Additional Simulation with Skewed Normal Distribution
1 Simulate Skewed Data
latentaffect as skewed
set.seed(0405191)
n <- 200
days_per_person <- 30
n_days <- n*days_per_person
people <- tibble(
id = 1:n,
neurot = rnorm(n),
latentaffect = brms::rskew_normal(n, alpha = 100),
latentaffectsd =rnorm(n),
)
sd(people$neurot)## [1] 0.9622981qplot(people$latentaffect, binwidth= 0.1)## Warning: `qplot()` was deprecated in ggplot2 3.4.0.
Measure Function no Error, cuts off skewed distribution (since these do not have their own minimum value)
measure <- function(x) {
x[x < 0] <- 0
x[x > 4] <- 4
round(x,1) +1
}1.1 3 true models
True model 1: Associations with the mean (diary4) True model 2: Associations with SD (diary5) True model 3: Both (diary6)
diary4 <- people %>% full_join(
tibble(
id = rep(1:n, each = days_per_person),
), by = 'id') %>%
mutate(
Aff1 = brms::rskew_normal(n = n_days, xi = 0.1 + 0.3* neurot + 0.3* latentaffect, sigma = exp(-0.6 + 0 * neurot + 0.3* latentaffectsd), alpha = 10)
)
qplot(diary4$Aff1, binwidth = .1)
qplot(measure(diary4$Aff1), binwidth = .1)
diary4 %>% group_by(id, neurot) %>%
summarise(Aff1 = mean(Aff1)) %>% ungroup() %>% summarise(cor(Aff1, neurot))## `summarise()` has grouped output by 'id'. You can override using the `.groups`
## argument.## # A tibble: 1 × 1
## `cor(Aff1, neurot)`
## <dbl>
## 1 0.579sd(diary4$neurot)## [1] 0.9599693sd(diary4$Aff1)## [1] 0.7703411hist(diary4$Aff1)
qplot(diary4$neurot, diary4$Aff1)
diary5 <- people %>% full_join(
tibble(
id = rep(1:n, each = days_per_person),
), by = 'id') %>%
mutate(
Aff2 =brms::rskew_normal(n = n_days, xi = 0.1 + 0* neurot + 0.3* latentaffect, sigma = exp(-.8 + 0.15 * neurot + 0.3* latentaffectsd), alpha = 10)
)
sd(diary5$Aff2)## [1] 0.636185qplot(diary5$Aff2)## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
qplot(diary5$neurot, diary5$Aff2, alpha = I(0.1)) + geom_hline(yintercept = -2, linetype = "dashed")
qplot(measure(diary5$Aff2), binwidth = .1)
diary6 <- people %>% full_join(
tibble(
id = rep(1:n, each = days_per_person),
), by = 'id') %>%
mutate(
Aff3 =brms::rskew_normal(n = n_days, xi =0.3+ 0.3*neurot + 0.3*latentaffect, sigma = exp(-.8 + 0.15 * neurot + 0.3*latentaffectsd), alpha = 10)
)
qplot(diary6$Aff3, binwidth = .1)
qplot(measure(diary6$Aff3), binwidth = .1)
sd(diary6$neurot)## [1] 0.9599693sd(diary6$Aff3)## [1] 0.7285978qplot(diary6$neurot, diary6$Aff3, alpha = I(0.1)) + geom_hline(yintercept = -2, linetype = "dashed")
Add measured Affect to all three Simulations
diary4 <- diary4 %>%
mutate(
Affect_m = measure(Aff1)
)
sd(diary4$Affect_m)## [1] 0.7325122round(cor(diary4 %>% select(Aff1, Affect_m)),2) ## Aff1 Affect_m
## Aff1 1.00 0.99
## Affect_m 0.99 1.00qplot(diary4$Affect_m, binwidth=.1)
diary5 <- diary5 %>%
mutate(
Affect_m = measure(Aff2 )
)
sd(diary5$Affect_m)## [1] 0.6240122round(cor(diary5 %>% select(Aff2, Affect_m)),2) ## Aff2 Affect_m
## Aff2 1 1
## Affect_m 1 1qplot(diary5$Affect_m, binwidth=.1)
diary6 <- diary6 %>%
mutate(
Affect_m = measure(Aff3)
)
sd(diary6$Affect_m)## [1] 0.7079548#round(cor(diary6 %>% select(Aff3, Affect_m)),2)
qplot(diary6$Affect_m, binwidth=.1)
diary4$Acens <- case_when(diary4$Affect_m == 1 ~ "left",
diary4$Affect_m == 5 ~ "right",
TRUE ~ "none")
table(diary4$Acens)##
## left none right
## 750 5237 13diary5$Acens <- case_when(diary5$Affect_m == 1 ~ "left",
diary5$Affect_m == 5 ~ "right",
TRUE ~ "none")
table(diary5$Acens)##
## left none right
## 607 5385 8diary6$Acens <- case_when(diary6$Affect_m == 1 ~ "left",
diary6$Affect_m == 5 ~ "right",
TRUE ~ "none")
table(diary6$Acens)##
## left none right
## 484 5507 9Add measured neuroticism to all three Simulations
measure_n <- function(x) {
# expects that x is N(0,1)
x <- x
round(x,1)
}
diary4 <- diary4 %>%
mutate(
neurot_m = measure_n(neurot)
)
sd(diary4$neurot_m)## [1] 0.9618098#round(cor(diary4 %>% select(neurot, neurot_m)),2)
qplot(diary4$neurot_m, binwidth=.1)
diary5 <- diary5 %>%
mutate(
neurot_m = measure_n(neurot)
)
sd(diary5$neurot_m)## [1] 0.9618098#round(cor(diary5 %>% select(neurot, neurot_m)),2)
#qplot(diary5$neurot_m, binwidth=.1)
diary6 <- diary6 %>%
mutate(
neurot_m = measure_n(neurot)
)
sd(diary6$neurot_m)## [1] 0.9618098#round(cor(diary6 %>% select(neurot, neurot_m)),2)
#qplot(diary6$neurot_m, binwidth=.1)2 Estimated models
model 1: naiv: only associations with the mean, normal distribution assumption model 2: associations with the mean, censored model 3: association with mean and variability, censored
2.1 Simulation 4 (effect on mean)
w4model_neuro3 <- brm(bf(Affect_m | cens(Acens) ~ neurot_m + (1|id),
sigma ~ neurot_m + (1|id)), data = diary4,
iter = 6000, warmup = 2000, init = 0.1,
file = "w4skew")
print(w4model_neuro3)## Family: gaussian
## Links: mu = identity; sigma = log
## Formula: Affect_m | cens(Acens) ~ neurot_m + (1 | id)
## sigma ~ neurot_m + (1 | id)
## Data: diary4 (Number of observations: 6000)
## Draws: 4 chains, each with iter = 6000; warmup = 2000; thin = 1;
## total post-warmup draws = 16000
##
## Group-Level Effects:
## ~id (Number of levels: 200)
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## sd(Intercept) 0.42 0.02 0.38 0.47 1.00 3160 6200
## sd(sigma_Intercept) 0.31 0.02 0.28 0.35 1.00 4987 6670
##
## Population-Level Effects:
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## Intercept 1.80 0.03 1.74 1.86 1.00 1611 3574
## sigma_Intercept -0.53 0.02 -0.58 -0.49 1.00 4485 7760
## neurot_m 0.34 0.03 0.28 0.40 1.00 1902 3594
## sigma_neurot_m -0.05 0.03 -0.10 -0.00 1.00 4964 8347
##
## Draws were sampled using sample(hmc). For each parameter, Bulk_ESS
## and Tail_ESS are effective sample size measures, and Rhat is the potential
## scale reduction factor on split chains (at convergence, Rhat = 1).plot(w4model_neuro3)
prior_summary(w4model_neuro3)## prior class coef group resp dpar nlpar lb ub
## (flat) b
## (flat) b neurot_m
## (flat) b sigma
## (flat) b neurot_m sigma
## student_t(3, 1.8, 2.5) Intercept
## student_t(3, 0, 2.5) Intercept sigma
## student_t(3, 0, 2.5) sd 0
## student_t(3, 0, 2.5) sd sigma 0
## student_t(3, 0, 2.5) sd id 0
## student_t(3, 0, 2.5) sd Intercept id 0
## student_t(3, 0, 2.5) sd id sigma 0
## student_t(3, 0, 2.5) sd Intercept id sigma 0
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## (vectorized)2.2 Simulation 5 (effect on SD)
w5model_neuro3 <- brm(bf(Affect_m | cens(Acens) ~ neurot_m + (1|id),
sigma ~ neurot_m + (1|id)), data = diary5,
control = list(adapt_delta = .99),chains = 4,
iter = 6000, warmup = 2000, init = 0.1,
file = "w5skew")
print(w5model_neuro3)## Family: gaussian
## Links: mu = identity; sigma = log
## Formula: Affect_m | cens(Acens) ~ neurot_m + (1 | id)
## sigma ~ neurot_m + (1 | id)
## Data: diary5 (Number of observations: 6000)
## Draws: 4 chains, each with iter = 6000; warmup = 2000; thin = 1;
## total post-warmup draws = 16000
##
## Group-Level Effects:
## ~id (Number of levels: 200)
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## sd(Intercept) 0.38 0.02 0.34 0.42 1.00 2699 5252
## sd(sigma_Intercept) 0.28 0.02 0.25 0.32 1.00 5818 9148
##
## Population-Level Effects:
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## Intercept 1.70 0.03 1.64 1.75 1.00 1620 2974
## sigma_Intercept -0.71 0.02 -0.76 -0.67 1.00 5621 8199
## neurot_m 0.11 0.03 0.05 0.17 1.00 1761 3491
## sigma_neurot_m 0.19 0.02 0.14 0.24 1.00 5542 9003
##
## Draws were sampled using sample(hmc). For each parameter, Bulk_ESS
## and Tail_ESS are effective sample size measures, and Rhat is the potential
## scale reduction factor on split chains (at convergence, Rhat = 1).prior_summary(w5model_neuro3)## prior class coef group resp dpar nlpar lb ub
## (flat) b
## (flat) b neurot_m
## (flat) b sigma
## (flat) b neurot_m sigma
## student_t(3, 1.6, 2.5) Intercept
## student_t(3, 0, 2.5) Intercept sigma
## student_t(3, 0, 2.5) sd 0
## student_t(3, 0, 2.5) sd sigma 0
## student_t(3, 0, 2.5) sd id 0
## student_t(3, 0, 2.5) sd Intercept id 0
## student_t(3, 0, 2.5) sd id sigma 0
## student_t(3, 0, 2.5) sd Intercept id sigma 0
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## (vectorized)plot(w5model_neuro3)
2.3 Simulation 6 (effects on both)
w6model_neuro3 <- brm(bf(Affect_m| cens(Acens) ~ neurot_m + (1|id),
sigma ~ neurot_m + (1|id)), data = diary6,
iter = 7000, warmup = 2000,chains = 4,
control = list(adapt_delta = .99), inits = 0.1 , #options = list(adapt_delta = 0.99)
file = "w6skew")## Warning: Argument 'inits' is deprecated. Please use argument 'init' instead.print(w6model_neuro3)## Family: gaussian
## Links: mu = identity; sigma = log
## Formula: Affect_m | cens(Acens) ~ neurot_m + (1 | id)
## sigma ~ neurot_m + (1 | id)
## Data: diary6 (Number of observations: 6000)
## Draws: 4 chains, each with iter = 7000; warmup = 2000; thin = 1;
## total post-warmup draws = 20000
##
## Group-Level Effects:
## ~id (Number of levels: 200)
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## sd(Intercept) 0.38 0.02 0.34 0.43 1.00 2628 5133
## sd(sigma_Intercept) 0.31 0.02 0.27 0.35 1.00 5413 9542
##
## Population-Level Effects:
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## Intercept 1.90 0.03 1.84 1.95 1.01 1036 2057
## sigma_Intercept -0.74 0.02 -0.79 -0.69 1.00 4179 7985
## neurot_m 0.41 0.03 0.36 0.47 1.00 1446 3315
## sigma_neurot_m 0.10 0.03 0.05 0.15 1.00 4533 7692
##
## Draws were sampled using sample(hmc). For each parameter, Bulk_ESS
## and Tail_ESS are effective sample size measures, and Rhat is the potential
## scale reduction factor on split chains (at convergence, Rhat = 1).plot(w6model_neuro3)
prior_summary(w6model_neuro3)## prior class coef group resp dpar nlpar lb ub
## (flat) b
## (flat) b neurot_m
## (flat) b sigma
## (flat) b neurot_m sigma
## student_t(3, 1.8, 2.5) Intercept
## student_t(3, 0, 2.5) Intercept sigma
## student_t(3, 0, 2.5) sd 0
## student_t(3, 0, 2.5) sd sigma 0
## student_t(3, 0, 2.5) sd id 0
## student_t(3, 0, 2.5) sd Intercept id 0
## student_t(3, 0, 2.5) sd id sigma 0
## student_t(3, 0, 2.5) sd Intercept id sigma 0
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## (vectorized)3 Results
extract_param2 <- function(model, parameter) {
ci <- posterior_summary(model, variable = parameter)
est <- sprintf("%.4f %.4f %.4f", ci[,"Estimate"], ci[,"Q2.5"], ci[,"Q97.5"])
est
}
results_sim <- data.frame(matrix(nrow = 7, # Modelle & RVI
ncol = 9+1))
names(results_sim) <- c("model", "w4_b_neuro", "w4_b_neuro_sigma", "w4_sigma",
"w5_b_neuro", "w5_b_neuro_sigma", "w5_sigma",
"w6_b_neuro", "w6_b_neuro_sigma", "w6_sigma"
)
results_sim$model <- c("model1", "model2", "model3",
"RVI", "RVI_weight", "SD", "SD*")
#summary(w4model_neuro)$fixed
#posterior_summary(w4model_neuro3)results_sim[3, "w4_b_neuro"] <- extract_param2(w4model_neuro3, "b_neurot_m")
results_sim[3, "w4_b_neuro_sigma"] <- extract_param2(w4model_neuro3, "b_sigma_neurot_m")
results_sim[3, "w4_sigma"] <- extract_param2(w4model_neuro3, "b_sigma_Intercept")results_sim[3, "w5_b_neuro"] <- extract_param2(w5model_neuro3, "b_neurot_m")
results_sim[3, "w5_b_neuro_sigma"] <- extract_param2(w5model_neuro3, "b_sigma_neurot_m")
results_sim[3, "w5_sigma"] <- extract_param2(w5model_neuro3, "b_sigma_Intercept")results_sim[3, "w6_b_neuro"] <- extract_param2(w6model_neuro3, "b_neurot_m")
results_sim[3, "w6_b_neuro_sigma"] <- extract_param2(w6model_neuro3, "b_sigma_neurot_m")
results_sim[3, "w6_sigma"] <- extract_param2(w6model_neuro3, "b_sigma_Intercept")4 RVI (Relative-Variability-Index)
4.1 Unweighted RVI for all three Simulations
people <- people %>%
mutate(
neurot = measure_n(neurot)
)
id <- unique(diary4$id)
id <- as.data.frame(id)
people$RSD_d4 <- NA
for (i in 1:nrow(id)) {
if (id$id[i] %in% diary4$id) {
people$RSD_d4[i] <- relativeSD(diary4$Affect_m[diary4$id == id$id[i]],
1, 5)
}
}
people$logrsd_d4 <- log(people$RSD_d4)
m_rsd_d4 <- brm(logrsd_d4 ~ neurot, data= people)## Start sampling## Running MCMC with 4 chains, at most 64 in parallel...
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## Total execution time: 0.4 seconds.print(m_rsd_d4, digits=4)## Family: gaussian
## Links: mu = identity; sigma = identity
## Formula: logrsd_d4 ~ neurot
## Data: people (Number of observations: 200)
## Draws: 4 chains, each with iter = 2000; warmup = 1000; thin = 1;
## total post-warmup draws = 4000
##
## Population-Level Effects:
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## Intercept -1.0840 0.0182 -1.1197 -1.0494 1.0010 4168 2975
## neurot -0.0891 0.0194 -0.1273 -0.0504 1.0042 4437 2814
##
## Family Specific Parameters:
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## sigma 0.2647 0.0136 0.2402 0.2938 1.0001 3998 2676
##
## Draws were sampled using sample(hmc). For each parameter, Bulk_ESS
## and Tail_ESS are effective sample size measures, and Rhat is the potential
## scale reduction factor on split chains (at convergence, Rhat = 1).results_sim[4,3] <- extract_param2(m_rsd_d4, "b_neurot")
people$RSD_d5 <- NA
for (i in 1:nrow(id)) {
if (id$id[i] %in% diary5$id) {
people$RSD_d5[i] <- relativeSD(diary5$Affect_m[diary5$id == id$id[i]],
1, 5)
}
}
people$logrsd_d5 <- log(people$RSD_d5)
m_rsd_d5 <- brm( logrsd_d5~ neurot, data= people)## Start sampling## Running MCMC with 4 chains, at most 64 in parallel...
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## Total execution time: 0.3 seconds.m_rsd_d5## Family: gaussian
## Links: mu = identity; sigma = identity
## Formula: logrsd_d5 ~ neurot
## Data: people (Number of observations: 200)
## Draws: 4 chains, each with iter = 2000; warmup = 1000; thin = 1;
## total post-warmup draws = 4000
##
## Population-Level Effects:
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## Intercept -1.18 0.02 -1.22 -1.15 1.00 4279 2917
## neurot 0.12 0.02 0.08 0.15 1.00 3953 2897
##
## Family Specific Parameters:
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## sigma 0.25 0.01 0.23 0.28 1.00 3328 2769
##
## Draws were sampled using sample(hmc). For each parameter, Bulk_ESS
## and Tail_ESS are effective sample size measures, and Rhat is the potential
## scale reduction factor on split chains (at convergence, Rhat = 1).results_sim[4,6] <- extract_param2(m_rsd_d5, "b_neurot")
people$RSD_d6 <- NA
for (i in 1:nrow(id)) {
if (id$id[i] %in% diary6$id) {
people$RSD_d6[i] <- relativeSD(diary6$Affect_m[diary6$id == id$id[i]],
1, 5)
}
} ## Warning in checkOutput(M, MIN, MAX): NaN returned. Data has a mean equal the
## minimumpeople$logrsd_d6 <- log(people$RSD_d6)
m_rsd_d6 <- brm(logrsd_d6 ~ neurot, data= people)## Warning: Rows containing NAs were excluded from the model.## Start sampling## Running MCMC with 4 chains, at most 64 in parallel...
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## Total execution time: 0.3 seconds.m_rsd_d6## Family: gaussian
## Links: mu = identity; sigma = identity
## Formula: logrsd_d6 ~ neurot
## Data: people (Number of observations: 199)
## Draws: 4 chains, each with iter = 2000; warmup = 1000; thin = 1;
## total post-warmup draws = 4000
##
## Population-Level Effects:
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## Intercept -1.25 0.02 -1.29 -1.21 1.00 3717 2692
## neurot -0.02 0.02 -0.06 0.02 1.00 3329 2928
##
## Family Specific Parameters:
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## sigma 0.29 0.01 0.26 0.32 1.00 3769 2893
##
## Draws were sampled using sample(hmc). For each parameter, Bulk_ESS
## and Tail_ESS are effective sample size measures, and Rhat is the potential
## scale reduction factor on split chains (at convergence, Rhat = 1).results_sim[4,9] <- extract_param2(m_rsd_d6, "b_neurot")4.2 weighted RVI for all three Simulations
people$mean_Aff_d4 <- NA
for (i in 1:nrow(id)) {
if (id$id[i] %in% diary4$id) {
people$mean_Aff_d4[i] <- mean(diary4$Affect_m[diary4$id == id$id[i]],
na.rm = T)
}
}
range(people$mean_Aff_d4)## [1] 1.026667 3.740000people$mean_Aff_d5 <- NA
for (i in 1:nrow(id)) {
if (id$id[i] %in% diary5$id) {
people$mean_Aff_d5[i] <- mean(diary5$Affect_m[diary5$id == id$id[i]],
na.rm = T)
}
}
range(people$mean_Aff_d5)## [1] 1.186667 2.966667people$mean_Aff_d6 <- NA
for (i in 1:nrow(id)) {
if (id$id[i] %in% diary6$id) {
people$mean_Aff_d6[i] <- mean(diary6$Affect_m[diary6$id == id$id[i]],
na.rm = T)
}
}
range(people$mean_Aff_d6)## [1] 1.000000 3.503333people$weight_d4 <- NA
for (i in 1:nrow(id)) {
if (id$id[i] %in% diary4$id) {
people$weight_d4[i] <- maximumSD(people$mean_Aff_d4[i],
1, # Minimum
5, # Maximum
sum(!is.na(diary4$Affect_m[diary4$id == id$id[i]])) # Anzahl Beobachtungen in var eingeflossen/30
)
# W as reported in paper
people$weight_d4[i] <- people$weight_d4[i]^2
}
}
people$weight_d5 <- NA
for (i in 1:nrow(id)) {
if (id$id[i] %in% diary5$id) {
people$weight_d5[i] <- maximumSD(people$mean_Aff_d5[i],
1, # Minimum
5, # Maximum
sum(!is.na(diary5$Affect_m[diary5$id == id$id[i]])) # Anzahl Beobachtungen in var eingeflossen/30
)
# W as reported in paper
people$weight_d5[i] <- people$weight_d5[i]^2
}
}
people$weight_d6 <- NA
for (i in 1:nrow(id)) {
if (id$id[i] %in% diary6$id) {
people$weight_d6[i] <- maximumSD(people$mean_Aff_d6[i],
1, # Minimum
5, # Maximum
sum(!is.na(diary6$Affect_m[diary6$id == id$id[i]])) # Anzahl Beobachtungen in var eingeflossen/30
)
# W as reported in paper
people$weight_d6[i] <- people$weight_d6[i]^2
}
}m_rsd_d4_w <- brm(logrsd_d4| weights(weight_d4) ~ neurot, data= people)## Start sampling## Running MCMC with 4 chains, at most 64 in parallel...
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## Total execution time: 0.5 seconds.m_rsd_d4_w## Family: gaussian
## Links: mu = identity; sigma = identity
## Formula: logrsd_d4 | weights(weight_d4) ~ neurot
## Data: people (Number of observations: 200)
## Draws: 4 chains, each with iter = 2000; warmup = 1000; thin = 1;
## total post-warmup draws = 4000
##
## Population-Level Effects:
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## Intercept -1.08 0.01 -1.10 -1.05 1.00 3721 3273
## neurot -0.08 0.01 -0.10 -0.05 1.00 3341 2930
##
## Family Specific Parameters:
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## sigma 0.28 0.01 0.26 0.30 1.00 3280 2738
##
## Draws were sampled using sample(hmc). For each parameter, Bulk_ESS
## and Tail_ESS are effective sample size measures, and Rhat is the potential
## scale reduction factor on split chains (at convergence, Rhat = 1).results_sim[5,3] <- extract_param2(m_rsd_d4_w, "b_neurot")
m_rsd_d5_w <- brm(logrsd_d5| weights(weight_d5) ~ neurot, data= people)## Start sampling## Running MCMC with 4 chains, at most 64 in parallel...
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## Total execution time: 0.4 seconds.m_rsd_d5_w## Family: gaussian
## Links: mu = identity; sigma = identity
## Formula: logrsd_d5 | weights(weight_d5) ~ neurot
## Data: people (Number of observations: 200)
## Draws: 4 chains, each with iter = 2000; warmup = 1000; thin = 1;
## total post-warmup draws = 4000
##
## Population-Level Effects:
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## Intercept -1.19 0.01 -1.22 -1.16 1.00 3707 2687
## neurot 0.14 0.01 0.11 0.17 1.00 3380 3106
##
## Family Specific Parameters:
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## sigma 0.27 0.01 0.25 0.29 1.00 3305 2764
##
## Draws were sampled using sample(hmc). For each parameter, Bulk_ESS
## and Tail_ESS are effective sample size measures, and Rhat is the potential
## scale reduction factor on split chains (at convergence, Rhat = 1).results_sim[5,6] <- extract_param2(m_rsd_d5_w, "b_neurot")
m_rsd_d6_w <- brm(logrsd_d6| weights(weight_d6) ~ neurot, data= people)## Warning: Rows containing NAs were excluded from the model.## Start sampling## Running MCMC with 4 chains, at most 64 in parallel...
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## Total execution time: 0.4 seconds.m_rsd_d6_w## Family: gaussian
## Links: mu = identity; sigma = identity
## Formula: logrsd_d6 | weights(weight_d6) ~ neurot
## Data: people (Number of observations: 199)
## Draws: 4 chains, each with iter = 2000; warmup = 1000; thin = 1;
## total post-warmup draws = 4000
##
## Population-Level Effects:
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## Intercept -1.26 0.01 -1.29 -1.24 1.00 2992 2769
## neurot 0.02 0.01 -0.01 0.05 1.00 3655 3060
##
## Family Specific Parameters:
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## sigma 0.29 0.01 0.27 0.31 1.00 3340 2389
##
## Draws were sampled using sample(hmc). For each parameter, Bulk_ESS
## and Tail_ESS are effective sample size measures, and Rhat is the potential
## scale reduction factor on split chains (at convergence, Rhat = 1).results_sim[5,9] <- extract_param2(m_rsd_d6_w, "b_neurot")5 SD
people$sd_Aff_d4 <- NA
for (i in 1:nrow(id)) {
if (id$id[i] %in% diary4$id) {
people$sd_Aff_d4[i] <- sd(diary4$Affect_m[diary4$id == id$id[i]],
na.rm = T)
}
}
people$sd_Aff_d5 <- NA
for (i in 1:nrow(id)) {
if (id$id[i] %in% diary5$id) {
people$sd_Aff_d5[i] <- sd(diary5$Affect_m[diary5$id == id$id[i]],
na.rm = T)
}
}
people$sd_Aff_d6 <- NA
for (i in 1:nrow(id)) {
if (id$id[i] %in% diary6$id) {
people$sd_Aff_d6[i] <- sd(diary6$Affect_m[diary6$id == id$id[i]],
na.rm = T)
}
}
people$sd_Aff_d4[people$sd_Aff_d4 == 0] <- NA
people$sd_Aff_d5[people$sd_Aff_d5 == 0] <- NA
people$sd_Aff_d6[people$sd_Aff_d6 == 0] <- NA
people$logsd_d4 <- log(people$sd_Aff_d4)
people$logsd_d5 <- log(people$sd_Aff_d5)
people$logsd_d6 <- log(people$sd_Aff_d6)
mean(people$sd_Aff_d4) ## [1] 0.5382563mean(people$sd_Aff_d5) ## [1] 0.4707706mean(people$sd_Aff_d6, na.rm = T) ## [1] 0.46634635.1 Regression with SD
m_sd_d4 <- brm(logsd_d4 ~ neurot, data= people, file = "logsd_d4")
m_sd_d4## Family: gaussian
## Links: mu = identity; sigma = identity
## Formula: logsd_d4 ~ neurot
## Data: people (Number of observations: 200)
## Draws: 4 chains, each with iter = 2000; warmup = 1000; thin = 1;
## total post-warmup draws = 4000
##
## Population-Level Effects:
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## Intercept -0.69 0.03 -0.75 -0.64 1.00 3653 3210
## neurot 0.10 0.03 0.05 0.16 1.00 4425 3013
##
## Family Specific Parameters:
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## sigma 0.39 0.02 0.35 0.43 1.00 3546 2763
##
## Draws were sampled using sample(hmc). For each parameter, Bulk_ESS
## and Tail_ESS are effective sample size measures, and Rhat is the potential
## scale reduction factor on split chains (at convergence, Rhat = 1).results_sim[6,3] <- extract_param2(m_sd_d4, "b_neurot")
m_sd_d5 <- brm(logsd_d5 ~ neurot, data= people, file = "logsd_d5")
m_sd_d5## Family: gaussian
## Links: mu = identity; sigma = identity
## Formula: logsd_d5 ~ neurot
## Data: people (Number of observations: 200)
## Draws: 4 chains, each with iter = 2000; warmup = 1000; thin = 1;
## total post-warmup draws = 4000
##
## Population-Level Effects:
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## Intercept -0.83 0.02 -0.87 -0.78 1.00 4555 3000
## neurot 0.20 0.02 0.15 0.24 1.00 4336 3143
##
## Family Specific Parameters:
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## sigma 0.32 0.02 0.30 0.36 1.00 4174 3072
##
## Draws were sampled using sample(hmc). For each parameter, Bulk_ESS
## and Tail_ESS are effective sample size measures, and Rhat is the potential
## scale reduction factor on split chains (at convergence, Rhat = 1).results_sim[6,6] <- extract_param2(m_sd_d5, "b_neurot")
m_sd_d6 <- brm(logsd_d6 ~ neurot, data= people, file = "logsd_d6")## Warning: Rows containing NAs were excluded from the model.m_sd_d6## Family: gaussian
## Links: mu = identity; sigma = identity
## Formula: logsd_d6 ~ neurot
## Data: people (Number of observations: 199)
## Draws: 4 chains, each with iter = 2000; warmup = 1000; thin = 1;
## total post-warmup draws = 4000
##
## Population-Level Effects:
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## Intercept -0.85 0.03 -0.90 -0.80 1.00 4239 2712
## neurot 0.22 0.03 0.16 0.27 1.00 4194 2685
##
## Family Specific Parameters:
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## sigma 0.36 0.02 0.33 0.40 1.00 4383 2702
##
## Draws were sampled using sample(hmc). For each parameter, Bulk_ESS
## and Tail_ESS are effective sample size measures, and Rhat is the potential
## scale reduction factor on split chains (at convergence, Rhat = 1).results_sim[6,9] <- extract_param2(m_sd_d6, "b_neurot")5.2 Regression with SD + controlling for mean values of negative Emotion
m_sd_d4c <- brm(logsd_d4 ~ neurot + mean_Aff_d4, data= people)## Start sampling## Running MCMC with 4 chains, at most 64 in parallel...
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## Total execution time: 0.3 seconds.m_sd_d4c## Family: gaussian
## Links: mu = identity; sigma = identity
## Formula: logsd_d4 ~ neurot + mean_Aff_d4
## Data: people (Number of observations: 200)
## Draws: 4 chains, each with iter = 2000; warmup = 1000; thin = 1;
## total post-warmup draws = 4000
##
## Population-Level Effects:
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## Intercept -1.77 0.11 -1.98 -1.55 1.00 3469 2895
## neurot -0.05 0.03 -0.11 0.00 1.00 3365 2864
## mean_Aff_d4 0.57 0.06 0.46 0.69 1.00 3404 3109
##
## Family Specific Parameters:
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## sigma 0.32 0.02 0.29 0.35 1.00 3638 2796
##
## Draws were sampled using sample(hmc). For each parameter, Bulk_ESS
## and Tail_ESS are effective sample size measures, and Rhat is the potential
## scale reduction factor on split chains (at convergence, Rhat = 1).results_sim[7,3] <- extract_param2(m_sd_d4c, "b_neurot")
m_sd_d5c <- brm(logsd_d5 ~ neurot + mean_Aff_d5, data= people)## Start sampling## Running MCMC with 4 chains, at most 64 in parallel...
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## Total execution time: 0.3 seconds.m_sd_d5c## Family: gaussian
## Links: mu = identity; sigma = identity
## Formula: logsd_d5 ~ neurot + mean_Aff_d5
## Data: people (Number of observations: 200)
## Draws: 4 chains, each with iter = 2000; warmup = 1000; thin = 1;
## total post-warmup draws = 4000
##
## Population-Level Effects:
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## Intercept -1.69 0.09 -1.88 -1.52 1.00 4014 2781
## neurot 0.14 0.02 0.10 0.18 1.00 3775 2719
## mean_Aff_d5 0.50 0.05 0.40 0.61 1.00 3858 2699
##
## Family Specific Parameters:
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## sigma 0.27 0.01 0.24 0.30 1.00 3966 3036
##
## Draws were sampled using sample(hmc). For each parameter, Bulk_ESS
## and Tail_ESS are effective sample size measures, and Rhat is the potential
## scale reduction factor on split chains (at convergence, Rhat = 1).results_sim[7,6] <- extract_param2(m_sd_d5c, "b_neurot")
m_sd_d6c <- brm(logsd_d6 ~ neurot + mean_Aff_d6, data= people)## Warning: Rows containing NAs were excluded from the model.## Start sampling## Running MCMC with 4 chains, at most 64 in parallel...
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## Total execution time: 0.3 seconds.m_sd_d6c## Family: gaussian
## Links: mu = identity; sigma = identity
## Formula: logsd_d6 ~ neurot + mean_Aff_d6
## Data: people (Number of observations: 199)
## Draws: 4 chains, each with iter = 2000; warmup = 1000; thin = 1;
## total post-warmup draws = 4000
##
## Population-Level Effects:
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## Intercept -1.79 0.12 -2.03 -1.55 1.00 2632 2984
## neurot 0.04 0.03 -0.02 0.10 1.00 2591 2613
## mean_Aff_d6 0.49 0.06 0.37 0.61 1.00 2617 2548
##
## Family Specific Parameters:
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## sigma 0.32 0.02 0.29 0.35 1.00 3329 2614
##
## Draws were sampled using sample(hmc). For each parameter, Bulk_ESS
## and Tail_ESS are effective sample size measures, and Rhat is the potential
## scale reduction factor on split chains (at convergence, Rhat = 1).results_sim[7,9] <- extract_param2(m_sd_d6c, "b_neurot")results_sim## model w4_b_neuro w4_b_neuro_sigma
## 1 model1 <NA> <NA>
## 2 model2 <NA> <NA>
## 3 model3 0.3397 0.2763 0.4040 -0.0538 -0.1046 -0.0022
## 4 RVI <NA> -0.0891 -0.1273 -0.0504
## 5 RVI_weight <NA> -0.0779 -0.1043 -0.0500
## 6 SD <NA> 0.1012 0.0461 0.1571
## 7 SD* <NA> -0.0532 -0.1085 0.0026
## w4_sigma w5_b_neuro w5_b_neuro_sigma
## 1 <NA> <NA> <NA>
## 2 <NA> <NA> <NA>
## 3 -0.5331 -0.5815 -0.4853 0.1102 0.0546 0.1682 0.1900 0.1447 0.2358
## 4 <NA> <NA> 0.1191 0.0828 0.1547
## 5 <NA> <NA> 0.1415 0.1146 0.1680
## 6 <NA> <NA> 0.1976 0.1516 0.2437
## 7 <NA> <NA> 0.1404 0.1007 0.1810
## w5_sigma w6_b_neuro w6_b_neuro_sigma
## 1 <NA> <NA> <NA>
## 2 <NA> <NA> <NA>
## 3 -0.7119 -0.7567 -0.6686 0.4149 0.3566 0.4726 0.0977 0.0476 0.1479
## 4 <NA> <NA> -0.0195 -0.0627 0.0220
## 5 <NA> <NA> 0.0219 -0.0051 0.0489
## 6 <NA> <NA> 0.2180 0.1643 0.2704
## 7 <NA> <NA> 0.0387 -0.0249 0.1042
## w6_sigma
## 1 <NA>
## 2 <NA>
## 3 -0.7416 -0.7887 -0.6946
## 4 <NA>
## 5 <NA>
## 6 <NA>
## 7 <NA>library("writexl")
write_xlsx(results_sim,"results_sim2.xlsx")6 Plot Simulaion
apatheme = theme_bw() +
theme(panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
panel.background = element_blank(),
panel.border = element_blank(),
text=element_text(family='Arial'),
legend.title=element_blank(),
legend.position=c(.7,.8),
axis.line.x = element_line(color='black'),
axis.line.y = element_line(color='black'))
#getwd()
#results_sim <- read_xlsx("~/results_sim2.xlsx")
results_sim2 <- results_sim
results_sim2$w4_sigma <- NULL
results_sim2$w5_sigma <- NULL
results_sim2$w6_sigma <- NULL
results_sim2 <- results_sim2 %>%
tidyr::separate(w4_b_neuro_sigma,
c("w4_b_neuro_sigma","lowerw4", "upperw4"), sep = " ")
results_sim2 <- results_sim2 %>%
tidyr::separate(w5_b_neuro_sigma,
c("w5_b_neuro_sigma", "lowerw5", "upperw5"), sep = " ")
results_sim2 <- results_sim2 %>%
tidyr::separate(w6_b_neuro_sigma,
c("w6_b_neuro_sigma", "lowerw6", "upperw6"), sep = " ")
resultsw4sig <- results_sim2
resultsw4sig <- resultsw4sig[-c(1,2), ]
resultsw4sig <- resultsw4sig[-6, ]
resultsw4sig[1,1] <- "BCLSM"
resultsw4sig[3,1] <- "weighted RVI"
resultsw4sig$w4_b_neuro_sigma <- as.numeric(resultsw4sig$w4_b_neuro_sigma )
resultsw4sig$w5_b_neuro_sigma <- as.numeric(resultsw4sig$w5_b_neuro_sigma )
resultsw4sig$w6_b_neuro_sigma <- as.numeric(resultsw4sig$w6_b_neuro_sigma )
resultsw4sig$lowerw4 <- as.numeric(resultsw4sig$lowerw4 )
resultsw4sig$upperw4 <- as.numeric(resultsw4sig$upperw4 )
resultsw4sig$lowerw5 <- as.numeric(resultsw4sig$lowerw5 )
resultsw4sig$upperw5 <- as.numeric(resultsw4sig$upperw5 )
resultsw4sig$lowerw6 <- as.numeric(resultsw4sig$lowerw6 )
resultsw4sig$upperw6 <- as.numeric(resultsw4sig$upperw6 )
resultsw4sig$model <- as.character(resultsw4sig$model)
resultsw4sig$model <- factor(resultsw4sig$model, levels=unique(resultsw4sig$model))
w4sig <- ggplot(resultsw4sig, aes(x=model , y = w4_b_neuro_sigma))+ geom_point() +
geom_hline(yintercept=0, linetype='dotted', col = 'black') +
labs(x = " Statistical Approach", y = "b estimates")+ ggtitle("Negative Emotion")
w4sig+apatheme + geom_errorbar(aes(ymin = lowerw4, ymax = upperw4), width = 0.2)+coord_cartesian(ylim = c(-0.40, 0.5))+theme(plot.title = element_text(hjust = 0.5))
ggsave("w4skew.png", width = 4, height = 3)
w5sig <- ggplot(resultsw4sig, aes(x=model , y = w5_b_neuro_sigma))+ geom_point() +
geom_hline(yintercept= 0.15, linetype='dotted', col = 'black') +
labs(x = "Statistical Approach", y = "b estimates") + geom_errorbar(aes(ymin = lowerw5, ymax = upperw5), width = 0.2)+ ggtitle("Negative Emotion")
w5sig + apatheme +coord_cartesian(ylim = c(-0.20, 0.4))+theme(plot.title = element_text(hjust = 0.5))
ggsave("w5skew.png", width = 4, height = 3)
w6sig <- ggplot(resultsw4sig, aes(x=model , y = w6_b_neuro_sigma))+ geom_point() +
geom_hline(yintercept= 0.15, linetype='dotted', col = 'black') +
labs(x = "Statistical Approach", y = "b estimates") + geom_errorbar(aes(ymin = lowerw6, ymax = upperw6), width = 0.2)+ ggtitle("Negative Emotion")
w6sig + apatheme +coord_cartesian(ylim = c(-0.30, 0.5))+theme(plot.title = element_text(hjust = 0.5))
ggsave("w6skew.png", width = 4, height = 3)