library(brms)
## Loading required package: Rcpp
## Loading required package: ggplot2
## Loading 'brms' package (version 2.7.0). Useful instructions
## can be found by typing help('brms'). A more detailed introduction
## to the package is available through vignette('brms_overview').
## Run theme_set(theme_default()) to use the default bayesplot theme.
library(insight)
b1
## Family: gaussian
## Links: mu = identity; sigma = identity
## Formula: neg_c_7 ~ e42dep + c12hour + c172code
## Data: efc (Number of observations: 834)
## Samples: 1 chains, each with iter = 500; warmup = 250; thin = 1;
## total post-warmup samples = 250
##
## Population-Level Effects:
## Estimate Est.Error l-95% CI u-95% CI Eff.Sample Rhat
## Intercept 8.92 0.51 8.00 9.95 132 1.00
## e42dep2 1.17 0.52 0.15 2.10 116 1.00
## e42dep3 2.43 0.50 1.42 3.37 125 1.00
## e42dep4 4.00 0.52 2.97 5.10 138 1.00
## c12hour 0.01 0.00 0.00 0.01 250 1.00
## c172code2 0.10 0.29 -0.45 0.73 167 1.00
## c172code3 0.61 0.37 -0.09 1.43 151 1.03
##
## Family Specific Parameters:
## Estimate Est.Error l-95% CI u-95% CI Eff.Sample Rhat
## sigma 3.61 0.10 3.42 3.82 224 1.00
##
## Samples were drawn using sampling(NUTS). For each parameter, Eff.Sample
## is a crude measure of effective sample size, and Rhat is the potential
## scale reduction factor on split chains (at convergence, Rhat = 1).
find_formula(b1)
## $conditional
## neg_c_7 ~ e42dep + c12hour + c172code
## <environment: 0x0000000037fe53b0>
find_terms(b1)
## $response
## [1] "neg_c_7"
##
## $conditional
## [1] "e42dep" "c12hour" "c172code"
head(get_data(b1))
## neg_c_7 e42dep c12hour c172code
## 1 12 3 16 2
## 2 20 3 148 2
## 3 11 3 70 1
## 4 10 4 168 2
## 5 12 4 168 2
## 6 19 4 16 2
b2
## Family: gaussian
## Links: mu = identity; sigma = identity
## Formula: neg_c_7 ~ e42dep + c12hour + c172code + (1 | e15relat)
## Data: efc (Number of observations: 834)
## Samples: 1 chains, each with iter = 500; warmup = 250; thin = 1;
## total post-warmup samples = 250
##
## Group-Level Effects:
## ~e15relat (Number of levels: 8)
## Estimate Est.Error l-95% CI u-95% CI Eff.Sample Rhat
## sd(Intercept) 0.66 0.43 0.08 1.83 81 1.00
##
## Population-Level Effects:
## Estimate Est.Error l-95% CI u-95% CI Eff.Sample Rhat
## Intercept 8.81 0.59 7.62 9.90 169 1.00
## e42dep2 1.07 0.50 0.12 2.02 160 1.00
## e42dep3 2.28 0.51 1.31 3.26 169 1.00
## e42dep4 3.85 0.54 2.82 4.87 142 1.00
## c12hour 0.01 0.00 -0.00 0.01 250 1.00
## c172code2 0.15 0.33 -0.50 0.77 104 1.02
## c172code3 0.67 0.40 -0.11 1.39 120 1.02
##
## Family Specific Parameters:
## Estimate Est.Error l-95% CI u-95% CI Eff.Sample Rhat
## sigma 3.58 0.09 3.43 3.76 250 1.01
##
## Samples were drawn using sampling(NUTS). For each parameter, Eff.Sample
## is a crude measure of effective sample size, and Rhat is the potential
## scale reduction factor on split chains (at convergence, Rhat = 1).
find_formula(b2)
## $conditional
## neg_c_7 ~ e42dep + c12hour + c172code
## <environment: 0x000000003bb9b520>
##
## $random
## ~1 | e15relat
## <environment: 0x000000003bbc9c08>
find_terms(b2)
## $response
## [1] "neg_c_7"
##
## $conditional
## [1] "e42dep" "c12hour" "c172code"
##
## $random
## [1] "e15relat"
head(get_data(b2))
## neg_c_7 e42dep c12hour c172code e15relat
## 1 12 3 16 2 2
## 2 20 3 148 2 2
## 3 11 3 70 1 1
## 4 10 4 168 2 1
## 5 12 4 168 2 2
## 6 19 4 16 2 2
b3
## Warning: The model has not converged (some Rhats are > 1.1). Do not analyse the results!
## We recommend running more iterations and/or setting stronger priors.
## Warning: There were 149 divergent transitions after warmup. Increasing adapt_delta above 0.8 may help.
## See http://mc-stan.org/misc/warnings.html#divergent-transitions-after-warmup
## Family: gaussian
## Links: mu = identity; sigma = identity
## Formula: mpg ~ wt + (1 | cyl) + (1 + wt | gear)
## Data: mtcars (Number of observations: 32)
## Samples: 1 chains, each with iter = 500; warmup = 250; thin = 1;
## total post-warmup samples = 250
##
## Group-Level Effects:
## ~cyl (Number of levels: 3)
## Estimate Est.Error l-95% CI u-95% CI Eff.Sample Rhat
## sd(Intercept) 4.41 1.26 3.07 6.71 7 1.34
##
## ~gear (Number of levels: 3)
## Estimate Est.Error l-95% CI u-95% CI Eff.Sample Rhat
## sd(Intercept) 7.74 1.61 4.14 10.12 8 1.27
## sd(wt) 5.08 1.04 1.59 6.08 15 1.00
## cor(Intercept,wt) -0.43 0.26 -0.84 0.49 48 1.08
##
## Population-Level Effects:
## Estimate Est.Error l-95% CI u-95% CI Eff.Sample Rhat
## Intercept 26.47 2.11 25.24 34.67 12 1.07
## wt -4.98 0.51 -5.80 -3.40 24 1.10
##
## Family Specific Parameters:
## Estimate Est.Error l-95% CI u-95% CI Eff.Sample Rhat
## sigma 2.61 0.15 2.51 3.18 43 1.00
##
## Samples were drawn using sampling(NUTS). For each parameter, Eff.Sample
## is a crude measure of effective sample size, and Rhat is the potential
## scale reduction factor on split chains (at convergence, Rhat = 1).
find_formula(b3)
## $conditional
## mpg ~ wt
## <environment: 0x000000003d7d1d68>
##
## $random
## $random[[1]]
## ~1 | cyl
## <environment: 0x000000003d80f660>
##
## $random[[2]]
## ~1 + wt | gear
## <environment: 0x000000003d818c88>
find_terms(b3)
## $response
## [1] "mpg"
##
## $conditional
## [1] "wt"
##
## $random
## [1] "cyl" "gear"
head(get_data(b3))
## mpg wt cyl gear
## Mazda RX4 21.0 2.620 6 4
## Mazda RX4 Wag 21.0 2.875 6 4
## Datsun 710 22.8 2.320 4 4
## Hornet 4 Drive 21.4 3.215 6 3
## Hornet Sportabout 18.7 3.440 8 3
## Valiant 18.1 3.460 6 3
b4
## Family: gaussian
## Links: mu = identity; sigma = identity
## Formula: mpg ~ wt + cyl + gear
## Data: mtcars (Number of observations: 32)
## Samples: 1 chains, each with iter = 500; warmup = 250; thin = 1;
## total post-warmup samples = 250
##
## Population-Level Effects:
## Estimate Est.Error l-95% CI u-95% CI Eff.Sample Rhat
## Intercept 42.46 5.01 32.47 52.50 250 1.00
## wt -3.47 0.84 -5.16 -1.68 139 1.00
## cyl -1.49 0.41 -2.23 -0.71 146 1.00
## gear -0.53 0.85 -2.22 1.09 250 1.00
##
## Family Specific Parameters:
## Estimate Est.Error l-95% CI u-95% CI Eff.Sample Rhat
## sigma 2.71 0.35 2.12 3.47 213 1.00
##
## Samples were drawn using sampling(NUTS). For each parameter, Eff.Sample
## is a crude measure of effective sample size, and Rhat is the potential
## scale reduction factor on split chains (at convergence, Rhat = 1).
find_formula(b4)
## $conditional
## mpg ~ wt + cyl + gear
## <environment: 0x000000003eaed740>
find_terms(b4)
## $response
## [1] "mpg"
##
## $conditional
## [1] "wt" "cyl" "gear"
head(get_data(b4))
## mpg wt cyl gear
## Mazda RX4 21.0 2.620 6 4
## Mazda RX4 Wag 21.0 2.875 6 4
## Datsun 710 22.8 2.320 4 4
## Hornet 4 Drive 21.4 3.215 6 3
## Hornet Sportabout 18.7 3.440 8 3
## Valiant 18.1 3.460 6 3
b5
## Family: gaussian
## Links: mu = identity; sigma = identity
## Formula: Reaction ~ Days + (1 + Days | Subject)
## Data: sleepstudy (Number of observations: 180)
## Samples: 4 chains, each with iter = 2000; warmup = 1000; thin = 1;
## total post-warmup samples = 4000
##
## Group-Level Effects:
## ~Subject (Number of levels: 18)
## Estimate Est.Error l-95% CI u-95% CI Eff.Sample Rhat
## sd(Intercept) 27.00 6.70 15.88 42.11 1792 1.00
## sd(Days) 6.63 1.57 4.15 10.18 1465 1.00
## cor(Intercept,Days) 0.09 0.30 -0.49 0.67 1012 1.00
##
## Population-Level Effects:
## Estimate Est.Error l-95% CI u-95% CI Eff.Sample Rhat
## Intercept 251.37 7.35 236.96 265.93 1629 1.00
## Days 10.46 1.69 7.12 13.79 1341 1.00
##
## Family Specific Parameters:
## Estimate Est.Error l-95% CI u-95% CI Eff.Sample Rhat
## sigma 25.89 1.53 23.10 29.13 4000 1.00
##
## Samples were drawn using sampling(NUTS). For each parameter, Eff.Sample
## is a crude measure of effective sample size, and Rhat is the potential
## scale reduction factor on split chains (at convergence, Rhat = 1).
find_formula(b5)
## $conditional
## Reaction ~ Days
## <environment: 0x0000000036ea5538>
##
## $random
## ~1 + Days | Subject
## <environment: 0x0000000036f08300>
find_terms(b5)
## $response
## [1] "Reaction"
##
## $conditional
## [1] "Days"
##
## $random
## [1] "Subject"
head(get_data(b5))
## Reaction Days Subject
## 1 249.5600 0 308
## 2 258.7047 1 308
## 3 250.8006 2 308
## 4 321.4398 3 308
## 5 356.8519 4 308
## 6 414.6901 5 308
b6
## Family: gaussian
## Links: mu = identity; sigma = identity
## Formula: Reaction ~ Days + (1 | grp/subgrp) + (1 | Subject)
## Data: sleepstudy (Number of observations: 180)
## Samples: 1 chains, each with iter = 500; warmup = 250; thin = 1;
## total post-warmup samples = 250
##
## Group-Level Effects:
## ~grp (Number of levels: 5)
## Estimate Est.Error l-95% CI u-95% CI Eff.Sample Rhat
## sd(Intercept) 7.91 6.21 0.47 24.29 79 1.00
##
## ~grp:subgrp (Number of levels: 71)
## Estimate Est.Error l-95% CI u-95% CI Eff.Sample Rhat
## sd(Intercept) 3.91 2.78 0.21 9.60 110 1.00
##
## ~Subject (Number of levels: 18)
## Estimate Est.Error l-95% CI u-95% CI Eff.Sample Rhat
## sd(Intercept) 38.68 6.99 28.26 53.51 97 1.00
##
## Population-Level Effects:
## Estimate Est.Error l-95% CI u-95% CI Eff.Sample Rhat
## Intercept 251.62 11.78 225.97 272.81 38 1.07
## Days 10.67 0.82 8.95 12.31 545 1.00
##
## Family Specific Parameters:
## Estimate Est.Error l-95% CI u-95% CI Eff.Sample Rhat
## sigma 30.61 1.51 27.86 33.71 205 1.00
##
## Samples were drawn using sampling(NUTS). For each parameter, Eff.Sample
## is a crude measure of effective sample size, and Rhat is the potential
## scale reduction factor on split chains (at convergence, Rhat = 1).
find_formula(b6)
## $conditional
## Reaction ~ Days
## <environment: 0x000000003b26efc0>
##
## $random
## $random[[1]]
## ~1 | subgrp:grp
## <environment: 0x000000003b2ac850>
##
## $random[[2]]
## ~1 | grp
## <environment: 0x000000003b2b7eb0>
##
## $random[[3]]
## ~1 | Subject
## <environment: 0x000000003b2bd7b8>
find_terms(b6)
## $response
## [1] "Reaction"
##
## $conditional
## [1] "Days"
##
## $random
## [1] "subgrp" "grp" "Subject"
head(get_data(b6))
## Reaction Days grp Subject subgrp grp:subgrp
## 1 249.5600 0 2 308 7 2_7
## 2 258.7047 1 2 308 6 2_6
## 3 250.8006 2 4 308 9 4_9
## 4 321.4398 3 4 308 12 4_12
## 5 356.8519 4 2 308 6 2_6
## 6 414.6901 5 1 308 6 1_6
b7
## Family: MV(gaussian, gaussian, gaussian)
## Links: mu = identity; sigma = identity
## mu = identity; sigma = identity
## mu = identity; sigma = identity
## Formula: c82cop1 ~ c161sex + e42dep
## c83cop2 ~ c161sex + e42dep
## c84cop3 ~ c161sex + e42dep
## Data: efc (Number of observations: 899)
## Samples: 1 chains, each with iter = 500; warmup = 250; thin = 1;
## total post-warmup samples = 250
##
## Population-Level Effects:
## Estimate Est.Error l-95% CI u-95% CI Eff.Sample Rhat
## c82cop1_Intercept 3.41 0.08 3.24 3.55 156 1.00
## c83cop2_Intercept 1.51 0.09 1.35 1.68 176 1.00
## c84cop3_Intercept 1.16 0.12 0.91 1.38 114 1.00
## c82cop1_c161sex2 -0.06 0.05 -0.15 0.04 250 1.00
## c82cop1_e42dep2 -0.11 0.08 -0.25 0.06 158 1.00
## c82cop1_e42dep3 -0.29 0.08 -0.44 -0.13 148 1.00
## c82cop1_e42dep4 -0.34 0.08 -0.48 -0.19 160 1.00
## c83cop2_c161sex2 0.06 0.06 -0.04 0.17 250 1.00
## c83cop2_e42dep2 0.21 0.09 0.02 0.39 133 1.02
## c83cop2_e42dep3 0.50 0.09 0.32 0.67 155 1.00
## c83cop2_e42dep4 0.73 0.09 0.54 0.90 152 1.00
## c84cop3_c161sex2 -0.03 0.07 -0.15 0.10 250 1.00
## c84cop3_e42dep2 0.23 0.12 0.01 0.46 102 1.00
## c84cop3_e42dep3 0.49 0.11 0.27 0.68 102 1.00
## c84cop3_e42dep4 0.81 0.12 0.60 1.02 100 1.00
##
## Family Specific Parameters:
## Estimate Est.Error l-95% CI u-95% CI Eff.Sample Rhat
## sigma_c82cop1 0.57 0.01 0.55 0.60 250 1.00
## sigma_c83cop2 0.69 0.02 0.65 0.72 250 1.00
## sigma_c84cop3 0.83 0.02 0.80 0.87 250 1.00
##
## Residual Correlations:
## Estimate Est.Error l-95% CI u-95% CI Eff.Sample
## rescor(c82cop1,c83cop2) -0.35 0.03 -0.41 -0.29 243
## rescor(c82cop1,c84cop3) -0.17 0.03 -0.23 -0.10 250
## rescor(c83cop2,c84cop3) 0.31 0.03 0.25 0.38 250
## Rhat
## rescor(c82cop1,c83cop2) 1.00
## rescor(c82cop1,c84cop3) 1.00
## rescor(c83cop2,c84cop3) 1.00
##
## Samples were drawn using sampling(NUTS). For each parameter, Eff.Sample
## is a crude measure of effective sample size, and Rhat is the potential
## scale reduction factor on split chains (at convergence, Rhat = 1).
find_formula(b7)
## $c82cop1
## $c82cop1$conditional
## c82cop1 ~ c161sex + e42dep
## <environment: 0x000000003dd8b780>
##
##
## $c83cop2
## $c83cop2$conditional
## c83cop2 ~ c161sex + e42dep
## <environment: 0x000000003dda9588>
##
##
## $c84cop3
## $c84cop3$conditional
## c84cop3 ~ c161sex + e42dep
## <environment: 0x000000003ddc8170>
##
##
## attr(,"is_mv")
## [1] "1"
find_terms(b7)
## $response
## c82cop1 c83cop2 c84cop3
## "c82cop1" "c83cop2" "c84cop3"
##
## $c82cop1
## $c82cop1$conditional
## [1] "c161sex" "e42dep"
##
##
## $c83cop2
## $c83cop2$conditional
## [1] "c161sex" "e42dep"
##
##
## $c84cop3
## $c84cop3$conditional
## [1] "c161sex" "e42dep"
head(get_data(b7))
## c82cop1 c161sex e42dep c83cop2 c84cop3
## 1 3 2 3 2 2
## 2 3 2 3 3 3
## 3 2 1 3 2 1
## 4 4 1 4 1 3
## 5 3 2 4 2 1
## 6 2 1 4 2 3
b8
## Family: MV(gaussian, gaussian)
## Links: mu = identity; sigma = identity
## mu = identity; sigma = identity
## Formula: neg_c_7 ~ e42dep + c12hour + c172code
## c12hour ~ c172code
## Data: efc (Number of observations: 834)
## Samples: 1 chains, each with iter = 500; warmup = 250; thin = 1;
## total post-warmup samples = 250
##
## Population-Level Effects:
## Estimate Est.Error l-95% CI u-95% CI Eff.Sample Rhat
## negc7_Intercept 8.91 0.53 7.89 9.94 168 1.00
## c12hour_Intercept 50.02 3.71 42.40 56.46 250 1.00
## negc7_e42dep2 1.16 0.55 0.19 2.18 143 1.01
## negc7_e42dep3 2.44 0.54 1.38 3.59 138 1.02
## negc7_e42dep4 4.02 0.58 2.91 5.16 130 1.00
## negc7_c12hour 0.01 0.00 0.00 0.01 250 1.00
## negc7_c172code2 0.09 0.35 -0.57 0.67 250 1.00
## negc7_c172code3 0.63 0.45 -0.26 1.43 250 1.00
## c12hour_c172code2 -8.30 4.28 -16.00 0.07 250 1.00
## c12hour_c172code3 -15.45 5.54 -26.15 -5.42 250 1.00
##
## Family Specific Parameters:
## Estimate Est.Error l-95% CI u-95% CI Eff.Sample Rhat
## sigma_negc7 3.59 0.09 3.45 3.79 250 1.01
## sigma_c12hour 50.37 1.37 47.75 53.23 250 1.00
##
## Samples were drawn using sampling(NUTS). For each parameter, Eff.Sample
## is a crude measure of effective sample size, and Rhat is the potential
## scale reduction factor on split chains (at convergence, Rhat = 1).
find_formula(b8)
## $negc7
## $negc7$conditional
## neg_c_7 ~ e42dep + c12hour + c172code
## <environment: 0x00000000400225a0>
##
##
## $c12hour
## $c12hour$conditional
## c12hour ~ c172code
## <environment: 0x0000000040043880>
##
##
## attr(,"is_mv")
## [1] "1"
find_terms(b8)
## $response
## negc7 c12hour
## "neg_c_7" "c12hour"
##
## $negc7
## $negc7$conditional
## [1] "e42dep" "c12hour" "c172code"
##
##
## $c12hour
## $c12hour$conditional
## [1] "c172code"
head(get_data(b8))
## neg_c_7 e42dep c12hour c172code
## 1 12 3 16 2
## 2 20 3 148 2
## 3 11 3 70 1
## 4 10 4 168 2
## 5 12 4 168 2
## 6 19 4 16 2
b9
## Family: zero_inflated_poisson
## Links: mu = log; zi = identity
## Formula: count ~ persons + child + camper
## Data: zinb (Number of observations: 250)
## Samples: 1 chains, each with iter = 500; warmup = 250; thin = 1;
## total post-warmup samples = 250
##
## Population-Level Effects:
## Estimate Est.Error l-95% CI u-95% CI Eff.Sample Rhat
## Intercept -1.00 0.17 -1.31 -0.69 174 1.00
## persons 0.87 0.04 0.78 0.97 179 1.00
## child -1.36 0.09 -1.53 -1.20 156 1.01
## camper 0.79 0.09 0.61 0.94 169 1.01
##
## Family Specific Parameters:
## Estimate Est.Error l-95% CI u-95% CI Eff.Sample Rhat
## zi 0.41 0.04 0.31 0.48 134 1.02
##
## Samples were drawn using sampling(NUTS). For each parameter, Eff.Sample
## is a crude measure of effective sample size, and Rhat is the potential
## scale reduction factor on split chains (at convergence, Rhat = 1).
find_formula(b9)
## $conditional
## count ~ persons + child + camper
## <environment: 0x00000000412c3b40>
find_terms(b9)
## $response
## [1] "count"
##
## $conditional
## [1] "persons" "child" "camper"
head(get_data(b9))
## count persons child camper
## 1 0 1 0 0
## 2 0 1 0 1
## 3 0 1 0 0
## 4 0 2 1 1
## 5 1 1 0 0
## 6 0 4 2 1
b10
## Family: zero_inflated_poisson
## Links: mu = log; zi = logit
## Formula: count ~ persons + child + camper
## zi ~ child + camper
## Data: zinb (Number of observations: 250)
## Samples: 1 chains, each with iter = 500; warmup = 250; thin = 1;
## total post-warmup samples = 250
##
## Population-Level Effects:
## Estimate Est.Error l-95% CI u-95% CI Eff.Sample Rhat
## Intercept -1.07 0.16 -1.35 -0.77 218 1.00
## zi_Intercept -0.58 0.33 -1.19 0.06 250 1.00
## persons 0.90 0.04 0.81 0.97 250 1.00
## child -1.18 0.09 -1.36 -1.00 136 1.00
## camper 0.75 0.10 0.58 0.96 250 1.00
## zi_child 1.24 0.27 0.74 1.77 250 1.00
## zi_camper -0.61 0.36 -1.22 0.02 203 1.00
##
## Samples were drawn using sampling(NUTS). For each parameter, Eff.Sample
## is a crude measure of effective sample size, and Rhat is the potential
## scale reduction factor on split chains (at convergence, Rhat = 1).
find_formula(b10)
## $conditional
## count ~ persons + child + camper
## <environment: 0x00000000368f9ab0>
##
## $zero_inflated
## ~child + camper
## <environment: 0x00000000368f9ab0>
find_terms(b10)
## $response
## [1] "count"
##
## $conditional
## [1] "persons" "child" "camper"
##
## $zero_inflated
## [1] "child" "camper"
head(get_data(b10))
## count persons child camper
## 1 0 1 0 0
## 2 0 1 0 1
## 3 0 1 0 0
## 4 0 2 1 1
## 5 1 1 0 0
## 6 0 4 2 1
b11
## Warning: There were 4 divergent transitions after warmup. Increasing adapt_delta above 0.8 may help.
## See http://mc-stan.org/misc/warnings.html#divergent-transitions-after-warmup
## Family: zero_inflated_poisson
## Links: mu = log; zi = logit
## Formula: count ~ child + camper + (1 | persons)
## zi ~ child + camper
## Data: zinb (Number of observations: 250)
## Samples: 1 chains, each with iter = 500; warmup = 250; thin = 1;
## total post-warmup samples = 250
##
## Group-Level Effects:
## ~persons (Number of levels: 4)
## Estimate Est.Error l-95% CI u-95% CI Eff.Sample Rhat
## sd(Intercept) 2.09 1.13 0.79 5.33 85 1.01
##
## Population-Level Effects:
## Estimate Est.Error l-95% CI u-95% CI Eff.Sample Rhat
## Intercept 1.09 1.12 -1.27 2.98 53 1.02
## zi_Intercept -0.65 0.34 -1.32 -0.01 244 1.00
## child -1.18 0.09 -1.36 -0.99 250 1.00
## camper 0.74 0.10 0.56 0.92 250 1.00
## zi_child 1.38 0.32 0.76 1.97 205 1.00
## zi_camper -0.74 0.39 -1.57 -0.00 241 1.00
##
## Samples were drawn using sampling(NUTS). For each parameter, Eff.Sample
## is a crude measure of effective sample size, and Rhat is the potential
## scale reduction factor on split chains (at convergence, Rhat = 1).
find_formula(b11)
## $conditional
## count ~ child + camper
## <environment: 0x000000003910b9b0>
##
## $random
## ~1 | persons
## <environment: 0x0000000039136638>
##
## $zero_inflated
## ~child + camper
## <environment: 0x000000003910b9b0>
find_terms(b11)
## $response
## [1] "count"
##
## $conditional
## [1] "child" "camper"
##
## $random
## [1] "persons"
##
## $zero_inflated
## [1] "child" "camper"
head(get_data(b11))
## count child camper persons
## 1 0 0 0 1
## 2 0 0 1 1
## 3 0 0 0 1
## 4 0 1 1 2
## 5 1 0 0 1
## 6 0 2 1 4
b12
## Warning: There were 8 divergent transitions after warmup. Increasing adapt_delta above 0.8 may help.
## See http://mc-stan.org/misc/warnings.html#divergent-transitions-after-warmup
## Family: zero_inflated_poisson
## Links: mu = log; zi = logit
## Formula: count ~ child + camper + (1 | persons)
## zi ~ child + camper + (1 | persons)
## Data: zinb (Number of observations: 250)
## Samples: 1 chains, each with iter = 500; warmup = 250; thin = 1;
## total post-warmup samples = 250
##
## Group-Level Effects:
## ~persons (Number of levels: 4)
## Estimate Est.Error l-95% CI u-95% CI Eff.Sample Rhat
## sd(Intercept) 1.58 0.67 0.71 3.13 134 1.00
## sd(zi_Intercept) 1.74 0.90 0.60 3.80 88 1.00
##
## Population-Level Effects:
## Estimate Est.Error l-95% CI u-95% CI Eff.Sample Rhat
## Intercept 1.34 0.78 -0.36 3.36 68 1.00
## zi_Intercept -0.64 0.83 -2.30 0.98 110 1.01
## child -1.15 0.10 -1.36 -0.99 137 1.02
## camper 0.72 0.09 0.56 0.91 144 1.00
## zi_child 1.89 0.32 1.35 2.55 242 1.00
## zi_camper -0.84 0.36 -1.54 -0.22 208 1.00
##
## Samples were drawn using sampling(NUTS). For each parameter, Eff.Sample
## is a crude measure of effective sample size, and Rhat is the potential
## scale reduction factor on split chains (at convergence, Rhat = 1).
find_formula(b12)
## $conditional
## count ~ child + camper
## <environment: 0x000000003b815388>
##
## $random
## ~1 | persons
## <environment: 0x000000003b84c550>
##
## $zero_inflated
## ~child + camper
## <environment: 0x000000003b815388>
##
## $zero_inflated_random
## ~1 | persons
## <environment: 0x000000003b88a058>
find_terms(b12)
## $response
## [1] "count"
##
## $conditional
## [1] "child" "camper"
##
## $random
## [1] "persons"
##
## $zero_inflated
## [1] "child" "camper"
##
## $zero_inflated_random
## [1] "persons"
head(get_data(b12))
## count child camper persons
## 1 0 0 0 1
## 2 0 0 1 1
## 3 0 0 0 1
## 4 0 1 1 2
## 5 1 0 0 1
## 6 0 2 1 4
b13
## Warning: There were 2 divergent transitions after warmup. Increasing adapt_delta above 0.8 may help.
## See http://mc-stan.org/misc/warnings.html#divergent-transitions-after-warmup
## Family: MV(gaussian, gaussian, gaussian)
## Links: mu = identity; sigma = identity
## mu = identity; sigma = identity
## mu = identity; sigma = identity
## Formula: c82cop1 ~ c161sex + e42dep + (1 | e15relat)
## c83cop2 ~ c161sex + e42dep + (1 | e15relat)
## c84cop3 ~ c161sex + e42dep + (1 | e15relat)
## Data: efc (Number of observations: 898)
## Samples: 1 chains, each with iter = 500; warmup = 250; thin = 1;
## total post-warmup samples = 250
##
## Group-Level Effects:
## ~e15relat (Number of levels: 8)
## Estimate Est.Error l-95% CI u-95% CI Eff.Sample Rhat
## sd(c82cop1_Intercept) 0.09 0.07 0.01 0.27 58 1.00
## sd(c83cop2_Intercept) 0.05 0.04 0.00 0.14 112 1.00
## sd(c84cop3_Intercept) 0.09 0.07 0.01 0.27 98 1.00
##
## Population-Level Effects:
## Estimate Est.Error l-95% CI u-95% CI Eff.Sample Rhat
## c82cop1_Intercept 3.41 0.08 3.27 3.58 89 1.00
## c83cop2_Intercept 1.50 0.09 1.34 1.69 111 1.00
## c84cop3_Intercept 1.13 0.11 0.92 1.34 110 1.00
## c82cop1_c161sex2 -0.06 0.04 -0.14 0.03 190 1.00
## c82cop1_e42dep2 -0.11 0.07 -0.25 0.04 102 1.00
## c82cop1_e42dep3 -0.30 0.07 -0.45 -0.16 108 1.00
## c82cop1_e42dep4 -0.34 0.08 -0.50 -0.20 112 1.00
## c83cop2_c161sex2 0.07 0.05 -0.05 0.17 202 1.00
## c83cop2_e42dep2 0.20 0.09 0.00 0.37 132 1.00
## c83cop2_e42dep3 0.49 0.09 0.30 0.67 115 1.00
## c83cop2_e42dep4 0.72 0.10 0.51 0.87 117 1.00
## c84cop3_c161sex2 -0.02 0.06 -0.14 0.11 250 1.00
## c84cop3_e42dep2 0.25 0.11 0.04 0.47 124 1.00
## c84cop3_e42dep3 0.49 0.10 0.28 0.68 95 1.00
## c84cop3_e42dep4 0.81 0.10 0.63 1.02 106 1.00
##
## Family Specific Parameters:
## Estimate Est.Error l-95% CI u-95% CI Eff.Sample Rhat
## sigma_c82cop1 0.57 0.01 0.55 0.60 250 1.00
## sigma_c83cop2 0.69 0.02 0.65 0.72 250 1.00
## sigma_c84cop3 0.83 0.02 0.78 0.87 250 1.00
##
## Residual Correlations:
## Estimate Est.Error l-95% CI u-95% CI Eff.Sample
## rescor(c82cop1,c83cop2) -0.35 0.03 -0.41 -0.29 240
## rescor(c82cop1,c84cop3) -0.17 0.03 -0.23 -0.11 250
## rescor(c83cop2,c84cop3) 0.31 0.03 0.25 0.37 250
## Rhat
## rescor(c82cop1,c83cop2) 1.01
## rescor(c82cop1,c84cop3) 1.00
## rescor(c83cop2,c84cop3) 1.00
##
## Samples were drawn using sampling(NUTS). For each parameter, Eff.Sample
## is a crude measure of effective sample size, and Rhat is the potential
## scale reduction factor on split chains (at convergence, Rhat = 1).
find_formula(b13)
## $c82cop1
## $c82cop1$conditional
## c82cop1 ~ c161sex + e42dep
## <environment: 0x0000000040a20f20>
##
## $c82cop1$random
## ~1 | e15relat
## <environment: 0x0000000040482d88>
##
##
## $c83cop2
## $c83cop2$conditional
## c83cop2 ~ c161sex + e42dep
## <environment: 0x0000000040338d50>
##
## $c83cop2$random
## ~1 | e15relat
## <environment: 0x0000000040157458>
##
##
## $c84cop3
## $c84cop3$conditional
## c84cop3 ~ c161sex + e42dep
## <environment: 0x000000003ff29c50>
##
## $c84cop3$random
## ~1 | e15relat
## <environment: 0x0000000038639020>
##
##
## attr(,"is_mv")
## [1] "1"
find_terms(b13)
## $response
## c82cop1 c83cop2 c84cop3
## "c82cop1" "c83cop2" "c84cop3"
##
## $c82cop1
## $c82cop1$conditional
## [1] "c161sex" "e42dep"
##
## $c82cop1$random
## [1] "e15relat"
##
##
## $c83cop2
## $c83cop2$conditional
## [1] "c161sex" "e42dep"
##
## $c83cop2$random
## [1] "e15relat"
##
##
## $c84cop3
## $c84cop3$conditional
## [1] "c161sex" "e42dep"
##
## $c84cop3$random
## [1] "e15relat"
head(get_data(b13))
## c82cop1 c161sex e42dep e15relat c83cop2 c84cop3
## 1 3 2 3 2 2 2
## 2 3 2 3 2 3 3
## 3 2 1 3 1 2 1
## 4 4 1 4 1 1 3
## 5 3 2 4 2 2 1
## 6 2 1 4 2 2 3
b14
## Warning: There were 3 divergent transitions after warmup. Increasing adapt_delta above 0.8 may help.
## See http://mc-stan.org/misc/warnings.html#divergent-transitions-after-warmup
## Family: MV(gaussian, gaussian)
## Links: mu = identity; sigma = identity
## mu = identity; sigma = identity
## Formula: neg_c_7 ~ e42dep + c12hour + c172code + (1 | ID | e15relat)
## c12hour ~ c172code + (1 | ID | e15relat)
## Data: efc (Number of observations: 834)
## Samples: 1 chains, each with iter = 500; warmup = 250; thin = 1;
## total post-warmup samples = 250
##
## Group-Level Effects:
## ~e15relat (Number of levels: 8)
## Estimate Est.Error l-95% CI
## sd(negc7_Intercept) 0.63 0.38 0.16
## sd(c12hour_Intercept) 28.69 8.29 16.06
## cor(negc7_Intercept,c12hour_Intercept) 0.54 0.33 -0.14
## u-95% CI Eff.Sample Rhat
## sd(negc7_Intercept) 1.61 85 1.00
## sd(c12hour_Intercept) 45.59 86 1.00
## cor(negc7_Intercept,c12hour_Intercept) 0.97 88 1.00
##
## Population-Level Effects:
## Estimate Est.Error l-95% CI u-95% CI Eff.Sample Rhat
## negc7_Intercept 8.64 0.59 7.52 9.89 142 1.00
## c12hour_Intercept 37.88 9.78 19.34 61.45 128 1.00
## negc7_e42dep2 1.19 0.52 0.11 2.23 136 1.00
## negc7_e42dep3 2.38 0.54 1.21 3.40 145 1.00
## negc7_e42dep4 3.97 0.55 2.89 5.02 121 1.00
## negc7_c12hour 0.01 0.00 0.00 0.01 250 1.00
## negc7_c172code2 0.20 0.30 -0.30 0.83 250 1.00
## negc7_c172code3 0.74 0.41 -0.09 1.51 250 1.00
## c12hour_c172code2 -0.73 3.82 -8.60 6.27 250 1.00
## c12hour_c172code3 -7.10 4.43 -16.48 1.36 250 1.00
##
## Family Specific Parameters:
## Estimate Est.Error l-95% CI u-95% CI Eff.Sample Rhat
## sigma_negc7 3.58 0.09 3.42 3.75 250 1.00
## sigma_c12hour 46.29 1.05 44.43 48.58 250 1.00
##
## Samples were drawn using sampling(NUTS). For each parameter, Eff.Sample
## is a crude measure of effective sample size, and Rhat is the potential
## scale reduction factor on split chains (at convergence, Rhat = 1).
find_formula(b14)
## $negc7
## $negc7$conditional
## neg_c_7 ~ e42dep + c12hour + c172code
## <environment: 0x0000000042e9b368>
##
## $negc7$random
## ~1 | ID | e15relat
## <environment: 0x0000000042ed03a8>
##
##
## $c12hour
## $c12hour$conditional
## c12hour ~ c172code
## <environment: 0x0000000042ee44c0>
##
## $c12hour$random
## ~1 | ID | e15relat
## <environment: 0x0000000042f06e00>
##
##
## attr(,"is_mv")
## [1] "1"
find_terms(b14)
## $response
## negc7 c12hour
## "neg_c_7" "c12hour"
##
## $negc7
## $negc7$conditional
## [1] "e42dep" "c12hour" "c172code"
##
## $negc7$random
## [1] "ID | e15relat"
##
##
## $c12hour
## $c12hour$conditional
## [1] "c172code"
##
## $c12hour$random
## [1] "ID | e15relat"
head(get_data(b14))
## neg_c_7 e42dep c12hour c172code e15relat
## 1 12 3 16 2 2
## 2 20 3 148 2 2
## 3 11 3 70 1 1
## 4 10 4 168 2 1
## 5 12 4 168 2 2
## 6 19 4 16 2 2
b15
## Family: cumulative
## Links: mu = logit; disc = identity
## Formula: rating ~ temp + contact + (1 | bottle) + (1 | judge)
## Data: wine (Number of observations: 72)
## Samples: 1 chains, each with iter = 500; warmup = 250; thin = 1;
## total post-warmup samples = 250
##
## Group-Level Effects:
## ~bottle (Number of levels: 8)
## Estimate Est.Error l-95% CI u-95% CI Eff.Sample Rhat
## sd(Intercept) 0.50 0.45 0.04 1.66 65 1.01
##
## ~judge (Number of levels: 9)
## Estimate Est.Error l-95% CI u-95% CI Eff.Sample Rhat
## sd(Intercept) 1.51 0.73 0.66 3.05 60 1.01
##
## Population-Level Effects:
## Estimate Est.Error l-95% CI u-95% CI Eff.Sample Rhat
## Intercept[1] -1.75 0.89 -3.46 -0.06 108 1.00
## Intercept[2] 1.62 0.83 0.03 3.42 140 1.00
## Intercept[3] 4.53 1.03 2.72 6.89 159 1.00
## Intercept[4] 6.61 1.12 4.63 9.00 163 1.00
## tempwarm 3.38 0.87 1.97 5.57 131 1.01
## contactyes 1.96 0.66 0.91 3.26 168 1.00
##
## Samples were drawn using sampling(NUTS). For each parameter, Eff.Sample
## is a crude measure of effective sample size, and Rhat is the potential
## scale reduction factor on split chains (at convergence, Rhat = 1).
find_formula(b15)
## $conditional
## rating ~ temp + contact
## <environment: 0x000000004171f218>
##
## $random
## $random[[1]]
## ~1 | bottle
## <environment: 0x00000000416dd400>
##
## $random[[2]]
## ~1 | judge
## <environment: 0x00000000416cfe58>
find_terms(b15)
## $response
## [1] "rating"
##
## $conditional
## [1] "temp" "contact"
##
## $random
## [1] "bottle" "judge"
head(get_data(b15))
## rating temp contact bottle judge
## 1 2 cold no 1 1
## 2 3 cold no 2 1
## 3 3 cold yes 3 1
## 4 4 cold yes 4 1
## 5 4 warm no 5 1
## 6 4 warm no 6 1