nstep <- 1e5
trait <- "N" # Neuroticism
set.seed(1)Experiences
Experiences Example
General definitions
Feature 1
For Feature 1, it is assumed that there is neither cognitive bias nor any preference. Accordingly, both distributions are uniform.
facet <- "A" # Anxiety
feature <- 1
weight <- 0.5
exp_1 <- data.frame(
step = 1:nstep,
sit = 1:nstep, # Situation ID
trait = rep(trait, nstep),
facet = rep(facet, nstep),
feature = factor(rep(feature, nstep)),
weight = rep(weight, nstep),
obj = seq(0, 1, length.out = nstep),
cog = rep(NA, nstep),
aff = rep(NA, nstep),
sit_aff = rep(NA, nstep),
sit_beh = rep(NA, nstep),
sit_acc_pos = rep(NA, nstep),
sit_acc_neg = rep(NA, nstep),
sit_acc =rep(NA, nstep)
)mean_1 <- 0.5
pvar_1 <- 1
par_1 <- calc_beta_par(mean_1, pvar_1, output = c("a", "b"))
par_1 a b
1 1 1exp_1$cog <- qbeta(pbeta(exp_1$obj, 1, 1), par_1$a, par_1$b)
exp_1$aff <- features_dist(exp_1$cog, mean_1, pvar_1, output = "probx")
head(exp_1) step sit trait facet feature weight obj cog aff sit_aff
1 1 1 N A 1 0.5 0.00000e+00 0.00000e+00 0.5 NA
2 2 2 N A 1 0.5 1.00001e-05 1.00001e-05 0.5 NA
3 3 3 N A 1 0.5 2.00002e-05 2.00002e-05 0.5 NA
4 4 4 N A 1 0.5 3.00003e-05 3.00003e-05 0.5 NA
5 5 5 N A 1 0.5 4.00004e-05 4.00004e-05 0.5 NA
6 6 6 N A 1 0.5 5.00005e-05 5.00005e-05 0.5 NA
sit_beh sit_acc_pos sit_acc_neg sit_acc
1 NA NA NA NA
2 NA NA NA NA
3 NA NA NA NA
4 NA NA NA NA
5 NA NA NA NA
6 NA NA NA NAFeature 2
For Feature 2, it is again assumed that no cognitive bias is present. However, a preference with a defined tendency (mean=0.4) and variance (pvar=0.6) is specified.
facet <- "A" # Anxiety
feature <- 2
weight <- 0.5
exp_2 <- data.frame(
step = 1:nstep,
sit = 1:nstep, # Situation ID
trait = rep(trait, nstep),
facet = rep(facet, nstep),
feature = factor(rep(feature, nstep)),
weight = rep(weight, nstep),
obj = seq(0, 1, length.out = nstep),
cog = rep(NA, nstep),
aff = rep(NA, nstep),
sit_aff = rep(NA, nstep),
sit_beh = rep(NA, nstep),
sit_acc_pos = rep(NA, nstep),
sit_acc_neg = rep(NA, nstep),
sit_acc =rep(NA, nstep)
)mean_2 <- 0.4
pvar_2 <- 0.6
par_2 <- calc_beta_par(mean_2, pvar_2, output = c("a", "b"))
par_2 a b
1 1.933333 2.9exp_2$cog <- qbeta(pbeta(exp_2$obj, 1, 1), par_1$a, par_1$b)
exp_2$aff <- features_dist(exp_2$cog, mean_2, pvar_2, output = "probx")
head(exp_2) step sit trait facet feature weight obj cog aff
1 1 1 N A 2 0.5 0.00000e+00 0.00000e+00 0.0000000000
2 2 2 N A 2 0.5 1.00001e-05 1.00001e-05 0.0002267586
3 3 3 N A 2 0.5 2.00002e-05 2.00002e-05 0.0004329395
4 4 4 N A 2 0.5 3.00003e-05 3.00003e-05 0.0006319524
5 5 5 N A 2 0.5 4.00004e-05 4.00004e-05 0.0008264205
6 6 6 N A 2 0.5 5.00005e-05 5.00005e-05 0.0010175578
sit_aff sit_beh sit_acc_pos sit_acc_neg sit_acc
1 NA NA NA NA NA
2 NA NA NA NA NA
3 NA NA NA NA NA
4 NA NA NA NA NA
5 NA NA NA NA NA
6 NA NA NA NA NAFeature 3
For Feature 3, the situation is reversed: no preference is defined, but a cognitive bias is specified (mean=0.45, pvar=0.6).
facet <- "D" # Depression
feature <- 1
weight <- 0.5
exp_3 <- data.frame(
step = 1:nstep,
sit = 1:nstep, # Situation ID
trait = rep(trait, nstep),
facet = rep(facet, nstep),
feature = factor(rep(feature, nstep)),
weight = rep(weight, nstep),
obj = seq(0, 1, length.out = nstep),
cog = rep(NA, nstep),
aff = rep(NA, nstep),
sit_aff = rep(NA, nstep),
sit_beh = rep(NA, nstep),
sit_acc_pos = rep(NA, nstep),
sit_acc_neg = rep(NA, nstep),
sit_acc =rep(NA, nstep)
)mean_3 <- 0.45
pvar_3 <- 0.6
par_3 <- calc_beta_par(mean_3, pvar_3, output = c("a", "b"))
par_3 a b
1 1.966667 2.403704exp_3$cog <- qbeta(pbeta(exp_3$obj, 1, 1) , par_3$a, par_3$b)
exp_3$aff <- features_dist(exp_3$cog, mean_1, pvar_1, output = "probx")
head(exp_3) step sit trait facet feature weight obj cog aff sit_aff
1 1 1 N D 1 0.5 0.00000e+00 0.000000000 0.5 NA
2 2 2 N D 1 0.5 1.00001e-05 0.001412874 0.5 NA
3 3 3 N D 1 0.5 2.00002e-05 0.002010446 0.5 NA
4 4 4 N D 1 0.5 3.00003e-05 0.002471298 0.5 NA
5 5 5 N D 1 0.5 4.00004e-05 0.002861104 0.5 NA
6 6 6 N D 1 0.5 5.00005e-05 0.003205389 0.5 NA
sit_beh sit_acc_pos sit_acc_neg sit_acc
1 NA NA NA NA
2 NA NA NA NA
3 NA NA NA NA
4 NA NA NA NA
5 NA NA NA NA
6 NA NA NA NAFeature 4
For Feature 4, both a cognitive bias (mean=0.4, pvar=0.6) and a preference (mean=0.55, pvar=0.4) are specified.
facet <- "D" # Depression
feature <- 2
weight <- 0.33
exp_4 <- data.frame(
step = 1:nstep,
sit = 1:nstep, # Situation ID
trait = rep(trait, nstep),
facet = rep(facet, nstep),
feature = factor(rep(feature, nstep)),
weight = rep(weight, nstep),
obj = seq(0, 1, length.out = nstep),
cog = rep(NA, nstep),
aff = rep(NA, nstep),
sit_aff = rep(NA, nstep),
sit_beh = rep(NA, nstep),
sit_acc_pos = rep(NA, nstep),
sit_acc_neg = rep(NA, nstep),
sit_acc =rep(NA, nstep)
)mean_4 <- 0.4
pvar_4 <- 0.6
par_4 <- calc_beta_par(mean_4, pvar_4, output = c("a", "b"))
par_4 a b
1 1.933333 2.9mean_5 <- 0.55
pvar_5 <- 0.4
par_5 <- calc_beta_par(mean_5, pvar_5, output = c("a", "b"))
par_5 a b
1 3.880556 3.175exp_4$cog <- qbeta(pbeta(exp_4$obj, 1, 1), par_4$a, par_4$b)
exp_4$aff <- features_dist(exp_4$cog, mean_5, pvar_5, output = "probx")
head(exp_4) step sit trait facet feature weight obj cog aff
1 1 1 N D 2 0.33 0.00000e+00 0.000000000 0.000000e+00
2 2 2 N D 2 0.33 1.00001e-05 0.001079931 1.855752e-07
3 3 3 N D 2 0.33 2.00002e-05 0.001546081 5.211639e-07
4 4 4 N D 2 0.33 3.00003e-05 0.001907286 9.534352e-07
5 5 5 N D 2 0.33 4.00004e-05 0.002213735 1.463528e-06
6 6 6 N D 2 0.33 5.00005e-05 0.002485008 2.040584e-06
sit_aff sit_beh sit_acc_pos sit_acc_neg sit_acc
1 NA NA NA NA NA
2 NA NA NA NA NA
3 NA NA NA NA NA
4 NA NA NA NA NA
5 NA NA NA NA NA
6 NA NA NA NA NAFeature 5
Feature 5 is a copy of Feature 1, but with the modification that the objective experiences are no longer uniformly distributed.
facet <- "E" # Emotional Volatility
feature <- 1
weight <- 0.25
exp_5 <- data.frame(
step = 1:nstep,
sit = 1:nstep, # Situation ID
trait = rep(trait, nstep),
facet = rep(facet, nstep),
feature = factor(rep(feature, nstep)),
weight = rep(weight, nstep),
obj = qbeta(seq(0, 1, length.out = nstep), 2, 2),
cog = rep(NA, nstep),
aff = rep(NA, nstep),
sit_aff = rep(NA, nstep),
sit_beh = rep(NA, nstep),
sit_acc_pos = rep(NA, nstep),
sit_acc_neg = rep(NA, nstep),
sit_acc =rep(NA, nstep)
)exp_5$cog <- qbeta(pbeta(exp_5$obj, 2, 2) , par_1$a, par_1$b)
exp_5$aff <- features_dist(exp_5$cog, mean_1, pvar_1, output = "probx")
head(exp_5) step sit trait facet feature weight obj cog aff sit_aff
1 1 1 N E 1 0.25 0.000000000 0.00000e+00 0.5 NA
2 2 2 N E 1 0.25 0.001826864 1.00001e-05 0.5 NA
3 3 3 N E 1 0.25 0.002584229 2.00002e-05 0.5 NA
4 4 4 N E 1 0.25 0.003165636 3.00003e-05 0.5 NA
5 5 5 N E 1 0.25 0.003655960 4.00004e-05 0.5 NA
6 6 6 N E 1 0.25 0.004088078 5.00005e-05 0.5 NA
sit_beh sit_acc_pos sit_acc_neg sit_acc
1 NA NA NA NA
2 NA NA NA NA
3 NA NA NA NA
4 NA NA NA NA
5 NA NA NA NA
6 NA NA NA NAFeature 6
Feature 6 is a copy of Feature 4, but also with the modification that the objective experiences are no longer uniformly distributed.
facet <- "E" # Emotional Volatility
feature <- 2
weight <- 0.25
exp_6 <- data.frame(
step = 1:nstep,
sit = 1:nstep, # Situation ID
trait = rep(trait, nstep),
facet = rep(facet, nstep),
feature = factor(rep(feature, nstep)),
weight = rep(weight, nstep),
obj = qbeta(seq(0, 1, length.out = nstep), 2, 2),
cog = rep(NA, nstep),
aff = rep(NA, nstep),
sit_aff = rep(NA, nstep),
sit_beh = rep(NA, nstep),
sit_acc_pos = rep(NA, nstep),
sit_acc_neg = rep(NA, nstep),
sit_acc =rep(NA, nstep)
)exp_6$cog <- qbeta(pbeta(exp_6$obj, 2, 2), par_4$a, par_4$b)
exp_6$aff <- features_dist(exp_6$cog, mean_5, pvar_5, output = "probx")
head(exp_6) step sit trait facet feature weight obj cog aff
1 1 1 N E 2 0.25 0.000000000 0.000000000 0.000000e+00
2 2 2 N E 2 0.25 0.001826864 0.001079931 1.855752e-07
3 3 3 N E 2 0.25 0.002584229 0.001546081 5.211639e-07
4 4 4 N E 2 0.25 0.003165636 0.001907286 9.534352e-07
5 5 5 N E 2 0.25 0.003655960 0.002213735 1.463528e-06
6 6 6 N E 2 0.25 0.004088078 0.002485008 2.040584e-06
sit_aff sit_beh sit_acc_pos sit_acc_neg sit_acc
1 NA NA NA NA NA
2 NA NA NA NA NA
3 NA NA NA NA NA
4 NA NA NA NA NA
5 NA NA NA NA NA
6 NA NA NA NA NAFeature 7
Feature 7 is a copy of Feature 6, but with the addition that the objective experiences are randomly generated.
facet <- "E" # Emotional Volatility
feature <- 3
weight <- 0.25
exp_7 <- data.frame(
step = 1:nstep,
sit = 1:nstep, # Situation ID
trait = rep(trait, nstep),
facet = rep(facet, nstep),
feature = factor(rep(feature, nstep)),
weight = rep(weight, nstep),
obj = rbeta(nstep, 2, 2),
cog = rep(NA, nstep),
aff = rep(NA, nstep),
sit_aff = rep(NA, nstep),
sit_beh = rep(NA, nstep),
sit_acc_pos = rep(NA, nstep),
sit_acc_neg = rep(NA, nstep),
sit_acc =rep(NA, nstep)
)exp_7$cog <- qbeta(pbeta(exp_7$obj, 2, 2), par_4$a, par_4$b)
exp_7$aff <- features_dist(exp_7$cog, mean_5, pvar_5, output = "probx")
head(exp_7) step sit trait facet feature weight obj cog aff sit_aff
1 1 1 N E 3 0.25 0.3275025 0.2411078 0.3732068 NA
2 2 2 N E 3 0.25 0.5516990 0.4311386 0.6291958 NA
3 3 3 N E 3 0.25 0.2743131 0.1990916 0.2783129 NA
4 4 4 N E 3 0.25 0.8814780 0.7784175 0.5448828 NA
5 5 5 N E 3 0.25 0.5923401 0.4683281 0.6502303 NA
6 6 6 N E 3 0.25 0.2780523 0.2020115 0.2851923 NA
sit_beh sit_acc_pos sit_acc_neg sit_acc
1 NA NA NA NA
2 NA NA NA NA
3 NA NA NA NA
4 NA NA NA NA
5 NA NA NA NA
6 NA NA NA NAFeature 8
The final Feature 8 is a copy of Feature 7, with the modification that the cognitive distribution uses a pvar greater than 1 (specifically, 1.6).
facet <- "E" # Emotional Volatility
feature <- 4
weight <- 0.25
exp_8 <- data.frame(
step = 1:nstep,
sit = 1:nstep, # Situation ID
trait = rep(trait, nstep),
facet = rep(facet, nstep),
feature = factor(rep(feature, nstep)),
weight = rep(weight, nstep),
obj = rbeta(nstep, 2, 2),
cog = rep(NA, nstep),
aff = rep(NA, nstep),
sit_aff = rep(NA, nstep),
sit_beh = rep(NA, nstep),
sit_acc_pos = rep(NA, nstep),
sit_acc_neg = rep(NA, nstep),
sit_acc =rep(NA, nstep)
)mean_6 <- 0.4
pvar_6 <- 1.6
par_6 <- calc_beta_par(mean_6, pvar_6, output = c("a", "b"))
par_6 a b
1 0.475 0.7125exp_8$cog <- qbeta(pbeta(exp_8$obj, 2, 2), par_6$a, par_6$b)
exp_8$aff <- features_dist(exp_8$cog, mean_5, pvar_5, output = "probx")
head(exp_8) step sit trait facet feature weight obj cog aff sit_aff
1 1 1 N E 4 0.25 0.5837254 0.50667818 0.664617629 NA
2 2 2 N E 4 0.25 0.2829150 0.04884107 0.009692856 NA
3 3 3 N E 4 0.25 0.5027416 0.33846776 0.539896739 NA
4 4 4 N E 4 0.25 0.2458955 0.02893792 0.002261821 NA
5 5 5 N E 4 0.25 0.3522969 0.10738620 0.076186716 NA
6 6 6 N E 4 0.25 0.3338101 0.08885347 0.047592014 NA
sit_beh sit_acc_pos sit_acc_neg sit_acc
1 NA NA NA NA
2 NA NA NA NA
3 NA NA NA NA
4 NA NA NA NA
5 NA NA NA NA
6 NA NA NA NAGenerating situational parameters
exp <- bind_rows(exp_1, exp_2, exp_3, exp_4, exp_5, exp_6, exp_7, exp_8)
exp <- exp %>%
group_by(step) %>%
mutate(sit_aff = mean(aff),
sit_beh = rbinom(1, 1, sit_aff),
sit_acc_pos = sit_aff,
sit_acc_neg = 1 - sit_aff,
acc = 1 - 2 * (sit_acc_pos * sit_acc_neg)
) %>%
ungroup() %>%
arrange(step)
head(exp, 12)# A tibble: 12 × 15
step sit trait facet feature weight obj cog aff sit_aff
<int> <int> <chr> <chr> <fct> <dbl> <dbl> <dbl> <dbl> <dbl>
1 1 1 N A 1 0.5 0 0 5 e-1 0.317
2 1 1 N A 2 0.5 0 0 0 0.317
3 1 1 N D 1 0.5 0 0 5 e-1 0.317
4 1 1 N D 2 0.33 0 0 0 0.317
5 1 1 N E 1 0.25 0 0 5 e-1 0.317
6 1 1 N E 2 0.25 0 0 0 0.317
7 1 1 N E 3 0.25 0.328 0.241 3.73e-1 0.317
8 1 1 N E 4 0.25 0.584 0.507 6.65e-1 0.317
9 2 2 N A 1 0.5 0.0000100 0.0000100 5 e-1 0.267
10 2 2 N A 2 0.5 0.0000100 0.0000100 2.27e-4 0.267
11 2 2 N D 1 0.5 0.0000100 0.00141 5 e-1 0.267
12 2 2 N D 2 0.33 0.0000100 0.00108 1.86e-7 0.267
# ℹ 5 more variables: sit_beh <int>, sit_acc_pos <dbl>, sit_acc_neg <dbl>,
# sit_acc <lgl>, acc <dbl>Testing results
Helper functions
round_res <- function(res, size = 2) {
res[] <- lapply(res, function(x) {
if (is.numeric(x)) round(x, size) else x
})
res
}
scaled_mean_sd <- function(x, x_prob = NULL, w = 1, eps = 1e-6, na.rm = FALSE) {
if (length(w) == 1) {
w <- rep(w, length(x))
}
if (is.null(x_prob)) {
x_prob <- x
}
if (length(x_prob) == 1) {
x_prob <- rep(x_prob, length(x))
}
if (na.rm) {
ok <- is.finite(x) & is.finite(x_prob) & is.finite(w)
x <- x[ok]
x_prob <- x_prob[ok]
w <- w[ok]
}
x_mu <- mean(x_prob)
x_mu <- pmin(pmax(x_mu, eps), 1 - eps)
x_var <- var(x_prob)
prec <- (x_mu * (1 - x_mu)) / x_var - 1
a <- prec * x_mu
b <- prec * (1 - x_mu)
prob <- dbeta(x_prob, a, b)
prob <- pmax(prob, eps)
w <- pmax(w, eps)
W <- w / prob
W_sum <- sum(W)
mu <- sum(W * x) / W_sum
sd <- sqrt(sum(W * (x - mu)^2) / W_sum)
list(mean = mu, sd = sd)
}Aggregation of distribution parameters
res <- exp %>%
group_by(trait, facet, feature) %>%
summarise(
obj_mean = mean(obj, na.rm = TRUE),
obj_sd = sd(obj, na.rm = TRUE),
cog_mean = mean(cog, na.rm = TRUE),
cog_sd = sd(cog, na.rm = TRUE),
aff_mean = scaled_mean_sd(x = cog, w = aff, na.rm = TRUE)$mean,
aff_sd = scaled_mean_sd(x = cog, w = aff, na.rm = TRUE)$sd,
aff_mean_1 = scaled_mean_sd(x = cog, w = aff * sit_beh, na.rm = TRUE)$mean,
aff_sd_1 = scaled_mean_sd(x = cog, w = aff * sit_beh, na.rm = TRUE)$sd,
aff_mean_0 = scaled_mean_sd(x = cog, w = aff * (1-sit_beh), na.rm = TRUE)$mean,
aff_sd_0 = scaled_mean_sd(x = cog, w = aff * (1-sit_beh), na.rm = TRUE)$sd,
beh_mean = scaled_mean_sd(x = cog, x_prob = 1, w = sit_beh, na.rm = TRUE)$mean,
beh_sd = scaled_mean_sd(x = cog, x_prob = 1, w = sit_beh, na.rm = TRUE)$sd,
.groups = "drop"
) %>%
mutate(
obj_var_max = obj_mean * (1 - obj_mean) / (pmax(1 / obj_mean,1 / (1 - obj_mean)) + 1),
obj_pvar = pmax(obj_sd^2 / obj_var_max, 0.01),
cog_var_max = cog_mean * (1 - cog_mean) / (pmax(1 / cog_mean,1 / (1 - cog_mean)) + 1),
cog_pvar = pmax(cog_sd^2 / cog_var_max, 0.01),
aff_var_max = aff_mean * (1 - aff_mean) / (pmax(1 / aff_mean,1 / (1 - aff_mean)) + 1),
aff_pvar = pmax(pmin(aff_sd^2 / aff_var_max, 1), 0.01), # limitet to max = 1
aff_var_max_1 = aff_mean_1 * (1 - aff_mean_1) / (pmax(1 / aff_mean_1,1 / (1 - aff_mean_1)) + 1),
aff_pvar_1 = pmax(pmin(aff_sd_1^2 / aff_var_max_1, 1), 0.01), # limitet to max = 1
aff_var_max_0 = aff_mean_0 * (1 - aff_mean_0) / (pmax(1 / aff_mean_0,1 / (1 - aff_mean_0)) + 1),
aff_pvar_0 = pmax(pmin(aff_sd_0^2 / aff_var_max_0, 1), 0.01), # limitet to max = 1
beh_var_max = beh_mean * (1 - beh_mean) / (pmax(1 / beh_mean,1 / (1 - beh_mean)) + 1),
beh_pvar = pmax(beh_sd^2 / beh_var_max, 0.01)
)Results for objective features
round_res(res[, c("trait", "facet", "feature", "obj_mean", "obj_pvar")], 2)# A tibble: 8 × 5
trait facet feature obj_mean obj_pvar
<chr> <chr> <fct> <dbl> <dbl>
1 N A 1 0.5 1
2 N A 2 0.5 1
3 N D 1 0.5 1
4 N D 2 0.5 1
5 N E 1 0.5 0.6
6 N E 2 0.5 0.6
7 N E 3 0.5 0.6
8 N E 4 0.5 0.6Results for cognitive and affective interpreted features (Self-Concept)
goal <- data.frame(
cog_mean = c(mean_1, mean_1, mean_3, mean_4, mean_1, mean_4, mean_4, mean_6),
cog_pvar = c(pvar_1, pvar_1, pvar_3, pvar_4, pvar_1, pvar_4, pvar_4, pvar_6),
aff_mean = c(mean_1, mean_2, mean_1, mean_5, mean_1, mean_5, mean_5, mean_5),
aff_pvar = c(pvar_1, pvar_2, pvar_1, pvar_5, pvar_1, pvar_5, pvar_5, pvar_5)
)
goal cog_mean cog_pvar aff_mean aff_pvar
1 0.50 1.0 0.50 1.0
2 0.50 1.0 0.40 0.6
3 0.45 0.6 0.50 1.0
4 0.40 0.6 0.55 0.4
5 0.50 1.0 0.50 1.0
6 0.40 0.6 0.55 0.4
7 0.40 0.6 0.55 0.4
8 0.40 1.6 0.55 0.4round_res(res[, c("trait", "facet", "feature", "cog_mean", "cog_pvar", "aff_mean", "aff_pvar")], 3)# A tibble: 8 × 7
trait facet feature cog_mean cog_pvar aff_mean aff_pvar
<chr> <chr> <fct> <dbl> <dbl> <dbl> <dbl>
1 N A 1 0.5 1 0.5 1
2 N A 2 0.5 1 0.421 0.713
3 N D 1 0.45 0.6 0.5 1
4 N D 2 0.4 0.6 0.543 0.526
5 N E 1 0.5 1 0.5 1
6 N E 2 0.4 0.6 0.543 0.526
7 N E 3 0.4 0.604 0.543 0.529
8 N E 4 0.402 1.60 0.543 0.526Results for real Self-Concept and ideal Self-Concept
round_res(res[, c("trait", "facet", "feature", "aff_mean_1", "aff_pvar_1", "aff_mean_0", "aff_pvar_0")], 3)# A tibble: 8 × 7
trait facet feature aff_mean_1 aff_pvar_1 aff_mean_0 aff_pvar_0
<chr> <chr> <fct> <dbl> <dbl> <dbl> <dbl>
1 N A 1 0.516 0.926 0.486 1
2 N A 2 0.439 0.644 0.405 0.773
3 N D 1 0.505 0.861 0.5 1
4 N D 2 0.533 0.471 0.551 0.574
5 N E 1 0.516 0.926 0.486 1
6 N E 2 0.533 0.471 0.551 0.574
7 N E 3 0.545 0.505 0.542 0.55
8 N E 4 0.544 0.509 0.542 0.542Results for Behavior (Habituation)
round_res(res[, c("trait", "facet", "feature", "beh_mean", "beh_pvar")], 2)# A tibble: 8 × 5
trait facet feature beh_mean beh_pvar
<chr> <chr> <fct> <dbl> <dbl>
1 N A 1 0.52 0.93
2 N A 2 0.52 0.93
3 N D 1 0.46 0.52
4 N D 2 0.41 0.52
5 N E 1 0.52 0.93
6 N E 2 0.41 0.52
7 N E 3 0.41 0.57
8 N E 4 0.41 1.49