HTW All Exp Testing

Analyses

Bayesian

Code

pacman::p_load(dplyr,purrr,tidyr,tibble,ggplot2,
  brms,tidybayes, rstanarm,emmeans,broom,bayestestR,
  stringr, here,conflicted, patchwork, knitr,kableExtra,ggh4x,ggpattern)

conflict_prefer_all("dplyr", quiet = TRUE)
options(scipen = 999)
walk(c("Display_Functions","fun_alm","fun_indv_fit","fun_model"), ~ source(here::here(paste0("Functions/", .x, ".R"))))

e1 <- readRDS(here("data/e1_08-21-23.rds")) 
e2 <- readRDS(here("data/e2_08-04-23.rds")) 
e3 <- readRDS(here("data/e3_08-04-23.rds")) 

# combine all 3 experiments
d <- rbind(e1,e2,e3)

d <- d |> 
    mutate(trainCon=case_when(
    bandOrder=="Original" ~ "800",
    bandOrder=="Reverse" ~ "600",
    TRUE ~ NA_character_
    ), trainCon=as.numeric(trainCon)) 


nbins=5
train <-  d |> filter(expMode2=="Train") |> group_by(id,condit,fb,bandOrder, vb) |> 
    mutate(Trial_Bin = cut( gt.train, breaks = seq(1, max(gt.train),length.out=nbins+1),include.lowest = TRUE, labels=FALSE)) 
train_max <- train |> filter(Trial_Bin == nbins, bandInt==trainCon)
train_avg <- train_max |> group_by(id,condit,fb,bandOrder) |> summarise(train_end = mean(dist))
train_avg2 <- train_max |> select(id,condit,fb,bandOrder,expMode2,vb,bandInt,dist,vx)

test2 <- d |> filter(expMode2=="Test") |> 
  select(id,condit,fb,bandOrder,expMode2,vb,bandInt,dist,vx) |> 
  rbind(train_avg2) |>
  left_join(train_avg, by=c("id","condit", "fb", "bandOrder")) 

test <- d |> filter(expMode2=="Test") |> left_join(train_avg, by=c("id","condit", "fb", "bandOrder")) |>
  select(id,condit,bandType,bandInt,vb,vx,dist,train_end,fb,bandOrder)

Testing Deviation

Code

d |> filter(expMode2=="Test") |> ggplot(aes(x = vb, y = dist,fill=condit)) +
  stat_bar

Code

d |> filter(expMode2=="Test") |> ggplot(aes(x = condit, y = dist,fill=vb)) +
  stat_bar + facet_wrap(~Exp)

Code

d |> filter(expMode2=="Test") |> ggplot(aes(x = condit, y = dist,fill=vb)) +
  stat_bar + facet_wrap(fb~bandOrder)

Code

k1 <- d |>
  filter(expMode2 == "Test") |>
  group_by(condit, bandType, fb, bandOrder, vb) |>  # No need for 'id' if not used later
  summarise(se_dist = sd(dist) / sqrt(n()),dist = mean(dist))

ep1 <- d |>
  filter(expMode2 == "Test") |>
  group_by(condit, bandType, fb, bandOrder, vb) |>  # No need for 'id' if not used later
  summarise(se_dist = sd(dist) / sqrt(n()),dist = mean(dist)) |>
  ggplot(aes(x = condit, y = dist, fill = vb,
             pattern=bandType,
             pattern_density=0.35,
             pattern_fill=bandType, 
             ppatern_angle=bandType,
            )) +
   geom_bar_pattern(stat = "identity", 
                   # pattern_density=.25,
                    position = position_dodge()) + 
  geom_errorbar(aes(ymin = dist - se_dist, ymax = dist + se_dist),position = position_dodge()) +
  facet_wrap2(~fb*bandOrder,axes="all") +
  labs(title="Testing Deviation",pattern = "Band Type") +
  # scale_pattern_manual(values = c("stripe", "circle"))
  scale_pattern_spacing_discrete(range = c(0.01, 0.05)) 


ep2 <- d |>
  filter(expMode2 == "Test") |>
  group_by(condit, bandType, fb, bandOrder, vb) |>  # No need for 'id' if not used later
  summarise(se_dist = sd(dist) / sqrt(n()),dist = mean(dist)) |>
  ggplot(aes(x = vb, y = dist, fill = condit, pattern=bandType)) +
   geom_bar_pattern(stat = "identity", 
                    
                    position = position_dodge()) + 
  geom_errorbar(aes(ymin = dist - se_dist, ymax = dist + se_dist),position = position_dodge()) +
  facet_wrap2(~fb*bandOrder,axes="all") +
  labs(title="Testing Deviation",pattern = "Band Type") +
   scale_pattern_manual(values = c("stripe", "circle"))

ep1/ep2

Warning: The `scale_name` argument of `discrete_scale()` is deprecated as of ggplot2
3.5.0.

Warning: The `scale_name` argument of `continuous_scale()` is deprecated as of ggplot2
3.5.0.

Warning: The `trans` argument of `continuous_scale()` is deprecated as of ggplot2 3.5.0.
ℹ Please use the `transform` argument instead.

Testing Vx

Code

ep1 <- d |>
  filter(expMode2 == "Test") |>
  group_by(condit, bandType, fb, bandOrder, vb) |>  # No need for 'id' if not used later
  summarise(se_vx = sd(vx) / sqrt(n()),vx = mean(vx)) |>
  ggplot(aes(x = condit, y = vx, fill = vb, pattern=bandType)) +
   geom_bar_pattern(stat = "identity", 
                    position = position_dodge()) + 
  geom_errorbar(aes(ymin = vx - se_vx, ymax = vx + se_vx),position = position_dodge()) +
  facet_wrap2(~fb*bandOrder,axes="all") +
  labs(title="Testing Vx",pattern = "Band Type") +
   scale_pattern_manual(values = c("stripe", "circle"))


ep2 <- d |>
  filter(expMode2 == "Test") |>
  group_by(condit, bandType, fb, bandOrder, vb) |>  # No need for 'id' if not used later
  summarise(se_vx = sd(vx) / sqrt(n()),vx = mean(vx)) |>
  ggplot(aes(x = vb, y = vx, fill = condit, pattern=bandType)) +
   geom_bar_pattern(stat = "identity", 
                    position = position_dodge()) + 
  geom_errorbar(aes(ymin = vx - se_vx, ymax = vx + se_vx),position = position_dodge()) +
  facet_wrap2(~fb*bandOrder,axes="all") +
  labs(title="Testing Vx",pattern = "Band Type") +
   scale_pattern_manual(values = c("stripe", "circle"))

ep1/ep2

Control for end of training performance

Code

test|> group_by(id,condit) |> pivot_longer(c("dist","train_end"),names_to="var",values_to="value") |> 
  ggplot(aes(x=var,y=value, fill=condit)) + stat_bar + facet_wrap(~var)

Code

test2 |> ggplot(aes(x=bandInt,y=dist,fill=expMode2)) + stat_bar + facet_wrap(~condit+fb+bandOrder)

Code

test2 |> mutate(train_end_q = ntile(train_end,4)) |>  
  ggplot(aes(x=bandInt,y=dist,fill=expMode2)) + stat_bar + facet_wrap(~condit+train_end_q+bandOrder)

Code

test2 |> mutate(train_end_q = ntile(train_end,4)) |>  
  ggplot(aes(x=condit,y=dist,fill=vb)) + stat_bar + 
  facet_wrap(~expMode2+train_end_q+bandOrder,scales="free")

Code

test2 |> mutate(train_end_q = ntile(train_end,4)) |>  
  ggplot(aes(x=vb,y=dist,fill=condit)) + stat_bar + 
  facet_wrap(~expMode2+train_end_q+bandOrder,scales="free")

Code

test2 |> mutate(train_end_q = ntile(train_end,4)) |>  
  ggplot(aes(x=train_end_q,y=dist,fill=condit)) + stat_bar + 
  facet_wrap(~expMode2+vb+bandOrder,scales="free")

Code

test |> ggplot(aes(x=train_end,y=dist,fill=condit)) + 
  stat_summary(geom = "line", position=position_dodge(), fun = mean) +
    stat_summary(geom = "errorbar", position=position_dodge(.9), fun.data = mean_se, width = .4, alpha = .7) + 
    facet_wrap(~vb) +
  labs(x="Band", y="Deviation From Target")

Warning: Width not defined
ℹ Set with `position_dodge(width = ...)`

Warning: `position_dodge()` requires non-overlapping x intervals.
`position_dodge()` requires non-overlapping x intervals.
`position_dodge()` requires non-overlapping x intervals.
`position_dodge()` requires non-overlapping x intervals.
`position_dodge()` requires non-overlapping x intervals.
`position_dodge()` requires non-overlapping x intervals.

Code

test |> filter(bandType=="Extrapolation") |>
  ggplot(aes(x=train_end,y=dist,fill=condit,col=condit)) + 
  #geom_point() +
  geom_smooth(method="loess") +
    facet_nested_wrap(~bandOrder+vb+fb) +
  labs(x="Band", y="Deviation From Target")

`geom_smooth()` using formula = 'y ~ x'

Code

test |> filter(bandType=="Extrapolation") |>
  ggplot(aes(x=train_end,y=dist,fill=condit,col=condit)) + 
  #geom_point() +
  geom_smooth() +
    facet_nested_wrap(~bandOrder+vb+fb) +
  labs(x="Band", y="Deviation From Target")

`geom_smooth()` using method = 'gam' and formula = 'y ~ s(x, bs = "cs")'

Code

test |> filter(bandType=="Extrapolation") |>
  ggplot(aes(x=train_end,y=dist,fill=condit,col=condit)) + 
  #geom_point() +
  geom_smooth(method="lm") +
    facet_nested_wrap(~bandOrder+vb) +
  labs(x="Band", y="Deviation From Target")

`geom_smooth()` using formula = 'y ~ x'

Code

# create quartiles for train_end
test |> group_by(condit,vb,fb,bandOrder) |>  filter(bandType=="Extrapolation") |>
  mutate(train_end_q = ntile(train_end,4)) |> 
  ggplot(aes(x=train_end_q,y=dist,fill=condit)) + 
 stat_bar + 
    ggh4x::facet_wrap2(bandOrder~vb~fb) +
  labs(x="quartile", y="Deviation From Target")

Code

test |> group_by(condit,vb,fb,bandOrder) |>  #filter(bandType=="Extrapolation") |>
  mutate(train_end_q = ntile(train_end,4)) |> 
  ggplot(aes(x=condit,y=vx,fill=vb)) +
  stat_bar + 
    ggh4x::facet_wrap2(~train_end_q+bandOrder+fb)

dist ~ condit * fb * bandOrder * bandInt + (1 + bandInt | id)

Code

# dist ~ condit * fb * bandOrder * bandInt + (1 + bandInt | id)
bmm_combo_testDistBand <- readRDS(paste0(here::here("data/model_cache","combo_testDistBand_RF_5k.rds")))

mted1 <- as.data.frame(describe_posterior(bmm_combo_testDistBand, centrality = "Mean"))[, c(1,2,4,5,6)]
colnames(mted1) <- c("Term", "Estimate","95% CrI Lower", "95% CrI Upper", "pd")

mted1 |> mutate(across(where(is.numeric), \(x) round(x, 2))) |>
  tibble::remove_rownames() |> 
  mutate(Term = stringr::str_remove(Term, "b_")) |> kable(booktabs = TRUE, caption=(bmm_combo_testDistBand$formula[1]))

dist ~ condit * fb * bandOrder * bandInt + (1 + bandInt | id)
Term	Estimate	95% CrI Lower	95% CrI Upper	pd
Intercept	205.97	147.49	267.42	1.00
conditVaried	157.04	68.71	240.80	1.00
fbOrdinal	138.32	43.63	240.16	1.00
bandOrderReverse	-54.71	-153.03	40.96	0.86
bandInt	0.01	-0.06	0.07	0.58
conditVaried:fbOrdinal	-152.04	-301.04	-12.59	0.98
conditVaried:bandOrderReverse	-225.89	-366.28	-84.64	1.00
fbOrdinal:bandOrderReverse	-13.77	-163.82	130.61	0.57
conditVaried:bandInt	-0.16	-0.25	-0.06	1.00
fbOrdinal:bandInt	-0.14	-0.25	-0.03	1.00
bandOrderReverse:bandInt	0.10	-0.01	0.20	0.96
conditVaried:fbOrdinal:bandOrderReverse	46.25	-163.02	268.66	0.65
conditVaried:fbOrdinal:bandInt	0.16	0.00	0.32	0.98
conditVaried:bandOrderReverse:bandInt	0.28	0.13	0.44	1.00
fbOrdinal:bandOrderReverse:bandInt	0.02	-0.14	0.18	0.60
conditVaried:fbOrdinal:bandOrderReverse:bandInt	-0.10	-0.35	0.13	0.79

Code

ce_bmm1 <- plot(conditional_effects(bmm_combo_testDistBand),points=FALSE,plot=FALSE)
wrap_plots(ce_bmm1)

vx ~ condit * fb * bandOrder * bandInt + (1 + bandInt | id)

Code

# vx ~ condit * fb * bandOrder * bandInt + (1 + bandInt | id)
bmm_combo_testVxBand<- readRDS(paste0(here::here("data/model_cache","combo_testVxBand_RF_5K.rds")))

mted2 <- as.data.frame(describe_posterior(bmm_combo_testVxBand, centrality = "Mean"))[, c(1,2,4,5,6)]
colnames(mted2) <- c("Term", "Estimate","95% CrI Lower", "95% CrI Upper", "pd")

mted2 |> mutate(across(where(is.numeric), \(x) round(x, 2))) |>
  tibble::remove_rownames() |> 
  mutate(Term = stringr::str_remove(Term, "b_")) |> kable(booktabs = TRUE, caption=(bmm_combo_testVxBand$formula[1]))

vx ~ condit * fb * bandOrder * bandInt + (1 + bandInt | id)
Term	Estimate	95% CrI Lower	95% CrI Upper	pd
Intercept	415.31	333.21	494.51	1.00
conditVaried	163.41	45.67	275.80	1.00
fbOrdinal	186.14	59.22	314.65	1.00
bandOrderReverse	-53.30	-173.43	68.10	0.81
bandInt	0.71	0.61	0.81	1.00
conditVaried:fbOrdinal	-147.56	-329.93	41.42	0.94
conditVaried:bandOrderReverse	-169.21	-340.18	11.54	0.97
fbOrdinal:bandOrderReverse	63.60	-117.65	242.70	0.76
conditVaried:bandInt	-0.14	-0.29	0.00	0.97
fbOrdinal:bandInt	-0.22	-0.39	-0.06	1.00
bandOrderReverse:bandInt	0.00	-0.15	0.15	0.51
conditVaried:fbOrdinal:bandOrderReverse	-169.57	-438.52	93.73	0.90
conditVaried:fbOrdinal:bandInt	0.09	-0.15	0.32	0.78
conditVaried:bandOrderReverse:bandInt	0.08	-0.13	0.30	0.78
fbOrdinal:bandOrderReverse:bandInt	-0.09	-0.33	0.13	0.80
conditVaried:fbOrdinal:bandOrderReverse:bandInt	0.34	0.01	0.68	0.98

Code

ce_bmm1 <- plot(conditional_effects(bmm_combo_testVxBand),points=FALSE,plot=FALSE)
wrap_plots(ce_bmm1)

dist ~ condit * bandType * bandOrder * fb + (1 + bandType | id)

Code

# dist ~ condit * bandType * bandOrder * fb + (1 + bandType | id)
bmm_combo_testBT_dist<- readRDS(paste0(here::here("data/model_cache/brms","combo_TeDistBt_test6__4_5000_0.93_12_Bt_test6_1537.rds")))

mted4 <- as.data.frame(describe_posterior(bmm_combo_testBT_dist, centrality = "Mean"))[, c(1,2,4,5,6)]
colnames(mted4) <- c("Term", "Estimate","95% CrI Lower", "95% CrI Upper", "pd")

mted4 |> mutate(across(where(is.numeric), \(x) round(x, 2))) |>
  tibble::remove_rownames() |> 
  mutate(Term = stringr::str_remove(Term, "b_")) |> kable(booktabs = TRUE, caption=(bmm_combo_testBT_dist$formula[1]))

dist ~ condit * bandType * bandOrder * fb + (1 + bandType | id)
Term	Estimate	95% CrI Lower	95% CrI Upper	pd
Intercept	186.62	150.21	222.49	1.00
conditVaried	39.03	-9.09	88.02	0.94
bandTypeExtrapolation	23.83	-19.81	68.55	0.85
bandOrderReverse	-2.40	-58.78	55.98	0.53
fbOrdinal	41.49	-17.95	100.89	0.91
conditVaried:bandTypeExtrapolation	54.72	-6.85	115.38	0.96
conditVaried:bandOrderReverse	-73.30	-149.35	1.68	0.97
bandTypeExtrapolation:bandOrderReverse	31.52	-39.14	104.13	0.80
conditVaried:fbOrdinal	-34.88	-114.89	47.95	0.80
bandTypeExtrapolation:fbOrdinal	26.82	-46.39	98.89	0.77
bandOrderReverse:fbOrdinal	-70.56	-154.91	14.86	0.95
conditVaried:bandTypeExtrapolation:bandOrderReverse	51.22	-45.23	146.02	0.85
conditVaried:bandTypeExtrapolation:fbOrdinal	-41.83	-145.96	62.54	0.79
conditVaried:bandOrderReverse:fbOrdinal	70.87	-45.83	183.42	0.89
bandTypeExtrapolation:bandOrderReverse:fbOrdinal	36.67	-66.72	139.66	0.75
conditVaried:bandTypeExtrapolation:bandOrderReverse:fbOrdinal	-76.72	-221.77	71.82	0.85

Code

ce_bmm1 <- plot(conditional_effects(bmm_combo_testBT_dist),points=FALSE,plot=FALSE)
wrap_plots(ce_bmm1)

dist ~ condit * bandType * bandOrder * fb + (1 | id) + (1 | bandInt)

Code

# dist ~ condit * bandType * bandOrder * fb + (1 | id) + (1 | bandInt)
bmm_combo_testBT_dist3<- readRDS(paste0(here::here("data/model_cache/brms","combo_TeDistBand_test6_RF_2_2_8000_0.94_13_Band_test6_RF_21817.rds")))

mted7 <- as.data.frame(describe_posterior(bmm_combo_testBT_dist3, centrality = "Mean"))[, c(1,2,4,5,6)]
colnames(mted7) <- c("Term", "Estimate","95% CrI Lower", "95% CrI Upper", "pd")

mted7 |> mutate(across(where(is.numeric), \(x) round(x, 2))) |>
  tibble::remove_rownames() |> 
  mutate(Term = stringr::str_remove(Term, "b_")) |> kable(booktabs = TRUE, caption=(bmm_combo_testBT_dist3$formula[1]))

dist ~ condit * bandType * bandOrder * fb + (1 | id) + (1 | bandInt)
Term	Estimate	95% CrI Lower	95% CrI Upper	pd
Intercept	219.02	150.88	288.15	1.00
conditVaried	-2.12	-52.80	47.57	0.53
bandTypeExtrapolation	-9.66	-35.89	16.09	0.77
bandOrderReverse	29.28	-30.80	89.14	0.83
fbOrdinal	40.26	-19.25	99.48	0.91
conditVaried:bandTypeExtrapolation	101.40	72.52	130.37	1.00
conditVaried:bandOrderReverse	-89.61	-168.58	-13.13	0.99
bandTypeExtrapolation:bandOrderReverse	-11.29	-53.56	31.35	0.70
conditVaried:fbOrdinal	-33.43	-118.57	52.94	0.78
bandTypeExtrapolation:fbOrdinal	27.05	-10.96	65.49	0.92
bandOrderReverse:fbOrdinal	-68.09	-156.41	17.84	0.94
conditVaried:bandTypeExtrapolation:bandOrderReverse	64.61	19.80	109.63	1.00
conditVaried:bandTypeExtrapolation:fbOrdinal	-42.78	-89.10	3.82	0.96
conditVaried:bandOrderReverse:fbOrdinal	65.56	-53.40	183.10	0.87
bandTypeExtrapolation:bandOrderReverse:fbOrdinal	34.46	-19.89	88.51	0.89
conditVaried:bandTypeExtrapolation:bandOrderReverse:fbOrdinal	-69.97	-135.85	-3.96	0.98

Code

ce_bmm1 <- plot(conditional_effects(bmm_combo_testBT_dist3),points=FALSE,plot=FALSE)
wrap_plots(ce_bmm1)

dist ~ condit * bandType * bandOrder * fb + (1 | id) + (1 | bandInt)

Code

# dist ~ condit * bandType * bandOrder * fb + (1 | id) + (1 | bandInt)
bmm_combo_testBT_dist2<- readRDS(paste0(here::here("data/model_cache/brms","combo_TeDistBand_test6_RF_2_4_5000_0.94_14_Band_test6_RF_25437.rds")))

mted5 <- as.data.frame(describe_posterior(bmm_combo_testBT_dist2, centrality = "Mean"))[, c(1,2,4,5,6)]
colnames(mted5) <- c("Term", "Estimate","95% CrI Lower", "95% CrI Upper", "pd")

mted5 |> mutate(across(where(is.numeric), \(x) round(x, 2))) |>
  tibble::remove_rownames() |> 
  mutate(Term = stringr::str_remove(Term, "b_")) |> kable(booktabs = TRUE, caption=(bmm_combo_testBT_dist2$formula[1]))

dist ~ condit * bandType * bandOrder * fb + (1 | id) + (1 | bandInt)
Term	Estimate	95% CrI Lower	95% CrI Upper	pd
Intercept	218.19	143.92	288.36	1.00
conditVaried	-1.11	-51.46	48.60	0.52
bandTypeExtrapolation	-9.24	-35.42	17.02	0.76
bandOrderReverse	30.19	-28.56	86.72	0.84
fbOrdinal	41.44	-16.83	100.84	0.92
conditVaried:bandTypeExtrapolation	101.14	72.70	129.45	1.00
conditVaried:bandOrderReverse	-91.21	-165.93	-13.21	0.99
bandTypeExtrapolation:bandOrderReverse	-12.11	-53.77	29.59	0.72
conditVaried:fbOrdinal	-33.01	-113.59	46.80	0.79
bandTypeExtrapolation:fbOrdinal	26.60	-11.27	64.33	0.92
bandOrderReverse:fbOrdinal	-70.04	-154.19	13.95	0.95
conditVaried:bandTypeExtrapolation:bandOrderReverse	65.30	20.37	110.21	1.00
conditVaried:bandTypeExtrapolation:fbOrdinal	-42.23	-88.30	3.79	0.96
conditVaried:bandOrderReverse:fbOrdinal	65.45	-54.03	181.29	0.87
bandTypeExtrapolation:bandOrderReverse:fbOrdinal	35.32	-18.02	89.89	0.90
conditVaried:bandTypeExtrapolation:bandOrderReverse:fbOrdinal	-70.89	-137.60	-4.83	0.98

Code

ce_bmm1 <- plot(conditional_effects(bmm_combo_testBT_dist2),points=FALSE,plot=FALSE)
wrap_plots(ce_bmm1)

dist ~ condit * bandType * bandOrder * fb + (1 | id * bandInt)

Code

# dist ~ condit * bandType * bandOrder * fb + (1 | id * bandInt)
bmm_combo_testBT_dist4<- readRDS(paste0(here::here("data/model_cache/brms","combo_TeDistBt_test6_RF_int_2_8000_0.94_13_Bt_test6_RF_int4808.rds")))

mted9 <- as.data.frame(describe_posterior(bmm_combo_testBT_dist4, centrality = "Mean"))[, c(1,2,4,5,6)]
colnames(mted9) <- c("Term", "Estimate","95% CrI Lower", "95% CrI Upper", "pd")

mted9 |> mutate(across(where(is.numeric), \(x) round(x, 2))) |>
  tibble::remove_rownames() |> 
  mutate(Term = stringr::str_remove(Term, "b_")) |> kable(booktabs = TRUE, caption=(bmm_combo_testBT_dist4$formula[1]))

dist ~ condit * bandType * bandOrder * fb + (1 | id * bandInt)
Term	Estimate	95% CrI Lower	95% CrI Upper	pd
Intercept	213.95	138.03	285.77	1.00
conditVaried	1.57	-52.63	57.54	0.51
bandTypeExtrapolation	1.25	-43.39	46.38	0.52
bandOrderReverse	26.73	-47.18	99.90	0.76
fbOrdinal	40.87	-28.66	110.50	0.88
conditVaried:bandTypeExtrapolation	91.75	40.69	142.93	1.00
conditVaried:bandOrderReverse	-86.93	-171.08	-1.71	0.98
bandTypeExtrapolation:bandOrderReverse	-21.29	-92.94	50.87	0.71
conditVaried:fbOrdinal	-32.89	-120.92	55.22	0.77
bandTypeExtrapolation:fbOrdinal	13.60	-48.78	76.13	0.66
bandOrderReverse:fbOrdinal	-71.63	-171.52	31.15	0.92
conditVaried:bandTypeExtrapolation:bandOrderReverse	73.15	-5.64	152.36	0.97
conditVaried:bandTypeExtrapolation:fbOrdinal	-30.15	-110.05	48.97	0.77
conditVaried:bandOrderReverse:fbOrdinal	69.36	-56.34	192.48	0.86
bandTypeExtrapolation:bandOrderReverse:fbOrdinal	67.39	-23.76	157.12	0.93
conditVaried:bandTypeExtrapolation:bandOrderReverse:fbOrdinal	-102.77	-215.69	10.23	0.96

Code

ce_bmm1 <- plot(conditional_effects(bmm_combo_testBT_dist4),points=FALSE,plot=FALSE)
wrap_plots(ce_bmm1)

dist ~ condit * bandType * bandOrder * fb + bandInt + (1 | id)

Code

# dist ~ condit * bandType * bandOrder * fb + (1 | id) + (1 | bandInt)
bmm_combo_testBT_dist3<- readRDS(paste0(here::here("data/model_cache/brms","combo_TeDistBand_test6_fixBandInt_RF_1_4_5000_0.94_14_Band_test6_fixBandInt_RF_10858.rds")))

mted5 <- as.data.frame(describe_posterior(bmm_combo_testBT_dist3, centrality = "Mean"))[, c(1,2,4,5,6)]
colnames(mted5) <- c("Term", "Estimate","95% CrI Lower", "95% CrI Upper", "pd")

mted5 |> mutate(across(where(is.numeric), \(x) round(x, 2))) |>
  tibble::remove_rownames() |> 
  mutate(Term = stringr::str_remove(Term, "b_")) |> kable(booktabs = TRUE, caption=(bmm_combo_testBT_dist3$formula[1]))

dist ~ condit * bandType * bandOrder * fb + bandInt + (1 | id)
Term	Estimate	95% CrI Lower	95% CrI Upper	pd
Intercept	207.20	168.97	245.78	1.00
conditVaried	42.62	-6.68	91.45	0.96
bandTypeExtrapolation	16.24	-7.68	40.81	0.90
bandOrderReverse	-10.08	-68.95	49.91	0.64
fbOrdinal	39.23	-20.79	97.66	0.90
bandInt	-0.02	-0.03	-0.01	1.00
conditVaried:bandTypeExtrapolation	45.25	16.68	74.29	1.00
conditVaried:bandOrderReverse	-80.96	-158.95	-2.68	0.98
bandTypeExtrapolation:bandOrderReverse	45.64	7.41	84.36	0.99
conditVaried:fbOrdinal	-30.79	-110.29	49.26	0.77
bandTypeExtrapolation:fbOrdinal	27.26	-10.81	66.45	0.92
bandOrderReverse:fbOrdinal	-66.90	-154.53	19.05	0.93
conditVaried:bandTypeExtrapolation:bandOrderReverse	67.94	23.37	113.09	1.00
conditVaried:bandTypeExtrapolation:fbOrdinal	-42.98	-91.17	3.58	0.96
conditVaried:bandOrderReverse:fbOrdinal	63.08	-54.57	182.68	0.85
bandTypeExtrapolation:bandOrderReverse:fbOrdinal	35.08	-22.23	90.81	0.89
conditVaried:bandTypeExtrapolation:bandOrderReverse:fbOrdinal	-70.38	-138.05	-1.13	0.98

Code

ce_bmm1 <- plot(conditional_effects(bmm_combo_testBT_dist3),points=FALSE,plot=FALSE)
wrap_plots(ce_bmm1)

vx ~ condit * bandType * bandOrder * fb + (1 + bandType | id)

Code

# vx ~ condit * bandType * bandOrder * fb + (1 + bandType | id)
bmm_combo_testBT<- readRDS(paste0(here::here("data/model_cache/brms","combo_TeVxBt_test6__4_5000_0.93_12_Bt_test6_3033.rds")))

mted3 <- as.data.frame(describe_posterior(bmm_combo_testBT, centrality = "Mean"))[, c(1,2,4,5,6)]
colnames(mted3) <- c("Term", "Estimate","95% CrI Lower", "95% CrI Upper", "pd")

mted3 |> mutate(across(where(is.numeric), \(x) round(x, 2))) |>
  tibble::remove_rownames() |> 
  mutate(Term = stringr::str_remove(Term, "b_")) |> kable(booktabs = TRUE, caption=(bmm_combo_testBT$formula[1]))

vx ~ condit * bandType * bandOrder * fb + (1 + bandType | id)
Term	Estimate	95% CrI Lower	95% CrI Upper	pd
Intercept	1006.75	947.60	1067.18	1.00
conditVaried	167.24	84.29	249.31	1.00
bandTypeExtrapolation	-241.09	-309.10	-173.08	1.00
bandOrderReverse	-274.23	-368.69	-179.03	1.00
fbOrdinal	4.84	-92.33	98.77	0.54
conditVaried:bandTypeExtrapolation	-161.31	-253.24	-71.39	1.00
conditVaried:bandOrderReverse	-337.34	-465.68	-209.79	1.00
bandTypeExtrapolation:bandOrderReverse	470.18	363.86	575.32	1.00
conditVaried:fbOrdinal	-118.92	-251.26	16.32	0.96
bandTypeExtrapolation:fbOrdinal	71.23	-35.26	177.28	0.91
bandOrderReverse:fbOrdinal	38.34	-100.45	179.61	0.71
conditVaried:bandTypeExtrapolation:bandOrderReverse	387.75	243.41	529.99	1.00
conditVaried:bandTypeExtrapolation:fbOrdinal	43.02	-105.25	191.99	0.71
conditVaried:bandOrderReverse:fbOrdinal	41.08	-152.15	237.75	0.66
bandTypeExtrapolation:bandOrderReverse:fbOrdinal	-128.62	-284.77	27.32	0.94
conditVaried:bandTypeExtrapolation:bandOrderReverse:fbOrdinal	102.72	-118.70	318.92	0.82

Code

ce_bmm1 <- plot(conditional_effects(bmm_combo_testBT),points=FALSE,plot=FALSE)
wrap_plots(ce_bmm1)