr(outcomes2, function(outcome) {
map_dfr(predictors2, function(predictor) {
ear2 %>%
select(x = {{predictor}},
y = {{outcome}},
MASQ_GD, MASQ_AD, MASQ_AA) %>%
lm(data = .,
y ~ x + MASQ_GD + MASQ_AD + MASQ_AA) %>%
broom::tidy(conf.int = TRUE) %>%
mutate(term = case_when(term == "x" ~ predictor,
TRUE ~ term)) %>%
select(-std.error) %>%
mutate(predictor = predictor)
}) %>%
mutate(outcome = outcome) %>%
relocate(outcome)
})
table2 <- table2 %>%
mutate(sig = ifelse(p.value < .05,
"*",
NA_character_),
matters = case_when(
p.value < .05 &
term == "RosenbergSelfEsteem_Sum" ~
"highlight",
p.value < .05 &
term == "RSQ_Score_Fixed" ~ "highlight",
TRUE ~ NA_character_
))
outcome_names2 <- c(
"Explicit_OverallNegEmo_Avg" = "Neg Emotions to Explicit Rejection",
"OverallLikelyEnd_Avg" = "Likelihood Overall",
"Ghost_OverallDifficulty_Avg" = "Difficulty Ghosting",
"Explicit_OverallDifficulty_Avg" = "Difficulty Explicit",
"Ghost_OverallLikelyEnd_Avg" = "Likelihood Ghosting",
"Explicit_OverallLikelyEnd_Avg" = "Likelihood Explicit",
"Ghost_OverallNegEmo_Avg" = "Neg Emotions to Ghosting",
"Explicit_OverallNegEmo_Avg" = "Overall Negative Emotions"
)
predictor_names2 <- c(
"RosenbergSelfEsteem_Sum" = "Self-Esteem (Rosenberg)",
"RSQ_Score_Fixed" = "Rejection Sensitivity (RSQ)",
"MASQ_GD" = "General Distress (MASQ)",
"MASQ_AD" = "Anhedonic Depression (MASQ)",
"MASQ_AA" = "Anxious Arousal (MASQ)",
"(Intercept)" = "(Intercept)"
)
table2 %>%
mutate(outcome = outcome_names2[outcome],
term = predictor_names2[term],
predictor = predictor_names2[predictor]) %>%
group_by(outcome, predictor) %>%
gt::gt(rowname_col = "term") %>%
gt::cols_merge(columns = c(estimate, conf.low,
conf.high),
pattern = "{1} [{2}–{3}]") %>%
gt::cols_label(
estimate = md("*b* [95% CI]"),
statistic = md("*t*"),
p.value = md("*p*"),
sig = md("*p* < .05")
) %>%
gt::sub_missing(sig, missing_text = "") %>%
gt::sub_small_vals(columns = c(estimate,
statistic,
conf.low,
conf.high),
threshold = .01) %>%
gt::sub_small_vals(columns = p.value,
threshold = .001,
small_pattern = "<.001") %>%
gt::fmt_number(p.value, decimals = 3) %>%
gt::fmt_number(c(estimate, statistic,
conf.low, conf.high),
decimals = 2) %>%
gt::tab_style(
style = list(cell_fill(color = "lightblue"),
cell_text(weight = "bold")),
locations = cells_body(
rows = matters == "highlight"
)
) %>%
gt::cols_hide(matters) #%>%| b [95% CI] | t | p | p < .05 | |
|---|---|---|---|---|
| Likelihood Overall - Self-Esteem (Rosenberg) | ||||
| (Intercept) | 3.31 [2.35–4.27] | 6.80 | <.001 | * |
| Self-Esteem (Rosenberg) | 0.01 [−0.01–0.03] | 1.20 | 0.230 | |
| General Distress (MASQ) | <0.01 [−0.01–0.02] | 0.30 | 0.767 | |
| Anhedonic Depression (MASQ) | <0.01 [−0.01–0.02] | 0.73 | 0.467 | |
| Anxious Arousal (MASQ) | <0.01 [−0.01–0.01] | 0.06 | 0.951 | |
| Likelihood Overall - Rejection Sensitivity (RSQ) | ||||
| (Intercept) | 3.93 [3.59–4.28] | 22.33 | <.001 | * |
| Rejection Sensitivity (RSQ) | −0.03 [−0.04–−0.01] | −2.87 | 0.004 | * |
| General Distress (MASQ) | <0.01 [−0.01–0.01] | 0.30 | 0.764 | |
| Anhedonic Depression (MASQ) | <0.01 [−0.01–0.01] | 0.68 | 0.499 | |
| Anxious Arousal (MASQ) | <0.01 [−0.01–0.01] | 0.14 | 0.889 | |
| Difficulty Explicit - Self-Esteem (Rosenberg) | ||||
| (Intercept) | 5.58 [3.90–7.26] | 6.53 | <.001 | * |
| Self-Esteem (Rosenberg) | −0.04 [−0.08–−0.01] | −2.50 | 0.013 | * |
| General Distress (MASQ) | 0.03 [<0.01–0.05] | 2.57 | 0.011 | * |
| Anhedonic Depression (MASQ) | −0.02 [−0.04–<0.01] | −1.80 | 0.073 | |
| Anxious Arousal (MASQ) | −0.02 [−0.04–<0.01] | −1.65 | 0.099 | |
| Difficulty Explicit - Rejection Sensitivity (RSQ) | ||||
| (Intercept) | 3.48 [2.86–4.09] | 11.07 | <.001 | * |
| Rejection Sensitivity (RSQ) | 0.04 [<0.01–0.07] | 2.33 | 0.021 | * |
| General Distress (MASQ) | 0.04 [0.02–0.06] | 3.94 | <.001 | * |
| Anhedonic Depression (MASQ) | −0.01 [−0.03–<0.01] | −1.02 | 0.309 | |
| Anxious Arousal (MASQ) | −0.02 [−0.04–<0.01] | −1.58 | 0.114 | |
| Likelihood Explicit - Self-Esteem (Rosenberg) | ||||
| (Intercept) | 3.89 [2.92–4.86] | 7.91 | <.001 | * |
| Self-Esteem (Rosenberg) | <0.01 [−0.01–0.03] | 0.89 | 0.373 | |
| General Distress (MASQ) | <0.01 [−0.01–0.02] | 0.40 | 0.691 | |
| Anhedonic Depression (MASQ) | <0.01 [0.00–0.02] | 1.12 | 0.263 | |
| Anxious Arousal (MASQ) | −0.01 [−0.03–<0.01] | −1.84 | 0.066 | |
| Likelihood Explicit - Rejection Sensitivity (RSQ) | ||||
| (Intercept) | 4.29 [3.94–4.65] | 23.79 | <.001 | * |
| Rejection Sensitivity (RSQ) | <0.01 [−0.02–0.02] | 0.19 | 0.853 | |
| General Distress (MASQ) | 0.00 [−0.01–0.01] | −0.18 | 0.855 | |
| Anhedonic Depression (MASQ) | <0.01 [−0.01–0.01] | 0.78 | 0.436 | |
| Anxious Arousal (MASQ) | −0.01 [−0.03–<0.01] | −1.94 | 0.054 | |
| Neg Emotions to Explicit Rejection - Self-Esteem (Rosenberg) | ||||
| (Intercept) | 4.83 [3.48–6.19] | 7.03 | <.001 | * |
| Self-Esteem (Rosenberg) | −0.02 [−0.05–<0.01] | −1.70 | 0.091 | |
| General Distress (MASQ) | 0.02 [<0.01–0.04] | 2.42 | 0.016 | * |
| Anhedonic Depression (MASQ) | −0.02 [−0.04–0.00] | −2.43 | 0.016 | * |
| Anxious Arousal (MASQ) | 0.00 [−0.02–0.02] | −0.39 | 0.694 | |
| Neg Emotions to Explicit Rejection - Rejection Sensitivity (RSQ) | ||||
| (Intercept) | 3.71 [3.21–4.21] | 14.68 | <.001 | * |
| Rejection Sensitivity (RSQ) | 0.01 [−0.01–0.04] | 1.02 | 0.311 | |
| General Distress (MASQ) | 0.03 [0.01–0.04] | 3.63 | <.001 | * |
| Anhedonic Depression (MASQ) | −0.01 [−0.03–<0.01] | −1.95 | 0.052 | |
| Anxious Arousal (MASQ) | 0.00 [−0.02–0.02] | −0.33 | 0.745 | |
| Difficulty Ghosting - Self-Esteem (Rosenberg) | ||||
| (Intercept) | 4.45 [2.65–6.24] | 4.88 | <.001 | * |
| Self-Esteem (Rosenberg) | −0.02 [−0.05–0.02] | −0.96 | 0.339 | |
| General Distress (MASQ) | 0.03 [<0.01–0.05] | 2.29 | 0.023 | * |
| Anhedonic Depression (MASQ) | −0.02 [−0.04–<0.01] | −1.59 | 0.112 | |
| Anxious Arousal (MASQ) | 0.00 [−0.03–0.02] | −0.22 | 0.825 | |
| Difficulty Ghosting - Rejection Sensitivity (RSQ) | ||||
| (Intercept) | 3.55 [2.90–4.20] | 10.69 | <.001 | * |
| Rejection Sensitivity (RSQ) | 0.03 [0.00–0.06] | 1.71 | 0.089 | |
| General Distress (MASQ) | 0.03 [<0.01–0.05] | 2.87 | 0.004 | * |
| Anhedonic Depression (MASQ) | −0.01 [−0.03–<0.01] | −1.55 | 0.123 | |
| Anxious Arousal (MASQ) | 0.00 [−0.03–0.02] | −0.25 | 0.805 | |
| Likelihood Ghosting - Self-Esteem (Rosenberg) | ||||
| (Intercept) | 2.53 [1.06–3.99] | 3.41 | <.001 | * |
| Self-Esteem (Rosenberg) | 0.00 [−0.03–0.03] | −0.27 | 0.786 | |
| General Distress (MASQ) | −0.01 [−0.03–0.01] | −0.59 | 0.553 | |
| Anhedonic Depression (MASQ) | −0.01 [−0.02–0.01] | −0.64 | 0.524 | |
| Anxious Arousal (MASQ) | 0.01 [−0.01–0.03] | 1.41 | 0.159 | |
| Likelihood Ghosting - Rejection Sensitivity (RSQ) | ||||
| (Intercept) | 2.40 [1.87–2.93] | 8.86 | <.001 | * |
| Rejection Sensitivity (RSQ) | −0.02 [−0.05–<0.01] | −1.42 | 0.158 | |
| General Distress (MASQ) | 0.00 [−0.02–0.02] | −0.09 | 0.928 | |
| Anhedonic Depression (MASQ) | 0.00 [−0.02–0.01] | −0.38 | 0.708 | |
| Anxious Arousal (MASQ) | 0.02 [0.00–0.04] | 1.50 | 0.135 | |
| Neg Emotions to Ghosting - Self-Esteem (Rosenberg) | ||||
| (Intercept) | 5.13 [3.58–6.68] | 6.53 | <.001 | * |
| Self-Esteem (Rosenberg) | −0.02 [−0.05–0.01] | −1.23 | 0.219 | |
| General Distress (MASQ) | 0.02 [<0.01–0.04] | 2.18 | 0.030 | * |
| Anhedonic Depression (MASQ) | −0.01 [−0.03–<0.01] | −1.07 | 0.286 | |
| Anxious Arousal (MASQ) | −0.01 [−0.03–0.01] | −0.87 | 0.388 | |
| Neg Emotions to Ghosting - Rejection Sensitivity (RSQ) | ||||
| (Intercept) | 4.17 [3.60–4.74] | 14.44 | <.001 | * |
| Rejection Sensitivity (RSQ) | 0.02 [−0.01–0.05] | 1.36 | 0.175 | |
| General Distress (MASQ) | 0.03 [<0.01–0.04] | 2.94 | 0.004 | * |
| Anhedonic Depression (MASQ) | −0.01 [−0.02–0.01] | −0.71 | 0.481 | |
| Anxious Arousal (MASQ) | −0.01 [−0.03–0.01] | −0.88 | 0.379 | |
#gt::as_latex() %>% as.character()ear3 <- read_csv(here::here("data and codebooks", "study2_processed.csv"))## Rows: 259 Columns: 33
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (7): responseid, gender, race, reject_experience, when_reject, reject_m...
## dbl (23): age, reject_timing, difficult, effort, distressed, upset, guilty, ...
## lgl (3): usedata, when_reject_remove, attention_check_state
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.outcomes3 <- list("neg_emotions", "difficulty")
predictors3 <- list("rse", "arsq_full")
table3 <- map_dfr(outcomes3, function(outcome) {
map_dfr(predictors3, function(predictor) {
ear3 %>%
select(x = {{predictor}},
y = {{outcome}},
masq_gd, masq_ad, masq_aa) %>%
lm(data = .,
y ~ x + masq_gd + masq_ad + masq_aa) %>%
broom::tidy(conf.int = TRUE) %>%
mutate(term = case_when(term == "x" ~ predictor,
TRUE ~ term)) %>%
select(-std.error) %>%
mutate(predictor = predictor)
}) %>%
mutate(outcome = outcome) %>%
relocate(outcome)
})
# add the four interaction versions
table3 <- bind_rows(table3,
ear3 %>%
filter(reject_method != "different") %>%
select(arsq_full, rse, difficulty,
neg_emotions, reject_method,
masq_gd, masq_ad, masq_aa) %>%
nest(data = everything()) %>%
mutate(t1 = map(data, ~ lm(data = .x,
difficulty ~ rse * reject_method +
masq_gd + masq_ad + masq_aa)),
t2 = map(data, ~ lm(data = .x,
difficulty ~ arsq_full * reject_method +
masq_gd + masq_ad + masq_aa)),
t3 = map(data, ~ lm(data = .x,
neg_emotions ~ rse * reject_method +
masq_gd + masq_ad + masq_aa)),
t4 = map(data, ~ lm(data = .x,
neg_emotions ~ arsq_full * reject_method +
masq_gd + masq_ad + masq_aa)),
across(c(t1, t2, t3, t4),
~ map(.x,
~ broom::tidy(.x,
conf.int = TRUE) %>%
select(-std.error)),
.names = "tidy_{.col}")
) %>%
pivot_longer(cols = starts_with("tidy"),
names_to = "which",
values_to = "regression") %>%
select(-starts_with("t"), -data) %>%
unnest(regression) %>%
mutate(outcome = case_when(
which == "tidy_t1" ~ "difficulty",
which == "tidy_t2" ~ "difficulty",
which == "tidy_t3" ~ "neg_emotions",
which == "tidy_t4" ~ "neg_emotions"
),
predictor = case_when(
which == "tidy_t1" ~ "rse:reject_methodindirect",
which == "tidy_t2" ~ "arsq_full:reject_methodindirect",
which == "tidy_t3" ~ "rse:reject_methodindirect",
which == "tidy_t4" ~ "arsq_full:reject_methodindirect"
)) %>%
select(outcome, everything(), predictor, -which)
)
table3 <- table3 %>%
mutate(sig = ifelse(p.value < .05,
"*",
NA_character_),
matters = case_when(
p.value < .05 &
term == "rse" ~"highlight",
p.value < .05 &
term == "arsq_full" ~ "highlight",
p.value < .05 &
term == "rse:reject_methodindirect" ~ "highlight",
p.value < .05 &
term == "arsq_full:reject_methodindirect" ~ "highlight",
TRUE ~ NA_character_
))
outcome_names3 <- c(
"neg_emotions" = "Negative emotions after rejecting",
"difficulty" = "Difficulty rejecting"
)
predictor_names3 <- c(
"rse" = "Self-Esteem (Rosenberg)",
"arsq_full" = "Rejection Sensitivity (RSQ)",
"masq_gd" = "General Distress (MASQ)",
"masq_ad" = "Anhedonic Depression (MASQ)",
"masq_aa" = "Anxious Arousal (MASQ)",
"(Intercept)" = "(Intercept)",
"reject_methodindirect" = "Rejection Method",
"rse:reject_methodindirect" = "Interaction between Self-Esteem and Rejection Method",
"arsq_full:reject_methodindirect" = "Interaction between Rejection Sensitivity and Rejection Method"
)
table3 %>%
mutate(outcome = outcome_names3[outcome],
term = predictor_names3[term],
predictor = predictor_names3[predictor]) %>%
group_by(outcome, predictor) %>%
gt::gt(rowname_col = "term") %>%
gt::cols_merge(columns = c(estimate, conf.low,
conf.high),
pattern = "{1} [{2}–{3}]") %>%
gt::cols_label(
estimate = md("*b* [95% CI]"),
statistic = md("*t*"),
p.value = md("*p*"),
sig = md("*p* < .05")
) %>%
gt::sub_missing(sig, missing_text = "") %>%
gt::sub_small_vals(columns = c(estimate,
statistic,
conf.low,
conf.high),
threshold = .01) %>%
gt::sub_small_vals(columns = p.value,
threshold = .001,
small_pattern = "<.001") %>%
gt::fmt_number(p.value, decimals = 3) %>%
gt::fmt_number(c(estimate, statistic,
conf.low, conf.high),
decimals = 2) %>%
gt::tab_style(
style = list(cell_fill(color = "lightblue"),
cell_text(weight = "bold")),
locations = cells_body(
rows = matters == "highlight"
)
) %>%
gt::cols_hide(matters)# %>%| b [95% CI] | t | p | p < .05 | |
|---|---|---|---|---|
| Negative emotions after rejecting - Self-Esteem (Rosenberg) | ||||
| (Intercept) | −0.27 [−2.72–2.17] | −0.22 | 0.827 | |
| Self-Esteem (Rosenberg) | 0.06 [<0.01–0.10] | 2.35 | 0.020 | * |
| General Distress (MASQ) | 0.08 [0.05–0.11] | 5.37 | <.001 | * |
| Anhedonic Depression (MASQ) | <0.01 [−0.02–0.04] | 0.54 | 0.589 | |
| Anxious Arousal (MASQ) | 0.02 [−0.01–0.05] | 1.46 | 0.145 | |
| Negative emotions after rejecting - Rejection Sensitivity (RSQ) | ||||
| (Intercept) | 2.26 [1.31–3.21] | 4.68 | <.001 | * |
| Rejection Sensitivity (RSQ) | <0.01 [−0.03–0.05] | 0.45 | 0.651 | |
| General Distress (MASQ) | 0.06 [0.03–0.08] | 4.27 | <.001 | * |
| Anhedonic Depression (MASQ) | −0.01 [−0.04–0.02] | −0.58 | 0.563 | |
| Anxious Arousal (MASQ) | 0.03 [0.00–0.06] | 1.70 | 0.091 | |
| Difficulty rejecting - Self-Esteem (Rosenberg) | ||||
| (Intercept) | −0.57 [−3.61–2.46] | −0.37 | 0.711 | |
| Self-Esteem (Rosenberg) | 0.09 [0.03–0.15] | 3.12 | 0.002 | * |
| General Distress (MASQ) | 0.09 [0.06–0.13] | 4.81 | <.001 | * |
| Anhedonic Depression (MASQ) | −0.01 [−0.05–0.02] | −0.68 | 0.500 | |
| Anxious Arousal (MASQ) | 0.00 [−0.04–0.03] | −0.10 | 0.919 | |
| Difficulty rejecting - Rejection Sensitivity (RSQ) | ||||
| (Intercept) | 3.79 [2.61–4.98] | 6.30 | <.001 | * |
| Rejection Sensitivity (RSQ) | −0.01 [−0.07–0.04] | −0.54 | 0.587 | |
| General Distress (MASQ) | 0.05 [0.02–0.09] | 3.32 | 0.001 | * |
| Anhedonic Depression (MASQ) | −0.04 [−0.07–0.00] | −2.23 | 0.027 | * |
| Anxious Arousal (MASQ) | <0.01 [−0.03–0.04] | 0.17 | 0.865 | |
| Difficulty rejecting - Interaction between Self-Esteem and Rejection Method | ||||
| (Intercept) | −0.25 [−3.42–2.92] | −0.16 | 0.875 | |
| Self-Esteem (Rosenberg) | 0.08 [0.02–0.14] | 2.44 | 0.016 | * |
| Rejection Method | −1.43 [−3.14–0.28] | −1.65 | 0.100 | |
| General Distress (MASQ) | 0.09 [0.05–0.13] | 4.64 | <.001 | * |
| Anhedonic Depression (MASQ) | −0.01 [−0.05–0.03] | −0.40 | 0.688 | |
| Anxious Arousal (MASQ) | 0.00 [−0.04–0.04] | −0.07 | 0.943 | |
| Interaction between Self-Esteem and Rejection Method | 0.04 [−0.02–0.11] | 1.25 | 0.212 | |
| Difficulty rejecting - Interaction between Rejection Sensitivity and Rejection Method | ||||
| (Intercept) | 3.47 [2.20–4.73] | 5.39 | <.001 | * |
| Rejection Sensitivity (RSQ) | 0.03 [−0.04–0.09] | 0.87 | 0.386 | |
| Rejection Method | 0.93 [−0.33–2.20] | 1.46 | 0.146 | |
| General Distress (MASQ) | 0.05 [0.02–0.08] | 3.10 | 0.002 | * |
| Anhedonic Depression (MASQ) | −0.04 [−0.07–0.00] | −2.17 | 0.031 | * |
| Anxious Arousal (MASQ) | <0.01 [−0.03–0.04] | 0.28 | 0.781 | |
| Interaction between Rejection Sensitivity and Rejection Method | −0.10 [−0.19–−0.01] | −2.20 | 0.029 | * |
| Negative emotions after rejecting - Interaction between Self-Esteem and Rejection Method | ||||
| (Intercept) | 0.35 [−2.21–2.91] | 0.27 | 0.789 | |
| Self-Esteem (Rosenberg) | 0.03 [−0.02–0.08] | 1.20 | 0.231 | |
| Rejection Method | −1.60 [−2.98–−0.21] | −2.27 | 0.024 | * |
| General Distress (MASQ) | 0.08 [0.05–0.11] | 5.03 | <.001 | * |
| Anhedonic Depression (MASQ) | 0.01 [−0.02–0.04] | 0.69 | 0.489 | |
| Anxious Arousal (MASQ) | 0.02 [−0.01–0.05] | 1.51 | 0.132 | |
| Interaction between Self-Esteem and Rejection Method | 0.06 [<0.01–0.12] | 2.28 | 0.024 | * |
| Negative emotions after rejecting - Interaction between Rejection Sensitivity and Rejection Method | ||||
| (Intercept) | 1.97 [0.94–2.99] | 3.79 | <.001 | * |
| Rejection Sensitivity (RSQ) | 0.04 [−0.01–0.09] | 1.63 | 0.105 | |
| Rejection Method | 0.89 [−0.13–1.90] | 1.72 | 0.087 | |
| General Distress (MASQ) | 0.05 [0.03–0.08] | 3.95 | <.001 | * |
| Anhedonic Depression (MASQ) | −0.01 [−0.04–0.02] | −0.61 | 0.540 | |
| Anxious Arousal (MASQ) | 0.03 [0.00–0.06] | 1.82 | 0.070 | |
| Interaction between Rejection Sensitivity and Rejection Method | −0.08 [−0.15–0.00] | −2.10 | 0.037 | * |
#gt::as_latex() %>% as.character()Simple effects for interactions, since direction seemed to flip. Code follows and can be replicated with data stored on OSF.
#cor.test(ear3$difficulty, ear3$rse)
summary(lm(data = ear3 %>% filter(reject_method != "different"), neg_emotions ~ rse * reject_method + masq_gd + masq_ad + masq_aa))
plot(effects::effect("rse:reject_method",
lm(data = ear3 %>% filter(reject_method != "different"), neg_emotions ~ rse * reject_method + masq_gd + masq_ad + masq_aa)))
# simple effects
ear3 %>%
filter(reject_method == "indirect") %>%
lm(data = ., neg_emotions ~ rse + masq_gd + masq_ad + masq_aa) %>%
summary(.)
ear3 %>%
filter(reject_method == "direct") %>%
lm(data = ., neg_emotions ~ rse + masq_gd + masq_ad + masq_aa) %>%
summary(.)
# cor.test(ear3$arsq_full, ear3$neg_emotions)
summary(lm(data = ear3 %>% filter(reject_method != "different"), neg_emotions ~ arsq_full * reject_method + masq_gd + masq_ad + masq_aa))
# plot(effects::effect("arsq_full:reject_method",
# lm(data = ear3 %>% filter(reject_method != "different"), neg_emotions ~ arsq_full * reject_method + masq_gd + masq_ad + masq_aa)))
# simple effects
ear3 %>%
filter(reject_method == "indirect") %>%
lm(data = ., neg_emotions ~ arsq_full) %>% # + masq_gd + masq_ad + masq_aa) %>%
summary(.)
ear3 %>%
filter(reject_method == "direct") %>%
lm(data = ., neg_emotions ~ arsq_full) %>% # + masq_gd + masq_ad + masq_aa) %>%
summary(.)