Main source of income
table(DF_anly$ECCOCC)##
## Business Other Farming Worker
## 77 7 496 111Nyabyeya quantative data exploration
This walk through proceeds through the following steps:
- Examining the meta data
- Exploring the individual variables and instruments (including patterns within instruments)
- Explore selected bi-variate associations
- Perform model diagnostics, following the WAMBS check list (not shown yet)
- Examine the patterns within the scenarios
Examining the metadata
Survey duration
### Identify training and test observations (for later) ###
train = sort(sample(nrow(DF_anly), nrow(DF_anly)*2/3))
###### Meta-data exploration ######
### Survey duration ###
# Mean duration
hist(as.numeric(DF_anly$Duration))
mean(DF_anly$Duration)## Time difference of 44.67338 minssd(DF_anly$Duration)## [1] 17.60006# Remove one outlier - must have forgot to submit the survey
subset_duration <- DF_anly[which(DF_anly$Duration < 100), ]
# Duration by enumerator
subset_duration %>%
group_by(Int_ID) %>%
summarise(Mean = mean(Duration, na.rm=TRUE))## # A tibble: 4 x 2
## Int_ID Mean
## <chr> <drtn>
## 1 7vX 45.07692 mins
## 2 8vZ 41.90230 mins
## 3 oKF 42.81081 mins
## 4 YEX 44.52174 mins# Duration by date and enumerator
subset_duration %>%
group_by(Date, Int_ID) %>%
dplyr::summarise(Mean = mean(Duration, na.rm=TRUE)) -> meanda## `summarise()` has grouped output by 'Date'. You can override using the `.groups` argument.# Plot variation by enumerator over time
ggplot(meanda, aes(x=as.Date(Date,"%d/%m/%Y"), y=as.numeric(Mean), group = Int_ID, colour = Int_ID)) +
geom_line() +
xlab("") 
Patterns of missing data
# The observations and variables missing data
vis_miss(DF_anly)+
theme(axis.text.x = element_text(angle = 90)) + theme(text = element_text(size=8))
# Patterns of missingness between variables
gg_miss_upset(DF_anly, nsets = n_var_miss(DF_anly))
# High-level pattern of missingness
gg_miss_var(DF_anly, show_pct = TRUE) + theme(text = element_text(size=8))
# Specific percentages of missing data
round(table(is.na(DF_anly$SOCJOY))[2]/ nrow(DF_anly)*100,1)## TRUE
## 4.6Exploring the individual variables and instruments
Survey language
table(DF_anly$LANG)##
## English Kiswahili Runyoro
## 96 502 97Location
table(DF_anly$LOCB)##
## EWAF KADONE KADTWO KARO KYEM MARA NONE NTC NTWO NYAB NYAK
## 41 52 55 154 41 82 50 92 61 36 31Gender
table(DF_anly$SEX)##
## Female Male
## 414 281Age
hist(DF_anly$DOBB)
Education
table(DF_anly$EDUCAT)##
## No.educ Start.pri Fin.prim Start.sec Fin.sec Bey.sec
## 63 400 65 81 63 23Marital status
table(DF_anly$MSTATUS)##
## Div.wid Mar.pol Single
## 117 532 46Health
table(DF_anly$Health)##
## V.bad Bad Fair Good V.good
## 31 75 364 159 66Number of children
hist(DF_anly$HOUCHI)
Number of adults
hist(DF_anly$HOUADU)
Asset ownership
##
## 0 1
## 11444 10096ggplot(data=assets_DF_l, aes(x=key, y=value)) +
geom_bar(stat="identity") + coord_flip()## Warning: Removed 5 rows containing missing values (position_stack).
### Asset indexes constructed from the asset ownership variables ###
# Excluding some assets
exclude <- c("BNSGAS" , "BNSFRID", "BNSTANK", "BNSELEC" , "BNSCAR" , "BNSTELV" , "BNSMOTO" , "BNSBATT")
assets_sub <- setdiff(assets, exclude)
# Conduct the logistic principal component analysis (following https://cran.r-project.org/web/packages/logisticPCA/vignettes/logisticPCA.html)
# k = number of principal components to return
# m = value to approximate the saturated model
# ms = the different approximations to the saturated model m to try
# Optimise the number of components to extract based on how strongly it is associated with subjective financial strain (1-10 components)
association_strengh <- data.frame(K = NA, X.Intercept. = NA, Strain.L = NA, Strain.Q = NA, Strain.C = NA)
for (i in seq_along(1:10)){
# Decide which m to use with cross validation
logpca_cv = cv.lpca(DF_stat.list[[1]][assets_sub], ks = i, ms = 1:10)
# Fit the logistic PCA using the minimum m
logpca_asset = logisticPCA(DF_stat.list[[1]][assets_sub], k = i, m = which.min(logpca_cv))
# Association between the first component score and subjective financial strain
strain_asset <- data.frame(Asset = logpca_asset$PCs[,1], Strain = DF_stat.list[[1]]$Strain)
association_strengh <- rbind(association_strengh, data.frame(K = i, t(coef(lm(Asset ~ Strain, strain_asset)))))
}
# Select the k with that yielded the highest association with subjective financial strain
association_strengh <- association_strengh[c(2:10),]
K_op <- association_strengh[association_strengh$Strain.L == max(association_strengh$Strain.L, na.rm = T),]
K_op$K## [1] 3# Decide which m to use with cross validation using k that yielded the strongest estimate
logpca_cv = cv.lpca(DF_stat.list[[1]][assets_sub], ks = K_op$K, ms = 1:10)
plot(logpca_cv)
# Fit the logistic PCA using the minimum m
logpca_asset = logisticPCA(DF_stat.list[[1]][assets_sub], k = K_op$K, m = which.min(logpca_cv))
# Summary
logpca_asset## 695 rows and 23 columns
## Rank 3 solution with m = 6
##
## 42% of deviance explained
## 127 iterations to converge# Plot against the sum of asset ownership, split into quantiles
survey_response_group <- cut(
rowSums(DF_stat.list[[1]][assets_sub]),
breaks = quantile(rowSums(DF_stat.list[[1]][assets_sub]), c(0, 0.25, 0.5, 0.75, 1)),
labels = c("Poorest", "Poor", "Wealthy", "Wealthiest"),
right = FALSE,
include.lowest = TRUE)
plot(logpca_asset, type = "scores") + geom_point(aes(colour = survey_response_group)) 
# Plot PCA against subjective financial strain (remember the sign of PCA scores can be flipped)
plot(logpca_asset, type = "scores") + geom_point(aes(colour = DF_stat.list[[1]]$Strain)) 
# Association between the first component score and subjective financial strain
strain_asset <- data.frame(Asset = logpca_asset$PCs[,1], Strain = DF_stat.list[[1]]$Strain)
summary(lm(Asset ~ Strain, strain_asset))##
## Call:
## lm(formula = Asset ~ Strain, data = strain_asset)
##
## Residuals:
## Min 1Q Median 3Q Max
## -22.1987 -6.0206 -0.3054 5.8007 30.2354
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -7.1525 0.4039 -17.707 <2e-16 ***
## Strain.L 7.5731 0.8930 8.480 <2e-16 ***
## Strain.Q -0.4553 0.8079 -0.564 0.573
## Strain.C 0.7290 0.7126 1.023 0.307
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 9.354 on 691 degrees of freedom
## Multiple R-squared: 0.1095, Adjusted R-squared: 0.1056
## F-statistic: 28.32 on 3 and 691 DF, p-value: < 2.2e-16# Repeat this model for each of the ten imputed datasets - extracting the 1st component in each
for (i in seq_along(1:length(DF_stat.list))) {
# Fit the logistic PCA using the minimum m and optimal k
logpca_asset <- logisticPCA(DF_stat.list[[i]][assets_sub], k = K_op$K, m = which.min(logpca_cv))
DF_stat.list[[i]]$Assets_index <- logpca_asset$PCs[,1]
# Switching the sign of the Asset index since it currently has an unintuitive direction
DF_stat.list[[i]]$Assets_index <- DF_stat.list[[i]]$Assets_index*-1
# Scale and centrer
DF_stat.list[[i]]$Assets_index <- scale(DF_stat.list[[i]]$Assets_index, center = T, scale = T)
# Create the economic poverty variable - simply the inverse of the asset index
DF_stat.list[[i]]$Economic_poverty <- DF_stat.list[[i]]$Assets_index*-1
}
# Check that switching the sign worked
ggplot() + geom_point(aes(x = survey_response_group, y = DF_stat.list[[1]]$Assets_index)) 
ggplot() + geom_point(aes(x = survey_response_group, y = DF_stat.list[[1]]$Economic_poverty)) 
Subjective financial strain
table(DF_anly$Strain)##
## N.hard B.hard Hard V.hard
## 66 173 200 255Subjective land size
table(DF_anly$Land_sub)##
## Very.small Small Middle Large Very.large
## 236 246 173 27 6Estimated land size (ha)
hist(DF_anly$Land_size) # All data 
hist(DF_anly[which(DF_anly$Land_size <1),]$Land_size) # Under 1 hectare 
Growing sugar cane?
table(DF_anly$GROWSUG)##
## No Yes
## 586 107Land size instrument
# Likert scaled plot
HH::likert(Variable~.,land_prop, positive.order=TRUE,as.percent = TRUE,
main="Land instrument",
xlab="Percentage", ylab="Variable")
# Parallel analysis (with training data)
fa.parallel(land_DF[complete.cases(land_DF),][train,] , cor = "poly", fm="wls", fa="fa", main = "Parallel analysis")
## Parallel analysis suggests that the number of factors = 1 and the number of components = NA# Factor analysis, with polychoric correlation, WLS estimator, and oblimin rotation
fa_mod1 <- efaUnrotate(land_DF[train,], estimator = "WLS", nf = 1)
# Factor loadings
summary(fa_mod1)## lavaan 0.6-8 ended normally after 19 iterations
##
## Estimator WLS
## Optimization method NLMINB
## Number of model parameters 12
##
## Used Total
## Number of observations 450 463
##
## Model Test User Model:
##
## Test statistic 28.014
## Degrees of freedom 9
## P-value (Chi-square) 0.001
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Expected
## Information saturated (h1) model Unstructured
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|)
## factor1 =~
## LANDBIG (l1_1) 0.534 0.045 11.867 0.000
## LANDBAS (l2_1) 0.931 0.047 19.700 0.000
## LANDSTR (l3_1) 0.639 0.056 11.315 0.000
## LANDWEL (l4_1) 0.836 0.052 16.161 0.000
## LANDGOO (l5_1) 0.775 0.051 15.118 0.000
## LANDSMA (l6_1) 0.494 0.057 8.618 0.000
##
## Variances:
## Estimate Std.Err z-value P(>|z|)
## factor1 1.000
## .LANDBIG 0.620 0.046 13.455 0.000
## .LANDBAS 0.554 0.067 8.244 0.000
## .LANDSTR 0.767 0.093 8.272 0.000
## .LANDWELL 0.614 0.065 9.390 0.000
## .LANDGOOD 0.667 0.061 10.872 0.000
## .LANDSMALL 1.012 0.082 12.366 0.000# RMSEA
fitmeasures(fa_mod1)["rmsea"]## rmsea
## 0.06859556This suggests good model fit, when using the training data.
# Confirmatory analysis with test data
CFA_1 <- 'factor.1 =~ LANDBIG + LANDBAS + LANDSTR + LANDWELL + LANDGOOD + LANDSMALL'
CFA_mod1 <- cfa(CFA_1, data = land_DF[-train,])
# Factor loadings
summary(CFA_mod1)## lavaan 0.6-8 ended normally after 25 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 12
##
## Used Total
## Number of observations 224 232
##
## Model Test User Model:
##
## Test statistic 32.527
## Degrees of freedom 9
## P-value (Chi-square) 0.000
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Expected
## Information saturated (h1) model Structured
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|)
## factor.1 =~
## LANDBIG 1.000
## LANDBAS 1.374 0.209 6.581 0.000
## LANDSTR 0.979 0.173 5.652 0.000
## LANDWELL 1.661 0.239 6.947 0.000
## LANDGOOD 1.363 0.207 6.575 0.000
## LANDSMALL 0.747 0.170 4.398 0.000
##
## Variances:
## Estimate Std.Err z-value P(>|z|)
## .LANDBIG 0.684 0.073 9.429 0.000
## .LANDBAS 0.671 0.081 8.316 0.000
## .LANDSTR 0.738 0.077 9.561 0.000
## .LANDWELL 0.579 0.086 6.759 0.000
## .LANDGOOD 0.664 0.080 8.330 0.000
## .LANDSMALL 0.988 0.097 10.144 0.000
## factor.1 0.276 0.072 3.852 0.000# RMSEA - this suggests poor model fit
fitmeasures(CFA_mod1)["rmsea"]## rmsea
## 0.1080277This suggests poor model fit, when using the test data. This is probably because of the small sample size.
# Factor analysis, with polychoric correlation, WLS estimator, and oblimin rotation using all data
fa_mod1b <- efaUnrotate(land_DF, estimator = "WLS", nf = 1)
# Factor loadings - much better than the above
summary(fa_mod1b)## lavaan 0.6-8 ended normally after 18 iterations
##
## Estimator WLS
## Optimization method NLMINB
## Number of model parameters 12
##
## Used Total
## Number of observations 674 695
##
## Model Test User Model:
##
## Test statistic 38.372
## Degrees of freedom 9
## P-value (Chi-square) 0.000
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Expected
## Information saturated (h1) model Unstructured
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|)
## factor1 =~
## LANDBIG (l1_1) 0.544 0.038 14.299 0.000
## LANDBAS (l2_1) 0.850 0.041 20.949 0.000
## LANDSTR (l3_1) 0.634 0.045 14.184 0.000
## LANDWEL (l4_1) 0.825 0.042 19.834 0.000
## LANDGOO (l5_1) 0.725 0.042 17.174 0.000
## LANDSMA (l6_1) 0.498 0.046 10.926 0.000
##
## Variances:
## Estimate Std.Err z-value P(>|z|)
## factor1 1.000
## .LANDBIG 0.632 0.041 15.316 0.000
## .LANDBAS 0.621 0.056 10.995 0.000
## .LANDSTR 0.712 0.072 9.849 0.000
## .LANDWELL 0.601 0.054 11.196 0.000
## .LANDGOOD 0.686 0.050 13.657 0.000
## .LANDSMALL 0.965 0.067 14.392 0.000# RMSEA
fitmeasures(fa_mod1b)["rmsea"]## rmsea
## 0.06963674This suggests good model fit, when using all the data.
# Inspect fit statistics
M2(Land_GRM, type = "C2", calcNULL = FALSE)## M2 df p RMSEA RMSEA_5 RMSEA_95 SRMSR
## stats 57.37776 9 4.277959e-09 0.08800794 0.06702113 0.1103414 0.06047478
## TLI CFI
## stats 0.9405491 0.9643294# Item information curve (or 'information and trace lines') and information and SE
plot(Land_GRM, type = 'infotrace', facet_items= F)
plot(Land_GRM, type = 'infoSE', facet_items= F) 
# Item characteristic curve (or 'item scoring traceline plots')
plot(Land_GRM, type = 'trace') 
# Extract ten sets of plausible values and inspect them
Land_PV <- fscores(Land_GRM, plausible.draws = 10)
Land_PV_combined <- data.frame(Iteration = "1", Land = Land_PV[[1]] )
for (i in seq_along(2:length(Land_PV))){
Land_PV_combined <- rbind(Land_PV_combined, data.frame(Iteration = as.factor(i+1), Land = Land_PV[[i+1]] ))
}
# Plot
ggplot(Land_PV_combined, aes(x=Land, color=Iteration)) +
geom_density()
Forest dependency instrument
# Likert scaled plot
HH::likert(Variable~.,fore_prop, positive.order=TRUE,as.percent = TRUE,
main="Forest instrument",
xlab="Percentage", ylab="Variable")
# Parallel analysis
fa.parallel(fore_DF[complete.cases(fore_DF),][train,], cor = "poly", fm="wls", fa="fa", main = "Parallel analysis")
## Parallel analysis suggests that the number of factors = 2 and the number of components = NA# Factor analysis, with polychoric correlation, WLS estimator, and oblimin rotation
fa_mod2 <- efaUnrotate(fore_DF[train,], estimator = "WLS", nf = 2)
# Factor loadings
summary(fa_mod2)## lavaan 0.6-8 ended normally after 87 iterations
##
## Estimator WLS
## Optimization method NLMINB
## Number of model parameters 21
##
## Used Total
## Number of observations 460 463
##
## Model Test User Model:
##
## Test statistic 34.093
## Degrees of freedom 8
## P-value (Chi-square) 0.000
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Expected
## Information saturated (h1) model Unstructured
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|)
## factor1 =~
## FORELOT (l1_1) 0.316 0.074 4.278 0.000
## FOREMON (l2_1) 0.961 0.065 14.685 0.000
## FOREBUY (l3_1) 0.954 0.066 14.482 0.000
## FOREBAD (l4_1) 0.624 0.082 7.641 0.000
## FORFOOD (l5_1) 0.787 0.061 12.836 0.000
## FORESUR (l6_1) 0.697 0.073 9.516 0.000
## FORENO (l7_1) 0.768 0.060 12.815 0.000
## factor2 =~
## FORELOT (l1_2) 0.601 0.054 11.218 0.000
## FOREMON (l2_2) -0.392 0.058 -6.746 0.000
## FOREBUY (l3_2) -0.369 0.057 -6.489 0.000
## FOREBAD (l4_2) 0.678 0.058 11.708 0.000
## FORFOOD (l5_2) -0.217 0.049 -4.396 0.000
## FORESUR (l6_2) 0.632 0.054 11.640 0.000
## FORENO (l7_2) -0.202 0.057 -3.565 0.000
##
## Covariances:
## Estimate Std.Err z-value P(>|z|)
## factor1 ~~
## factor2 0.000
##
## Variances:
## Estimate Std.Err z-value P(>|z|)
## factor1 1.000
## factor2 1.000
## .FORELOT 0.634 0.073 8.725 0.000
## .FOREMON 0.354 0.079 4.510 0.000
## .FOREBUY 0.477 0.091 5.236 0.000
## .FOREBAD 0.821 0.104 7.907 0.000
## .FORFOOD 0.681 0.079 8.609 0.000
## .FORESUR 0.753 0.096 7.869 0.000
## .FORENO 0.867 0.086 10.114 0.000
##
## Constraints:
## |Slack|
## 0-(1_2*1_1+2_2*2_1+3_2*3_1+4_2*4_1+5_2*5_ 0.000# RMSEA
fitmeasures(fa_mod2)["rmsea"]## rmsea
## 0.0842969# Confirmatory analysis with test data
CFA_2 <- '
factor.1 =~ FOREMON + FOREBUY + FOREBAD + FORFOOD + FORESUR + FORENO
factor.2 =~ FORELOT + FOREBAD + FORESUR
'
CFA_mod2 <- cfa(CFA_2, data = fore_DF[-train,])
# Factor loadings
summary(CFA_mod2)## lavaan 0.6-8 ended normally after 28 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 17
##
## Used Total
## Number of observations 231 232
##
## Model Test User Model:
##
## Test statistic 25.141
## Degrees of freedom 11
## P-value (Chi-square) 0.009
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Expected
## Information saturated (h1) model Structured
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|)
## factor.1 =~
## FOREMON 1.000
## FOREBUY 1.204 0.104 11.547 0.000
## FOREBAD 0.328 0.097 3.395 0.001
## FORFOOD 1.009 0.102 9.908 0.000
## FORESUR 0.307 0.094 3.265 0.001
## FORENO 1.081 0.102 10.560 0.000
## factor.2 =~
## FORELOT 1.000
## FOREBAD 0.561 0.144 3.909 0.000
## FORESUR 0.879 0.216 4.071 0.000
##
## Covariances:
## Estimate Std.Err z-value P(>|z|)
## factor.1 ~~
## factor.2 0.116 0.070 1.654 0.098
##
## Variances:
## Estimate Std.Err z-value P(>|z|)
## .FOREMON 0.667 0.078 8.497 0.000
## .FOREBUY 0.470 0.076 6.161 0.000
## .FOREBAD 1.138 0.118 9.671 0.000
## .FORFOOD 0.848 0.095 8.969 0.000
## .FORESUR 0.753 0.141 5.333 0.000
## .FORENO 0.735 0.088 8.355 0.000
## .FORELOT 0.487 0.167 2.925 0.003
## factor.1 0.799 0.131 6.118 0.000
## factor.2 0.698 0.189 3.690 0.000# RMSEA
fitmeasures(CFA_mod2)["rmsea"]## rmsea
## 0.0745994These results suggests good model fit.
# Graded response model
Forest_GRM <- mirt(DF_stat.list[[1]][names_fore], model = CFA_2_mirt, "graded") ##
Iteration: 1, Log-Lik: -6021.173, Max-Change: 0.75574
Iteration: 2, Log-Lik: -5875.754, Max-Change: 0.75689
Iteration: 3, Log-Lik: -5807.541, Max-Change: 0.29903
Iteration: 4, Log-Lik: -5780.830, Max-Change: 0.25532
Iteration: 5, Log-Lik: -5767.929, Max-Change: 0.18657
Iteration: 6, Log-Lik: -5761.920, Max-Change: 0.16540
Iteration: 7, Log-Lik: -5758.909, Max-Change: 0.12808
Iteration: 8, Log-Lik: -5757.300, Max-Change: 0.09056
Iteration: 9, Log-Lik: -5756.411, Max-Change: 0.05654
Iteration: 10, Log-Lik: -5755.964, Max-Change: 0.04091
Iteration: 11, Log-Lik: -5755.738, Max-Change: 0.03089
Iteration: 12, Log-Lik: -5755.612, Max-Change: 0.02438
Iteration: 13, Log-Lik: -5755.474, Max-Change: 0.01420
Iteration: 14, Log-Lik: -5755.440, Max-Change: 0.00967
Iteration: 15, Log-Lik: -5755.417, Max-Change: 0.00776
Iteration: 16, Log-Lik: -5755.372, Max-Change: 0.00285
Iteration: 17, Log-Lik: -5755.366, Max-Change: 0.00164
Iteration: 18, Log-Lik: -5755.364, Max-Change: 0.00097
Iteration: 19, Log-Lik: -5755.363, Max-Change: 0.00087
Iteration: 20, Log-Lik: -5755.362, Max-Change: 0.00034
Iteration: 21, Log-Lik: -5755.362, Max-Change: 0.00017
Iteration: 22, Log-Lik: -5755.362, Max-Change: 0.00062
Iteration: 23, Log-Lik: -5755.362, Max-Change: 0.00059
Iteration: 24, Log-Lik: -5755.361, Max-Change: 0.00029
Iteration: 25, Log-Lik: -5755.361, Max-Change: 0.00013
Iteration: 26, Log-Lik: -5755.361, Max-Change: 0.00031
Iteration: 27, Log-Lik: -5755.361, Max-Change: 0.00040
Iteration: 28, Log-Lik: -5755.361, Max-Change: 0.00020
Iteration: 29, Log-Lik: -5755.361, Max-Change: 0.00043
Iteration: 30, Log-Lik: -5755.361, Max-Change: 0.00015
Iteration: 31, Log-Lik: -5755.361, Max-Change: 0.00034
Iteration: 32, Log-Lik: -5755.361, Max-Change: 0.00014
Iteration: 33, Log-Lik: -5755.361, Max-Change: 0.00030
Iteration: 34, Log-Lik: -5755.361, Max-Change: 0.00011
Iteration: 35, Log-Lik: -5755.361, Max-Change: 0.00026
Iteration: 36, Log-Lik: -5755.361, Max-Change: 0.00046
Iteration: 37, Log-Lik: -5755.361, Max-Change: 0.00018
Iteration: 38, Log-Lik: -5755.361, Max-Change: 0.00036
Iteration: 39, Log-Lik: -5755.361, Max-Change: 0.00014
Iteration: 40, Log-Lik: -5755.361, Max-Change: 0.00032
Iteration: 41, Log-Lik: -5755.360, Max-Change: 0.00012
Iteration: 42, Log-Lik: -5755.360, Max-Change: 0.00025
Iteration: 43, Log-Lik: -5755.360, Max-Change: 0.00010
Iteration: 44, Log-Lik: -5755.360, Max-Change: 0.00024
Iteration: 45, Log-Lik: -5755.360, Max-Change: 0.00008# Inspect fit statistics
M2(Forest_GRM, type = "C2", calcNULL = FALSE)## M2 df p RMSEA RMSEA_5 RMSEA_95 SRMSR
## stats 74.74743 12 4.101008e-11 0.08680158 0.06849493 0.106091 0.04771463
## TLI CFI
## stats 0.9409071 0.9662326# Item information curve (or 'information and trace lines') and information and SE
plot(Forest_GRM, type = 'infotrace', facet_items= F)
# plot(Forest_GRM, type = 'infoSE', facet_items= F)
# Item characteristic curve (or 'item scoring traceline plots')
plot(Forest_GRM, type = 'trace', facet_items= T) 
# Extract ten sets of plausible values and inspect them
Forest_PV <- fscores(Forest_GRM, plausible.draws = 10)
Forest_PV_combined <- data.frame(Iteration = "1", Forest = Forest_PV[[1]] )
for (i in seq_along(2:length(Forest_PV))){
Forest_PV_combined <- rbind(Forest_PV_combined, data.frame(Iteration = as.factor(i+1), Forest = Forest_PV[[i+1]] ))
}
# Plot
ggplot(Forest_PV_combined, aes(x=Forest.1, color=Iteration)) +
geom_density()
ggplot(Forest_PV_combined, aes(x=Forest.2, color=Iteration)) +
geom_density()
Food Insecurity Experience Scale (FIES)
##
## 0 1
## 2134 3425# Response to each item
ggplot(data=FIES_DF_l, aes(x=factor(key, levels = rev(c(FIES_vars))), y=value)) +
geom_bar(stat="identity") + coord_flip()## Warning: Removed 1 rows containing missing values (position_stack).
# Inspect fit statistics
M2(FIES_RM_2, type = "C2", calcNULL = FALSE)## M2 df p RMSEA RMSEA_5 RMSEA_95 SRMSR TLI
## stats 125.6329 20 0 0.08723784 0.07293298 0.1020767 0.05090469 0.9594735
## CFI
## stats 0.9710525# Item information curve (or 'information and trace lines') and information and SE
plot(FIES_RM_2, type = 'infotrace', facet_items= F)
plot(FIES_RM_2, type = 'infoSE', facet_items= F) 
# Item characteristic curve (or 'item scoring traceline plots')
plot(FIES_RM_2, type = 'trace', facet_items = F) 
# Extract ten sets of plausible values and inspect them
FIES_PV <- fscores(FIES_RM_2, plausible.draws = 10)
FIES_PV_combined <- data.frame(Iteration = "1", FIES = FIES_PV[[1]] )
for (i in seq_along(2:length(FIES_PV))){
FIES_PV_combined <- rbind(FIES_PV_combined, data.frame(Iteration = as.factor(i+1), FIES = FIES_PV[[i+1]] ))
}
# Plot
ggplot(FIES_PV_combined, aes(x=FIES, color=Iteration)) +
geom_density()
Smoking
table(DF_anly$SMOKING)##
## No Yes
## 619 76Drinking days per week
table(DF_anly$Alcohol)##
## 0 1 2 3 4 5 6 7
## 561 52 42 19 5 2 1 13PHQ-8 plus ‘strong throughts’
# Likert scaled plot
HH::likert(Variable~.,PHQ8_prop, positive.order=TRUE,as.percent = TRUE,
main="PHQ-8 & strong thoughts instrument",
xlab="Percentage", ylab="Variable")
# How many crossed the diagnostic for referral? (In total, 20 agreed to be referred, and 6 actually went to the hospital.)
table(rowSums(PHQ8_DF[names_PHQ8], na.rm = T) > 17)##
## FALSE TRUE
## 655 40# Parallel analysis
fa.parallel(PHQ8_DF[complete.cases(PHQ8_DF),][train,], cor = "poly", fm="wls", fa="fa", main = "Parallel analysis")
## Parallel analysis suggests that the number of factors = 1 and the number of components = NA# Factor analysis, with polychoric correlation, WLS estimator, and oblimin rotation
fa_mod4 <- efaUnrotate(PHQ8_DF[train,], estimator = "WLS", nf = 1)
# Factor loadings
summary(fa_mod4)## lavaan 0.6-8 ended normally after 17 iterations
##
## Estimator WLS
## Optimization method NLMINB
## Number of model parameters 16
##
## Number of observations 463
##
## Model Test User Model:
##
## Test statistic 45.943
## Degrees of freedom 20
## P-value (Chi-square) 0.001
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Expected
## Information saturated (h1) model Unstructured
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|)
## factor1 =~
## PH9INTE (l1_1) 0.532 0.046 11.592 0.000
## PH9FEEL (l2_1) 0.641 0.041 15.468 0.000
## PH9TROU (l3_1) 0.435 0.050 8.665 0.000
## PH9TIRE (l4_1) 0.519 0.047 10.983 0.000
## PH9APPE (l5_1) 0.564 0.046 12.392 0.000
## PH9BADA (l6_1) 0.558 0.046 12.235 0.000
## PH9CONC (l7_1) 0.511 0.045 11.275 0.000
## PH9MOVI (l8_1) 0.429 0.051 8.402 0.000
##
## Variances:
## Estimate Std.Err z-value P(>|z|)
## factor1 1.000
## .PH9INTERST 0.725 0.052 13.971 0.000
## .PH9FEEL 0.594 0.049 12.114 0.000
## .PH9TROUBL 0.739 0.057 12.866 0.000
## .PH9TIRED 0.702 0.052 13.598 0.000
## .PH9APPETIT 0.679 0.053 12.919 0.000
## .PH9BADABT 0.685 0.055 12.349 0.000
## .PH9CONCEN 0.725 0.054 13.407 0.000
## .PH9MOVING 0.649 0.054 12.130 0.000# RMSEA
fitmeasures(fa_mod4)["rmsea"]## rmsea
## 0.0529875This suggests good model fit.
# Confirmatory analysis with test data
CFA_4 <- '
factor.1 =~ PH9INTERST + PH9FEEL + PH9TROUBL + PH9TIRED + PH9APPETIT + PH9BADABT + PH9CONCEN + PH9MOVING
'
CFA_mod4 <- cfa(CFA_4, data = PHQ8_DF[-train,])
# Factor loadings
summary(CFA_mod4)## lavaan 0.6-8 ended normally after 29 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 16
##
## Number of observations 232
##
## Model Test User Model:
##
## Test statistic 33.872
## Degrees of freedom 20
## P-value (Chi-square) 0.027
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Expected
## Information saturated (h1) model Structured
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|)
## factor.1 =~
## PH9INTERST 1.000
## PH9FEEL 1.217 0.203 5.984 0.000
## PH9TROUBL 1.002 0.193 5.189 0.000
## PH9TIRED 1.165 0.203 5.741 0.000
## PH9APPETIT 1.032 0.191 5.410 0.000
## PH9BADABT 1.435 0.232 6.193 0.000
## PH9CONCEN 1.251 0.211 5.937 0.000
## PH9MOVING 1.223 0.204 5.989 0.000
##
## Variances:
## Estimate Std.Err z-value P(>|z|)
## .PH9INTERST 0.862 0.086 9.972 0.000
## .PH9FEEL 0.617 0.068 9.108 0.000
## .PH9TROUBL 0.870 0.087 9.976 0.000
## .PH9TIRED 0.726 0.077 9.481 0.000
## .PH9APPETIT 0.769 0.078 9.816 0.000
## .PH9BADABT 0.665 0.077 8.622 0.000
## .PH9CONCEN 0.686 0.075 9.194 0.000
## .PH9MOVING 0.620 0.068 9.099 0.000
## factor.1 0.258 0.074 3.489 0.000# RMSEA - this suggests poor model fit
fitmeasures(CFA_mod4)["rmsea"]## rmsea
## 0.05467807This suggests poor model fit, so repeating the analysis with all data.
# Repeat the parallel analysis using all data
fa.parallel(PHQ8_DF[complete.cases(PHQ8_DF),], cor = "poly", fm="wls", fa="fa", main = "Parallel analysis")
## Parallel analysis suggests that the number of factors = 1 and the number of components = NA# Factor analysis, with polychoric correlation, WLS estimator, and oblimin rotation
fa_mod4b <- efaUnrotate(PHQ8_DF, estimator = "WLS", nf = 1)
# Factor loadings
summary(fa_mod4b)## lavaan 0.6-8 ended normally after 17 iterations
##
## Estimator WLS
## Optimization method NLMINB
## Number of model parameters 16
##
## Number of observations 695
##
## Model Test User Model:
##
## Test statistic 52.647
## Degrees of freedom 20
## P-value (Chi-square) 0.000
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Expected
## Information saturated (h1) model Unstructured
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|)
## factor1 =~
## PH9INTE (l1_1) 0.516 0.038 13.515 0.000
## PH9FEEL (l2_1) 0.638 0.034 18.763 0.000
## PH9TROU (l3_1) 0.484 0.041 11.727 0.000
## PH9TIRE (l4_1) 0.554 0.037 14.832 0.000
## PH9APPE (l5_1) 0.546 0.037 14.594 0.000
## PH9BADA (l6_1) 0.613 0.037 16.678 0.000
## PH9CONC (l7_1) 0.564 0.037 15.115 0.000
## PH9MOVI (l8_1) 0.496 0.042 11.772 0.000
##
## Variances:
## Estimate Std.Err z-value P(>|z|)
## factor1 1.000
## .PH9INTERST 0.773 0.043 17.963 0.000
## .PH9FEEL 0.591 0.040 14.700 0.000
## .PH9TROUBL 0.763 0.048 16.026 0.000
## .PH9TIRED 0.702 0.042 16.677 0.000
## .PH9APPETIT 0.717 0.043 16.516 0.000
## .PH9BADABT 0.711 0.045 15.626 0.000
## .PH9CONCEN 0.719 0.045 16.006 0.000
## .PH9MOVING 0.642 0.047 13.807 0.000# RMSEA - better than the above
fitmeasures(fa_mod4b)["rmsea"]## rmsea
## 0.04849806Again, the model fits well when using all data.
# Inspect fit statistics
M2(PHQ8_GRM, type = "C2", calcNULL = FALSE)## M2 df p RMSEA RMSEA_5 RMSEA_95 SRMSR
## stats 73.78763 20 4.334076e-08 0.06225102 0.04738457 0.07771271 0.05049266
## TLI CFI
## stats 0.9518307 0.9655934# Item information curve (or 'information and trace lines') and information and SE
plot(PHQ8_GRM, type = 'infotrace', facet_items= F)
plot(PHQ8_GRM, type = 'infoSE', facet_items= F) 
# Item characteristic curve (or 'item scoring traceline plots')
plot(PHQ8_GRM, type = 'trace') 
# Extract ten sets of plausible values and inspect them
PHQ8_PV <- fscores(PHQ8_GRM, plausible.draws = 10)
PHQ8_PV_combined <- data.frame(Iteration = "1", PHQ8 = PHQ8_PV[[1]] )
for (i in seq_along(2:length(PHQ8_PV))){
PHQ8_PV_combined <- rbind(PHQ8_PV_combined, data.frame(Iteration = as.factor(i+1), PHQ8 = PHQ8_PV[[i+1]] ))
}
# Plot
ggplot(PHQ8_PV_combined, aes(x=PHQ8, color=Iteration)) +
geom_density()
Distance to the nearest forest reserve
hist(DF_anly$FR.dist)
Distance to Budongo
hist(DF_anly$Budongo.dist)
Selected bi-variate associations
# Correlations between all continuous data (or ordinal variables treated as continuous).
plot_correlation(na.omit(explor_df[key_vars]), maxcat = 5L)
Gender and economic poverty
ggplot(explor_df, aes(x=as.factor(SEX), y=Economic_poverty)) +
geom_boxplot()
Gender and food insecurity
ggplot(explor_df, aes(x=as.factor(SEX), y=FIES_est)) +
geom_boxplot()
Marital status and economic poverty
ggplot(explor_df, aes(x=as.factor(MSTATUS), y=Economic_poverty)) +
geom_boxplot()
Marital status and food insecurity
ggplot(explor_df, aes(x=as.factor(MSTATUS), y=FIES_est)) +
geom_boxplot()
Community and economic poverty
ggplot(explor_df, aes(x=as.factor(LOCB), y=Economic_poverty)) +
geom_boxplot()
Community and food insecurity
ggplot(explor_df, aes(x=as.factor(LOCB), y=FIES_est)) +
geom_boxplot()
Perform model diagnostics, following the WAMBS check list
The model diagnostic steps from the WAMBS check list (Depaoli & van de Schoot 2017):
- ‘Do you understand the priors?’
- ‘Does the trace-plot exhibit convergence?’ (After running the model.)
- ‘Does convergence remain after doubling the number of iterations?’
- ‘Does the histogram have enough information?’
- ‘Do the chains exhibit a strong degree of autocorrelation?’
- ‘Does the posterior distribution make substantive sense?’
- ‘Do different specifications of the multivariate variance priors influence the results?’
- ‘Is there a notable effect of the prior when compared with noninformative priors?’
- ‘Are the results stable from a sensitivity analysis?’
- ‘Is the Bayesian way of interpreting and reporting model results used?’
1) ‘Do you understand the priors?’
# See the main text and SX for further details. The following is simply used to generate the plots used in SX.
# Function for creating plots for SX.
prior.plot <- function(type="normal", mean=0, variance=1, shape.a=1, shape.b=1, sec.min=-6, sec.max=6, step=.01, label=label) {
x <- seq(sec.min, sec.max, by = step)
# For a normally distributed prior
if (type == "normal") {
prior.d <- dnorm(x,mean = mean, sd = sqrt(variance))
}
# For a beta distributed prior
if (type == "beta") {
prior.d <- dbeta(x, shape1 = shape.a, shape2 = shape.b)
}
# Plot
df <- data.frame(x = x, prior.d = prior.d)
print(ggplot(data=df, aes(x=x, y=prior.d, group=1)) +
geom_line(size = .5) +
xlab("Beta") +
ylab("Prob. den.") +
scale_x_continuous(
labels = scales::number_format(accuracy = 0.1)))
}
# Depression ~ Food insecurity
prior.plot(type = "normal", mean = 1, variance = 1,
label="1. Depression ~ Food insecurity")
# Depression ~ Economic poverty
prior.plot(type = "normal", mean = 1, variance = 1,
label="2. Depression ~ Economic poverty")
# Food insecurity ~ Forest dependence
prior.plot(type = "beta", shape.a = 3, shape.b = 2, sec.min=0, sec.max=1,
label="3. Food insecurity ~ Forest dependence")
# Food insecurity ~ Farm size
prior.plot(type = "normal", mean = -0.5, variance = 4,
label="4. Food insecurity ~ Farm size")
# Food insecurity ~ Economic poverty
prior.plot(type = "normal", mean = 1, variance = 1,
label="5. Food insecurity ~ Economic poverty")
# Food insecurity ~ Distance for forest reserve
prior.plot(type = "normal", mean = -0.5, variance = 4,
label="6. Food insecurity ~ Distance for forest reserve")
# Economic poverty ~ Forest dependence
prior.plot(type = "beta", shape.a = 3, shape.b = 2, sec.min=0, sec.max=1,
label="7. Economic poverty ~ Forest dependence")
# Economic poverty ~ Farm size
prior.plot(type = "beta", shape.a = 2, shape.b = 3, sec.min=0, sec.max=1,
label="8. Economic poverty ~ Farm size")
# Depression ~ Age
prior.plot(type = "normal", mean = 0.5, variance = 4,
label="9. Depression ~ Age")
# Depression ~ Gender
prior.plot(type = "normal", mean = 0.5, variance = 4,
label="10. Depression ~ Gender")
# Depression ~ Education
prior.plot(type = "normal", mean = -0.5, variance = 4,
label="11. Depression ~ Education")
# Depression ~ Social support
prior.plot(type = "normal", mean = -0.5, variance = 4,
label="12. Depression ~ Social support")
# Depression ~ Divorced or widowed
prior.plot(type = "normal", mean = 0.5, variance = 4,
label="13. Depression ~ Divorced or widowed")
# Depression ~ Never married
prior.plot(type = "normal", mean = 0, variance = 9, sec.min=-9, sec.max=9,
label="14. Depression ~ Never married")
# Depression ~ General health
prior.plot(type = "beta", shape.a = 2, shape.b = 3, sec.min=0, sec.max=1,
label="15. Depression ~ General health")
# Depression ~ Alcohol consumption
prior.plot(type = "normal", mean = 0.5, variance = 4,
label="16. Depression ~ Alcohol consumption")
# Depression ~ Smoking
prior.plot(type = "normal", mean = 0.5, variance = 4,
label="17. Depression ~ Smoking")
# Depression ~ Community (all)
prior.plot(type = "normal", mean = 0, variance = 9, sec.min=-9, sec.max=9,
label="18. Depression ~ Community (all)")
2) ‘Does the trace-plot exhibit convergence?’
### Define the model and set the priors ###
# Stan uses standard deviations as the priors (rather than variance, like in Jags). See here for useful details: http://ecmerkle.github.io/blavaan/articles/prior.html
# This means we have to take the square root of the variance to get the standard deviation, which is supplied as the prior.
# Diagnostics are performed using the first of the ten imputed datasets.
# Chains
chains = 4
# Burn-in iterations
burnin <- 4000 # 4000
# Post-burn-in iterations
sample <- 4000 # 4000
# Set seed
seed = 4343
# Check - standard deviation = square root of variance
sqrt(1) # = Variance = 1
sqrt(4) # = Variance = 4
sqrt(9) # = Variance = 9
# The model and priors
model_main_1 <- '
### Main regression part
# Key variables of interest
PHQ8_est ~ prior("normal(1, 1)")*FIES_est + prior("normal(1, 1)")*Economic_poverty
FIES_est ~ prior("normal(-0.5, 2)")*Land_est + prior("normal(1, 1)")*Economic_poverty
# Distance to forest reserve
FIES_est ~ prior("normal(-0.5, 2)")*FR.dist
### Covariance
# Between socio-ecological variables
FIES_est ~~ prior("beta(3, 2)")*Forest_est.1
Economic_poverty ~~ prior("beta(3, 2)")*Forest_est.1
Economic_poverty ~~ prior("beta(2, 3)")*Land_est
# Health (treated as numeric)
PHQ8_est ~~ prior("beta(2, 3)")*Health
### Covariates
# Age
PHQ8_est ~ prior("normal(0.5, 2)")*DOBB +
# Sex
prior("normal(0.5, 2)")*SEX +
# Education (treated as numeric)
prior("normal(-0.5, 2)")*EDUCAT +
# Social support
prior("normal(-0.5, 2)")*Soci_est.1 +
# Marital status (RL = MSTATUS_Mar.pol)
prior("normal(0.5, 2)")*MSTATUS_Div.wid + prior("normal(0, 9)")*MSTATUS_Single +
# Alchohol consumption
prior("normal(0.5, 2)")*Alcohol +
# Smoking
prior("normal(0.5, 2)")*SMOKING
### Location (RL = LOCB_NTC)
PHQ8_est ~ prior("normal(0, 9)")*LOCB_EWAF + prior("normal(0, 9)")*LOCB_KADONE + prior("normal(0, 9)")*LOCB_KADTWO + prior("normal(0, 9)")*LOCB_KARO + prior("normal(0, 9)")*LOCB_KYEM + prior("normal(0, 9)")*LOCB_MARA + prior("normal(0, 9)")*LOCB_NONE + prior("normal(0, 9)")*LOCB_NTWO + prior("normal(0, 9)")*LOCB_NYAB
'
# Bayesian SEM
fit_main_1 <- bsem(model_main_1, data=DF_analy.list[[1]], fixed.x = FALSE, n.chains = chains, burnin = burnin, sample = sample, seed = seed, target = "stan", bcontrol = list(cores = 4))# Load the model (if needed)
fit_main_1 <- readRDS("fit_main_1.rds")
# Plot estimates
plot(fit_main_1, plot.type = "areas", pars = 1:26, prob = 0.90,
prob_outer = 0.95)
# Traceplots for key parameters - first ten
key_params <- 26
plot(fit_main_1, pars = 1:10, plot.type = "trace")
# Traceplots for key parameters - secound ten
plot(fit_main_1, pars = 11:20, plot.type = "trace")
# Traceplots for key parameters - remaining
plot(fit_main_1, pars = 21:key_params, plot.type = "trace")
fit_main_1_mcmc.list <- blavInspect(fit_main_1, what = "mcmc")### Not shown since it returns pages and pages of plots ###
# Geweke diagnostic
geweke.plot(fit_main_1_mcmc.list) # We expect about 5% to be out more than +/- 1.96.
# Gelman and Rubin diagnostic (rule of thumb is that everything below 1.1 is OK): https://theoreticalecology.wordpress.com/2011/12/09/mcmc-chain-analysis-and-convergence-diagnostics-with-coda-in-r/
gelman.diag(fit_main_1_mcmc.list)
gelman.plot(fit_main_1_mcmc.list)3) ‘Does convergence remain after doubling the number of iterations?’
# Double the burn-in
burnin.d <- burnin*2
# Double the post burn-in
sample.d <- sample*2
# Bayesian SEM
fit_main_2 <- bsem(model_main_1, data=DF_analy.list[[1]], fixed.x = FALSE, n.chains = chains, burnin = burnin.d, sample = sample.d, seed = seed, target = "stan", bcontrol = list(cores = 4))# Load the model (if needed)
fit_main_2 <- readRDS("fit_main_2.rds")
# Traceplots for key parameters - first ten
plot(fit_main_2, pars = 1:10, plot.type = "trace")
# Traceplots for key parameters - secound ten
plot(fit_main_2, pars = 11:20, plot.type = "trace")
# Traceplots for key parameters - remaining
plot(fit_main_2, pars = 21:key_params, plot.type = "trace")
fit_main_2_mcmc.list <- blavInspect(fit_main_2, what = "mcmc")### Not shown since it returns pages and pages of plots ###
# Geweke diagnostic
geweke.plot(fit_main_2_mcmc.list) # We expect about 5% to be out more than +/- 1.96.
# Gelman and Rubin diagnostic (rule of thumb is that everything below 1.1 is OK): https://theoreticalecology.wordpress.com/2011/12/09/mcmc-chain-analysis-and-convergence-diagnostics-with-coda-in-r/
gelman.diag(fit_main_2_mcmc.list)
gelman.plot(fit_main_2_mcmc.list)4) ‘Does the histogram have enough information?’
plot(fit_main_1 , pars = 1:key_params, plot.type = "hist")## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
5) ‘Do the chains exhibit a strong degree of autocorrelation?’
# Params 1 - 4
plot(fit_main_1, pars = 1:4, plot.type = "acf")
# Params 5 - 8
plot(fit_main_1, pars = 5:8, plot.type = "acf")
# Params 9 - 12
plot(fit_main_1, pars = 9:12, plot.type = "acf")
# Params 13 - 16
plot(fit_main_1, pars = 14:16, plot.type = "acf")
# Params 17 - 20
plot(fit_main_1, pars = 17:20, plot.type = "acf")
# Params 21 - 24
plot(fit_main_1, pars = 21:24, plot.type = "acf")
# Params 25 - 26
plot(fit_main_1, pars = 22:key_params, plot.type = "acf")
6) ‘Does the posterior distribution make substantive sense?’
# Combing the results of the three chains together
fit_1_MCMCbinded <- as.matrix(fit_main_1_mcmc.list)
# Examine the posterior for each key variable
par(mfrow = c(4,7))
for (i in seq_along(1:key_params)) {
plot(density(fit_1_MCMCbinded[,i]))
}
dev.off()## null device
## 17) ‘Do different specifications of the multivariate variance priors influence the results?’
# Examine the observed variable precision parameter (observed, because we are using plausible valued) - theta (which appears to be called 'itheta' in the manual?) - which has a default prior of gamma(1, 0.5).
dpriors(target = "stan")
# Respecify the model with a different variance associated with PHQ8_est: prior("gamma(.5, .5)")
model_main_3 <- '
### Main regression part
# Key variables of interest
PHQ8_est ~ prior("normal(1, 1)")*FIES_est + prior("normal(1, 1)")*Economic_poverty
FIES_est ~ prior("normal(-0.5, 2)")*Land_est + prior("normal(1, 1)")*Economic_poverty
# Distance to forest reserve
FIES_est ~ prior("normal(-0.5, 2)")*FR.dist
### Covariance
# Between socio-ecological variables
FIES_est ~~ prior("beta(3, 2)")*Forest_est.1
Economic_poverty ~~ prior("beta(3, 2)")*Forest_est.1
Economic_poverty ~~ prior("beta(2, 3)")*Land_est
# Health (treated as numeric)
PHQ8_est ~~ prior("beta(2, 3)")*Health
### Covariates
# Age
PHQ8_est ~ prior("normal(0.5, 2)")*DOBB +
# Sex
prior("normal(0.5, 2)")*SEX +
# Education (treated as numeric)
prior("normal(-0.5, 2)")*EDUCAT +
# Social support
prior("normal(-0.5, 2)")*Soci_est.1 +
# Marital status (RL = MSTATUS_Mar.pol)
prior("normal(0.5, 2)")*MSTATUS_Div.wid + prior("normal(0, 9)")*MSTATUS_Single +
# Alchohol consumption
prior("normal(0.5, 2)")*Alcohol +
# Smoking
prior("normal(0.5, 2)")*SMOKING
### Location (RL = LOCB_NTC)
PHQ8_est ~ prior("normal(0, 9)")*LOCB_EWAF + prior("normal(0, 9)")*LOCB_KADONE + prior("normal(0, 9)")*LOCB_KADTWO + prior("normal(0, 9)")*LOCB_KARO + prior("normal(0, 9)")*LOCB_KYEM + prior("normal(0, 9)")*LOCB_MARA + prior("normal(0, 9)")*LOCB_NONE + prior("normal(0, 9)")*LOCB_NTWO + prior("normal(0, 9)")*LOCB_NYAB
# Using a alternative to the default
PHQ8_est ~~ gamma(1,.05)[sd]*PHQ8_est
'
# Bayesian SEM
fit_main_3 <- bsem(model_main_3, data=DF_analy.list[[1]], fixed.x = FALSE, n.chains = chains, burnin = burnin, sample = sample, seed = seed, target = "stan", bcontrol = list(cores = 4))# Load the model (if needed)
fit_main_3 <- readRDS("fit_main_3.rds")
# Calculate the bias associated with using this different specification
fit_main_1_sum <- data.frame(summary(fit_main_1))[,1:2]## blavaan (0.3-15) results of 4000 samples after 4000 adapt/burnin iterations
##
## Number of observations 695
##
## Number of missing patterns 1
##
## Statistic MargLogLik PPP
## Value -12480.392 0.000
##
## Regressions:
## Estimate Post.SD pi.lower pi.upper Rhat Prior
## PHQ8_est ~
## FIES_est 0.212 0.038 0.137 0.288 1.000 normal(1,1)
## Economic_pvrty 0.119 0.042 0.036 0.202 1.000 normal(1,1)
## FIES_est ~
## Land_est -0.158 0.035 -0.226 -0.089 1.000 normal(-0.5,2)
## Economic_pvrty 0.356 0.035 0.286 0.426 1.000 normal(1,1)
## FR.dist -0.012 0.034 -0.079 0.056 1.000 normal(-0.5,2)
## PHQ8_est ~
## DOBB 0.013 0.039 -0.063 0.089 1.000 normal(0.5,2)
## SEX 0.088 0.077 -0.061 0.24 1.000 normal(0.5,2)
## EDUCAT -0.093 0.031 -0.154 -0.031 1.000 normal(-0.5,2)
## Soci_est.1 -0.048 0.037 -0.12 0.024 1.000 normal(-0.5,2)
## MSTATUS_Div.wd -0.018 0.101 -0.215 0.178 1.000 normal(0.5,2)
## MSTATUS_Single 0.121 0.150 -0.17 0.413 1.000 normal(0,9)
## Alcohol 0.005 0.029 -0.053 0.063 1.000 normal(0.5,2)
## SMOKING 0.075 0.118 -0.158 0.307 1.000 normal(0.5,2)
## LOCB_EWAF 0.149 0.162 -0.166 0.468 1.000 normal(0,9)
## LOCB_KADONE 0.253 0.151 -0.045 0.55 1.000 normal(0,9)
## LOCB_KADTWO 0.057 0.148 -0.236 0.347 1.000 normal(0,9)
## LOCB_KARO 0.284 0.109 0.069 0.498 1.000 normal(0,9)
## LOCB_KYEM 0.482 0.161 0.167 0.794 1.000 normal(0,9)
## LOCB_MARA 0.084 0.129 -0.17 0.337 1.000 normal(0,9)
## LOCB_NONE 0.044 0.152 -0.252 0.343 1.000 normal(0,9)
## LOCB_NTWO 0.040 0.144 -0.243 0.322 1.000 normal(0,9)
## LOCB_NYAB 0.347 0.171 0.01 0.679 1.000 normal(0,9)
##
## Covariances:
## Estimate Post.SD pi.lower pi.upper Rhat
## .FIES_est ~~
## Forest_est.1 0.136 0.036 0.067 0.207 1.000
## Economic_poverty ~~
## Forest_est.1 0.033 0.037 -0.039 0.107 1.000
## Land_est -0.272 0.040 -0.352 -0.195 1.000
## .PHQ8_est ~~
## Health -0.165 0.038 -0.24 -0.093 1.000
## FR.dist ~~
## DOBB 0.038 0.038 -0.038 0.113 1.000
## SEX -0.026 0.019 -0.063 0.011 1.000
## EDUCAT 0.005 0.047 -0.089 0.097 1.000
## Soci_est.1 0.012 0.039 -0.065 0.088 1.000
## MSTATUS_Div.wd -0.013 0.014 -0.041 0.015 1.000
## MSTATUS_Single 0.001 0.009 -0.018 0.019 1.000
## Alcohol -0.068 0.048 -0.162 0.023 1.000
## SMOKING -0.007 0.012 -0.031 0.016 1.000
## LOCB_EWAF -0.024 0.009 -0.042 -0.007 1.000
## LOCB_KADONE 0.186 0.012 0.163 0.211 1.001
## LOCB_KADTWO 0.124 0.011 0.102 0.147 1.000
## LOCB_KARO -0.066 0.016 -0.098 -0.035 1.000
## LOCB_KYEM -0.039 0.009 -0.056 -0.021 1.000
## LOCB_MARA -0.088 0.013 -0.114 -0.063 1.001
## LOCB_NONE 0.005 0.010 -0.014 0.025 1.001
## LOCB_NTWO -0.051 0.011 -0.073 -0.03 1.000
## LOCB_NYAB -0.024 0.009 -0.041 -0.007 1.000
## DOBB ~~
## SEX -0.043 0.019 -0.081 -0.007 1.000
## EDUCAT -0.249 0.050 -0.349 -0.154 1.000
## Soci_est.1 -0.004 0.038 -0.079 0.07 1.000
## MSTATUS_Div.wd 0.098 0.015 0.07 0.128 1.000
## MSTATUS_Single -0.063 0.010 -0.083 -0.044 1.000
## Alcohol 0.179 0.048 0.085 0.275 1.000
## SMOKING 0.062 0.012 0.039 0.087 1.000
## LOCB_EWAF 0.013 0.009 -0.004 0.031 1.000
## LOCB_KADONE -0.002 0.010 -0.022 0.017 1.000
## LOCB_KADTWO 0.016 0.010 -0.005 0.036 1.000
## LOCB_KARO 0.000 0.016 -0.032 0.032 1.000
## LOCB_KYEM 0.004 0.009 -0.014 0.022 1.000
## LOCB_MARA -0.023 0.012 -0.048 0.001 1.000
## LOCB_NONE 0.000 0.010 -0.019 0.02 1.000
## LOCB_NTWO -0.011 0.011 -0.032 0.011 1.000
## LOCB_NYAB 0.009 0.009 -0.008 0.025 1.000
## SEX ~~
## EDUCAT -0.140 0.025 -0.19 -0.093 1.000
## Soci_est.1 -0.027 0.019 -0.065 0.011 1.000
## MSTATUS_Div.wd 0.037 0.007 0.023 0.051 1.000
## MSTATUS_Single -0.009 0.005 -0.019 0 1.000
## Alcohol -0.097 0.023 -0.143 -0.052 1.000
## SMOKING -0.038 0.006 -0.05 -0.026 1.000
## LOCB_EWAF -0.009 0.005 -0.018 0 1.000
## LOCB_KADONE 0.003 0.005 -0.007 0.013 1.000
## LOCB_KADTWO -0.007 0.005 -0.017 0.003 1.000
## LOCB_KARO -0.015 0.008 -0.031 0 1.000
## LOCB_KYEM 0.001 0.004 -0.008 0.009 1.000
## LOCB_MARA 0.000 0.006 -0.012 0.012 1.000
## LOCB_NONE 0.005 0.005 -0.005 0.015 1.000
## LOCB_NTWO 0.005 0.005 -0.006 0.015 1.000
## LOCB_NYAB 0.002 0.004 -0.006 0.01 1.000
## EDUCAT ~~
## Soci_est.1 0.195 0.050 0.098 0.294 1.000
## MSTATUS_Div.wd -0.075 0.019 -0.112 -0.04 1.000
## MSTATUS_Single 0.086 0.013 0.062 0.111 1.000
## Alcohol -0.048 0.060 -0.163 0.069 1.000
## SMOKING -0.043 0.015 -0.073 -0.013 1.000
## LOCB_EWAF -0.005 0.012 -0.028 0.018 1.000
## LOCB_KADONE -0.008 0.013 -0.032 0.017 1.000
## LOCB_KADTWO 0.001 0.013 -0.025 0.027 1.000
## LOCB_KARO 0.015 0.020 -0.025 0.054 1.000
## LOCB_KYEM 0.019 0.011 -0.004 0.041 1.000
## LOCB_MARA -0.002 0.016 -0.033 0.028 1.000
## LOCB_NONE 0.029 0.013 0.004 0.053 1.000
## LOCB_NTWO -0.056 0.014 -0.084 -0.029 1.000
## LOCB_NYAB -0.017 0.011 -0.039 0.004 1.000
## Soci_est.1 ~~
## MSTATUS_Div.wd -0.037 0.015 -0.067 -0.009 1.000
## MSTATUS_Single 0.030 0.010 0.011 0.049 1.000
## Alcohol -0.022 0.048 -0.116 0.073 1.000
## SMOKING -0.019 0.012 -0.043 0.004 1.000
## LOCB_EWAF -0.007 0.009 -0.025 0.011 1.000
## LOCB_KADONE 0.009 0.010 -0.011 0.029 1.000
## LOCB_KADTWO -0.018 0.010 -0.039 0.002 1.000
## LOCB_KARO -0.006 0.016 -0.038 0.025 1.000
## LOCB_KYEM 0.000 0.009 -0.018 0.019 1.000
## LOCB_MARA 0.010 0.013 -0.014 0.035 1.000
## LOCB_NONE -0.001 0.010 -0.021 0.018 1.000
## LOCB_NTWO -0.014 0.011 -0.035 0.007 1.000
## LOCB_NYAB 0.011 0.009 -0.006 0.028 1.000
## MSTATUS_Div.wid ~~
## MSTATUS_Single -0.011 0.004 -0.018 -0.004 1.000
## Alcohol 0.023 0.018 -0.012 0.059 1.000
## SMOKING 0.005 0.005 -0.004 0.013 1.000
## LOCB_EWAF -0.007 0.003 -0.014 -0.001 1.000
## LOCB_KADONE -0.007 0.004 -0.014 0 1.000
## LOCB_KADTWO 0.004 0.004 -0.004 0.012 1.000
## LOCB_KARO 0.001 0.006 -0.01 0.013 1.000
## LOCB_KYEM 0.009 0.003 0.002 0.016 1.000
## LOCB_MARA -0.003 0.005 -0.012 0.007 1.000
## LOCB_NONE -0.008 0.004 -0.015 0 1.000
## LOCB_NTWO -0.003 0.004 -0.012 0.005 1.000
## LOCB_NYAB -0.000 0.003 -0.007 0.006 1.000
## MSTATUS_Single ~~
## Alcohol -0.010 0.012 -0.033 0.013 1.000
## SMOKING -0.006 0.003 -0.012 0 1.000
## LOCB_EWAF -0.001 0.002 -0.005 0.003 1.000
## LOCB_KADONE -0.001 0.003 -0.005 0.004 1.000
## LOCB_KADTWO 0.001 0.003 -0.005 0.006 1.000
## LOCB_KARO -0.003 0.004 -0.011 0.005 1.000
## LOCB_KYEM -0.001 0.002 -0.005 0.003 1.000
## LOCB_MARA 0.002 0.003 -0.004 0.008 1.000
## LOCB_NONE 0.002 0.003 -0.003 0.007 1.000
## LOCB_NTWO -0.003 0.003 -0.008 0.002 1.000
## LOCB_NYAB -0.002 0.002 -0.006 0.002 1.000
## Alcohol ~~
## SMOKING 0.083 0.015 0.054 0.113 1.000
## LOCB_EWAF 0.039 0.011 0.017 0.061 1.000
## LOCB_KADONE -0.014 0.012 -0.039 0.011 1.000
## LOCB_KADTWO -0.006 0.013 -0.032 0.019 1.000
## LOCB_KARO -0.019 0.020 -0.058 0.019 1.000
## LOCB_KYEM -0.014 0.011 -0.036 0.008 1.000
## LOCB_MARA -0.001 0.015 -0.031 0.029 1.000
## LOCB_NONE -0.003 0.012 -0.027 0.022 1.000
## LOCB_NTWO 0.018 0.013 -0.008 0.045 1.000
## LOCB_NYAB -0.002 0.011 -0.023 0.018 1.000
## SMOKING ~~
## LOCB_EWAF 0.002 0.003 -0.004 0.008 1.000
## LOCB_KADONE -0.002 0.003 -0.009 0.004 1.000
## LOCB_KADTWO -0.006 0.003 -0.012 0 1.000
## LOCB_KARO 0.009 0.005 -0.001 0.019 1.000
## LOCB_KYEM -0.002 0.003 -0.008 0.003 1.000
## LOCB_MARA -0.001 0.004 -0.009 0.006 1.000
## LOCB_NONE 0.007 0.003 0.001 0.013 1.000
## LOCB_NTWO 0.005 0.003 -0.002 0.011 1.000
## LOCB_NYAB -0.001 0.003 -0.007 0.004 1.000
## LOCB_EWAF ~~
## LOCB_KADONE -0.004 0.002 -0.009 0 1.000
## LOCB_KADTWO -0.005 0.002 -0.01 0 1.000
## LOCB_KARO -0.013 0.004 -0.021 -0.006 1.000
## LOCB_KYEM -0.004 0.002 -0.008 0.001 1.000
## LOCB_MARA -0.007 0.003 -0.013 -0.001 1.000
## LOCB_NONE -0.004 0.002 -0.009 0 1.000
## LOCB_NTWO -0.005 0.003 -0.01 0 1.000
## LOCB_NYAB -0.003 0.002 -0.007 0.001 1.000
## LOCB_KADONE ~~
## LOCB_KADTWO -0.007 0.003 -0.012 -0.001 1.000
## LOCB_KARO -0.017 0.004 -0.025 -0.008 1.000
## LOCB_KYEM -0.004 0.002 -0.009 0 1.000
## LOCB_MARA -0.009 0.003 -0.015 -0.002 1.000
## LOCB_NONE -0.006 0.003 -0.011 -0.001 1.000
## LOCB_NTWO -0.006 0.003 -0.012 -0.001 1.000
## LOCB_NYAB -0.004 0.002 -0.008 0 1.000
## LOCB_KADTWO ~~
## LOCB_KARO -0.018 0.004 -0.026 -0.009 1.000
## LOCB_KYEM -0.005 0.002 -0.01 0 1.000
## LOCB_MARA -0.009 0.003 -0.016 -0.003 1.000
## LOCB_NONE -0.006 0.003 -0.011 -0.001 1.000
## LOCB_NTWO -0.007 0.003 -0.013 -0.001 1.000
## LOCB_NYAB -0.004 0.002 -0.009 0 1.000
## LOCB_KARO ~~
## LOCB_KYEM -0.013 0.004 -0.021 -0.006 1.000
## LOCB_MARA -0.027 0.005 -0.037 -0.017 1.000
## LOCB_NONE -0.016 0.004 -0.025 -0.008 1.000
## LOCB_NTWO -0.020 0.005 -0.029 -0.011 1.000
## LOCB_NYAB -0.012 0.004 -0.019 -0.005 1.000
## LOCB_KYEM ~~
## LOCB_MARA -0.007 0.003 -0.013 -0.001 1.000
## LOCB_NONE -0.004 0.002 -0.009 0 1.000
## LOCB_NTWO -0.005 0.003 -0.011 0 1.000
## LOCB_NYAB -0.003 0.002 -0.007 0.001 1.000
## LOCB_MARA ~~
## LOCB_NONE -0.009 0.003 -0.015 -0.002 1.000
## LOCB_NTWO -0.011 0.004 -0.018 -0.004 1.000
## LOCB_NYAB -0.006 0.003 -0.012 -0.001 1.000
## LOCB_NONE ~~
## LOCB_NTWO -0.006 0.003 -0.012 -0.001 1.000
## LOCB_NYAB -0.004 0.002 -0.008 0.001 1.000
## LOCB_NTWO ~~
## LOCB_NYAB -0.005 0.002 -0.01 0 1.000
## Prior
##
## beta(3,2)
##
## beta(3,2)
## beta(2,3)
##
## beta(2,3)
##
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
##
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
##
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
##
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
##
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
##
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
##
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
##
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
##
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
##
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
##
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
##
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
##
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
##
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
##
## beta(1,1)
## beta(1,1)
## beta(1,1)
##
## beta(1,1)
## beta(1,1)
##
## beta(1,1)
##
## Intercepts:
## Estimate Post.SD pi.lower pi.upper Rhat Prior
## .PHQ8_est 0.019 0.139 -0.256 0.289 1.000 normal(0,10)
## .FIES_est -0.000 0.035 -0.068 0.068 1.000 normal(0,10)
## Economic_pvrty 0.000 0.039 -0.077 0.076 1.000 normal(0,10)
## Land_est 0.000 0.038 -0.074 0.074 1.000 normal(0,10)
## FR.dist 0.000 0.038 -0.075 0.076 1.000 normal(0,10)
## DOBB 0.000 0.038 -0.074 0.074 1.000 normal(0,10)
## SEX 0.596 0.019 0.559 0.633 1.000 normal(0,10)
## EDUCAT 2.641 0.048 2.548 2.735 1.000 normal(0,10)
## Soci_est.1 -0.000 0.039 -0.076 0.076 1.000 normal(0,10)
## MSTATUS_Div.wd 0.168 0.015 0.14 0.197 1.000 normal(0,10)
## MSTATUS_Single 0.066 0.009 0.048 0.085 1.000 normal(0,10)
## Alcohol 0.460 0.048 0.366 0.554 1.000 normal(0,10)
## SMOKING 0.109 0.012 0.086 0.133 1.000 normal(0,10)
## LOCB_EWAF 0.059 0.009 0.042 0.077 1.000 normal(0,10)
## LOCB_KADONE 0.075 0.010 0.055 0.095 1.000 normal(0,10)
## LOCB_KADTWO 0.079 0.010 0.059 0.1 1.000 normal(0,10)
## LOCB_KARO 0.222 0.016 0.19 0.253 1.000 normal(0,10)
## LOCB_KYEM 0.059 0.009 0.041 0.077 1.000 normal(0,10)
## LOCB_MARA 0.118 0.012 0.094 0.142 1.000 normal(0,10)
## LOCB_NONE 0.072 0.010 0.052 0.091 1.000 normal(0,10)
## LOCB_NTWO 0.088 0.011 0.066 0.109 1.000 normal(0,10)
## LOCB_NYAB 0.052 0.008 0.035 0.068 1.000 normal(0,10)
## Forest_est.1 -0.000 0.038 -0.074 0.072 1.000 normal(0,10)
## Health 3.222 0.035 3.153 3.291 1.000 normal(0,10)
##
## Variances:
## Estimate Post.SD pi.lower pi.upper Rhat Prior
## .PHQ8_est 0.828 0.046 0.745 0.924 1.000 gamma(1,.5)[sd]
## .FIES_est 0.835 0.045 0.751 0.926 1.000 gamma(1,.5)[sd]
## Economic_pvrty 1.008 0.054 0.906 1.118 1.000 gamma(1,.5)[sd]
## Land_est 1.005 0.054 0.903 1.116 1.000 gamma(1,.5)[sd]
## Forest_est.1 1.006 0.055 0.905 1.119 1.000 gamma(1,.5)[sd]
## Health 0.852 0.046 0.768 0.946 1.000 gamma(1,.5)[sd]
## FR.dist 1.005 0.054 0.904 1.115 1.001 gamma(1,.5)[sd]
## DOBB 1.021 0.055 0.919 1.134 1.000 gamma(1,.5)[sd]
## SEX 0.247 0.013 0.222 0.274 1.000 gamma(1,.5)[sd]
## EDUCAT 1.622 0.088 1.459 1.806 1.000 gamma(1,.5)[sd]
## Soci_est.1 1.026 0.057 0.921 1.144 1.000 gamma(1,.5)[sd]
## MSTATUS_Div.wd 0.144 0.008 0.129 0.16 1.000 gamma(1,.5)[sd]
## MSTATUS_Single 0.063 0.003 0.057 0.07 1.000 gamma(1,.5)[sd]
## Alcohol 1.545 0.083 1.391 1.716 1.000 gamma(1,.5)[sd]
## SMOKING 0.100 0.005 0.09 0.111 1.000 gamma(1,.5)[sd]
## LOCB_EWAF 0.057 0.003 0.051 0.063 1.000 gamma(1,.5)[sd]
## LOCB_KADONE 0.070 0.004 0.063 0.078 1.000 gamma(1,.5)[sd]
## LOCB_KADTWO 0.075 0.004 0.067 0.083 1.000 gamma(1,.5)[sd]
## LOCB_KARO 0.177 0.010 0.159 0.196 1.000 gamma(1,.5)[sd]
## LOCB_KYEM 0.057 0.003 0.051 0.064 1.000 gamma(1,.5)[sd]
## LOCB_MARA 0.107 0.006 0.096 0.119 1.000 gamma(1,.5)[sd]
## LOCB_NONE 0.069 0.004 0.062 0.076 1.000 gamma(1,.5)[sd]
## LOCB_NTWO 0.082 0.004 0.074 0.091 1.000 gamma(1,.5)[sd]
## LOCB_NYAB 0.051 0.003 0.045 0.056 1.000 gamma(1,.5)[sd]fit_main_2_sum <- data.frame(summary(fit_main_3))[,1:2]## blavaan (0.3-15) results of 4000 samples after 4000 adapt/burnin iterations
##
## Number of observations 695
##
## Number of missing patterns 1
##
## Statistic MargLogLik PPP
## Value -12480.169 0.000
##
## Regressions:
## Estimate Post.SD pi.lower pi.upper Rhat Prior
## PHQ8_est ~
## FIES_est 0.212 0.039 0.136 0.288 1.000 normal(1,1)
## Economic_pvrty 0.119 0.042 0.036 0.202 1.000 normal(1,1)
## FIES_est ~
## Land_est -0.157 0.036 -0.228 -0.089 1.000 normal(-0.5,2)
## Economic_pvrty 0.356 0.036 0.285 0.427 1.000 normal(1,1)
## FR.dist -0.012 0.035 -0.08 0.054 1.000 normal(-0.5,2)
## PHQ8_est ~
## DOBB 0.013 0.039 -0.064 0.089 1.000 normal(0.5,2)
## SEX 0.089 0.079 -0.064 0.243 1.000 normal(0.5,2)
## EDUCAT -0.092 0.031 -0.154 -0.03 1.000 normal(-0.5,2)
## Soci_est.1 -0.048 0.037 -0.12 0.026 1.000 normal(-0.5,2)
## MSTATUS_Div.wd -0.018 0.101 -0.212 0.185 1.000 normal(0.5,2)
## MSTATUS_Single 0.120 0.148 -0.167 0.412 1.000 normal(0,9)
## Alcohol 0.005 0.029 -0.052 0.062 1.000 normal(0.5,2)
## SMOKING 0.076 0.119 -0.159 0.311 1.000 normal(0.5,2)
## LOCB_EWAF 0.148 0.162 -0.17 0.465 1.000 normal(0,9)
## LOCB_KADONE 0.251 0.150 -0.046 0.542 1.001 normal(0,9)
## LOCB_KADTWO 0.055 0.146 -0.232 0.342 1.001 normal(0,9)
## LOCB_KARO 0.282 0.110 0.068 0.499 1.001 normal(0,9)
## LOCB_KYEM 0.481 0.161 0.168 0.799 1.000 normal(0,9)
## LOCB_MARA 0.083 0.130 -0.172 0.337 1.001 normal(0,9)
## LOCB_NONE 0.042 0.155 -0.263 0.342 1.000 normal(0,9)
## LOCB_NTWO 0.037 0.143 -0.244 0.318 1.001 normal(0,9)
## LOCB_NYAB 0.345 0.172 0.007 0.685 1.001 normal(0,9)
##
## Covariances:
## Estimate Post.SD pi.lower pi.upper Rhat
## .FIES_est ~~
## Forest_est.1 0.137 0.036 0.068 0.21 1.000
## Economic_poverty ~~
## Forest_est.1 0.033 0.037 -0.04 0.107 1.000
## Land_est -0.272 0.040 -0.353 -0.195 1.000
## .PHQ8_est ~~
## Health -0.165 0.038 -0.24 -0.092 1.000
## FR.dist ~~
## DOBB 0.039 0.038 -0.035 0.114 1.000
## SEX -0.026 0.019 -0.064 0.01 1.000
## EDUCAT 0.005 0.048 -0.09 0.098 1.000
## Soci_est.1 0.011 0.038 -0.063 0.086 1.000
## MSTATUS_Div.wd -0.013 0.014 -0.041 0.015 1.001
## MSTATUS_Single 0.001 0.009 -0.018 0.019 1.000
## Alcohol -0.067 0.047 -0.159 0.024 1.000
## SMOKING -0.007 0.012 -0.03 0.017 1.000
## LOCB_EWAF -0.024 0.009 -0.042 -0.006 1.000
## LOCB_KADONE 0.185 0.012 0.162 0.21 1.000
## LOCB_KADTWO 0.124 0.011 0.102 0.147 1.000
## LOCB_KARO -0.066 0.016 -0.098 -0.035 1.000
## LOCB_KYEM -0.039 0.009 -0.057 -0.021 1.000
## LOCB_MARA -0.088 0.013 -0.114 -0.064 1.000
## LOCB_NONE 0.005 0.010 -0.014 0.025 1.000
## LOCB_NTWO -0.051 0.011 -0.073 -0.03 1.001
## LOCB_NYAB -0.024 0.009 -0.041 -0.007 1.000
## DOBB ~~
## SEX -0.044 0.019 -0.082 -0.007 1.000
## EDUCAT -0.249 0.049 -0.346 -0.155 1.000
## Soci_est.1 -0.004 0.039 -0.081 0.072 1.000
## MSTATUS_Div.wd 0.098 0.015 0.069 0.128 1.000
## MSTATUS_Single -0.063 0.010 -0.083 -0.044 1.000
## Alcohol 0.179 0.049 0.086 0.277 1.000
## SMOKING 0.063 0.012 0.039 0.087 1.000
## LOCB_EWAF 0.013 0.009 -0.005 0.032 1.000
## LOCB_KADONE -0.002 0.010 -0.022 0.018 1.000
## LOCB_KADTWO 0.016 0.010 -0.004 0.036 1.000
## LOCB_KARO -0.000 0.016 -0.032 0.031 1.000
## LOCB_KYEM 0.005 0.009 -0.013 0.022 1.000
## LOCB_MARA -0.024 0.012 -0.048 0.001 1.000
## LOCB_NONE 0.000 0.010 -0.02 0.02 1.000
## LOCB_NTWO -0.011 0.011 -0.032 0.011 1.000
## LOCB_NYAB 0.009 0.009 -0.008 0.026 1.000
## SEX ~~
## EDUCAT -0.140 0.024 -0.189 -0.093 1.000
## Soci_est.1 -0.027 0.019 -0.065 0.01 1.000
## MSTATUS_Div.wd 0.037 0.007 0.023 0.051 1.000
## MSTATUS_Single -0.009 0.005 -0.018 0 1.000
## Alcohol -0.097 0.023 -0.144 -0.051 1.000
## SMOKING -0.038 0.006 -0.051 -0.026 1.000
## LOCB_EWAF -0.009 0.004 -0.018 0 1.000
## LOCB_KADONE 0.003 0.005 -0.007 0.013 1.000
## LOCB_KADTWO -0.007 0.005 -0.017 0.003 1.000
## LOCB_KARO -0.015 0.008 -0.031 0 1.000
## LOCB_KYEM 0.001 0.004 -0.008 0.01 1.000
## LOCB_MARA 0.000 0.006 -0.012 0.012 1.000
## LOCB_NONE 0.005 0.005 -0.005 0.014 1.000
## LOCB_NTWO 0.005 0.005 -0.005 0.016 1.000
## LOCB_NYAB 0.002 0.004 -0.006 0.011 1.000
## EDUCAT ~~
## Soci_est.1 0.195 0.049 0.099 0.293 1.000
## MSTATUS_Div.wd -0.075 0.018 -0.111 -0.039 1.000
## MSTATUS_Single 0.086 0.013 0.062 0.111 1.000
## Alcohol -0.048 0.059 -0.165 0.067 1.000
## SMOKING -0.042 0.015 -0.072 -0.013 1.000
## LOCB_EWAF -0.005 0.012 -0.027 0.018 1.000
## LOCB_KADONE -0.008 0.013 -0.033 0.017 1.000
## LOCB_KADTWO 0.001 0.013 -0.024 0.027 1.000
## LOCB_KARO 0.015 0.020 -0.024 0.054 1.000
## LOCB_KYEM 0.018 0.012 -0.004 0.041 1.000
## LOCB_MARA -0.002 0.016 -0.033 0.028 1.000
## LOCB_NONE 0.028 0.013 0.004 0.054 1.000
## LOCB_NTWO -0.056 0.014 -0.084 -0.029 1.000
## LOCB_NYAB -0.017 0.011 -0.039 0.004 1.000
## Soci_est.1 ~~
## MSTATUS_Div.wd -0.038 0.015 -0.066 -0.009 1.000
## MSTATUS_Single 0.030 0.010 0.011 0.049 1.000
## Alcohol -0.022 0.048 -0.117 0.071 1.000
## SMOKING -0.019 0.012 -0.043 0.004 1.000
## LOCB_EWAF -0.007 0.009 -0.025 0.011 1.000
## LOCB_KADONE 0.009 0.010 -0.011 0.028 1.000
## LOCB_KADTWO -0.018 0.011 -0.039 0.003 1.000
## LOCB_KARO -0.006 0.016 -0.038 0.025 1.000
## LOCB_KYEM 0.000 0.009 -0.018 0.018 1.000
## LOCB_MARA 0.011 0.012 -0.014 0.035 1.000
## LOCB_NONE -0.001 0.010 -0.021 0.018 1.000
## LOCB_NTWO -0.014 0.011 -0.036 0.008 1.000
## LOCB_NYAB 0.011 0.009 -0.006 0.028 1.000
## MSTATUS_Div.wid ~~
## MSTATUS_Single -0.011 0.004 -0.018 -0.004 1.000
## Alcohol 0.023 0.018 -0.012 0.058 1.000
## SMOKING 0.004 0.005 -0.004 0.013 1.000
## LOCB_EWAF -0.007 0.003 -0.014 0 1.000
## LOCB_KADONE -0.007 0.004 -0.014 0.001 1.000
## LOCB_KADTWO 0.004 0.004 -0.004 0.012 1.000
## LOCB_KARO 0.001 0.006 -0.01 0.013 1.000
## LOCB_KYEM 0.009 0.003 0.002 0.016 1.000
## LOCB_MARA -0.003 0.005 -0.012 0.007 1.000
## LOCB_NONE -0.008 0.004 -0.015 -0.001 1.000
## LOCB_NTWO -0.003 0.004 -0.012 0.004 1.000
## LOCB_NYAB -0.000 0.003 -0.006 0.006 1.000
## MSTATUS_Single ~~
## Alcohol -0.010 0.012 -0.033 0.014 1.000
## SMOKING -0.006 0.003 -0.012 0 1.000
## LOCB_EWAF -0.001 0.002 -0.006 0.003 1.000
## LOCB_KADONE -0.001 0.003 -0.006 0.004 1.000
## LOCB_KADTWO 0.000 0.003 -0.005 0.006 1.000
## LOCB_KARO -0.003 0.004 -0.011 0.005 1.000
## LOCB_KYEM -0.001 0.002 -0.006 0.003 1.000
## LOCB_MARA 0.002 0.003 -0.004 0.008 1.000
## LOCB_NONE 0.002 0.002 -0.003 0.007 1.000
## LOCB_NTWO -0.003 0.003 -0.008 0.002 1.000
## LOCB_NYAB -0.002 0.002 -0.006 0.002 1.000
## Alcohol ~~
## SMOKING 0.083 0.015 0.053 0.113 1.000
## LOCB_EWAF 0.039 0.011 0.017 0.061 1.000
## LOCB_KADONE -0.014 0.013 -0.039 0.01 1.000
## LOCB_KADTWO -0.006 0.013 -0.031 0.019 1.000
## LOCB_KARO -0.019 0.020 -0.058 0.02 1.000
## LOCB_KYEM -0.014 0.011 -0.036 0.008 1.000
## LOCB_MARA -0.001 0.015 -0.031 0.028 1.000
## LOCB_NONE -0.003 0.012 -0.027 0.021 1.000
## LOCB_NTWO 0.018 0.013 -0.008 0.044 1.000
## LOCB_NYAB -0.002 0.011 -0.023 0.019 1.000
## SMOKING ~~
## LOCB_EWAF 0.002 0.003 -0.004 0.008 1.000
## LOCB_KADONE -0.002 0.003 -0.008 0.004 1.000
## LOCB_KADTWO -0.006 0.003 -0.012 0.001 1.000
## LOCB_KARO 0.009 0.005 -0.001 0.019 1.000
## LOCB_KYEM -0.002 0.003 -0.008 0.004 1.000
## LOCB_MARA -0.001 0.004 -0.009 0.006 1.000
## LOCB_NONE 0.007 0.003 0 0.013 1.000
## LOCB_NTWO 0.005 0.003 -0.002 0.012 1.000
## LOCB_NYAB -0.001 0.003 -0.007 0.004 1.000
## LOCB_EWAF ~~
## LOCB_KADONE -0.004 0.002 -0.009 0 1.000
## LOCB_KADTWO -0.005 0.002 -0.01 0 1.000
## LOCB_KARO -0.013 0.004 -0.021 -0.006 1.000
## LOCB_KYEM -0.004 0.002 -0.008 0.001 1.000
## LOCB_MARA -0.007 0.003 -0.013 -0.002 1.000
## LOCB_NONE -0.004 0.002 -0.009 0 1.000
## LOCB_NTWO -0.005 0.003 -0.01 0 1.000
## LOCB_NYAB -0.003 0.002 -0.007 0.001 1.000
## LOCB_KADONE ~~
## LOCB_KADTWO -0.007 0.003 -0.012 -0.001 1.000
## LOCB_KARO -0.017 0.004 -0.025 -0.009 1.000
## LOCB_KYEM -0.004 0.002 -0.009 0 1.000
## LOCB_MARA -0.009 0.003 -0.015 -0.002 1.000
## LOCB_NONE -0.006 0.003 -0.011 0 1.000
## LOCB_NTWO -0.006 0.003 -0.012 -0.001 1.001
## LOCB_NYAB -0.004 0.002 -0.008 0.001 1.000
## LOCB_KADTWO ~~
## LOCB_KARO -0.018 0.004 -0.026 -0.009 1.000
## LOCB_KYEM -0.005 0.002 -0.009 0 1.000
## LOCB_MARA -0.009 0.003 -0.016 -0.003 1.000
## LOCB_NONE -0.006 0.003 -0.011 -0.001 1.000
## LOCB_NTWO -0.007 0.003 -0.013 -0.001 1.000
## LOCB_NYAB -0.004 0.002 -0.009 0 1.000
## LOCB_KARO ~~
## LOCB_KYEM -0.013 0.004 -0.021 -0.006 1.000
## LOCB_MARA -0.027 0.005 -0.037 -0.017 1.000
## LOCB_NONE -0.016 0.004 -0.025 -0.008 1.000
## LOCB_NTWO -0.020 0.005 -0.029 -0.011 1.000
## LOCB_NYAB -0.012 0.004 -0.019 -0.005 1.000
## LOCB_KYEM ~~
## LOCB_MARA -0.007 0.003 -0.013 -0.001 1.000
## LOCB_NONE -0.004 0.002 -0.009 0 1.000
## LOCB_NTWO -0.005 0.003 -0.011 0 1.000
## LOCB_NYAB -0.003 0.002 -0.007 0.001 1.000
## LOCB_MARA ~~
## LOCB_NONE -0.009 0.003 -0.015 -0.002 1.000
## LOCB_NTWO -0.011 0.004 -0.018 -0.004 1.000
## LOCB_NYAB -0.006 0.003 -0.012 -0.001 1.000
## LOCB_NONE ~~
## LOCB_NTWO -0.006 0.003 -0.012 -0.001 1.000
## LOCB_NYAB -0.004 0.002 -0.008 0.001 1.000
## LOCB_NTWO ~~
## LOCB_NYAB -0.005 0.002 -0.01 0 1.000
## Prior
##
## beta(3,2)
##
## beta(3,2)
## beta(2,3)
##
## beta(2,3)
##
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
##
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
##
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
##
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
##
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
##
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
##
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
##
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
##
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
##
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
##
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
##
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
##
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
##
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
##
## beta(1,1)
## beta(1,1)
## beta(1,1)
##
## beta(1,1)
## beta(1,1)
##
## beta(1,1)
##
## Intercepts:
## Estimate Post.SD pi.lower pi.upper Rhat Prior
## .PHQ8_est 0.018 0.139 -0.257 0.291 1.001 normal(0,10)
## .FIES_est -0.000 0.034 -0.067 0.066 1.000 normal(0,10)
## Economic_pvrty -0.000 0.038 -0.075 0.074 1.000 normal(0,10)
## Land_est -0.000 0.038 -0.074 0.074 1.000 normal(0,10)
## FR.dist 0.000 0.038 -0.074 0.076 1.000 normal(0,10)
## DOBB -0.000 0.039 -0.076 0.076 1.000 normal(0,10)
## SEX 0.596 0.019 0.559 0.633 1.000 normal(0,10)
## EDUCAT 2.641 0.049 2.545 2.736 1.000 normal(0,10)
## Soci_est.1 0.000 0.039 -0.075 0.075 1.000 normal(0,10)
## MSTATUS_Div.wd 0.168 0.014 0.141 0.196 1.000 normal(0,10)
## MSTATUS_Single 0.066 0.010 0.047 0.085 1.000 normal(0,10)
## Alcohol 0.461 0.047 0.369 0.553 1.000 normal(0,10)
## SMOKING 0.109 0.012 0.086 0.133 1.000 normal(0,10)
## LOCB_EWAF 0.059 0.009 0.041 0.076 1.000 normal(0,10)
## LOCB_KADONE 0.075 0.010 0.055 0.095 1.000 normal(0,10)
## LOCB_KADTWO 0.079 0.010 0.059 0.099 1.000 normal(0,10)
## LOCB_KARO 0.221 0.016 0.19 0.253 1.000 normal(0,10)
## LOCB_KYEM 0.059 0.009 0.041 0.077 1.000 normal(0,10)
## LOCB_MARA 0.118 0.012 0.094 0.142 1.000 normal(0,10)
## LOCB_NONE 0.072 0.010 0.053 0.092 1.000 normal(0,10)
## LOCB_NTWO 0.088 0.011 0.067 0.109 1.000 normal(0,10)
## LOCB_NYAB 0.052 0.009 0.035 0.069 1.000 normal(0,10)
## Forest_est.1 -0.001 0.038 -0.075 0.074 1.000 normal(0,10)
## Health 3.222 0.035 3.153 3.292 1.000 normal(0,10)
##
## Variances:
## Estimate Post.SD pi.lower pi.upper Rhat Prior
## .PHQ8_est (sd) 0.828 0.046 0.743 0.923 1.000 gamma(1,.5)[sd]
## .FIES_est 0.835 0.045 0.752 0.927 1.000 gamma(1,.5)[sd]
## Ecnmc_pvr 1.009 0.055 0.906 1.122 1.000 gamma(1,.5)[sd]
## Land_est 1.004 0.054 0.903 1.116 1.000 gamma(1,.5)[sd]
## Frst_st.1 1.007 0.055 0.904 1.12 1.000 gamma(1,.5)[sd]
## Health 0.851 0.046 0.765 0.947 1.000 gamma(1,.5)[sd]
## FR.dist 1.004 0.054 0.907 1.115 1.000 gamma(1,.5)[sd]
## DOBB 1.021 0.056 0.917 1.136 1.000 gamma(1,.5)[sd]
## SEX 0.247 0.013 0.222 0.274 1.000 gamma(1,.5)[sd]
## EDUCAT 1.621 0.086 1.463 1.798 1.000 gamma(1,.5)[sd]
## Soci_st.1 1.027 0.056 0.923 1.141 1.000 gamma(1,.5)[sd]
## MSTATUS_D 0.144 0.008 0.129 0.16 1.000 gamma(1,.5)[sd]
## MSTATUS_S 0.063 0.003 0.057 0.07 1.000 gamma(1,.5)[sd]
## Alcohol 1.546 0.084 1.391 1.719 1.000 gamma(1,.5)[sd]
## SMOKING 0.100 0.005 0.09 0.111 1.000 gamma(1,.5)[sd]
## LOCB_EWAF 0.057 0.003 0.051 0.064 1.000 gamma(1,.5)[sd]
## LOCB_KADO 0.070 0.004 0.063 0.078 1.000 gamma(1,.5)[sd]
## LOCB_KADT 0.075 0.004 0.067 0.083 1.000 gamma(1,.5)[sd]
## LOCB_KARO 0.177 0.009 0.159 0.196 1.000 gamma(1,.5)[sd]
## LOCB_KYEM 0.057 0.003 0.051 0.063 1.000 gamma(1,.5)[sd]
## LOCB_MARA 0.107 0.006 0.096 0.119 1.000 gamma(1,.5)[sd]
## LOCB_NONE 0.069 0.004 0.062 0.076 1.000 gamma(1,.5)[sd]
## LOCB_NTWO 0.082 0.004 0.074 0.091 1.000 gamma(1,.5)[sd]
## LOCB_NYAB 0.051 0.003 0.045 0.056 1.000 gamma(1,.5)[sd]round(100*(as.numeric(fit_main_1_sum$Estimate)-as.numeric(fit_main_2_sum$Estimate))/as.numeric(fit_main_2_sum$Estimate),2) # Fine, if it is only small ## [1] 0.00 0.00 0.64 0.00 0.00 -0.73 0.00 0.00 0.00
## [10] 0.00 -1.12 1.09 0.00 0.00 0.83 0.00 -1.32 0.68
## [19] 0.80 3.64 0.71 0.21 1.20 4.76 8.11 0.58 0.00
## [28] 0.00 -0.10 0.10 -0.10 0.12 0.10 -2.56 0.00 0.00
## [37] 9.09 0.00 0.00 1.49 0.00 0.00 0.54 0.00 0.00
## [46] 0.00 0.00 0.00 0.00 0.00 0.00 -2.27 0.00 0.00
## [55] 0.00 0.00 0.00 -1.59 0.00 0.00 0.00 NaN -20.00
## [64] -4.17 NaN 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## [73] 0.00 0.00 0.00 0.00 0.00 0.00 0.00 NaN 0.00
## [82] 0.00 0.00 0.06 0.00 0.00 0.00 0.00 2.38 0.00
## [91] 0.00 0.00 0.00 5.56 0.00 3.57 0.00 0.00 -0.10
## [100] -2.63 0.00 0.00 0.00 0.00 0.00 0.00 0.00 NaN
## [109] -9.09 0.00 0.00 0.00 0.00 0.00 0.00 25.00 0.00
## [118] 0.00 0.00 0.00 0.00 0.00 0.00 0.00 NaN 0.00
## [127] 0.00 0.00 0.00 0.00 Inf 0.00 0.00 0.00 0.00
## [136] 0.00 0.00 -0.06 0.00 0.00 0.00 0.00 0.00 0.00
## [145] 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## [154] 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## [163] 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## [172] 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## [181] 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## [190] 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## [199] 0.00 0.00 0.00 0.00 0.00 5.56 NaN NaN NaN
## [208] NaN NaN 0.00 0.00 NaN 0.00 0.00 -0.22 0.00
## [217] 0.00 0.00 0.00 0.45 0.00 0.00 0.00 0.00 0.00
## [226] -100.00 0.008) ‘Is there a notable effect of the prior when compared with noninformative priors?’
# blavaan has default very weakly informative priors, so the analysis is repeated without specified priors
# Respecify the model with default weakly informative priors
model_main_4 <- '
### Main regression part
# Key variables of interest
PHQ8_est ~ FIES_est + Economic_poverty
FIES_est ~ Land_est + Economic_poverty
# Distance to forest reserve
FIES_est ~ FR.dist
### Covariance
# Between socio-ecological variables
FIES_est ~~ Forest_est.1
Economic_poverty ~~ Forest_est.1
Economic_poverty ~~ Land_est
# Health (treated as numeric)
PHQ8_est ~~ Health
### Covariates
# Age
PHQ8_est ~ DOBB +
# Sex
SEX +
# Education (treated as numeric)
EDUCAT +
# Social support
Soci_est.1 +
# Marital status (RL = MSTATUS_Mar.pol)
MSTATUS_Div.wid + MSTATUS_Single +
# Alchohol consumption
Alcohol +
# Smoking
SMOKING
### Location (RL = LOCB_NTC)
PHQ8_est ~ LOCB_EWAF + LOCB_KADONE + LOCB_KADTWO + LOCB_KARO + LOCB_KYEM + LOCB_MARA + LOCB_NONE + LOCB_NTWO + LOCB_NYAB
'
# Bayesian SEM
fit_main_4 <- bsem(model_main_4, data=DF_analy.list[[1]], fixed.x = FALSE, n.chains = chains, burnin = burnin, sample = sample, seed = seed, target = "stan", bcontrol = list(cores = 4))# Load the model (if needed)
fit_main_4 <- readRDS("fit_main_4.rds")
# Calculate the bias associated with using weakly informative priors
fit_main_1_sum <- data.frame(summary(fit_main_1))[,1:2]## blavaan (0.3-15) results of 4000 samples after 4000 adapt/burnin iterations
##
## Number of observations 695
##
## Number of missing patterns 1
##
## Statistic MargLogLik PPP
## Value -12480.392 0.000
##
## Regressions:
## Estimate Post.SD pi.lower pi.upper Rhat Prior
## PHQ8_est ~
## FIES_est 0.212 0.038 0.137 0.288 1.000 normal(1,1)
## Economic_pvrty 0.119 0.042 0.036 0.202 1.000 normal(1,1)
## FIES_est ~
## Land_est -0.158 0.035 -0.226 -0.089 1.000 normal(-0.5,2)
## Economic_pvrty 0.356 0.035 0.286 0.426 1.000 normal(1,1)
## FR.dist -0.012 0.034 -0.079 0.056 1.000 normal(-0.5,2)
## PHQ8_est ~
## DOBB 0.013 0.039 -0.063 0.089 1.000 normal(0.5,2)
## SEX 0.088 0.077 -0.061 0.24 1.000 normal(0.5,2)
## EDUCAT -0.093 0.031 -0.154 -0.031 1.000 normal(-0.5,2)
## Soci_est.1 -0.048 0.037 -0.12 0.024 1.000 normal(-0.5,2)
## MSTATUS_Div.wd -0.018 0.101 -0.215 0.178 1.000 normal(0.5,2)
## MSTATUS_Single 0.121 0.150 -0.17 0.413 1.000 normal(0,9)
## Alcohol 0.005 0.029 -0.053 0.063 1.000 normal(0.5,2)
## SMOKING 0.075 0.118 -0.158 0.307 1.000 normal(0.5,2)
## LOCB_EWAF 0.149 0.162 -0.166 0.468 1.000 normal(0,9)
## LOCB_KADONE 0.253 0.151 -0.045 0.55 1.000 normal(0,9)
## LOCB_KADTWO 0.057 0.148 -0.236 0.347 1.000 normal(0,9)
## LOCB_KARO 0.284 0.109 0.069 0.498 1.000 normal(0,9)
## LOCB_KYEM 0.482 0.161 0.167 0.794 1.000 normal(0,9)
## LOCB_MARA 0.084 0.129 -0.17 0.337 1.000 normal(0,9)
## LOCB_NONE 0.044 0.152 -0.252 0.343 1.000 normal(0,9)
## LOCB_NTWO 0.040 0.144 -0.243 0.322 1.000 normal(0,9)
## LOCB_NYAB 0.347 0.171 0.01 0.679 1.000 normal(0,9)
##
## Covariances:
## Estimate Post.SD pi.lower pi.upper Rhat
## .FIES_est ~~
## Forest_est.1 0.136 0.036 0.067 0.207 1.000
## Economic_poverty ~~
## Forest_est.1 0.033 0.037 -0.039 0.107 1.000
## Land_est -0.272 0.040 -0.352 -0.195 1.000
## .PHQ8_est ~~
## Health -0.165 0.038 -0.24 -0.093 1.000
## FR.dist ~~
## DOBB 0.038 0.038 -0.038 0.113 1.000
## SEX -0.026 0.019 -0.063 0.011 1.000
## EDUCAT 0.005 0.047 -0.089 0.097 1.000
## Soci_est.1 0.012 0.039 -0.065 0.088 1.000
## MSTATUS_Div.wd -0.013 0.014 -0.041 0.015 1.000
## MSTATUS_Single 0.001 0.009 -0.018 0.019 1.000
## Alcohol -0.068 0.048 -0.162 0.023 1.000
## SMOKING -0.007 0.012 -0.031 0.016 1.000
## LOCB_EWAF -0.024 0.009 -0.042 -0.007 1.000
## LOCB_KADONE 0.186 0.012 0.163 0.211 1.001
## LOCB_KADTWO 0.124 0.011 0.102 0.147 1.000
## LOCB_KARO -0.066 0.016 -0.098 -0.035 1.000
## LOCB_KYEM -0.039 0.009 -0.056 -0.021 1.000
## LOCB_MARA -0.088 0.013 -0.114 -0.063 1.001
## LOCB_NONE 0.005 0.010 -0.014 0.025 1.001
## LOCB_NTWO -0.051 0.011 -0.073 -0.03 1.000
## LOCB_NYAB -0.024 0.009 -0.041 -0.007 1.000
## DOBB ~~
## SEX -0.043 0.019 -0.081 -0.007 1.000
## EDUCAT -0.249 0.050 -0.349 -0.154 1.000
## Soci_est.1 -0.004 0.038 -0.079 0.07 1.000
## MSTATUS_Div.wd 0.098 0.015 0.07 0.128 1.000
## MSTATUS_Single -0.063 0.010 -0.083 -0.044 1.000
## Alcohol 0.179 0.048 0.085 0.275 1.000
## SMOKING 0.062 0.012 0.039 0.087 1.000
## LOCB_EWAF 0.013 0.009 -0.004 0.031 1.000
## LOCB_KADONE -0.002 0.010 -0.022 0.017 1.000
## LOCB_KADTWO 0.016 0.010 -0.005 0.036 1.000
## LOCB_KARO 0.000 0.016 -0.032 0.032 1.000
## LOCB_KYEM 0.004 0.009 -0.014 0.022 1.000
## LOCB_MARA -0.023 0.012 -0.048 0.001 1.000
## LOCB_NONE 0.000 0.010 -0.019 0.02 1.000
## LOCB_NTWO -0.011 0.011 -0.032 0.011 1.000
## LOCB_NYAB 0.009 0.009 -0.008 0.025 1.000
## SEX ~~
## EDUCAT -0.140 0.025 -0.19 -0.093 1.000
## Soci_est.1 -0.027 0.019 -0.065 0.011 1.000
## MSTATUS_Div.wd 0.037 0.007 0.023 0.051 1.000
## MSTATUS_Single -0.009 0.005 -0.019 0 1.000
## Alcohol -0.097 0.023 -0.143 -0.052 1.000
## SMOKING -0.038 0.006 -0.05 -0.026 1.000
## LOCB_EWAF -0.009 0.005 -0.018 0 1.000
## LOCB_KADONE 0.003 0.005 -0.007 0.013 1.000
## LOCB_KADTWO -0.007 0.005 -0.017 0.003 1.000
## LOCB_KARO -0.015 0.008 -0.031 0 1.000
## LOCB_KYEM 0.001 0.004 -0.008 0.009 1.000
## LOCB_MARA 0.000 0.006 -0.012 0.012 1.000
## LOCB_NONE 0.005 0.005 -0.005 0.015 1.000
## LOCB_NTWO 0.005 0.005 -0.006 0.015 1.000
## LOCB_NYAB 0.002 0.004 -0.006 0.01 1.000
## EDUCAT ~~
## Soci_est.1 0.195 0.050 0.098 0.294 1.000
## MSTATUS_Div.wd -0.075 0.019 -0.112 -0.04 1.000
## MSTATUS_Single 0.086 0.013 0.062 0.111 1.000
## Alcohol -0.048 0.060 -0.163 0.069 1.000
## SMOKING -0.043 0.015 -0.073 -0.013 1.000
## LOCB_EWAF -0.005 0.012 -0.028 0.018 1.000
## LOCB_KADONE -0.008 0.013 -0.032 0.017 1.000
## LOCB_KADTWO 0.001 0.013 -0.025 0.027 1.000
## LOCB_KARO 0.015 0.020 -0.025 0.054 1.000
## LOCB_KYEM 0.019 0.011 -0.004 0.041 1.000
## LOCB_MARA -0.002 0.016 -0.033 0.028 1.000
## LOCB_NONE 0.029 0.013 0.004 0.053 1.000
## LOCB_NTWO -0.056 0.014 -0.084 -0.029 1.000
## LOCB_NYAB -0.017 0.011 -0.039 0.004 1.000
## Soci_est.1 ~~
## MSTATUS_Div.wd -0.037 0.015 -0.067 -0.009 1.000
## MSTATUS_Single 0.030 0.010 0.011 0.049 1.000
## Alcohol -0.022 0.048 -0.116 0.073 1.000
## SMOKING -0.019 0.012 -0.043 0.004 1.000
## LOCB_EWAF -0.007 0.009 -0.025 0.011 1.000
## LOCB_KADONE 0.009 0.010 -0.011 0.029 1.000
## LOCB_KADTWO -0.018 0.010 -0.039 0.002 1.000
## LOCB_KARO -0.006 0.016 -0.038 0.025 1.000
## LOCB_KYEM 0.000 0.009 -0.018 0.019 1.000
## LOCB_MARA 0.010 0.013 -0.014 0.035 1.000
## LOCB_NONE -0.001 0.010 -0.021 0.018 1.000
## LOCB_NTWO -0.014 0.011 -0.035 0.007 1.000
## LOCB_NYAB 0.011 0.009 -0.006 0.028 1.000
## MSTATUS_Div.wid ~~
## MSTATUS_Single -0.011 0.004 -0.018 -0.004 1.000
## Alcohol 0.023 0.018 -0.012 0.059 1.000
## SMOKING 0.005 0.005 -0.004 0.013 1.000
## LOCB_EWAF -0.007 0.003 -0.014 -0.001 1.000
## LOCB_KADONE -0.007 0.004 -0.014 0 1.000
## LOCB_KADTWO 0.004 0.004 -0.004 0.012 1.000
## LOCB_KARO 0.001 0.006 -0.01 0.013 1.000
## LOCB_KYEM 0.009 0.003 0.002 0.016 1.000
## LOCB_MARA -0.003 0.005 -0.012 0.007 1.000
## LOCB_NONE -0.008 0.004 -0.015 0 1.000
## LOCB_NTWO -0.003 0.004 -0.012 0.005 1.000
## LOCB_NYAB -0.000 0.003 -0.007 0.006 1.000
## MSTATUS_Single ~~
## Alcohol -0.010 0.012 -0.033 0.013 1.000
## SMOKING -0.006 0.003 -0.012 0 1.000
## LOCB_EWAF -0.001 0.002 -0.005 0.003 1.000
## LOCB_KADONE -0.001 0.003 -0.005 0.004 1.000
## LOCB_KADTWO 0.001 0.003 -0.005 0.006 1.000
## LOCB_KARO -0.003 0.004 -0.011 0.005 1.000
## LOCB_KYEM -0.001 0.002 -0.005 0.003 1.000
## LOCB_MARA 0.002 0.003 -0.004 0.008 1.000
## LOCB_NONE 0.002 0.003 -0.003 0.007 1.000
## LOCB_NTWO -0.003 0.003 -0.008 0.002 1.000
## LOCB_NYAB -0.002 0.002 -0.006 0.002 1.000
## Alcohol ~~
## SMOKING 0.083 0.015 0.054 0.113 1.000
## LOCB_EWAF 0.039 0.011 0.017 0.061 1.000
## LOCB_KADONE -0.014 0.012 -0.039 0.011 1.000
## LOCB_KADTWO -0.006 0.013 -0.032 0.019 1.000
## LOCB_KARO -0.019 0.020 -0.058 0.019 1.000
## LOCB_KYEM -0.014 0.011 -0.036 0.008 1.000
## LOCB_MARA -0.001 0.015 -0.031 0.029 1.000
## LOCB_NONE -0.003 0.012 -0.027 0.022 1.000
## LOCB_NTWO 0.018 0.013 -0.008 0.045 1.000
## LOCB_NYAB -0.002 0.011 -0.023 0.018 1.000
## SMOKING ~~
## LOCB_EWAF 0.002 0.003 -0.004 0.008 1.000
## LOCB_KADONE -0.002 0.003 -0.009 0.004 1.000
## LOCB_KADTWO -0.006 0.003 -0.012 0 1.000
## LOCB_KARO 0.009 0.005 -0.001 0.019 1.000
## LOCB_KYEM -0.002 0.003 -0.008 0.003 1.000
## LOCB_MARA -0.001 0.004 -0.009 0.006 1.000
## LOCB_NONE 0.007 0.003 0.001 0.013 1.000
## LOCB_NTWO 0.005 0.003 -0.002 0.011 1.000
## LOCB_NYAB -0.001 0.003 -0.007 0.004 1.000
## LOCB_EWAF ~~
## LOCB_KADONE -0.004 0.002 -0.009 0 1.000
## LOCB_KADTWO -0.005 0.002 -0.01 0 1.000
## LOCB_KARO -0.013 0.004 -0.021 -0.006 1.000
## LOCB_KYEM -0.004 0.002 -0.008 0.001 1.000
## LOCB_MARA -0.007 0.003 -0.013 -0.001 1.000
## LOCB_NONE -0.004 0.002 -0.009 0 1.000
## LOCB_NTWO -0.005 0.003 -0.01 0 1.000
## LOCB_NYAB -0.003 0.002 -0.007 0.001 1.000
## LOCB_KADONE ~~
## LOCB_KADTWO -0.007 0.003 -0.012 -0.001 1.000
## LOCB_KARO -0.017 0.004 -0.025 -0.008 1.000
## LOCB_KYEM -0.004 0.002 -0.009 0 1.000
## LOCB_MARA -0.009 0.003 -0.015 -0.002 1.000
## LOCB_NONE -0.006 0.003 -0.011 -0.001 1.000
## LOCB_NTWO -0.006 0.003 -0.012 -0.001 1.000
## LOCB_NYAB -0.004 0.002 -0.008 0 1.000
## LOCB_KADTWO ~~
## LOCB_KARO -0.018 0.004 -0.026 -0.009 1.000
## LOCB_KYEM -0.005 0.002 -0.01 0 1.000
## LOCB_MARA -0.009 0.003 -0.016 -0.003 1.000
## LOCB_NONE -0.006 0.003 -0.011 -0.001 1.000
## LOCB_NTWO -0.007 0.003 -0.013 -0.001 1.000
## LOCB_NYAB -0.004 0.002 -0.009 0 1.000
## LOCB_KARO ~~
## LOCB_KYEM -0.013 0.004 -0.021 -0.006 1.000
## LOCB_MARA -0.027 0.005 -0.037 -0.017 1.000
## LOCB_NONE -0.016 0.004 -0.025 -0.008 1.000
## LOCB_NTWO -0.020 0.005 -0.029 -0.011 1.000
## LOCB_NYAB -0.012 0.004 -0.019 -0.005 1.000
## LOCB_KYEM ~~
## LOCB_MARA -0.007 0.003 -0.013 -0.001 1.000
## LOCB_NONE -0.004 0.002 -0.009 0 1.000
## LOCB_NTWO -0.005 0.003 -0.011 0 1.000
## LOCB_NYAB -0.003 0.002 -0.007 0.001 1.000
## LOCB_MARA ~~
## LOCB_NONE -0.009 0.003 -0.015 -0.002 1.000
## LOCB_NTWO -0.011 0.004 -0.018 -0.004 1.000
## LOCB_NYAB -0.006 0.003 -0.012 -0.001 1.000
## LOCB_NONE ~~
## LOCB_NTWO -0.006 0.003 -0.012 -0.001 1.000
## LOCB_NYAB -0.004 0.002 -0.008 0.001 1.000
## LOCB_NTWO ~~
## LOCB_NYAB -0.005 0.002 -0.01 0 1.000
## Prior
##
## beta(3,2)
##
## beta(3,2)
## beta(2,3)
##
## beta(2,3)
##
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
##
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
##
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
##
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
##
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
##
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
##
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
##
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
##
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
##
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
##
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
##
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
##
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
##
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
##
## beta(1,1)
## beta(1,1)
## beta(1,1)
##
## beta(1,1)
## beta(1,1)
##
## beta(1,1)
##
## Intercepts:
## Estimate Post.SD pi.lower pi.upper Rhat Prior
## .PHQ8_est 0.019 0.139 -0.256 0.289 1.000 normal(0,10)
## .FIES_est -0.000 0.035 -0.068 0.068 1.000 normal(0,10)
## Economic_pvrty 0.000 0.039 -0.077 0.076 1.000 normal(0,10)
## Land_est 0.000 0.038 -0.074 0.074 1.000 normal(0,10)
## FR.dist 0.000 0.038 -0.075 0.076 1.000 normal(0,10)
## DOBB 0.000 0.038 -0.074 0.074 1.000 normal(0,10)
## SEX 0.596 0.019 0.559 0.633 1.000 normal(0,10)
## EDUCAT 2.641 0.048 2.548 2.735 1.000 normal(0,10)
## Soci_est.1 -0.000 0.039 -0.076 0.076 1.000 normal(0,10)
## MSTATUS_Div.wd 0.168 0.015 0.14 0.197 1.000 normal(0,10)
## MSTATUS_Single 0.066 0.009 0.048 0.085 1.000 normal(0,10)
## Alcohol 0.460 0.048 0.366 0.554 1.000 normal(0,10)
## SMOKING 0.109 0.012 0.086 0.133 1.000 normal(0,10)
## LOCB_EWAF 0.059 0.009 0.042 0.077 1.000 normal(0,10)
## LOCB_KADONE 0.075 0.010 0.055 0.095 1.000 normal(0,10)
## LOCB_KADTWO 0.079 0.010 0.059 0.1 1.000 normal(0,10)
## LOCB_KARO 0.222 0.016 0.19 0.253 1.000 normal(0,10)
## LOCB_KYEM 0.059 0.009 0.041 0.077 1.000 normal(0,10)
## LOCB_MARA 0.118 0.012 0.094 0.142 1.000 normal(0,10)
## LOCB_NONE 0.072 0.010 0.052 0.091 1.000 normal(0,10)
## LOCB_NTWO 0.088 0.011 0.066 0.109 1.000 normal(0,10)
## LOCB_NYAB 0.052 0.008 0.035 0.068 1.000 normal(0,10)
## Forest_est.1 -0.000 0.038 -0.074 0.072 1.000 normal(0,10)
## Health 3.222 0.035 3.153 3.291 1.000 normal(0,10)
##
## Variances:
## Estimate Post.SD pi.lower pi.upper Rhat Prior
## .PHQ8_est 0.828 0.046 0.745 0.924 1.000 gamma(1,.5)[sd]
## .FIES_est 0.835 0.045 0.751 0.926 1.000 gamma(1,.5)[sd]
## Economic_pvrty 1.008 0.054 0.906 1.118 1.000 gamma(1,.5)[sd]
## Land_est 1.005 0.054 0.903 1.116 1.000 gamma(1,.5)[sd]
## Forest_est.1 1.006 0.055 0.905 1.119 1.000 gamma(1,.5)[sd]
## Health 0.852 0.046 0.768 0.946 1.000 gamma(1,.5)[sd]
## FR.dist 1.005 0.054 0.904 1.115 1.001 gamma(1,.5)[sd]
## DOBB 1.021 0.055 0.919 1.134 1.000 gamma(1,.5)[sd]
## SEX 0.247 0.013 0.222 0.274 1.000 gamma(1,.5)[sd]
## EDUCAT 1.622 0.088 1.459 1.806 1.000 gamma(1,.5)[sd]
## Soci_est.1 1.026 0.057 0.921 1.144 1.000 gamma(1,.5)[sd]
## MSTATUS_Div.wd 0.144 0.008 0.129 0.16 1.000 gamma(1,.5)[sd]
## MSTATUS_Single 0.063 0.003 0.057 0.07 1.000 gamma(1,.5)[sd]
## Alcohol 1.545 0.083 1.391 1.716 1.000 gamma(1,.5)[sd]
## SMOKING 0.100 0.005 0.09 0.111 1.000 gamma(1,.5)[sd]
## LOCB_EWAF 0.057 0.003 0.051 0.063 1.000 gamma(1,.5)[sd]
## LOCB_KADONE 0.070 0.004 0.063 0.078 1.000 gamma(1,.5)[sd]
## LOCB_KADTWO 0.075 0.004 0.067 0.083 1.000 gamma(1,.5)[sd]
## LOCB_KARO 0.177 0.010 0.159 0.196 1.000 gamma(1,.5)[sd]
## LOCB_KYEM 0.057 0.003 0.051 0.064 1.000 gamma(1,.5)[sd]
## LOCB_MARA 0.107 0.006 0.096 0.119 1.000 gamma(1,.5)[sd]
## LOCB_NONE 0.069 0.004 0.062 0.076 1.000 gamma(1,.5)[sd]
## LOCB_NTWO 0.082 0.004 0.074 0.091 1.000 gamma(1,.5)[sd]
## LOCB_NYAB 0.051 0.003 0.045 0.056 1.000 gamma(1,.5)[sd]fit_main_4_sum <- data.frame(summary(fit_main_4))[,1:2]## blavaan (0.3-15) results of 4000 samples after 4000 adapt/burnin iterations
##
## Number of observations 695
##
## Number of missing patterns 1
##
## Statistic MargLogLik PPP
## Value -12503.588 0.000
##
## Regressions:
## Estimate Post.SD pi.lower pi.upper Rhat Prior
## PHQ8_est ~
## FIES_est 0.212 0.038 0.137 0.288 1.000 normal(0,10)
## Economic_pvrty 0.119 0.042 0.037 0.201 1.000 normal(0,10)
## FIES_est ~
## Land_est -0.158 0.036 -0.229 -0.088 1.000 normal(0,10)
## Economic_pvrty 0.355 0.036 0.285 0.426 1.000 normal(0,10)
## FR.dist -0.012 0.035 -0.081 0.057 1.000 normal(0,10)
## PHQ8_est ~
## DOBB 0.013 0.038 -0.062 0.086 1.000 normal(0,10)
## SEX 0.088 0.079 -0.065 0.244 1.000 normal(0,10)
## EDUCAT -0.093 0.031 -0.153 -0.031 1.000 normal(0,10)
## Soci_est.1 -0.048 0.038 -0.122 0.026 1.000 normal(0,10)
## MSTATUS_Div.wd -0.018 0.102 -0.219 0.181 1.000 normal(0,10)
## MSTATUS_Single 0.120 0.149 -0.172 0.42 1.000 normal(0,10)
## Alcohol 0.005 0.029 -0.05 0.063 1.000 normal(0,10)
## SMOKING 0.074 0.117 -0.158 0.304 1.000 normal(0,10)
## LOCB_EWAF 0.149 0.165 -0.172 0.477 1.000 normal(0,10)
## LOCB_KADONE 0.255 0.148 -0.039 0.54 1.000 normal(0,10)
## LOCB_KADTWO 0.059 0.148 -0.234 0.351 1.000 normal(0,10)
## LOCB_KARO 0.285 0.110 0.07 0.5 1.000 normal(0,10)
## LOCB_KYEM 0.485 0.161 0.171 0.805 1.000 normal(0,10)
## LOCB_MARA 0.086 0.129 -0.169 0.339 1.000 normal(0,10)
## LOCB_NONE 0.046 0.153 -0.253 0.345 1.000 normal(0,10)
## LOCB_NTWO 0.041 0.142 -0.238 0.321 1.000 normal(0,10)
## LOCB_NYAB 0.351 0.170 0.016 0.687 1.000 normal(0,10)
##
## Covariances:
## Estimate Post.SD pi.lower pi.upper Rhat
## .FIES_est ~~
## Forest_est.1 0.137 0.036 0.068 0.207 1.000
## Economic_poverty ~~
## Forest_est.1 0.033 0.036 -0.038 0.105 1.000
## Land_est -0.271 0.039 -0.35 -0.197 1.000
## .PHQ8_est ~~
## Health -0.165 0.038 -0.242 -0.091 1.000
## FR.dist ~~
## DOBB 0.038 0.038 -0.034 0.114 1.000
## SEX -0.026 0.019 -0.063 0.01 1.000
## EDUCAT 0.004 0.047 -0.088 0.097 1.000
## Soci_est.1 0.012 0.039 -0.065 0.086 1.000
## MSTATUS_Div.wd -0.013 0.014 -0.04 0.015 1.000
## MSTATUS_Single 0.001 0.009 -0.018 0.019 1.000
## Alcohol -0.068 0.047 -0.16 0.024 1.000
## SMOKING -0.007 0.012 -0.03 0.016 1.000
## LOCB_EWAF -0.024 0.009 -0.042 -0.007 1.000
## LOCB_KADONE 0.185 0.012 0.162 0.211 1.000
## LOCB_KADTWO 0.124 0.011 0.102 0.147 1.000
## LOCB_KARO -0.066 0.016 -0.098 -0.035 1.000
## LOCB_KYEM -0.039 0.009 -0.057 -0.021 1.001
## LOCB_MARA -0.088 0.013 -0.114 -0.063 1.000
## LOCB_NONE 0.006 0.010 -0.014 0.025 1.000
## LOCB_NTWO -0.051 0.011 -0.073 -0.03 1.001
## LOCB_NYAB -0.024 0.008 -0.04 -0.007 1.000
## DOBB ~~
## SEX -0.044 0.019 -0.081 -0.007 1.000
## EDUCAT -0.249 0.050 -0.348 -0.153 1.000
## Soci_est.1 -0.004 0.039 -0.081 0.072 1.000
## MSTATUS_Div.wd 0.098 0.015 0.069 0.128 1.000
## MSTATUS_Single -0.063 0.010 -0.083 -0.044 1.000
## Alcohol 0.180 0.047 0.089 0.275 1.000
## SMOKING 0.063 0.012 0.04 0.088 1.000
## LOCB_EWAF 0.013 0.009 -0.004 0.032 1.000
## LOCB_KADONE -0.002 0.010 -0.022 0.018 1.000
## LOCB_KADTWO 0.016 0.010 -0.004 0.036 1.000
## LOCB_KARO 0.000 0.016 -0.032 0.031 1.000
## LOCB_KYEM 0.004 0.009 -0.013 0.022 1.000
## LOCB_MARA -0.024 0.013 -0.048 0.001 1.000
## LOCB_NONE 0.000 0.010 -0.019 0.02 1.000
## LOCB_NTWO -0.011 0.011 -0.032 0.011 1.000
## LOCB_NYAB 0.009 0.009 -0.008 0.026 1.000
## SEX ~~
## EDUCAT -0.140 0.025 -0.189 -0.093 1.000
## Soci_est.1 -0.027 0.019 -0.065 0.01 1.000
## MSTATUS_Div.wd 0.036 0.007 0.023 0.051 1.000
## MSTATUS_Single -0.009 0.005 -0.019 0 1.000
## Alcohol -0.097 0.024 -0.143 -0.051 1.000
## SMOKING -0.038 0.006 -0.05 -0.026 1.000
## LOCB_EWAF -0.009 0.004 -0.018 -0.001 1.000
## LOCB_KADONE 0.003 0.005 -0.007 0.013 1.000
## LOCB_KADTWO -0.007 0.005 -0.017 0.003 1.000
## LOCB_KARO -0.016 0.008 -0.031 0 1.000
## LOCB_KYEM 0.001 0.004 -0.008 0.01 1.000
## LOCB_MARA 0.000 0.006 -0.012 0.012 1.000
## LOCB_NONE 0.005 0.005 -0.005 0.014 1.000
## LOCB_NTWO 0.005 0.005 -0.005 0.016 1.000
## LOCB_NYAB 0.002 0.004 -0.006 0.011 1.000
## EDUCAT ~~
## Soci_est.1 0.195 0.050 0.097 0.293 1.000
## MSTATUS_Div.wd -0.075 0.018 -0.112 -0.04 1.000
## MSTATUS_Single 0.086 0.013 0.062 0.112 1.000
## Alcohol -0.049 0.059 -0.165 0.067 1.000
## SMOKING -0.042 0.015 -0.073 -0.013 1.000
## LOCB_EWAF -0.005 0.011 -0.027 0.018 1.000
## LOCB_KADONE -0.008 0.013 -0.033 0.017 1.000
## LOCB_KADTWO 0.001 0.013 -0.025 0.027 1.000
## LOCB_KARO 0.015 0.020 -0.025 0.054 1.000
## LOCB_KYEM 0.018 0.011 -0.004 0.041 1.000
## LOCB_MARA -0.003 0.016 -0.034 0.028 1.000
## LOCB_NONE 0.029 0.013 0.004 0.054 1.000
## LOCB_NTWO -0.056 0.014 -0.084 -0.029 1.000
## LOCB_NYAB -0.017 0.011 -0.039 0.004 1.000
## Soci_est.1 ~~
## MSTATUS_Div.wd -0.038 0.015 -0.066 -0.009 1.000
## MSTATUS_Single 0.030 0.010 0.011 0.049 1.000
## Alcohol -0.022 0.048 -0.117 0.072 1.000
## SMOKING -0.019 0.012 -0.043 0.004 1.000
## LOCB_EWAF -0.007 0.009 -0.025 0.012 1.000
## LOCB_KADONE 0.009 0.010 -0.011 0.029 1.000
## LOCB_KADTWO -0.018 0.010 -0.038 0.002 1.000
## LOCB_KARO -0.006 0.016 -0.038 0.025 1.000
## LOCB_KYEM 0.000 0.009 -0.018 0.018 1.000
## LOCB_MARA 0.010 0.012 -0.015 0.035 1.000
## LOCB_NONE -0.001 0.010 -0.021 0.018 1.000
## LOCB_NTWO -0.014 0.011 -0.036 0.007 1.000
## LOCB_NYAB 0.011 0.009 -0.006 0.028 1.000
## MSTATUS_Div.wid ~~
## MSTATUS_Single -0.011 0.004 -0.018 -0.004 1.000
## Alcohol 0.023 0.018 -0.012 0.058 1.000
## SMOKING 0.005 0.005 -0.004 0.013 1.000
## LOCB_EWAF -0.007 0.003 -0.014 0 1.000
## LOCB_KADONE -0.007 0.004 -0.014 0.001 1.000
## LOCB_KADTWO 0.004 0.004 -0.004 0.012 1.000
## LOCB_KARO 0.001 0.006 -0.01 0.013 1.000
## LOCB_KYEM 0.009 0.003 0.002 0.016 1.000
## LOCB_MARA -0.003 0.005 -0.012 0.006 1.000
## LOCB_NONE -0.008 0.004 -0.015 -0.001 1.000
## LOCB_NTWO -0.003 0.004 -0.012 0.005 1.000
## LOCB_NYAB -0.000 0.003 -0.006 0.006 1.000
## MSTATUS_Single ~~
## Alcohol -0.010 0.012 -0.033 0.013 1.000
## SMOKING -0.006 0.003 -0.012 0 1.000
## LOCB_EWAF -0.001 0.002 -0.005 0.003 1.000
## LOCB_KADONE -0.001 0.002 -0.005 0.004 1.000
## LOCB_KADTWO 0.001 0.003 -0.004 0.006 1.000
## LOCB_KARO -0.003 0.004 -0.011 0.005 1.000
## LOCB_KYEM -0.001 0.002 -0.005 0.003 1.000
## LOCB_MARA 0.002 0.003 -0.004 0.008 1.000
## LOCB_NONE 0.002 0.002 -0.003 0.007 1.000
## LOCB_NTWO -0.003 0.003 -0.008 0.003 1.000
## LOCB_NYAB -0.002 0.002 -0.006 0.002 1.000
## Alcohol ~~
## SMOKING 0.083 0.015 0.053 0.114 1.000
## LOCB_EWAF 0.039 0.011 0.017 0.061 1.000
## LOCB_KADONE -0.014 0.012 -0.038 0.01 1.000
## LOCB_KADTWO -0.006 0.013 -0.032 0.019 1.000
## LOCB_KARO -0.019 0.020 -0.057 0.019 1.000
## LOCB_KYEM -0.014 0.011 -0.036 0.008 1.000
## LOCB_MARA -0.001 0.015 -0.031 0.029 1.000
## LOCB_NONE -0.003 0.012 -0.028 0.021 1.000
## LOCB_NTWO 0.018 0.014 -0.008 0.045 1.000
## LOCB_NYAB -0.002 0.011 -0.024 0.019 1.000
## SMOKING ~~
## LOCB_EWAF 0.002 0.003 -0.004 0.008 1.000
## LOCB_KADONE -0.002 0.003 -0.009 0.004 1.000
## LOCB_KADTWO -0.006 0.003 -0.012 0 1.000
## LOCB_KARO 0.009 0.005 -0.001 0.019 1.000
## LOCB_KYEM -0.002 0.003 -0.008 0.004 1.000
## LOCB_MARA -0.001 0.004 -0.009 0.006 1.000
## LOCB_NONE 0.007 0.003 0.001 0.013 1.000
## LOCB_NTWO 0.005 0.003 -0.002 0.012 1.000
## LOCB_NYAB -0.001 0.003 -0.007 0.004 1.000
## LOCB_EWAF ~~
## LOCB_KADONE -0.004 0.002 -0.009 0 1.000
## LOCB_KADTWO -0.005 0.002 -0.01 0 1.000
## LOCB_KARO -0.013 0.004 -0.021 -0.006 1.000
## LOCB_KYEM -0.004 0.002 -0.008 0.001 1.000
## LOCB_MARA -0.007 0.003 -0.013 -0.001 1.000
## LOCB_NONE -0.004 0.002 -0.009 0 1.000
## LOCB_NTWO -0.005 0.003 -0.011 0 1.000
## LOCB_NYAB -0.003 0.002 -0.007 0.001 1.000
## LOCB_KADONE ~~
## LOCB_KADTWO -0.007 0.003 -0.012 -0.001 1.000
## LOCB_KARO -0.017 0.004 -0.025 -0.008 1.000
## LOCB_KYEM -0.004 0.002 -0.009 0 1.000
## LOCB_MARA -0.009 0.003 -0.015 -0.002 1.000
## LOCB_NONE -0.006 0.003 -0.011 0 1.000
## LOCB_NTWO -0.006 0.003 -0.012 -0.001 1.000
## LOCB_NYAB -0.004 0.002 -0.008 0.001 1.000
## LOCB_KADTWO ~~
## LOCB_KARO -0.018 0.004 -0.026 -0.009 1.000
## LOCB_KYEM -0.005 0.002 -0.009 0 1.000
## LOCB_MARA -0.009 0.003 -0.016 -0.002 1.000
## LOCB_NONE -0.006 0.003 -0.011 -0.001 1.000
## LOCB_NTWO -0.007 0.003 -0.013 -0.001 1.000
## LOCB_NYAB -0.004 0.002 -0.009 0 1.000
## LOCB_KARO ~~
## LOCB_KYEM -0.013 0.004 -0.021 -0.006 1.000
## LOCB_MARA -0.027 0.005 -0.037 -0.016 1.000
## LOCB_NONE -0.016 0.004 -0.025 -0.008 1.000
## LOCB_NTWO -0.020 0.005 -0.029 -0.011 1.000
## LOCB_NYAB -0.012 0.004 -0.019 -0.005 1.000
## LOCB_KYEM ~~
## LOCB_MARA -0.007 0.003 -0.013 -0.002 1.000
## LOCB_NONE -0.004 0.002 -0.009 0 1.000
## LOCB_NTWO -0.005 0.003 -0.01 0 1.000
## LOCB_NYAB -0.003 0.002 -0.007 0.001 1.000
## LOCB_MARA ~~
## LOCB_NONE -0.009 0.003 -0.015 -0.002 1.000
## LOCB_NTWO -0.011 0.004 -0.018 -0.004 1.000
## LOCB_NYAB -0.006 0.003 -0.012 -0.001 1.000
## LOCB_NONE ~~
## LOCB_NTWO -0.006 0.003 -0.012 -0.001 1.000
## LOCB_NYAB -0.004 0.002 -0.008 0.001 1.000
## LOCB_NTWO ~~
## LOCB_NYAB -0.005 0.002 -0.009 0 1.000
## Prior
##
## beta(1,1)
##
## beta(1,1)
## beta(1,1)
##
## beta(1,1)
##
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
##
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
##
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
##
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
##
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
##
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
##
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
##
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
##
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
##
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
##
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
##
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
##
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
##
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
##
## beta(1,1)
## beta(1,1)
## beta(1,1)
##
## beta(1,1)
## beta(1,1)
##
## beta(1,1)
##
## Intercepts:
## Estimate Post.SD pi.lower pi.upper Rhat Prior
## .PHQ8_est 0.018 0.138 -0.257 0.287 1.000 normal(0,10)
## .FIES_est 0.000 0.035 -0.067 0.068 1.000 normal(0,10)
## Economic_pvrty -0.000 0.038 -0.075 0.073 1.000 normal(0,10)
## Land_est -0.000 0.038 -0.075 0.074 1.000 normal(0,10)
## FR.dist 0.000 0.038 -0.075 0.076 1.000 normal(0,10)
## DOBB -0.000 0.038 -0.074 0.074 1.000 normal(0,10)
## SEX 0.596 0.019 0.559 0.633 1.000 normal(0,10)
## EDUCAT 2.640 0.049 2.546 2.735 1.000 normal(0,10)
## Soci_est.1 0.000 0.038 -0.073 0.075 1.000 normal(0,10)
## MSTATUS_Div.wd 0.168 0.014 0.14 0.197 1.000 normal(0,10)
## MSTATUS_Single 0.066 0.009 0.048 0.085 1.000 normal(0,10)
## Alcohol 0.460 0.047 0.368 0.554 1.000 normal(0,10)
## SMOKING 0.109 0.012 0.086 0.133 1.000 normal(0,10)
## LOCB_EWAF 0.059 0.009 0.041 0.077 1.000 normal(0,10)
## LOCB_KADONE 0.075 0.010 0.055 0.094 1.000 normal(0,10)
## LOCB_KADTWO 0.079 0.010 0.059 0.1 1.000 normal(0,10)
## LOCB_KARO 0.222 0.016 0.19 0.253 1.000 normal(0,10)
## LOCB_KYEM 0.059 0.009 0.041 0.077 1.000 normal(0,10)
## LOCB_MARA 0.118 0.012 0.093 0.143 1.000 normal(0,10)
## LOCB_NONE 0.072 0.010 0.052 0.092 1.000 normal(0,10)
## LOCB_NTWO 0.088 0.011 0.066 0.109 1.000 normal(0,10)
## LOCB_NYAB 0.052 0.008 0.035 0.068 1.000 normal(0,10)
## Forest_est.1 0.000 0.039 -0.074 0.076 1.000 normal(0,10)
## Health 3.222 0.035 3.153 3.291 1.000 normal(0,10)
##
## Variances:
## Estimate Post.SD pi.lower pi.upper Rhat Prior
## .PHQ8_est 0.828 0.046 0.742 0.924 1.000 gamma(1,.5)[sd]
## .FIES_est 0.836 0.045 0.753 0.93 1.000 gamma(1,.5)[sd]
## Economic_pvrty 1.009 0.055 0.907 1.122 1.000 gamma(1,.5)[sd]
## Land_est 1.005 0.054 0.904 1.116 1.000 gamma(1,.5)[sd]
## Forest_est.1 1.007 0.054 0.907 1.12 1.000 gamma(1,.5)[sd]
## Health 0.852 0.046 0.766 0.945 1.000 gamma(1,.5)[sd]
## FR.dist 1.005 0.054 0.904 1.115 1.000 gamma(1,.5)[sd]
## DOBB 1.022 0.054 0.921 1.133 1.000 gamma(1,.5)[sd]
## SEX 0.247 0.013 0.222 0.274 1.000 gamma(1,.5)[sd]
## EDUCAT 1.621 0.087 1.459 1.804 1.000 gamma(1,.5)[sd]
## Soci_est.1 1.025 0.055 0.921 1.139 1.000 gamma(1,.5)[sd]
## MSTATUS_Div.wd 0.143 0.008 0.129 0.16 1.000 gamma(1,.5)[sd]
## MSTATUS_Single 0.063 0.003 0.057 0.071 1.000 gamma(1,.5)[sd]
## Alcohol 1.545 0.082 1.391 1.714 1.000 gamma(1,.5)[sd]
## SMOKING 0.100 0.005 0.09 0.111 1.000 gamma(1,.5)[sd]
## LOCB_EWAF 0.057 0.003 0.051 0.063 1.000 gamma(1,.5)[sd]
## LOCB_KADONE 0.070 0.004 0.063 0.078 1.000 gamma(1,.5)[sd]
## LOCB_KADTWO 0.074 0.004 0.067 0.083 1.000 gamma(1,.5)[sd]
## LOCB_KARO 0.177 0.010 0.159 0.197 1.000 gamma(1,.5)[sd]
## LOCB_KYEM 0.057 0.003 0.051 0.063 1.000 gamma(1,.5)[sd]
## LOCB_MARA 0.107 0.006 0.096 0.119 1.000 gamma(1,.5)[sd]
## LOCB_NONE 0.069 0.004 0.062 0.076 1.000 gamma(1,.5)[sd]
## LOCB_NTWO 0.082 0.004 0.074 0.091 1.000 gamma(1,.5)[sd]
## LOCB_NYAB 0.051 0.003 0.045 0.056 1.000 gamma(1,.5)[sd]round(100*(as.numeric(fit_main_1_sum$Estimate)-as.numeric(fit_main_4_sum$Estimate))/as.numeric(fit_main_4_sum$Estimate),2) # Fine, if it is only small ## [1] 0.00 0.00 0.00 0.28 0.00 -0.73 0.00 0.37 0.00 0.00
## [11] 0.00 0.00 0.00 0.00 0.83 0.00 1.35 0.00 -0.78 -3.39
## [21] -0.35 -0.62 -2.33 -4.35 -2.44 -1.14 0.00 -0.12 -0.10 0.00
## [31] -0.10 0.00 0.00 0.00 0.00 25.00 0.00 0.00 0.00 0.00
## [41] 0.00 0.00 0.54 0.00 0.00 0.00 0.00 -16.67 0.00 0.00
## [51] -0.10 -2.27 0.00 0.00 0.00 0.00 -0.56 -1.59 0.00 0.00
## [61] 0.00 NaN 0.00 -4.17 NaN 0.00 0.00 0.00 0.00 0.00
## [71] 2.78 0.00 0.00 0.00 0.00 0.00 0.00 -6.25 0.00 NaN
## [81] 0.00 0.00 0.00 0.06 0.00 0.00 0.00 -2.04 2.38 0.00
## [91] 0.00 0.00 0.00 5.56 -33.33 0.00 0.00 0.00 0.10 -2.63
## [101] 0.00 0.00 0.00 0.00 0.00 0.00 0.00 NaN 0.00 0.00
## [111] 0.00 0.00 0.70 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## [121] 0.00 0.00 0.00 0.00 NaN 0.00 0.00 0.00 0.00 0.00
## [131] 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## [141] 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## [151] 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## [161] 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## [171] 0.00 0.00 0.00 0.00 0.00 1.35 0.00 0.00 0.00 0.00
## [181] 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## [191] 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## [201] 0.00 0.00 0.00 5.56 NaN NaN NaN NaN NaN 0.00
## [211] 0.04 NaN 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## [221] 0.00 0.00 0.00 0.00 0.00 NaN 0.002.9) ‘Are the results stable from a sensitivity analysis?’
# Shifting the hyperparameters 'up' (specifically the mean and the shape of the beta distribution)
model_main_5 <- '
### Main regression part
# Key variables of interest
PHQ8_est ~ prior("normal(2, 1)")*FIES_est + prior("normal(2, 1)")*Economic_poverty
FIES_est ~ prior("normal(0.5, 2)")*Land_est + prior("normal(2, 1)")*Economic_poverty
# Distance to forest reserve
FIES_est ~ prior("normal(0.5, 2)")*FR.dist
### Covariance
# Between socio-ecological variables
FIES_est ~~ prior("beta(4, 2)")*Forest_est.1
Economic_poverty ~~ prior("beta(4, 2)")*Forest_est.1
Economic_poverty ~~ prior("beta(3, 3)")*Land_est
# Health (treated as numeric)
PHQ8_est ~~ prior("beta(3, 3)")*Health
### Covariates
# Age
PHQ8_est ~ prior("normal(1.5, 2)")*DOBB +
# Sex
prior("normal(1.5, 2)")*SEX +
# Education (treated as numeric)
prior("normal(0.5, 2)")*EDUCAT +
# Social support
prior("normal(0.5, 2)")*Soci_est.1 +
# Marital status (RL = MSTATUS_Mar.pol)
prior("normal(1.5, 2)")*MSTATUS_Div.wid + prior("normal(0, 9)")*MSTATUS_Single +
# Alchohol consumption
prior("normal(1.5, 2)")*Alcohol +
# Smoking
prior("normal(1.5, 2)")*SMOKING
### Location (RL = LOCB_NTC)
PHQ8_est ~ prior("normal(1, 9)")*LOCB_EWAF + prior("normal(1, 9)")*LOCB_KADONE + prior("normal(1, 9)")*LOCB_KADTWO + prior("normal(1, 9)")*LOCB_KARO + prior("normal(1, 9)")*LOCB_KYEM + prior("normal(1, 9)")*LOCB_MARA + prior("normal(1, 9)")*LOCB_NONE + prior("normal(1, 9)")*LOCB_NTWO + prior("normal(1, 9)")*LOCB_NYAB
'
# Bayesian SEM
fit_main_5 <- bsem(model_main_5, data=DF_analy.list[[1]], fixed.x = FALSE, n.chains = chains, burnin = burnin, sample = sample, seed = seed, target = "stan", bcontrol = list(cores = 4))# Load the model (if needed)
fit_main_5 <- readRDS("fit_main_5.rds")
# Calculate the bias associated with using 'up' shifted hyperparameters
fit_main_1_sum <- data.frame(summary(fit_main_1))[,1:2]## blavaan (0.3-15) results of 4000 samples after 4000 adapt/burnin iterations
##
## Number of observations 695
##
## Number of missing patterns 1
##
## Statistic MargLogLik PPP
## Value -12480.392 0.000
##
## Regressions:
## Estimate Post.SD pi.lower pi.upper Rhat Prior
## PHQ8_est ~
## FIES_est 0.212 0.038 0.137 0.288 1.000 normal(1,1)
## Economic_pvrty 0.119 0.042 0.036 0.202 1.000 normal(1,1)
## FIES_est ~
## Land_est -0.158 0.035 -0.226 -0.089 1.000 normal(-0.5,2)
## Economic_pvrty 0.356 0.035 0.286 0.426 1.000 normal(1,1)
## FR.dist -0.012 0.034 -0.079 0.056 1.000 normal(-0.5,2)
## PHQ8_est ~
## DOBB 0.013 0.039 -0.063 0.089 1.000 normal(0.5,2)
## SEX 0.088 0.077 -0.061 0.24 1.000 normal(0.5,2)
## EDUCAT -0.093 0.031 -0.154 -0.031 1.000 normal(-0.5,2)
## Soci_est.1 -0.048 0.037 -0.12 0.024 1.000 normal(-0.5,2)
## MSTATUS_Div.wd -0.018 0.101 -0.215 0.178 1.000 normal(0.5,2)
## MSTATUS_Single 0.121 0.150 -0.17 0.413 1.000 normal(0,9)
## Alcohol 0.005 0.029 -0.053 0.063 1.000 normal(0.5,2)
## SMOKING 0.075 0.118 -0.158 0.307 1.000 normal(0.5,2)
## LOCB_EWAF 0.149 0.162 -0.166 0.468 1.000 normal(0,9)
## LOCB_KADONE 0.253 0.151 -0.045 0.55 1.000 normal(0,9)
## LOCB_KADTWO 0.057 0.148 -0.236 0.347 1.000 normal(0,9)
## LOCB_KARO 0.284 0.109 0.069 0.498 1.000 normal(0,9)
## LOCB_KYEM 0.482 0.161 0.167 0.794 1.000 normal(0,9)
## LOCB_MARA 0.084 0.129 -0.17 0.337 1.000 normal(0,9)
## LOCB_NONE 0.044 0.152 -0.252 0.343 1.000 normal(0,9)
## LOCB_NTWO 0.040 0.144 -0.243 0.322 1.000 normal(0,9)
## LOCB_NYAB 0.347 0.171 0.01 0.679 1.000 normal(0,9)
##
## Covariances:
## Estimate Post.SD pi.lower pi.upper Rhat
## .FIES_est ~~
## Forest_est.1 0.136 0.036 0.067 0.207 1.000
## Economic_poverty ~~
## Forest_est.1 0.033 0.037 -0.039 0.107 1.000
## Land_est -0.272 0.040 -0.352 -0.195 1.000
## .PHQ8_est ~~
## Health -0.165 0.038 -0.24 -0.093 1.000
## FR.dist ~~
## DOBB 0.038 0.038 -0.038 0.113 1.000
## SEX -0.026 0.019 -0.063 0.011 1.000
## EDUCAT 0.005 0.047 -0.089 0.097 1.000
## Soci_est.1 0.012 0.039 -0.065 0.088 1.000
## MSTATUS_Div.wd -0.013 0.014 -0.041 0.015 1.000
## MSTATUS_Single 0.001 0.009 -0.018 0.019 1.000
## Alcohol -0.068 0.048 -0.162 0.023 1.000
## SMOKING -0.007 0.012 -0.031 0.016 1.000
## LOCB_EWAF -0.024 0.009 -0.042 -0.007 1.000
## LOCB_KADONE 0.186 0.012 0.163 0.211 1.001
## LOCB_KADTWO 0.124 0.011 0.102 0.147 1.000
## LOCB_KARO -0.066 0.016 -0.098 -0.035 1.000
## LOCB_KYEM -0.039 0.009 -0.056 -0.021 1.000
## LOCB_MARA -0.088 0.013 -0.114 -0.063 1.001
## LOCB_NONE 0.005 0.010 -0.014 0.025 1.001
## LOCB_NTWO -0.051 0.011 -0.073 -0.03 1.000
## LOCB_NYAB -0.024 0.009 -0.041 -0.007 1.000
## DOBB ~~
## SEX -0.043 0.019 -0.081 -0.007 1.000
## EDUCAT -0.249 0.050 -0.349 -0.154 1.000
## Soci_est.1 -0.004 0.038 -0.079 0.07 1.000
## MSTATUS_Div.wd 0.098 0.015 0.07 0.128 1.000
## MSTATUS_Single -0.063 0.010 -0.083 -0.044 1.000
## Alcohol 0.179 0.048 0.085 0.275 1.000
## SMOKING 0.062 0.012 0.039 0.087 1.000
## LOCB_EWAF 0.013 0.009 -0.004 0.031 1.000
## LOCB_KADONE -0.002 0.010 -0.022 0.017 1.000
## LOCB_KADTWO 0.016 0.010 -0.005 0.036 1.000
## LOCB_KARO 0.000 0.016 -0.032 0.032 1.000
## LOCB_KYEM 0.004 0.009 -0.014 0.022 1.000
## LOCB_MARA -0.023 0.012 -0.048 0.001 1.000
## LOCB_NONE 0.000 0.010 -0.019 0.02 1.000
## LOCB_NTWO -0.011 0.011 -0.032 0.011 1.000
## LOCB_NYAB 0.009 0.009 -0.008 0.025 1.000
## SEX ~~
## EDUCAT -0.140 0.025 -0.19 -0.093 1.000
## Soci_est.1 -0.027 0.019 -0.065 0.011 1.000
## MSTATUS_Div.wd 0.037 0.007 0.023 0.051 1.000
## MSTATUS_Single -0.009 0.005 -0.019 0 1.000
## Alcohol -0.097 0.023 -0.143 -0.052 1.000
## SMOKING -0.038 0.006 -0.05 -0.026 1.000
## LOCB_EWAF -0.009 0.005 -0.018 0 1.000
## LOCB_KADONE 0.003 0.005 -0.007 0.013 1.000
## LOCB_KADTWO -0.007 0.005 -0.017 0.003 1.000
## LOCB_KARO -0.015 0.008 -0.031 0 1.000
## LOCB_KYEM 0.001 0.004 -0.008 0.009 1.000
## LOCB_MARA 0.000 0.006 -0.012 0.012 1.000
## LOCB_NONE 0.005 0.005 -0.005 0.015 1.000
## LOCB_NTWO 0.005 0.005 -0.006 0.015 1.000
## LOCB_NYAB 0.002 0.004 -0.006 0.01 1.000
## EDUCAT ~~
## Soci_est.1 0.195 0.050 0.098 0.294 1.000
## MSTATUS_Div.wd -0.075 0.019 -0.112 -0.04 1.000
## MSTATUS_Single 0.086 0.013 0.062 0.111 1.000
## Alcohol -0.048 0.060 -0.163 0.069 1.000
## SMOKING -0.043 0.015 -0.073 -0.013 1.000
## LOCB_EWAF -0.005 0.012 -0.028 0.018 1.000
## LOCB_KADONE -0.008 0.013 -0.032 0.017 1.000
## LOCB_KADTWO 0.001 0.013 -0.025 0.027 1.000
## LOCB_KARO 0.015 0.020 -0.025 0.054 1.000
## LOCB_KYEM 0.019 0.011 -0.004 0.041 1.000
## LOCB_MARA -0.002 0.016 -0.033 0.028 1.000
## LOCB_NONE 0.029 0.013 0.004 0.053 1.000
## LOCB_NTWO -0.056 0.014 -0.084 -0.029 1.000
## LOCB_NYAB -0.017 0.011 -0.039 0.004 1.000
## Soci_est.1 ~~
## MSTATUS_Div.wd -0.037 0.015 -0.067 -0.009 1.000
## MSTATUS_Single 0.030 0.010 0.011 0.049 1.000
## Alcohol -0.022 0.048 -0.116 0.073 1.000
## SMOKING -0.019 0.012 -0.043 0.004 1.000
## LOCB_EWAF -0.007 0.009 -0.025 0.011 1.000
## LOCB_KADONE 0.009 0.010 -0.011 0.029 1.000
## LOCB_KADTWO -0.018 0.010 -0.039 0.002 1.000
## LOCB_KARO -0.006 0.016 -0.038 0.025 1.000
## LOCB_KYEM 0.000 0.009 -0.018 0.019 1.000
## LOCB_MARA 0.010 0.013 -0.014 0.035 1.000
## LOCB_NONE -0.001 0.010 -0.021 0.018 1.000
## LOCB_NTWO -0.014 0.011 -0.035 0.007 1.000
## LOCB_NYAB 0.011 0.009 -0.006 0.028 1.000
## MSTATUS_Div.wid ~~
## MSTATUS_Single -0.011 0.004 -0.018 -0.004 1.000
## Alcohol 0.023 0.018 -0.012 0.059 1.000
## SMOKING 0.005 0.005 -0.004 0.013 1.000
## LOCB_EWAF -0.007 0.003 -0.014 -0.001 1.000
## LOCB_KADONE -0.007 0.004 -0.014 0 1.000
## LOCB_KADTWO 0.004 0.004 -0.004 0.012 1.000
## LOCB_KARO 0.001 0.006 -0.01 0.013 1.000
## LOCB_KYEM 0.009 0.003 0.002 0.016 1.000
## LOCB_MARA -0.003 0.005 -0.012 0.007 1.000
## LOCB_NONE -0.008 0.004 -0.015 0 1.000
## LOCB_NTWO -0.003 0.004 -0.012 0.005 1.000
## LOCB_NYAB -0.000 0.003 -0.007 0.006 1.000
## MSTATUS_Single ~~
## Alcohol -0.010 0.012 -0.033 0.013 1.000
## SMOKING -0.006 0.003 -0.012 0 1.000
## LOCB_EWAF -0.001 0.002 -0.005 0.003 1.000
## LOCB_KADONE -0.001 0.003 -0.005 0.004 1.000
## LOCB_KADTWO 0.001 0.003 -0.005 0.006 1.000
## LOCB_KARO -0.003 0.004 -0.011 0.005 1.000
## LOCB_KYEM -0.001 0.002 -0.005 0.003 1.000
## LOCB_MARA 0.002 0.003 -0.004 0.008 1.000
## LOCB_NONE 0.002 0.003 -0.003 0.007 1.000
## LOCB_NTWO -0.003 0.003 -0.008 0.002 1.000
## LOCB_NYAB -0.002 0.002 -0.006 0.002 1.000
## Alcohol ~~
## SMOKING 0.083 0.015 0.054 0.113 1.000
## LOCB_EWAF 0.039 0.011 0.017 0.061 1.000
## LOCB_KADONE -0.014 0.012 -0.039 0.011 1.000
## LOCB_KADTWO -0.006 0.013 -0.032 0.019 1.000
## LOCB_KARO -0.019 0.020 -0.058 0.019 1.000
## LOCB_KYEM -0.014 0.011 -0.036 0.008 1.000
## LOCB_MARA -0.001 0.015 -0.031 0.029 1.000
## LOCB_NONE -0.003 0.012 -0.027 0.022 1.000
## LOCB_NTWO 0.018 0.013 -0.008 0.045 1.000
## LOCB_NYAB -0.002 0.011 -0.023 0.018 1.000
## SMOKING ~~
## LOCB_EWAF 0.002 0.003 -0.004 0.008 1.000
## LOCB_KADONE -0.002 0.003 -0.009 0.004 1.000
## LOCB_KADTWO -0.006 0.003 -0.012 0 1.000
## LOCB_KARO 0.009 0.005 -0.001 0.019 1.000
## LOCB_KYEM -0.002 0.003 -0.008 0.003 1.000
## LOCB_MARA -0.001 0.004 -0.009 0.006 1.000
## LOCB_NONE 0.007 0.003 0.001 0.013 1.000
## LOCB_NTWO 0.005 0.003 -0.002 0.011 1.000
## LOCB_NYAB -0.001 0.003 -0.007 0.004 1.000
## LOCB_EWAF ~~
## LOCB_KADONE -0.004 0.002 -0.009 0 1.000
## LOCB_KADTWO -0.005 0.002 -0.01 0 1.000
## LOCB_KARO -0.013 0.004 -0.021 -0.006 1.000
## LOCB_KYEM -0.004 0.002 -0.008 0.001 1.000
## LOCB_MARA -0.007 0.003 -0.013 -0.001 1.000
## LOCB_NONE -0.004 0.002 -0.009 0 1.000
## LOCB_NTWO -0.005 0.003 -0.01 0 1.000
## LOCB_NYAB -0.003 0.002 -0.007 0.001 1.000
## LOCB_KADONE ~~
## LOCB_KADTWO -0.007 0.003 -0.012 -0.001 1.000
## LOCB_KARO -0.017 0.004 -0.025 -0.008 1.000
## LOCB_KYEM -0.004 0.002 -0.009 0 1.000
## LOCB_MARA -0.009 0.003 -0.015 -0.002 1.000
## LOCB_NONE -0.006 0.003 -0.011 -0.001 1.000
## LOCB_NTWO -0.006 0.003 -0.012 -0.001 1.000
## LOCB_NYAB -0.004 0.002 -0.008 0 1.000
## LOCB_KADTWO ~~
## LOCB_KARO -0.018 0.004 -0.026 -0.009 1.000
## LOCB_KYEM -0.005 0.002 -0.01 0 1.000
## LOCB_MARA -0.009 0.003 -0.016 -0.003 1.000
## LOCB_NONE -0.006 0.003 -0.011 -0.001 1.000
## LOCB_NTWO -0.007 0.003 -0.013 -0.001 1.000
## LOCB_NYAB -0.004 0.002 -0.009 0 1.000
## LOCB_KARO ~~
## LOCB_KYEM -0.013 0.004 -0.021 -0.006 1.000
## LOCB_MARA -0.027 0.005 -0.037 -0.017 1.000
## LOCB_NONE -0.016 0.004 -0.025 -0.008 1.000
## LOCB_NTWO -0.020 0.005 -0.029 -0.011 1.000
## LOCB_NYAB -0.012 0.004 -0.019 -0.005 1.000
## LOCB_KYEM ~~
## LOCB_MARA -0.007 0.003 -0.013 -0.001 1.000
## LOCB_NONE -0.004 0.002 -0.009 0 1.000
## LOCB_NTWO -0.005 0.003 -0.011 0 1.000
## LOCB_NYAB -0.003 0.002 -0.007 0.001 1.000
## LOCB_MARA ~~
## LOCB_NONE -0.009 0.003 -0.015 -0.002 1.000
## LOCB_NTWO -0.011 0.004 -0.018 -0.004 1.000
## LOCB_NYAB -0.006 0.003 -0.012 -0.001 1.000
## LOCB_NONE ~~
## LOCB_NTWO -0.006 0.003 -0.012 -0.001 1.000
## LOCB_NYAB -0.004 0.002 -0.008 0.001 1.000
## LOCB_NTWO ~~
## LOCB_NYAB -0.005 0.002 -0.01 0 1.000
## Prior
##
## beta(3,2)
##
## beta(3,2)
## beta(2,3)
##
## beta(2,3)
##
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
##
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
##
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
##
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
##
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
##
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
##
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
##
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
##
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
##
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
##
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
##
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
##
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
##
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
##
## beta(1,1)
## beta(1,1)
## beta(1,1)
##
## beta(1,1)
## beta(1,1)
##
## beta(1,1)
##
## Intercepts:
## Estimate Post.SD pi.lower pi.upper Rhat Prior
## .PHQ8_est 0.019 0.139 -0.256 0.289 1.000 normal(0,10)
## .FIES_est -0.000 0.035 -0.068 0.068 1.000 normal(0,10)
## Economic_pvrty 0.000 0.039 -0.077 0.076 1.000 normal(0,10)
## Land_est 0.000 0.038 -0.074 0.074 1.000 normal(0,10)
## FR.dist 0.000 0.038 -0.075 0.076 1.000 normal(0,10)
## DOBB 0.000 0.038 -0.074 0.074 1.000 normal(0,10)
## SEX 0.596 0.019 0.559 0.633 1.000 normal(0,10)
## EDUCAT 2.641 0.048 2.548 2.735 1.000 normal(0,10)
## Soci_est.1 -0.000 0.039 -0.076 0.076 1.000 normal(0,10)
## MSTATUS_Div.wd 0.168 0.015 0.14 0.197 1.000 normal(0,10)
## MSTATUS_Single 0.066 0.009 0.048 0.085 1.000 normal(0,10)
## Alcohol 0.460 0.048 0.366 0.554 1.000 normal(0,10)
## SMOKING 0.109 0.012 0.086 0.133 1.000 normal(0,10)
## LOCB_EWAF 0.059 0.009 0.042 0.077 1.000 normal(0,10)
## LOCB_KADONE 0.075 0.010 0.055 0.095 1.000 normal(0,10)
## LOCB_KADTWO 0.079 0.010 0.059 0.1 1.000 normal(0,10)
## LOCB_KARO 0.222 0.016 0.19 0.253 1.000 normal(0,10)
## LOCB_KYEM 0.059 0.009 0.041 0.077 1.000 normal(0,10)
## LOCB_MARA 0.118 0.012 0.094 0.142 1.000 normal(0,10)
## LOCB_NONE 0.072 0.010 0.052 0.091 1.000 normal(0,10)
## LOCB_NTWO 0.088 0.011 0.066 0.109 1.000 normal(0,10)
## LOCB_NYAB 0.052 0.008 0.035 0.068 1.000 normal(0,10)
## Forest_est.1 -0.000 0.038 -0.074 0.072 1.000 normal(0,10)
## Health 3.222 0.035 3.153 3.291 1.000 normal(0,10)
##
## Variances:
## Estimate Post.SD pi.lower pi.upper Rhat Prior
## .PHQ8_est 0.828 0.046 0.745 0.924 1.000 gamma(1,.5)[sd]
## .FIES_est 0.835 0.045 0.751 0.926 1.000 gamma(1,.5)[sd]
## Economic_pvrty 1.008 0.054 0.906 1.118 1.000 gamma(1,.5)[sd]
## Land_est 1.005 0.054 0.903 1.116 1.000 gamma(1,.5)[sd]
## Forest_est.1 1.006 0.055 0.905 1.119 1.000 gamma(1,.5)[sd]
## Health 0.852 0.046 0.768 0.946 1.000 gamma(1,.5)[sd]
## FR.dist 1.005 0.054 0.904 1.115 1.001 gamma(1,.5)[sd]
## DOBB 1.021 0.055 0.919 1.134 1.000 gamma(1,.5)[sd]
## SEX 0.247 0.013 0.222 0.274 1.000 gamma(1,.5)[sd]
## EDUCAT 1.622 0.088 1.459 1.806 1.000 gamma(1,.5)[sd]
## Soci_est.1 1.026 0.057 0.921 1.144 1.000 gamma(1,.5)[sd]
## MSTATUS_Div.wd 0.144 0.008 0.129 0.16 1.000 gamma(1,.5)[sd]
## MSTATUS_Single 0.063 0.003 0.057 0.07 1.000 gamma(1,.5)[sd]
## Alcohol 1.545 0.083 1.391 1.716 1.000 gamma(1,.5)[sd]
## SMOKING 0.100 0.005 0.09 0.111 1.000 gamma(1,.5)[sd]
## LOCB_EWAF 0.057 0.003 0.051 0.063 1.000 gamma(1,.5)[sd]
## LOCB_KADONE 0.070 0.004 0.063 0.078 1.000 gamma(1,.5)[sd]
## LOCB_KADTWO 0.075 0.004 0.067 0.083 1.000 gamma(1,.5)[sd]
## LOCB_KARO 0.177 0.010 0.159 0.196 1.000 gamma(1,.5)[sd]
## LOCB_KYEM 0.057 0.003 0.051 0.064 1.000 gamma(1,.5)[sd]
## LOCB_MARA 0.107 0.006 0.096 0.119 1.000 gamma(1,.5)[sd]
## LOCB_NONE 0.069 0.004 0.062 0.076 1.000 gamma(1,.5)[sd]
## LOCB_NTWO 0.082 0.004 0.074 0.091 1.000 gamma(1,.5)[sd]
## LOCB_NYAB 0.051 0.003 0.045 0.056 1.000 gamma(1,.5)[sd]fit_main_5_sum <- data.frame(summary(fit_main_5))[,1:2]## blavaan (0.3-15) results of 4000 samples after 4000 adapt/burnin iterations
##
## Number of observations 695
##
## Number of missing patterns 1
##
## Statistic MargLogLik PPP
## Value -12485.726 0.000
##
## Regressions:
## Estimate Post.SD pi.lower pi.upper Rhat Prior
## PHQ8_est ~
## FIES_est 0.214 0.038 0.138 0.289 1.000 normal(2,1)
## Economic_pvrty 0.121 0.042 0.038 0.203 1.000 normal(2,1)
## FIES_est ~
## Land_est -0.157 0.035 -0.227 -0.088 1.000 normal(0.5,2)
## Economic_pvrty 0.358 0.037 0.286 0.429 1.000 normal(2,1)
## FR.dist -0.011 0.035 -0.079 0.057 1.000 normal(0.5,2)
## PHQ8_est ~
## DOBB 0.014 0.038 -0.061 0.089 1.000 normal(1.5,2)
## SEX 0.090 0.078 -0.062 0.242 1.000 normal(1.5,2)
## EDUCAT -0.092 0.031 -0.153 -0.031 1.000 normal(0.5,2)
## Soci_est.1 -0.047 0.037 -0.119 0.026 1.000 normal(0.5,2)
## MSTATUS_Div.wd -0.015 0.101 -0.213 0.185 1.000 normal(1.5,2)
## MSTATUS_Single 0.124 0.144 -0.158 0.414 1.000 normal(0,9)
## Alcohol 0.006 0.029 -0.052 0.062 1.000 normal(1.5,2)
## SMOKING 0.078 0.118 -0.154 0.312 1.000 normal(1.5,2)
## LOCB_EWAF 0.149 0.165 -0.174 0.477 1.000 normal(1,9)
## LOCB_KADONE 0.254 0.150 -0.036 0.553 1.000 normal(1,9)
## LOCB_KADTWO 0.057 0.147 -0.229 0.344 1.000 normal(1,9)
## LOCB_KARO 0.284 0.110 0.067 0.5 1.000 normal(1,9)
## LOCB_KYEM 0.482 0.163 0.161 0.805 1.000 normal(1,9)
## LOCB_MARA 0.085 0.128 -0.165 0.337 1.000 normal(1,9)
## LOCB_NONE 0.045 0.154 -0.258 0.346 1.000 normal(1,9)
## LOCB_NTWO 0.040 0.144 -0.243 0.322 1.000 normal(1,9)
## LOCB_NYAB 0.349 0.170 0.017 0.68 1.000 normal(1,9)
##
## Covariances:
## Estimate Post.SD pi.lower pi.upper Rhat
## .FIES_est ~~
## Forest_est.1 0.137 0.036 0.068 0.209 1.000
## Economic_poverty ~~
## Forest_est.1 0.034 0.037 -0.039 0.109 1.000
## Land_est -0.272 0.040 -0.352 -0.197 1.000
## .PHQ8_est ~~
## Health -0.162 0.038 -0.239 -0.091 1.000
## FR.dist ~~
## DOBB 0.038 0.038 -0.039 0.113 1.000
## SEX -0.026 0.019 -0.063 0.01 1.000
## EDUCAT 0.004 0.048 -0.09 0.099 1.000
## Soci_est.1 0.011 0.038 -0.065 0.086 1.000
## MSTATUS_Div.wd -0.013 0.014 -0.041 0.015 1.000
## MSTATUS_Single 0.001 0.009 -0.018 0.019 1.000
## Alcohol -0.068 0.047 -0.161 0.024 1.000
## SMOKING -0.007 0.012 -0.031 0.016 1.000
## LOCB_EWAF -0.024 0.009 -0.042 -0.007 1.000
## LOCB_KADONE 0.185 0.012 0.162 0.21 1.000
## LOCB_KADTWO 0.124 0.011 0.103 0.147 1.000
## LOCB_KARO -0.066 0.016 -0.098 -0.035 1.000
## LOCB_KYEM -0.039 0.009 -0.057 -0.021 1.001
## LOCB_MARA -0.088 0.013 -0.113 -0.064 1.000
## LOCB_NONE 0.005 0.010 -0.014 0.025 1.000
## LOCB_NTWO -0.051 0.011 -0.073 -0.03 1.000
## LOCB_NYAB -0.024 0.009 -0.041 -0.007 1.001
## DOBB ~~
## SEX -0.044 0.019 -0.081 -0.007 1.000
## EDUCAT -0.249 0.050 -0.348 -0.153 1.000
## Soci_est.1 -0.004 0.039 -0.079 0.072 1.000
## MSTATUS_Div.wd 0.098 0.015 0.069 0.128 1.000
## MSTATUS_Single -0.063 0.010 -0.083 -0.044 1.000
## Alcohol 0.178 0.048 0.086 0.273 1.000
## SMOKING 0.062 0.012 0.039 0.087 1.000
## LOCB_EWAF 0.013 0.009 -0.005 0.031 1.000
## LOCB_KADONE -0.002 0.010 -0.022 0.018 1.000
## LOCB_KADTWO 0.016 0.010 -0.004 0.036 1.000
## LOCB_KARO 0.000 0.016 -0.031 0.031 1.000
## LOCB_KYEM 0.005 0.009 -0.013 0.022 1.000
## LOCB_MARA -0.023 0.012 -0.048 0.001 1.000
## LOCB_NONE -0.000 0.010 -0.019 0.019 1.000
## LOCB_NTWO -0.011 0.011 -0.032 0.01 1.000
## LOCB_NYAB 0.009 0.009 -0.008 0.026 1.000
## SEX ~~
## EDUCAT -0.140 0.024 -0.189 -0.092 1.000
## Soci_est.1 -0.027 0.019 -0.065 0.009 1.000
## MSTATUS_Div.wd 0.036 0.007 0.023 0.051 1.000
## MSTATUS_Single -0.009 0.005 -0.018 0 1.000
## Alcohol -0.097 0.024 -0.145 -0.051 1.000
## SMOKING -0.038 0.006 -0.05 -0.026 1.000
## LOCB_EWAF -0.009 0.004 -0.018 -0.001 1.000
## LOCB_KADONE 0.003 0.005 -0.007 0.013 1.000
## LOCB_KADTWO -0.007 0.005 -0.017 0.003 1.000
## LOCB_KARO -0.015 0.008 -0.031 0 1.000
## LOCB_KYEM 0.001 0.004 -0.008 0.01 1.000
## LOCB_MARA 0.000 0.006 -0.012 0.012 1.000
## LOCB_NONE 0.005 0.005 -0.005 0.014 1.000
## LOCB_NTWO 0.005 0.005 -0.006 0.016 1.000
## LOCB_NYAB 0.002 0.004 -0.006 0.01 1.000
## EDUCAT ~~
## Soci_est.1 0.194 0.049 0.1 0.292 1.000
## MSTATUS_Div.wd -0.075 0.019 -0.111 -0.038 1.000
## MSTATUS_Single 0.086 0.013 0.062 0.112 1.000
## Alcohol -0.047 0.060 -0.164 0.068 1.000
## SMOKING -0.043 0.015 -0.073 -0.012 1.000
## LOCB_EWAF -0.005 0.011 -0.027 0.018 1.000
## LOCB_KADONE -0.008 0.013 -0.033 0.017 1.000
## LOCB_KADTWO 0.001 0.013 -0.025 0.027 1.000
## LOCB_KARO 0.015 0.020 -0.025 0.055 1.000
## LOCB_KYEM 0.018 0.011 -0.004 0.041 1.000
## LOCB_MARA -0.003 0.016 -0.033 0.029 1.000
## LOCB_NONE 0.029 0.013 0.004 0.053 1.000
## LOCB_NTWO -0.056 0.014 -0.083 -0.029 1.000
## LOCB_NYAB -0.017 0.011 -0.039 0.004 1.000
## Soci_est.1 ~~
## MSTATUS_Div.wd -0.037 0.015 -0.067 -0.009 1.000
## MSTATUS_Single 0.030 0.010 0.011 0.049 1.000
## Alcohol -0.022 0.048 -0.116 0.071 1.000
## SMOKING -0.019 0.012 -0.044 0.005 1.000
## LOCB_EWAF -0.007 0.009 -0.025 0.012 1.000
## LOCB_KADONE 0.009 0.010 -0.011 0.029 1.000
## LOCB_KADTWO -0.018 0.010 -0.039 0.002 1.000
## LOCB_KARO -0.007 0.016 -0.038 0.025 1.000
## LOCB_KYEM 0.000 0.009 -0.018 0.018 1.000
## LOCB_MARA 0.011 0.013 -0.014 0.035 1.000
## LOCB_NONE -0.001 0.010 -0.021 0.018 1.000
## LOCB_NTWO -0.014 0.011 -0.036 0.007 1.000
## LOCB_NYAB 0.011 0.009 -0.006 0.028 1.000
## MSTATUS_Div.wid ~~
## MSTATUS_Single -0.011 0.004 -0.018 -0.004 1.000
## Alcohol 0.023 0.018 -0.012 0.058 1.000
## SMOKING 0.005 0.004 -0.004 0.013 1.000
## LOCB_EWAF -0.007 0.003 -0.014 0 1.000
## LOCB_KADONE -0.007 0.004 -0.014 0 1.000
## LOCB_KADTWO 0.004 0.004 -0.004 0.012 1.000
## LOCB_KARO 0.002 0.006 -0.01 0.014 1.000
## LOCB_KYEM 0.009 0.003 0.002 0.016 1.000
## LOCB_MARA -0.003 0.005 -0.012 0.007 1.000
## LOCB_NONE -0.008 0.004 -0.015 -0.001 1.000
## LOCB_NTWO -0.003 0.004 -0.011 0.005 1.000
## LOCB_NYAB -0.000 0.003 -0.006 0.006 1.000
## MSTATUS_Single ~~
## Alcohol -0.010 0.012 -0.033 0.014 1.000
## SMOKING -0.006 0.003 -0.012 0 1.000
## LOCB_EWAF -0.001 0.002 -0.005 0.003 1.000
## LOCB_KADONE -0.001 0.003 -0.006 0.004 1.000
## LOCB_KADTWO 0.001 0.003 -0.005 0.006 1.000
## LOCB_KARO -0.003 0.004 -0.011 0.005 1.000
## LOCB_KYEM -0.001 0.002 -0.006 0.003 1.000
## LOCB_MARA 0.002 0.003 -0.004 0.008 1.000
## LOCB_NONE 0.002 0.003 -0.003 0.007 1.000
## LOCB_NTWO -0.003 0.003 -0.008 0.003 1.000
## LOCB_NYAB -0.002 0.002 -0.006 0.002 1.000
## Alcohol ~~
## SMOKING 0.083 0.015 0.054 0.113 1.000
## LOCB_EWAF 0.039 0.011 0.017 0.062 1.000
## LOCB_KADONE -0.014 0.013 -0.039 0.01 1.000
## LOCB_KADTWO -0.006 0.013 -0.032 0.019 1.000
## LOCB_KARO -0.019 0.020 -0.058 0.02 1.000
## LOCB_KYEM -0.014 0.011 -0.036 0.007 1.000
## LOCB_MARA -0.001 0.015 -0.031 0.028 1.000
## LOCB_NONE -0.003 0.012 -0.027 0.021 1.000
## LOCB_NTWO 0.018 0.014 -0.008 0.045 1.000
## LOCB_NYAB -0.002 0.011 -0.023 0.019 1.000
## SMOKING ~~
## LOCB_EWAF 0.002 0.003 -0.004 0.008 1.000
## LOCB_KADONE -0.002 0.003 -0.009 0.004 1.000
## LOCB_KADTWO -0.006 0.003 -0.012 0.001 1.000
## LOCB_KARO 0.009 0.005 -0.001 0.019 1.000
## LOCB_KYEM -0.002 0.003 -0.008 0.004 1.000
## LOCB_MARA -0.001 0.004 -0.009 0.006 1.000
## LOCB_NONE 0.007 0.003 0 0.013 1.000
## LOCB_NTWO 0.005 0.003 -0.002 0.012 1.000
## LOCB_NYAB -0.001 0.003 -0.007 0.004 1.000
## LOCB_EWAF ~~
## LOCB_KADONE -0.004 0.002 -0.009 0 1.000
## LOCB_KADTWO -0.005 0.002 -0.01 0 1.000
## LOCB_KARO -0.013 0.004 -0.021 -0.006 1.000
## LOCB_KYEM -0.004 0.002 -0.008 0.001 1.000
## LOCB_MARA -0.007 0.003 -0.013 -0.001 1.000
## LOCB_NONE -0.004 0.002 -0.009 0 1.000
## LOCB_NTWO -0.005 0.003 -0.011 0 1.000
## LOCB_NYAB -0.003 0.002 -0.007 0.001 1.000
## LOCB_KADONE ~~
## LOCB_KADTWO -0.007 0.003 -0.012 -0.001 1.000
## LOCB_KARO -0.017 0.004 -0.025 -0.008 1.000
## LOCB_KYEM -0.004 0.002 -0.009 0 1.000
## LOCB_MARA -0.009 0.003 -0.015 -0.002 1.000
## LOCB_NONE -0.006 0.003 -0.011 0 1.000
## LOCB_NTWO -0.007 0.003 -0.012 -0.001 1.000
## LOCB_NYAB -0.004 0.002 -0.008 0.001 1.000
## LOCB_KADTWO ~~
## LOCB_KARO -0.018 0.004 -0.026 -0.009 1.000
## LOCB_KYEM -0.005 0.002 -0.009 0 1.000
## LOCB_MARA -0.009 0.003 -0.016 -0.003 1.000
## LOCB_NONE -0.006 0.003 -0.011 -0.001 1.000
## LOCB_NTWO -0.007 0.003 -0.013 -0.001 1.000
## LOCB_NYAB -0.004 0.002 -0.009 0 1.000
## LOCB_KARO ~~
## LOCB_KYEM -0.013 0.004 -0.021 -0.006 1.000
## LOCB_MARA -0.027 0.005 -0.037 -0.017 1.000
## LOCB_NONE -0.016 0.004 -0.025 -0.008 1.000
## LOCB_NTWO -0.020 0.005 -0.029 -0.011 1.000
## LOCB_NYAB -0.012 0.004 -0.019 -0.005 1.000
## LOCB_KYEM ~~
## LOCB_MARA -0.007 0.003 -0.013 -0.001 1.000
## LOCB_NONE -0.004 0.002 -0.009 0 1.000
## LOCB_NTWO -0.005 0.003 -0.011 0 1.000
## LOCB_NYAB -0.003 0.002 -0.007 0.001 1.000
## LOCB_MARA ~~
## LOCB_NONE -0.009 0.003 -0.015 -0.002 1.000
## LOCB_NTWO -0.011 0.004 -0.018 -0.004 1.000
## LOCB_NYAB -0.006 0.003 -0.012 -0.001 1.000
## LOCB_NONE ~~
## LOCB_NTWO -0.006 0.003 -0.012 -0.001 1.000
## LOCB_NYAB -0.004 0.002 -0.008 0.001 1.000
## LOCB_NTWO ~~
## LOCB_NYAB -0.005 0.002 -0.01 0 1.000
## Prior
##
## beta(4,2)
##
## beta(4,2)
## beta(3,3)
##
## beta(3,3)
##
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
##
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
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## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
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## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
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## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
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## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
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## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
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## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
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## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
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## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
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## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
##
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
##
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
##
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
##
## beta(1,1)
## beta(1,1)
## beta(1,1)
##
## beta(1,1)
## beta(1,1)
##
## beta(1,1)
##
## Intercepts:
## Estimate Post.SD pi.lower pi.upper Rhat Prior
## .PHQ8_est 0.014 0.137 -0.257 0.284 1.000 normal(0,10)
## .FIES_est -0.000 0.035 -0.069 0.068 1.000 normal(0,10)
## Economic_pvrty -0.000 0.038 -0.075 0.075 1.000 normal(0,10)
## Land_est -0.000 0.038 -0.075 0.074 1.000 normal(0,10)
## FR.dist 0.000 0.038 -0.073 0.074 1.000 normal(0,10)
## DOBB -0.000 0.038 -0.077 0.075 1.000 normal(0,10)
## SEX 0.596 0.019 0.559 0.632 1.000 normal(0,10)
## EDUCAT 2.640 0.048 2.546 2.735 1.000 normal(0,10)
## Soci_est.1 -0.000 0.038 -0.075 0.075 1.000 normal(0,10)
## MSTATUS_Div.wd 0.168 0.015 0.14 0.197 1.000 normal(0,10)
## MSTATUS_Single 0.066 0.009 0.048 0.085 1.000 normal(0,10)
## Alcohol 0.460 0.047 0.368 0.553 1.000 normal(0,10)
## SMOKING 0.109 0.012 0.086 0.133 1.000 normal(0,10)
## LOCB_EWAF 0.059 0.009 0.041 0.077 1.000 normal(0,10)
## LOCB_KADONE 0.075 0.010 0.055 0.094 1.000 normal(0,10)
## LOCB_KADTWO 0.079 0.010 0.059 0.099 1.000 normal(0,10)
## LOCB_KARO 0.222 0.016 0.191 0.253 1.000 normal(0,10)
## LOCB_KYEM 0.059 0.009 0.041 0.077 1.000 normal(0,10)
## LOCB_MARA 0.118 0.012 0.094 0.142 1.000 normal(0,10)
## LOCB_NONE 0.072 0.010 0.052 0.091 1.000 normal(0,10)
## LOCB_NTWO 0.088 0.011 0.067 0.109 1.000 normal(0,10)
## LOCB_NYAB 0.052 0.008 0.035 0.069 1.000 normal(0,10)
## Forest_est.1 0.000 0.038 -0.072 0.073 1.000 normal(0,10)
## Health 3.222 0.035 3.153 3.291 1.000 normal(0,10)
##
## Variances:
## Estimate Post.SD pi.lower pi.upper Rhat Prior
## .PHQ8_est 0.827 0.046 0.741 0.923 1.000 gamma(1,.5)[sd]
## .FIES_est 0.836 0.045 0.752 0.928 1.000 gamma(1,.5)[sd]
## Economic_pvrty 1.008 0.054 0.908 1.121 1.000 gamma(1,.5)[sd]
## Land_est 1.005 0.055 0.904 1.12 1.000 gamma(1,.5)[sd]
## Forest_est.1 1.006 0.054 0.905 1.118 1.000 gamma(1,.5)[sd]
## Health 0.851 0.046 0.765 0.946 1.000 gamma(1,.5)[sd]
## FR.dist 1.004 0.053 0.904 1.111 1.000 gamma(1,.5)[sd]
## DOBB 1.022 0.055 0.92 1.136 1.000 gamma(1,.5)[sd]
## SEX 0.247 0.013 0.222 0.274 1.000 gamma(1,.5)[sd]
## EDUCAT 1.620 0.089 1.453 1.802 1.000 gamma(1,.5)[sd]
## Soci_est.1 1.026 0.055 0.924 1.139 1.000 gamma(1,.5)[sd]
## MSTATUS_Div.wd 0.144 0.008 0.129 0.159 1.000 gamma(1,.5)[sd]
## MSTATUS_Single 0.063 0.003 0.057 0.07 1.000 gamma(1,.5)[sd]
## Alcohol 1.546 0.084 1.391 1.718 1.000 gamma(1,.5)[sd]
## SMOKING 0.100 0.006 0.09 0.111 1.000 gamma(1,.5)[sd]
## LOCB_EWAF 0.057 0.003 0.051 0.063 1.000 gamma(1,.5)[sd]
## LOCB_KADONE 0.070 0.004 0.063 0.078 1.000 gamma(1,.5)[sd]
## LOCB_KADTWO 0.075 0.004 0.067 0.083 1.000 gamma(1,.5)[sd]
## LOCB_KARO 0.177 0.010 0.159 0.196 1.000 gamma(1,.5)[sd]
## LOCB_KYEM 0.057 0.003 0.051 0.063 1.000 gamma(1,.5)[sd]
## LOCB_MARA 0.107 0.006 0.096 0.119 1.000 gamma(1,.5)[sd]
## LOCB_NONE 0.069 0.004 0.062 0.076 1.000 gamma(1,.5)[sd]
## LOCB_NTWO 0.082 0.004 0.074 0.091 1.000 gamma(1,.5)[sd]
## LOCB_NYAB 0.050 0.003 0.045 0.056 1.000 gamma(1,.5)[sd]round(100*(as.numeric(fit_main_1_sum$Estimate)-as.numeric(fit_main_5_sum$Estimate))/as.numeric(fit_main_5_sum$Estimate),2) # Fine, if it is only small ## [1] -0.93 -1.65 0.64 -0.56 9.09 -0.73 -2.94 0.00 1.85 -7.14
## [11] -2.22 1.09 2.13 20.00 -2.42 -16.67 -3.85 0.00 -0.39 0.00
## [21] 0.00 0.00 -1.18 -2.22 0.00 -0.57 0.12 -0.12 0.00 0.00
## [31] 0.00 0.12 0.10 0.00 0.00 25.00 9.09 0.00 0.00 0.00
## [41] 0.00 0.00 0.54 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## [51] -0.10 -2.27 0.00 0.00 0.00 0.00 0.56 0.00 0.00 0.00
## [61] 0.00 NaN -20.00 0.00 NaN 0.00 0.00 0.00 0.00 0.00
## [71] 2.78 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 NaN
## [81] 0.00 0.00 0.00 0.12 0.52 0.00 0.00 2.13 0.00 0.00
## [91] 0.00 0.00 0.00 5.56 -33.33 0.00 0.00 0.00 0.00 0.00
## [101] 0.00 0.00 0.00 0.00 0.00 0.00 -14.29 NaN -9.09 0.00
## [111] 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 -50.00
## [121] 0.00 0.00 0.00 0.00 NaN 0.00 0.00 0.00 0.00 0.00
## [131] 0.00 0.00 0.00 0.00 0.00 0.00 0.00 -0.06 0.00 0.00
## [141] 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## [151] 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## [161] 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## [171] 0.00 0.00 0.00 -14.29 0.00 0.00 0.00 0.00 0.00 0.00
## [181] 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## [191] 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## [201] 0.00 0.00 2.00 35.71 NaN NaN NaN NaN NaN 0.00
## [211] 0.04 NaN 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## [221] 0.00 0.00 0.00 0.00 0.00 NaN 0.00# Shifting the hyperparameters 'down' (specifically the mean and the shape of the beta distribution)
model_main_6 <- '
### Main regression part
# Key variables of interest
PHQ8_est ~ prior("normal(0, 1)")*FIES_est + prior("normal(0, 1)")*Economic_poverty
FIES_est ~ prior("normal(-1.5, 2)")*Land_est + prior("normal(0, 1)")*Economic_poverty
# Distance to forest reserve
FIES_est ~ prior("normal(-1.5, 2)")*FR.dist
### Covariance
# Between socio-ecological variables
FIES_est ~~ prior("beta(3, 3)")*Forest_est.1
Economic_poverty ~~ prior("beta(3, 3)")*Forest_est.1
Economic_poverty ~~ prior("beta(2, 4)")*Land_est
# Health (treated as numeric)
PHQ8_est ~~ prior("beta(2, 4)")*Health
### Covariates
# Age
PHQ8_est ~ prior("normal(-0.5, 2)")*DOBB +
# Sex
prior("normal(-0.5, 2)")*SEX +
# Education (treated as numeric)
prior("normal(-1.5, 2)")*EDUCAT +
# Social support
prior("normal(-1.5, 2)")*Soci_est.1 +
# Marital status (RL = MSTATUS_Mar.pol)
prior("normal(-0.5, 2)")*MSTATUS_Div.wid + prior("normal(0, 9)")*MSTATUS_Single +
# Alchohol consumption
prior("normal(-0.5, 2)")*Alcohol +
# Smoking
prior("normal(-0.5, 2)")*SMOKING
### Location (RL = LOCB_NTC)
PHQ8_est ~ prior("normal(-1, 9)")*LOCB_EWAF + prior("normal(-1, 9)")*LOCB_KADONE + prior("normal(-1, 9)")*LOCB_KADTWO + prior("normal(-1, 9)")*LOCB_KARO + prior("normal(-1, 9)")*LOCB_KYEM + prior("normal(-1, 9)")*LOCB_MARA + prior("normal(-1, 9)")*LOCB_NONE + prior("normal(-1, 9)")*LOCB_NTWO + prior("normal(-1, 9)")*LOCB_NYAB
'
# Bayesian SEM
fit_main_6 <- bsem(model_main_6, data=DF_analy.list[[1]], fixed.x = FALSE, n.chains = chains, burnin = burnin, sample = sample, seed = seed, target = "stan", bcontrol = list(cores = 4))# Load the model (if needed)
fit_main_6 <- readRDS("fit_main_6.rds")
# Calculate the bias associated with using 'down' shifted hyperparameters
fit_main_1_sum <- data.frame(summary(fit_main_1))[,1:2]## blavaan (0.3-15) results of 4000 samples after 4000 adapt/burnin iterations
##
## Number of observations 695
##
## Number of missing patterns 1
##
## Statistic MargLogLik PPP
## Value -12480.392 0.000
##
## Regressions:
## Estimate Post.SD pi.lower pi.upper Rhat Prior
## PHQ8_est ~
## FIES_est 0.212 0.038 0.137 0.288 1.000 normal(1,1)
## Economic_pvrty 0.119 0.042 0.036 0.202 1.000 normal(1,1)
## FIES_est ~
## Land_est -0.158 0.035 -0.226 -0.089 1.000 normal(-0.5,2)
## Economic_pvrty 0.356 0.035 0.286 0.426 1.000 normal(1,1)
## FR.dist -0.012 0.034 -0.079 0.056 1.000 normal(-0.5,2)
## PHQ8_est ~
## DOBB 0.013 0.039 -0.063 0.089 1.000 normal(0.5,2)
## SEX 0.088 0.077 -0.061 0.24 1.000 normal(0.5,2)
## EDUCAT -0.093 0.031 -0.154 -0.031 1.000 normal(-0.5,2)
## Soci_est.1 -0.048 0.037 -0.12 0.024 1.000 normal(-0.5,2)
## MSTATUS_Div.wd -0.018 0.101 -0.215 0.178 1.000 normal(0.5,2)
## MSTATUS_Single 0.121 0.150 -0.17 0.413 1.000 normal(0,9)
## Alcohol 0.005 0.029 -0.053 0.063 1.000 normal(0.5,2)
## SMOKING 0.075 0.118 -0.158 0.307 1.000 normal(0.5,2)
## LOCB_EWAF 0.149 0.162 -0.166 0.468 1.000 normal(0,9)
## LOCB_KADONE 0.253 0.151 -0.045 0.55 1.000 normal(0,9)
## LOCB_KADTWO 0.057 0.148 -0.236 0.347 1.000 normal(0,9)
## LOCB_KARO 0.284 0.109 0.069 0.498 1.000 normal(0,9)
## LOCB_KYEM 0.482 0.161 0.167 0.794 1.000 normal(0,9)
## LOCB_MARA 0.084 0.129 -0.17 0.337 1.000 normal(0,9)
## LOCB_NONE 0.044 0.152 -0.252 0.343 1.000 normal(0,9)
## LOCB_NTWO 0.040 0.144 -0.243 0.322 1.000 normal(0,9)
## LOCB_NYAB 0.347 0.171 0.01 0.679 1.000 normal(0,9)
##
## Covariances:
## Estimate Post.SD pi.lower pi.upper Rhat
## .FIES_est ~~
## Forest_est.1 0.136 0.036 0.067 0.207 1.000
## Economic_poverty ~~
## Forest_est.1 0.033 0.037 -0.039 0.107 1.000
## Land_est -0.272 0.040 -0.352 -0.195 1.000
## .PHQ8_est ~~
## Health -0.165 0.038 -0.24 -0.093 1.000
## FR.dist ~~
## DOBB 0.038 0.038 -0.038 0.113 1.000
## SEX -0.026 0.019 -0.063 0.011 1.000
## EDUCAT 0.005 0.047 -0.089 0.097 1.000
## Soci_est.1 0.012 0.039 -0.065 0.088 1.000
## MSTATUS_Div.wd -0.013 0.014 -0.041 0.015 1.000
## MSTATUS_Single 0.001 0.009 -0.018 0.019 1.000
## Alcohol -0.068 0.048 -0.162 0.023 1.000
## SMOKING -0.007 0.012 -0.031 0.016 1.000
## LOCB_EWAF -0.024 0.009 -0.042 -0.007 1.000
## LOCB_KADONE 0.186 0.012 0.163 0.211 1.001
## LOCB_KADTWO 0.124 0.011 0.102 0.147 1.000
## LOCB_KARO -0.066 0.016 -0.098 -0.035 1.000
## LOCB_KYEM -0.039 0.009 -0.056 -0.021 1.000
## LOCB_MARA -0.088 0.013 -0.114 -0.063 1.001
## LOCB_NONE 0.005 0.010 -0.014 0.025 1.001
## LOCB_NTWO -0.051 0.011 -0.073 -0.03 1.000
## LOCB_NYAB -0.024 0.009 -0.041 -0.007 1.000
## DOBB ~~
## SEX -0.043 0.019 -0.081 -0.007 1.000
## EDUCAT -0.249 0.050 -0.349 -0.154 1.000
## Soci_est.1 -0.004 0.038 -0.079 0.07 1.000
## MSTATUS_Div.wd 0.098 0.015 0.07 0.128 1.000
## MSTATUS_Single -0.063 0.010 -0.083 -0.044 1.000
## Alcohol 0.179 0.048 0.085 0.275 1.000
## SMOKING 0.062 0.012 0.039 0.087 1.000
## LOCB_EWAF 0.013 0.009 -0.004 0.031 1.000
## LOCB_KADONE -0.002 0.010 -0.022 0.017 1.000
## LOCB_KADTWO 0.016 0.010 -0.005 0.036 1.000
## LOCB_KARO 0.000 0.016 -0.032 0.032 1.000
## LOCB_KYEM 0.004 0.009 -0.014 0.022 1.000
## LOCB_MARA -0.023 0.012 -0.048 0.001 1.000
## LOCB_NONE 0.000 0.010 -0.019 0.02 1.000
## LOCB_NTWO -0.011 0.011 -0.032 0.011 1.000
## LOCB_NYAB 0.009 0.009 -0.008 0.025 1.000
## SEX ~~
## EDUCAT -0.140 0.025 -0.19 -0.093 1.000
## Soci_est.1 -0.027 0.019 -0.065 0.011 1.000
## MSTATUS_Div.wd 0.037 0.007 0.023 0.051 1.000
## MSTATUS_Single -0.009 0.005 -0.019 0 1.000
## Alcohol -0.097 0.023 -0.143 -0.052 1.000
## SMOKING -0.038 0.006 -0.05 -0.026 1.000
## LOCB_EWAF -0.009 0.005 -0.018 0 1.000
## LOCB_KADONE 0.003 0.005 -0.007 0.013 1.000
## LOCB_KADTWO -0.007 0.005 -0.017 0.003 1.000
## LOCB_KARO -0.015 0.008 -0.031 0 1.000
## LOCB_KYEM 0.001 0.004 -0.008 0.009 1.000
## LOCB_MARA 0.000 0.006 -0.012 0.012 1.000
## LOCB_NONE 0.005 0.005 -0.005 0.015 1.000
## LOCB_NTWO 0.005 0.005 -0.006 0.015 1.000
## LOCB_NYAB 0.002 0.004 -0.006 0.01 1.000
## EDUCAT ~~
## Soci_est.1 0.195 0.050 0.098 0.294 1.000
## MSTATUS_Div.wd -0.075 0.019 -0.112 -0.04 1.000
## MSTATUS_Single 0.086 0.013 0.062 0.111 1.000
## Alcohol -0.048 0.060 -0.163 0.069 1.000
## SMOKING -0.043 0.015 -0.073 -0.013 1.000
## LOCB_EWAF -0.005 0.012 -0.028 0.018 1.000
## LOCB_KADONE -0.008 0.013 -0.032 0.017 1.000
## LOCB_KADTWO 0.001 0.013 -0.025 0.027 1.000
## LOCB_KARO 0.015 0.020 -0.025 0.054 1.000
## LOCB_KYEM 0.019 0.011 -0.004 0.041 1.000
## LOCB_MARA -0.002 0.016 -0.033 0.028 1.000
## LOCB_NONE 0.029 0.013 0.004 0.053 1.000
## LOCB_NTWO -0.056 0.014 -0.084 -0.029 1.000
## LOCB_NYAB -0.017 0.011 -0.039 0.004 1.000
## Soci_est.1 ~~
## MSTATUS_Div.wd -0.037 0.015 -0.067 -0.009 1.000
## MSTATUS_Single 0.030 0.010 0.011 0.049 1.000
## Alcohol -0.022 0.048 -0.116 0.073 1.000
## SMOKING -0.019 0.012 -0.043 0.004 1.000
## LOCB_EWAF -0.007 0.009 -0.025 0.011 1.000
## LOCB_KADONE 0.009 0.010 -0.011 0.029 1.000
## LOCB_KADTWO -0.018 0.010 -0.039 0.002 1.000
## LOCB_KARO -0.006 0.016 -0.038 0.025 1.000
## LOCB_KYEM 0.000 0.009 -0.018 0.019 1.000
## LOCB_MARA 0.010 0.013 -0.014 0.035 1.000
## LOCB_NONE -0.001 0.010 -0.021 0.018 1.000
## LOCB_NTWO -0.014 0.011 -0.035 0.007 1.000
## LOCB_NYAB 0.011 0.009 -0.006 0.028 1.000
## MSTATUS_Div.wid ~~
## MSTATUS_Single -0.011 0.004 -0.018 -0.004 1.000
## Alcohol 0.023 0.018 -0.012 0.059 1.000
## SMOKING 0.005 0.005 -0.004 0.013 1.000
## LOCB_EWAF -0.007 0.003 -0.014 -0.001 1.000
## LOCB_KADONE -0.007 0.004 -0.014 0 1.000
## LOCB_KADTWO 0.004 0.004 -0.004 0.012 1.000
## LOCB_KARO 0.001 0.006 -0.01 0.013 1.000
## LOCB_KYEM 0.009 0.003 0.002 0.016 1.000
## LOCB_MARA -0.003 0.005 -0.012 0.007 1.000
## LOCB_NONE -0.008 0.004 -0.015 0 1.000
## LOCB_NTWO -0.003 0.004 -0.012 0.005 1.000
## LOCB_NYAB -0.000 0.003 -0.007 0.006 1.000
## MSTATUS_Single ~~
## Alcohol -0.010 0.012 -0.033 0.013 1.000
## SMOKING -0.006 0.003 -0.012 0 1.000
## LOCB_EWAF -0.001 0.002 -0.005 0.003 1.000
## LOCB_KADONE -0.001 0.003 -0.005 0.004 1.000
## LOCB_KADTWO 0.001 0.003 -0.005 0.006 1.000
## LOCB_KARO -0.003 0.004 -0.011 0.005 1.000
## LOCB_KYEM -0.001 0.002 -0.005 0.003 1.000
## LOCB_MARA 0.002 0.003 -0.004 0.008 1.000
## LOCB_NONE 0.002 0.003 -0.003 0.007 1.000
## LOCB_NTWO -0.003 0.003 -0.008 0.002 1.000
## LOCB_NYAB -0.002 0.002 -0.006 0.002 1.000
## Alcohol ~~
## SMOKING 0.083 0.015 0.054 0.113 1.000
## LOCB_EWAF 0.039 0.011 0.017 0.061 1.000
## LOCB_KADONE -0.014 0.012 -0.039 0.011 1.000
## LOCB_KADTWO -0.006 0.013 -0.032 0.019 1.000
## LOCB_KARO -0.019 0.020 -0.058 0.019 1.000
## LOCB_KYEM -0.014 0.011 -0.036 0.008 1.000
## LOCB_MARA -0.001 0.015 -0.031 0.029 1.000
## LOCB_NONE -0.003 0.012 -0.027 0.022 1.000
## LOCB_NTWO 0.018 0.013 -0.008 0.045 1.000
## LOCB_NYAB -0.002 0.011 -0.023 0.018 1.000
## SMOKING ~~
## LOCB_EWAF 0.002 0.003 -0.004 0.008 1.000
## LOCB_KADONE -0.002 0.003 -0.009 0.004 1.000
## LOCB_KADTWO -0.006 0.003 -0.012 0 1.000
## LOCB_KARO 0.009 0.005 -0.001 0.019 1.000
## LOCB_KYEM -0.002 0.003 -0.008 0.003 1.000
## LOCB_MARA -0.001 0.004 -0.009 0.006 1.000
## LOCB_NONE 0.007 0.003 0.001 0.013 1.000
## LOCB_NTWO 0.005 0.003 -0.002 0.011 1.000
## LOCB_NYAB -0.001 0.003 -0.007 0.004 1.000
## LOCB_EWAF ~~
## LOCB_KADONE -0.004 0.002 -0.009 0 1.000
## LOCB_KADTWO -0.005 0.002 -0.01 0 1.000
## LOCB_KARO -0.013 0.004 -0.021 -0.006 1.000
## LOCB_KYEM -0.004 0.002 -0.008 0.001 1.000
## LOCB_MARA -0.007 0.003 -0.013 -0.001 1.000
## LOCB_NONE -0.004 0.002 -0.009 0 1.000
## LOCB_NTWO -0.005 0.003 -0.01 0 1.000
## LOCB_NYAB -0.003 0.002 -0.007 0.001 1.000
## LOCB_KADONE ~~
## LOCB_KADTWO -0.007 0.003 -0.012 -0.001 1.000
## LOCB_KARO -0.017 0.004 -0.025 -0.008 1.000
## LOCB_KYEM -0.004 0.002 -0.009 0 1.000
## LOCB_MARA -0.009 0.003 -0.015 -0.002 1.000
## LOCB_NONE -0.006 0.003 -0.011 -0.001 1.000
## LOCB_NTWO -0.006 0.003 -0.012 -0.001 1.000
## LOCB_NYAB -0.004 0.002 -0.008 0 1.000
## LOCB_KADTWO ~~
## LOCB_KARO -0.018 0.004 -0.026 -0.009 1.000
## LOCB_KYEM -0.005 0.002 -0.01 0 1.000
## LOCB_MARA -0.009 0.003 -0.016 -0.003 1.000
## LOCB_NONE -0.006 0.003 -0.011 -0.001 1.000
## LOCB_NTWO -0.007 0.003 -0.013 -0.001 1.000
## LOCB_NYAB -0.004 0.002 -0.009 0 1.000
## LOCB_KARO ~~
## LOCB_KYEM -0.013 0.004 -0.021 -0.006 1.000
## LOCB_MARA -0.027 0.005 -0.037 -0.017 1.000
## LOCB_NONE -0.016 0.004 -0.025 -0.008 1.000
## LOCB_NTWO -0.020 0.005 -0.029 -0.011 1.000
## LOCB_NYAB -0.012 0.004 -0.019 -0.005 1.000
## LOCB_KYEM ~~
## LOCB_MARA -0.007 0.003 -0.013 -0.001 1.000
## LOCB_NONE -0.004 0.002 -0.009 0 1.000
## LOCB_NTWO -0.005 0.003 -0.011 0 1.000
## LOCB_NYAB -0.003 0.002 -0.007 0.001 1.000
## LOCB_MARA ~~
## LOCB_NONE -0.009 0.003 -0.015 -0.002 1.000
## LOCB_NTWO -0.011 0.004 -0.018 -0.004 1.000
## LOCB_NYAB -0.006 0.003 -0.012 -0.001 1.000
## LOCB_NONE ~~
## LOCB_NTWO -0.006 0.003 -0.012 -0.001 1.000
## LOCB_NYAB -0.004 0.002 -0.008 0.001 1.000
## LOCB_NTWO ~~
## LOCB_NYAB -0.005 0.002 -0.01 0 1.000
## Prior
##
## beta(3,2)
##
## beta(3,2)
## beta(2,3)
##
## beta(2,3)
##
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
##
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
##
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
##
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
##
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
##
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
##
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
##
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
##
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
##
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
##
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
##
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
##
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
##
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
##
## beta(1,1)
## beta(1,1)
## beta(1,1)
##
## beta(1,1)
## beta(1,1)
##
## beta(1,1)
##
## Intercepts:
## Estimate Post.SD pi.lower pi.upper Rhat Prior
## .PHQ8_est 0.019 0.139 -0.256 0.289 1.000 normal(0,10)
## .FIES_est -0.000 0.035 -0.068 0.068 1.000 normal(0,10)
## Economic_pvrty 0.000 0.039 -0.077 0.076 1.000 normal(0,10)
## Land_est 0.000 0.038 -0.074 0.074 1.000 normal(0,10)
## FR.dist 0.000 0.038 -0.075 0.076 1.000 normal(0,10)
## DOBB 0.000 0.038 -0.074 0.074 1.000 normal(0,10)
## SEX 0.596 0.019 0.559 0.633 1.000 normal(0,10)
## EDUCAT 2.641 0.048 2.548 2.735 1.000 normal(0,10)
## Soci_est.1 -0.000 0.039 -0.076 0.076 1.000 normal(0,10)
## MSTATUS_Div.wd 0.168 0.015 0.14 0.197 1.000 normal(0,10)
## MSTATUS_Single 0.066 0.009 0.048 0.085 1.000 normal(0,10)
## Alcohol 0.460 0.048 0.366 0.554 1.000 normal(0,10)
## SMOKING 0.109 0.012 0.086 0.133 1.000 normal(0,10)
## LOCB_EWAF 0.059 0.009 0.042 0.077 1.000 normal(0,10)
## LOCB_KADONE 0.075 0.010 0.055 0.095 1.000 normal(0,10)
## LOCB_KADTWO 0.079 0.010 0.059 0.1 1.000 normal(0,10)
## LOCB_KARO 0.222 0.016 0.19 0.253 1.000 normal(0,10)
## LOCB_KYEM 0.059 0.009 0.041 0.077 1.000 normal(0,10)
## LOCB_MARA 0.118 0.012 0.094 0.142 1.000 normal(0,10)
## LOCB_NONE 0.072 0.010 0.052 0.091 1.000 normal(0,10)
## LOCB_NTWO 0.088 0.011 0.066 0.109 1.000 normal(0,10)
## LOCB_NYAB 0.052 0.008 0.035 0.068 1.000 normal(0,10)
## Forest_est.1 -0.000 0.038 -0.074 0.072 1.000 normal(0,10)
## Health 3.222 0.035 3.153 3.291 1.000 normal(0,10)
##
## Variances:
## Estimate Post.SD pi.lower pi.upper Rhat Prior
## .PHQ8_est 0.828 0.046 0.745 0.924 1.000 gamma(1,.5)[sd]
## .FIES_est 0.835 0.045 0.751 0.926 1.000 gamma(1,.5)[sd]
## Economic_pvrty 1.008 0.054 0.906 1.118 1.000 gamma(1,.5)[sd]
## Land_est 1.005 0.054 0.903 1.116 1.000 gamma(1,.5)[sd]
## Forest_est.1 1.006 0.055 0.905 1.119 1.000 gamma(1,.5)[sd]
## Health 0.852 0.046 0.768 0.946 1.000 gamma(1,.5)[sd]
## FR.dist 1.005 0.054 0.904 1.115 1.001 gamma(1,.5)[sd]
## DOBB 1.021 0.055 0.919 1.134 1.000 gamma(1,.5)[sd]
## SEX 0.247 0.013 0.222 0.274 1.000 gamma(1,.5)[sd]
## EDUCAT 1.622 0.088 1.459 1.806 1.000 gamma(1,.5)[sd]
## Soci_est.1 1.026 0.057 0.921 1.144 1.000 gamma(1,.5)[sd]
## MSTATUS_Div.wd 0.144 0.008 0.129 0.16 1.000 gamma(1,.5)[sd]
## MSTATUS_Single 0.063 0.003 0.057 0.07 1.000 gamma(1,.5)[sd]
## Alcohol 1.545 0.083 1.391 1.716 1.000 gamma(1,.5)[sd]
## SMOKING 0.100 0.005 0.09 0.111 1.000 gamma(1,.5)[sd]
## LOCB_EWAF 0.057 0.003 0.051 0.063 1.000 gamma(1,.5)[sd]
## LOCB_KADONE 0.070 0.004 0.063 0.078 1.000 gamma(1,.5)[sd]
## LOCB_KADTWO 0.075 0.004 0.067 0.083 1.000 gamma(1,.5)[sd]
## LOCB_KARO 0.177 0.010 0.159 0.196 1.000 gamma(1,.5)[sd]
## LOCB_KYEM 0.057 0.003 0.051 0.064 1.000 gamma(1,.5)[sd]
## LOCB_MARA 0.107 0.006 0.096 0.119 1.000 gamma(1,.5)[sd]
## LOCB_NONE 0.069 0.004 0.062 0.076 1.000 gamma(1,.5)[sd]
## LOCB_NTWO 0.082 0.004 0.074 0.091 1.000 gamma(1,.5)[sd]
## LOCB_NYAB 0.051 0.003 0.045 0.056 1.000 gamma(1,.5)[sd]fit_main_6_sum <- data.frame(summary(fit_main_6))[,1:2]## blavaan (0.3-15) results of 4000 samples after 4000 adapt/burnin iterations
##
## Number of observations 695
##
## Number of missing patterns 1
##
## Statistic MargLogLik PPP
## Value -12480.188 0.000
##
## Regressions:
## Estimate Post.SD pi.lower pi.upper Rhat Prior
## PHQ8_est ~
## FIES_est 0.212 0.038 0.136 0.288 1.000 normal(0,1)
## Economic_pvrty 0.117 0.042 0.036 0.199 1.000 normal(0,1)
## FIES_est ~
## Land_est -0.158 0.036 -0.229 -0.087 1.000 normal(-1.5,2)
## Economic_pvrty 0.354 0.036 0.284 0.425 1.000 normal(0,1)
## FR.dist -0.012 0.034 -0.079 0.055 1.000 normal(-1.5,2)
## PHQ8_est ~
## DOBB 0.013 0.038 -0.06 0.086 1.000 normal(-0.5,2)
## SEX 0.087 0.079 -0.068 0.24 1.000 normal(-0.5,2)
## EDUCAT -0.094 0.031 -0.155 -0.032 1.000 normal(-1.5,2)
## Soci_est.1 -0.049 0.037 -0.121 0.025 1.000 normal(-1.5,2)
## MSTATUS_Div.wd -0.020 0.102 -0.22 0.179 1.000 normal(-0.5,2)
## MSTATUS_Single 0.123 0.148 -0.164 0.41 1.000 normal(0,9)
## Alcohol 0.005 0.029 -0.053 0.064 1.000 normal(-0.5,2)
## SMOKING 0.070 0.119 -0.164 0.304 1.000 normal(-0.5,2)
## LOCB_EWAF 0.147 0.164 -0.177 0.469 1.000 normal(-1,9)
## LOCB_KADONE 0.254 0.151 -0.036 0.55 1.001 normal(-1,9)
## LOCB_KADTWO 0.056 0.148 -0.236 0.341 1.000 normal(-1,9)
## LOCB_KARO 0.284 0.109 0.07 0.496 1.001 normal(-1,9)
## LOCB_KYEM 0.484 0.165 0.16 0.807 1.000 normal(-1,9)
## LOCB_MARA 0.085 0.129 -0.166 0.337 1.001 normal(-1,9)
## LOCB_NONE 0.045 0.153 -0.26 0.345 1.000 normal(-1,9)
## LOCB_NTWO 0.038 0.143 -0.241 0.316 1.000 normal(-1,9)
## LOCB_NYAB 0.347 0.172 0.008 0.683 1.000 normal(-1,9)
##
## Covariances:
## Estimate Post.SD pi.lower pi.upper Rhat
## .FIES_est ~~
## Forest_est.1 0.137 0.036 0.068 0.21 1.000
## Economic_poverty ~~
## Forest_est.1 0.033 0.037 -0.039 0.106 1.000
## Land_est -0.271 0.040 -0.35 -0.195 1.000
## .PHQ8_est ~~
## Health -0.167 0.037 -0.242 -0.095 1.000
## FR.dist ~~
## DOBB 0.037 0.038 -0.037 0.112 1.000
## SEX -0.026 0.019 -0.063 0.011 1.000
## EDUCAT 0.005 0.049 -0.091 0.1 1.000
## Soci_est.1 0.011 0.038 -0.065 0.086 1.000
## MSTATUS_Div.wd -0.013 0.014 -0.041 0.015 1.000
## MSTATUS_Single 0.001 0.009 -0.018 0.019 1.000
## Alcohol -0.068 0.047 -0.161 0.024 1.000
## SMOKING -0.007 0.012 -0.03 0.016 1.001
## LOCB_EWAF -0.024 0.009 -0.042 -0.007 1.001
## LOCB_KADONE 0.185 0.012 0.163 0.211 1.000
## LOCB_KADTWO 0.124 0.011 0.102 0.146 1.000
## LOCB_KARO -0.066 0.016 -0.097 -0.035 1.000
## LOCB_KYEM -0.039 0.009 -0.056 -0.021 1.000
## LOCB_MARA -0.088 0.013 -0.113 -0.064 1.000
## LOCB_NONE 0.005 0.010 -0.014 0.025 1.000
## LOCB_NTWO -0.051 0.011 -0.073 -0.03 1.000
## LOCB_NYAB -0.024 0.009 -0.041 -0.007 1.000
## DOBB ~~
## SEX -0.044 0.019 -0.081 -0.007 1.000
## EDUCAT -0.250 0.050 -0.349 -0.154 1.000
## Soci_est.1 -0.004 0.039 -0.08 0.071 1.000
## MSTATUS_Div.wd 0.098 0.015 0.069 0.128 1.000
## MSTATUS_Single -0.063 0.010 -0.083 -0.044 1.000
## Alcohol 0.179 0.047 0.088 0.274 1.000
## SMOKING 0.063 0.012 0.039 0.087 1.000
## LOCB_EWAF 0.013 0.009 -0.005 0.031 1.000
## LOCB_KADONE -0.002 0.010 -0.022 0.018 1.000
## LOCB_KADTWO 0.016 0.010 -0.005 0.036 1.000
## LOCB_KARO 0.000 0.016 -0.031 0.031 1.000
## LOCB_KYEM 0.005 0.009 -0.013 0.023 1.000
## LOCB_MARA -0.023 0.013 -0.048 0.001 1.000
## LOCB_NONE -0.000 0.010 -0.02 0.019 1.000
## LOCB_NTWO -0.011 0.011 -0.033 0.011 1.000
## LOCB_NYAB 0.009 0.009 -0.008 0.026 1.000
## SEX ~~
## EDUCAT -0.140 0.025 -0.189 -0.092 1.000
## Soci_est.1 -0.027 0.019 -0.065 0.01 1.000
## MSTATUS_Div.wd 0.036 0.007 0.022 0.051 1.000
## MSTATUS_Single -0.009 0.005 -0.018 0 1.000
## Alcohol -0.097 0.024 -0.144 -0.051 1.000
## SMOKING -0.038 0.006 -0.05 -0.026 1.000
## LOCB_EWAF -0.009 0.004 -0.018 -0.001 1.000
## LOCB_KADONE 0.003 0.005 -0.007 0.013 1.000
## LOCB_KADTWO -0.007 0.005 -0.017 0.003 1.000
## LOCB_KARO -0.015 0.008 -0.031 0 1.000
## LOCB_KYEM 0.001 0.004 -0.008 0.01 1.000
## LOCB_MARA 0.000 0.006 -0.012 0.012 1.000
## LOCB_NONE 0.005 0.005 -0.005 0.014 1.000
## LOCB_NTWO 0.005 0.005 -0.005 0.016 1.000
## LOCB_NYAB 0.002 0.004 -0.006 0.011 1.000
## EDUCAT ~~
## Soci_est.1 0.195 0.049 0.1 0.292 1.000
## MSTATUS_Div.wd -0.075 0.018 -0.111 -0.039 1.000
## MSTATUS_Single 0.086 0.013 0.062 0.112 1.000
## Alcohol -0.049 0.060 -0.167 0.068 1.000
## SMOKING -0.043 0.015 -0.073 -0.013 1.000
## LOCB_EWAF -0.005 0.012 -0.027 0.018 1.000
## LOCB_KADONE -0.008 0.013 -0.033 0.017 1.000
## LOCB_KADTWO 0.001 0.013 -0.025 0.027 1.000
## LOCB_KARO 0.015 0.020 -0.025 0.055 1.000
## LOCB_KYEM 0.018 0.011 -0.004 0.041 1.000
## LOCB_MARA -0.003 0.016 -0.034 0.028 1.000
## LOCB_NONE 0.029 0.012 0.004 0.053 1.000
## LOCB_NTWO -0.056 0.014 -0.084 -0.03 1.000
## LOCB_NYAB -0.017 0.011 -0.039 0.004 1.000
## Soci_est.1 ~~
## MSTATUS_Div.wd -0.038 0.015 -0.067 -0.009 1.000
## MSTATUS_Single 0.030 0.010 0.011 0.049 1.000
## Alcohol -0.023 0.047 -0.115 0.07 1.000
## SMOKING -0.019 0.012 -0.044 0.005 1.000
## LOCB_EWAF -0.007 0.009 -0.025 0.011 1.000
## LOCB_KADONE 0.008 0.010 -0.011 0.029 1.000
## LOCB_KADTWO -0.018 0.011 -0.039 0.002 1.000
## LOCB_KARO -0.006 0.016 -0.038 0.025 1.000
## LOCB_KYEM 0.000 0.009 -0.018 0.018 1.000
## LOCB_MARA 0.011 0.013 -0.014 0.035 1.000
## LOCB_NONE -0.001 0.010 -0.021 0.018 1.000
## LOCB_NTWO -0.014 0.011 -0.035 0.008 1.000
## LOCB_NYAB 0.011 0.009 -0.006 0.028 1.000
## MSTATUS_Div.wid ~~
## MSTATUS_Single -0.011 0.004 -0.018 -0.004 1.000
## Alcohol 0.023 0.018 -0.012 0.059 1.000
## SMOKING 0.005 0.005 -0.004 0.013 1.000
## LOCB_EWAF -0.007 0.003 -0.014 0 1.000
## LOCB_KADONE -0.007 0.004 -0.014 0.001 1.000
## LOCB_KADTWO 0.004 0.004 -0.004 0.012 1.000
## LOCB_KARO 0.001 0.006 -0.01 0.013 1.000
## LOCB_KYEM 0.009 0.003 0.002 0.016 1.000
## LOCB_MARA -0.003 0.005 -0.012 0.006 1.000
## LOCB_NONE -0.008 0.004 -0.015 0 1.000
## LOCB_NTWO -0.003 0.004 -0.012 0.005 1.000
## LOCB_NYAB -0.000 0.003 -0.007 0.006 1.000
## MSTATUS_Single ~~
## Alcohol -0.010 0.012 -0.033 0.013 1.000
## SMOKING -0.006 0.003 -0.012 0 1.000
## LOCB_EWAF -0.001 0.002 -0.005 0.003 1.000
## LOCB_KADONE -0.001 0.003 -0.006 0.004 1.000
## LOCB_KADTWO 0.001 0.003 -0.004 0.006 1.000
## LOCB_KARO -0.003 0.004 -0.011 0.005 1.000
## LOCB_KYEM -0.001 0.002 -0.005 0.003 1.000
## LOCB_MARA 0.002 0.003 -0.004 0.008 1.000
## LOCB_NONE 0.002 0.002 -0.002 0.007 1.000
## LOCB_NTWO -0.003 0.003 -0.008 0.002 1.000
## LOCB_NYAB -0.002 0.002 -0.006 0.002 1.000
## Alcohol ~~
## SMOKING 0.083 0.015 0.054 0.113 1.000
## LOCB_EWAF 0.039 0.011 0.017 0.061 1.000
## LOCB_KADONE -0.014 0.012 -0.038 0.01 1.000
## LOCB_KADTWO -0.006 0.013 -0.031 0.019 1.000
## LOCB_KARO -0.019 0.020 -0.059 0.02 1.000
## LOCB_KYEM -0.014 0.011 -0.036 0.008 1.000
## LOCB_MARA -0.001 0.015 -0.031 0.03 1.000
## LOCB_NONE -0.003 0.012 -0.027 0.021 1.000
## LOCB_NTWO 0.018 0.013 -0.008 0.045 1.000
## LOCB_NYAB -0.002 0.011 -0.023 0.018 1.000
## SMOKING ~~
## LOCB_EWAF 0.002 0.003 -0.004 0.008 1.000
## LOCB_KADONE -0.002 0.003 -0.008 0.004 1.000
## LOCB_KADTWO -0.006 0.003 -0.012 0.001 1.000
## LOCB_KARO 0.009 0.005 -0.001 0.019 1.000
## LOCB_KYEM -0.002 0.003 -0.008 0.003 1.000
## LOCB_MARA -0.001 0.004 -0.009 0.006 1.000
## LOCB_NONE 0.007 0.003 0.001 0.013 1.000
## LOCB_NTWO 0.005 0.003 -0.002 0.012 1.000
## LOCB_NYAB -0.001 0.003 -0.007 0.004 1.000
## LOCB_EWAF ~~
## LOCB_KADONE -0.004 0.002 -0.009 0 1.000
## LOCB_KADTWO -0.005 0.002 -0.01 0 1.001
## LOCB_KARO -0.013 0.004 -0.021 -0.006 1.000
## LOCB_KYEM -0.004 0.002 -0.008 0.001 1.000
## LOCB_MARA -0.007 0.003 -0.013 -0.001 1.000
## LOCB_NONE -0.004 0.002 -0.009 0 1.000
## LOCB_NTWO -0.005 0.003 -0.011 0 1.000
## LOCB_NYAB -0.003 0.002 -0.007 0.001 1.000
## LOCB_KADONE ~~
## LOCB_KADTWO -0.007 0.003 -0.012 -0.001 1.000
## LOCB_KARO -0.017 0.004 -0.025 -0.008 1.000
## LOCB_KYEM -0.004 0.002 -0.009 0 1.000
## LOCB_MARA -0.009 0.003 -0.015 -0.002 1.000
## LOCB_NONE -0.006 0.003 -0.011 0 1.000
## LOCB_NTWO -0.006 0.003 -0.012 -0.001 1.000
## LOCB_NYAB -0.004 0.002 -0.008 0.001 1.000
## LOCB_KADTWO ~~
## LOCB_KARO -0.018 0.004 -0.026 -0.009 1.000
## LOCB_KYEM -0.005 0.002 -0.01 0 1.000
## LOCB_MARA -0.009 0.003 -0.016 -0.003 1.000
## LOCB_NONE -0.006 0.003 -0.011 -0.001 1.000
## LOCB_NTWO -0.007 0.003 -0.013 -0.001 1.000
## LOCB_NYAB -0.004 0.002 -0.009 0 1.000
## LOCB_KARO ~~
## LOCB_KYEM -0.013 0.004 -0.021 -0.006 1.000
## LOCB_MARA -0.027 0.005 -0.038 -0.017 1.000
## LOCB_NONE -0.016 0.004 -0.025 -0.008 1.000
## LOCB_NTWO -0.020 0.005 -0.029 -0.011 1.000
## LOCB_NYAB -0.012 0.004 -0.019 -0.005 1.000
## LOCB_KYEM ~~
## LOCB_MARA -0.007 0.003 -0.013 -0.001 1.000
## LOCB_NONE -0.004 0.002 -0.009 0 1.000
## LOCB_NTWO -0.005 0.003 -0.01 0 1.000
## LOCB_NYAB -0.003 0.002 -0.007 0.001 1.000
## LOCB_MARA ~~
## LOCB_NONE -0.009 0.003 -0.015 -0.002 1.000
## LOCB_NTWO -0.011 0.004 -0.018 -0.004 1.000
## LOCB_NYAB -0.006 0.003 -0.012 -0.001 1.000
## LOCB_NONE ~~
## LOCB_NTWO -0.006 0.003 -0.012 -0.001 1.000
## LOCB_NYAB -0.004 0.002 -0.008 0.001 1.000
## LOCB_NTWO ~~
## LOCB_NYAB -0.005 0.002 -0.01 0 1.000
## Prior
##
## beta(3,3)
##
## beta(3,3)
## beta(2,4)
##
## beta(2,4)
##
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
##
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
##
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
##
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
##
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
##
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
##
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
##
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
##
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
##
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
##
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
##
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
##
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
##
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
##
## beta(1,1)
## beta(1,1)
## beta(1,1)
##
## beta(1,1)
## beta(1,1)
##
## beta(1,1)
##
## Intercepts:
## Estimate Post.SD pi.lower pi.upper Rhat Prior
## .PHQ8_est 0.023 0.139 -0.25 0.296 1.001 normal(0,10)
## .FIES_est 0.000 0.035 -0.068 0.067 1.000 normal(0,10)
## Economic_pvrty -0.000 0.037 -0.073 0.074 1.000 normal(0,10)
## Land_est 0.000 0.038 -0.074 0.074 1.000 normal(0,10)
## FR.dist 0.000 0.038 -0.074 0.075 1.000 normal(0,10)
## DOBB 0.001 0.039 -0.076 0.076 1.000 normal(0,10)
## SEX 0.595 0.019 0.558 0.633 1.000 normal(0,10)
## EDUCAT 2.640 0.048 2.546 2.733 1.000 normal(0,10)
## Soci_est.1 -0.000 0.039 -0.076 0.075 1.000 normal(0,10)
## MSTATUS_Div.wd 0.168 0.014 0.14 0.197 1.000 normal(0,10)
## MSTATUS_Single 0.066 0.010 0.047 0.085 1.000 normal(0,10)
## Alcohol 0.460 0.047 0.369 0.552 1.000 normal(0,10)
## SMOKING 0.109 0.012 0.085 0.133 1.000 normal(0,10)
## LOCB_EWAF 0.059 0.009 0.041 0.077 1.000 normal(0,10)
## LOCB_KADONE 0.075 0.010 0.055 0.095 1.000 normal(0,10)
## LOCB_KADTWO 0.079 0.010 0.059 0.1 1.000 normal(0,10)
## LOCB_KARO 0.222 0.016 0.19 0.253 1.000 normal(0,10)
## LOCB_KYEM 0.059 0.009 0.041 0.077 1.000 normal(0,10)
## LOCB_MARA 0.118 0.012 0.093 0.142 1.000 normal(0,10)
## LOCB_NONE 0.072 0.010 0.053 0.091 1.000 normal(0,10)
## LOCB_NTWO 0.088 0.011 0.066 0.109 1.000 normal(0,10)
## LOCB_NYAB 0.052 0.009 0.035 0.069 1.000 normal(0,10)
## Forest_est.1 0.000 0.038 -0.075 0.075 1.000 normal(0,10)
## Health 3.221 0.035 3.153 3.289 1.000 normal(0,10)
##
## Variances:
## Estimate Post.SD pi.lower pi.upper Rhat Prior
## .PHQ8_est 0.829 0.046 0.743 0.922 1.000 gamma(1,.5)[sd]
## .FIES_est 0.836 0.045 0.751 0.929 1.000 gamma(1,.5)[sd]
## Economic_pvrty 1.008 0.055 0.907 1.12 1.000 gamma(1,.5)[sd]
## Land_est 1.004 0.053 0.904 1.114 1.000 gamma(1,.5)[sd]
## Forest_est.1 1.007 0.055 0.905 1.122 1.000 gamma(1,.5)[sd]
## Health 0.851 0.046 0.767 0.946 1.000 gamma(1,.5)[sd]
## FR.dist 1.004 0.054 0.905 1.113 1.000 gamma(1,.5)[sd]
## DOBB 1.022 0.056 0.919 1.137 1.000 gamma(1,.5)[sd]
## SEX 0.247 0.013 0.222 0.274 1.000 gamma(1,.5)[sd]
## EDUCAT 1.621 0.088 1.46 1.802 1.000 gamma(1,.5)[sd]
## Soci_est.1 1.026 0.056 0.922 1.142 1.000 gamma(1,.5)[sd]
## MSTATUS_Div.wd 0.144 0.008 0.129 0.159 1.000 gamma(1,.5)[sd]
## MSTATUS_Single 0.063 0.003 0.057 0.07 1.000 gamma(1,.5)[sd]
## Alcohol 1.546 0.083 1.392 1.716 1.000 gamma(1,.5)[sd]
## SMOKING 0.100 0.005 0.09 0.111 1.000 gamma(1,.5)[sd]
## LOCB_EWAF 0.057 0.003 0.051 0.063 1.000 gamma(1,.5)[sd]
## LOCB_KADONE 0.070 0.004 0.063 0.078 1.000 gamma(1,.5)[sd]
## LOCB_KADTWO 0.075 0.004 0.067 0.083 1.000 gamma(1,.5)[sd]
## LOCB_KARO 0.177 0.010 0.159 0.197 1.000 gamma(1,.5)[sd]
## LOCB_KYEM 0.057 0.003 0.051 0.063 1.000 gamma(1,.5)[sd]
## LOCB_MARA 0.107 0.006 0.096 0.119 1.000 gamma(1,.5)[sd]
## LOCB_NONE 0.069 0.004 0.062 0.076 1.000 gamma(1,.5)[sd]
## LOCB_NTWO 0.082 0.004 0.074 0.091 1.000 gamma(1,.5)[sd]
## LOCB_NYAB 0.051 0.003 0.045 0.056 1.000 gamma(1,.5)[sd]round(100*(as.numeric(fit_main_1_sum$Estimate)-as.numeric(fit_main_6_sum$Estimate))/as.numeric(fit_main_6_sum$Estimate),2) # Fine, if it is only small ## [1] 0.00 1.71 0.00 0.56 0.00 -0.73 0.00 0.37 -1.20
## [10] 0.00 1.15 -1.06 -2.04 -10.00 -1.63 0.00 7.14 1.36
## [19] -0.39 1.79 0.00 -0.41 -1.18 -2.22 5.26 0.00 -0.12
## [28] -0.12 0.00 0.10 -0.10 0.12 0.10 2.70 0.00 0.00
## [37] 9.09 0.00 0.00 0.00 0.00 0.00 0.54 0.00 0.00
## [46] 0.00 0.00 0.00 0.00 0.00 -0.10 -2.27 -0.40 0.00
## [55] 0.00 0.00 0.00 -1.59 0.00 0.00 0.00 NaN -20.00
## [64] 0.00 NaN 0.00 0.00 0.00 0.00 0.00 2.78 0.00
## [73] 0.00 0.00 0.00 0.00 0.00 0.00 0.00 NaN 0.00
## [82] 0.00 0.00 0.06 0.00 0.00 0.00 -2.04 0.00 0.00
## [91] 0.00 0.00 0.00 5.56 -33.33 0.00 0.00 0.00 0.00
## [100] -2.63 0.00 -4.35 0.00 0.00 12.50 0.00 0.00 NaN
## [109] -9.09 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## [118] 0.00 0.00 0.00 0.00 0.00 0.00 0.00 NaN 0.00
## [127] 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## [136] 0.00 0.00 -0.06 0.00 0.00 0.00 0.00 0.00 0.00
## [145] 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## [154] 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## [163] 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## [172] 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## [181] 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## [190] 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## [199] 0.00 0.00 0.00 0.00 0.00 -17.39 NaN NaN NaN
## [208] NaN -100.00 0.17 0.04 NaN 0.00 0.00 0.00 0.00
## [217] 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## [226] NaN 0.032.10) ‘Is the Bayesian way of interpreting and reporting model results used?’
# Refer to this article: https://www.rensvandeschoot.com/wp-content/uploads/2017/02/2014-JA-RENS-Depaoli.pdf
# Credibility interval - there is a 95% probability that the true coefficient exists between the credibility interval.
summary(fit_main_1)## blavaan (0.3-15) results of 4000 samples after 4000 adapt/burnin iterations
##
## Number of observations 695
##
## Number of missing patterns 1
##
## Statistic MargLogLik PPP
## Value -12480.392 0.000
##
## Regressions:
## Estimate Post.SD pi.lower pi.upper Rhat Prior
## PHQ8_est ~
## FIES_est 0.212 0.038 0.137 0.288 1.000 normal(1,1)
## Economic_pvrty 0.119 0.042 0.036 0.202 1.000 normal(1,1)
## FIES_est ~
## Land_est -0.158 0.035 -0.226 -0.089 1.000 normal(-0.5,2)
## Economic_pvrty 0.356 0.035 0.286 0.426 1.000 normal(1,1)
## FR.dist -0.012 0.034 -0.079 0.056 1.000 normal(-0.5,2)
## PHQ8_est ~
## DOBB 0.013 0.039 -0.063 0.089 1.000 normal(0.5,2)
## SEX 0.088 0.077 -0.061 0.24 1.000 normal(0.5,2)
## EDUCAT -0.093 0.031 -0.154 -0.031 1.000 normal(-0.5,2)
## Soci_est.1 -0.048 0.037 -0.12 0.024 1.000 normal(-0.5,2)
## MSTATUS_Div.wd -0.018 0.101 -0.215 0.178 1.000 normal(0.5,2)
## MSTATUS_Single 0.121 0.150 -0.17 0.413 1.000 normal(0,9)
## Alcohol 0.005 0.029 -0.053 0.063 1.000 normal(0.5,2)
## SMOKING 0.075 0.118 -0.158 0.307 1.000 normal(0.5,2)
## LOCB_EWAF 0.149 0.162 -0.166 0.468 1.000 normal(0,9)
## LOCB_KADONE 0.253 0.151 -0.045 0.55 1.000 normal(0,9)
## LOCB_KADTWO 0.057 0.148 -0.236 0.347 1.000 normal(0,9)
## LOCB_KARO 0.284 0.109 0.069 0.498 1.000 normal(0,9)
## LOCB_KYEM 0.482 0.161 0.167 0.794 1.000 normal(0,9)
## LOCB_MARA 0.084 0.129 -0.17 0.337 1.000 normal(0,9)
## LOCB_NONE 0.044 0.152 -0.252 0.343 1.000 normal(0,9)
## LOCB_NTWO 0.040 0.144 -0.243 0.322 1.000 normal(0,9)
## LOCB_NYAB 0.347 0.171 0.01 0.679 1.000 normal(0,9)
##
## Covariances:
## Estimate Post.SD pi.lower pi.upper Rhat
## .FIES_est ~~
## Forest_est.1 0.136 0.036 0.067 0.207 1.000
## Economic_poverty ~~
## Forest_est.1 0.033 0.037 -0.039 0.107 1.000
## Land_est -0.272 0.040 -0.352 -0.195 1.000
## .PHQ8_est ~~
## Health -0.165 0.038 -0.24 -0.093 1.000
## FR.dist ~~
## DOBB 0.038 0.038 -0.038 0.113 1.000
## SEX -0.026 0.019 -0.063 0.011 1.000
## EDUCAT 0.005 0.047 -0.089 0.097 1.000
## Soci_est.1 0.012 0.039 -0.065 0.088 1.000
## MSTATUS_Div.wd -0.013 0.014 -0.041 0.015 1.000
## MSTATUS_Single 0.001 0.009 -0.018 0.019 1.000
## Alcohol -0.068 0.048 -0.162 0.023 1.000
## SMOKING -0.007 0.012 -0.031 0.016 1.000
## LOCB_EWAF -0.024 0.009 -0.042 -0.007 1.000
## LOCB_KADONE 0.186 0.012 0.163 0.211 1.001
## LOCB_KADTWO 0.124 0.011 0.102 0.147 1.000
## LOCB_KARO -0.066 0.016 -0.098 -0.035 1.000
## LOCB_KYEM -0.039 0.009 -0.056 -0.021 1.000
## LOCB_MARA -0.088 0.013 -0.114 -0.063 1.001
## LOCB_NONE 0.005 0.010 -0.014 0.025 1.001
## LOCB_NTWO -0.051 0.011 -0.073 -0.03 1.000
## LOCB_NYAB -0.024 0.009 -0.041 -0.007 1.000
## DOBB ~~
## SEX -0.043 0.019 -0.081 -0.007 1.000
## EDUCAT -0.249 0.050 -0.349 -0.154 1.000
## Soci_est.1 -0.004 0.038 -0.079 0.07 1.000
## MSTATUS_Div.wd 0.098 0.015 0.07 0.128 1.000
## MSTATUS_Single -0.063 0.010 -0.083 -0.044 1.000
## Alcohol 0.179 0.048 0.085 0.275 1.000
## SMOKING 0.062 0.012 0.039 0.087 1.000
## LOCB_EWAF 0.013 0.009 -0.004 0.031 1.000
## LOCB_KADONE -0.002 0.010 -0.022 0.017 1.000
## LOCB_KADTWO 0.016 0.010 -0.005 0.036 1.000
## LOCB_KARO 0.000 0.016 -0.032 0.032 1.000
## LOCB_KYEM 0.004 0.009 -0.014 0.022 1.000
## LOCB_MARA -0.023 0.012 -0.048 0.001 1.000
## LOCB_NONE 0.000 0.010 -0.019 0.02 1.000
## LOCB_NTWO -0.011 0.011 -0.032 0.011 1.000
## LOCB_NYAB 0.009 0.009 -0.008 0.025 1.000
## SEX ~~
## EDUCAT -0.140 0.025 -0.19 -0.093 1.000
## Soci_est.1 -0.027 0.019 -0.065 0.011 1.000
## MSTATUS_Div.wd 0.037 0.007 0.023 0.051 1.000
## MSTATUS_Single -0.009 0.005 -0.019 0 1.000
## Alcohol -0.097 0.023 -0.143 -0.052 1.000
## SMOKING -0.038 0.006 -0.05 -0.026 1.000
## LOCB_EWAF -0.009 0.005 -0.018 0 1.000
## LOCB_KADONE 0.003 0.005 -0.007 0.013 1.000
## LOCB_KADTWO -0.007 0.005 -0.017 0.003 1.000
## LOCB_KARO -0.015 0.008 -0.031 0 1.000
## LOCB_KYEM 0.001 0.004 -0.008 0.009 1.000
## LOCB_MARA 0.000 0.006 -0.012 0.012 1.000
## LOCB_NONE 0.005 0.005 -0.005 0.015 1.000
## LOCB_NTWO 0.005 0.005 -0.006 0.015 1.000
## LOCB_NYAB 0.002 0.004 -0.006 0.01 1.000
## EDUCAT ~~
## Soci_est.1 0.195 0.050 0.098 0.294 1.000
## MSTATUS_Div.wd -0.075 0.019 -0.112 -0.04 1.000
## MSTATUS_Single 0.086 0.013 0.062 0.111 1.000
## Alcohol -0.048 0.060 -0.163 0.069 1.000
## SMOKING -0.043 0.015 -0.073 -0.013 1.000
## LOCB_EWAF -0.005 0.012 -0.028 0.018 1.000
## LOCB_KADONE -0.008 0.013 -0.032 0.017 1.000
## LOCB_KADTWO 0.001 0.013 -0.025 0.027 1.000
## LOCB_KARO 0.015 0.020 -0.025 0.054 1.000
## LOCB_KYEM 0.019 0.011 -0.004 0.041 1.000
## LOCB_MARA -0.002 0.016 -0.033 0.028 1.000
## LOCB_NONE 0.029 0.013 0.004 0.053 1.000
## LOCB_NTWO -0.056 0.014 -0.084 -0.029 1.000
## LOCB_NYAB -0.017 0.011 -0.039 0.004 1.000
## Soci_est.1 ~~
## MSTATUS_Div.wd -0.037 0.015 -0.067 -0.009 1.000
## MSTATUS_Single 0.030 0.010 0.011 0.049 1.000
## Alcohol -0.022 0.048 -0.116 0.073 1.000
## SMOKING -0.019 0.012 -0.043 0.004 1.000
## LOCB_EWAF -0.007 0.009 -0.025 0.011 1.000
## LOCB_KADONE 0.009 0.010 -0.011 0.029 1.000
## LOCB_KADTWO -0.018 0.010 -0.039 0.002 1.000
## LOCB_KARO -0.006 0.016 -0.038 0.025 1.000
## LOCB_KYEM 0.000 0.009 -0.018 0.019 1.000
## LOCB_MARA 0.010 0.013 -0.014 0.035 1.000
## LOCB_NONE -0.001 0.010 -0.021 0.018 1.000
## LOCB_NTWO -0.014 0.011 -0.035 0.007 1.000
## LOCB_NYAB 0.011 0.009 -0.006 0.028 1.000
## MSTATUS_Div.wid ~~
## MSTATUS_Single -0.011 0.004 -0.018 -0.004 1.000
## Alcohol 0.023 0.018 -0.012 0.059 1.000
## SMOKING 0.005 0.005 -0.004 0.013 1.000
## LOCB_EWAF -0.007 0.003 -0.014 -0.001 1.000
## LOCB_KADONE -0.007 0.004 -0.014 0 1.000
## LOCB_KADTWO 0.004 0.004 -0.004 0.012 1.000
## LOCB_KARO 0.001 0.006 -0.01 0.013 1.000
## LOCB_KYEM 0.009 0.003 0.002 0.016 1.000
## LOCB_MARA -0.003 0.005 -0.012 0.007 1.000
## LOCB_NONE -0.008 0.004 -0.015 0 1.000
## LOCB_NTWO -0.003 0.004 -0.012 0.005 1.000
## LOCB_NYAB -0.000 0.003 -0.007 0.006 1.000
## MSTATUS_Single ~~
## Alcohol -0.010 0.012 -0.033 0.013 1.000
## SMOKING -0.006 0.003 -0.012 0 1.000
## LOCB_EWAF -0.001 0.002 -0.005 0.003 1.000
## LOCB_KADONE -0.001 0.003 -0.005 0.004 1.000
## LOCB_KADTWO 0.001 0.003 -0.005 0.006 1.000
## LOCB_KARO -0.003 0.004 -0.011 0.005 1.000
## LOCB_KYEM -0.001 0.002 -0.005 0.003 1.000
## LOCB_MARA 0.002 0.003 -0.004 0.008 1.000
## LOCB_NONE 0.002 0.003 -0.003 0.007 1.000
## LOCB_NTWO -0.003 0.003 -0.008 0.002 1.000
## LOCB_NYAB -0.002 0.002 -0.006 0.002 1.000
## Alcohol ~~
## SMOKING 0.083 0.015 0.054 0.113 1.000
## LOCB_EWAF 0.039 0.011 0.017 0.061 1.000
## LOCB_KADONE -0.014 0.012 -0.039 0.011 1.000
## LOCB_KADTWO -0.006 0.013 -0.032 0.019 1.000
## LOCB_KARO -0.019 0.020 -0.058 0.019 1.000
## LOCB_KYEM -0.014 0.011 -0.036 0.008 1.000
## LOCB_MARA -0.001 0.015 -0.031 0.029 1.000
## LOCB_NONE -0.003 0.012 -0.027 0.022 1.000
## LOCB_NTWO 0.018 0.013 -0.008 0.045 1.000
## LOCB_NYAB -0.002 0.011 -0.023 0.018 1.000
## SMOKING ~~
## LOCB_EWAF 0.002 0.003 -0.004 0.008 1.000
## LOCB_KADONE -0.002 0.003 -0.009 0.004 1.000
## LOCB_KADTWO -0.006 0.003 -0.012 0 1.000
## LOCB_KARO 0.009 0.005 -0.001 0.019 1.000
## LOCB_KYEM -0.002 0.003 -0.008 0.003 1.000
## LOCB_MARA -0.001 0.004 -0.009 0.006 1.000
## LOCB_NONE 0.007 0.003 0.001 0.013 1.000
## LOCB_NTWO 0.005 0.003 -0.002 0.011 1.000
## LOCB_NYAB -0.001 0.003 -0.007 0.004 1.000
## LOCB_EWAF ~~
## LOCB_KADONE -0.004 0.002 -0.009 0 1.000
## LOCB_KADTWO -0.005 0.002 -0.01 0 1.000
## LOCB_KARO -0.013 0.004 -0.021 -0.006 1.000
## LOCB_KYEM -0.004 0.002 -0.008 0.001 1.000
## LOCB_MARA -0.007 0.003 -0.013 -0.001 1.000
## LOCB_NONE -0.004 0.002 -0.009 0 1.000
## LOCB_NTWO -0.005 0.003 -0.01 0 1.000
## LOCB_NYAB -0.003 0.002 -0.007 0.001 1.000
## LOCB_KADONE ~~
## LOCB_KADTWO -0.007 0.003 -0.012 -0.001 1.000
## LOCB_KARO -0.017 0.004 -0.025 -0.008 1.000
## LOCB_KYEM -0.004 0.002 -0.009 0 1.000
## LOCB_MARA -0.009 0.003 -0.015 -0.002 1.000
## LOCB_NONE -0.006 0.003 -0.011 -0.001 1.000
## LOCB_NTWO -0.006 0.003 -0.012 -0.001 1.000
## LOCB_NYAB -0.004 0.002 -0.008 0 1.000
## LOCB_KADTWO ~~
## LOCB_KARO -0.018 0.004 -0.026 -0.009 1.000
## LOCB_KYEM -0.005 0.002 -0.01 0 1.000
## LOCB_MARA -0.009 0.003 -0.016 -0.003 1.000
## LOCB_NONE -0.006 0.003 -0.011 -0.001 1.000
## LOCB_NTWO -0.007 0.003 -0.013 -0.001 1.000
## LOCB_NYAB -0.004 0.002 -0.009 0 1.000
## LOCB_KARO ~~
## LOCB_KYEM -0.013 0.004 -0.021 -0.006 1.000
## LOCB_MARA -0.027 0.005 -0.037 -0.017 1.000
## LOCB_NONE -0.016 0.004 -0.025 -0.008 1.000
## LOCB_NTWO -0.020 0.005 -0.029 -0.011 1.000
## LOCB_NYAB -0.012 0.004 -0.019 -0.005 1.000
## LOCB_KYEM ~~
## LOCB_MARA -0.007 0.003 -0.013 -0.001 1.000
## LOCB_NONE -0.004 0.002 -0.009 0 1.000
## LOCB_NTWO -0.005 0.003 -0.011 0 1.000
## LOCB_NYAB -0.003 0.002 -0.007 0.001 1.000
## LOCB_MARA ~~
## LOCB_NONE -0.009 0.003 -0.015 -0.002 1.000
## LOCB_NTWO -0.011 0.004 -0.018 -0.004 1.000
## LOCB_NYAB -0.006 0.003 -0.012 -0.001 1.000
## LOCB_NONE ~~
## LOCB_NTWO -0.006 0.003 -0.012 -0.001 1.000
## LOCB_NYAB -0.004 0.002 -0.008 0.001 1.000
## LOCB_NTWO ~~
## LOCB_NYAB -0.005 0.002 -0.01 0 1.000
## Prior
##
## beta(3,2)
##
## beta(3,2)
## beta(2,3)
##
## beta(2,3)
##
## beta(1,1)
## beta(1,1)
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## beta(1,1)
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## beta(1,1)
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## beta(1,1)
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## beta(1,1)
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## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
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## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
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## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
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## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
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## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
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## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
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## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
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## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
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## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
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## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
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## beta(1,1)
## beta(1,1)
## beta(1,1)
## beta(1,1)
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## beta(1,1)
## beta(1,1)
## beta(1,1)
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## beta(1,1)
## beta(1,1)
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## beta(1,1)
##
## Intercepts:
## Estimate Post.SD pi.lower pi.upper Rhat Prior
## .PHQ8_est 0.019 0.139 -0.256 0.289 1.000 normal(0,10)
## .FIES_est -0.000 0.035 -0.068 0.068 1.000 normal(0,10)
## Economic_pvrty 0.000 0.039 -0.077 0.076 1.000 normal(0,10)
## Land_est 0.000 0.038 -0.074 0.074 1.000 normal(0,10)
## FR.dist 0.000 0.038 -0.075 0.076 1.000 normal(0,10)
## DOBB 0.000 0.038 -0.074 0.074 1.000 normal(0,10)
## SEX 0.596 0.019 0.559 0.633 1.000 normal(0,10)
## EDUCAT 2.641 0.048 2.548 2.735 1.000 normal(0,10)
## Soci_est.1 -0.000 0.039 -0.076 0.076 1.000 normal(0,10)
## MSTATUS_Div.wd 0.168 0.015 0.14 0.197 1.000 normal(0,10)
## MSTATUS_Single 0.066 0.009 0.048 0.085 1.000 normal(0,10)
## Alcohol 0.460 0.048 0.366 0.554 1.000 normal(0,10)
## SMOKING 0.109 0.012 0.086 0.133 1.000 normal(0,10)
## LOCB_EWAF 0.059 0.009 0.042 0.077 1.000 normal(0,10)
## LOCB_KADONE 0.075 0.010 0.055 0.095 1.000 normal(0,10)
## LOCB_KADTWO 0.079 0.010 0.059 0.1 1.000 normal(0,10)
## LOCB_KARO 0.222 0.016 0.19 0.253 1.000 normal(0,10)
## LOCB_KYEM 0.059 0.009 0.041 0.077 1.000 normal(0,10)
## LOCB_MARA 0.118 0.012 0.094 0.142 1.000 normal(0,10)
## LOCB_NONE 0.072 0.010 0.052 0.091 1.000 normal(0,10)
## LOCB_NTWO 0.088 0.011 0.066 0.109 1.000 normal(0,10)
## LOCB_NYAB 0.052 0.008 0.035 0.068 1.000 normal(0,10)
## Forest_est.1 -0.000 0.038 -0.074 0.072 1.000 normal(0,10)
## Health 3.222 0.035 3.153 3.291 1.000 normal(0,10)
##
## Variances:
## Estimate Post.SD pi.lower pi.upper Rhat Prior
## .PHQ8_est 0.828 0.046 0.745 0.924 1.000 gamma(1,.5)[sd]
## .FIES_est 0.835 0.045 0.751 0.926 1.000 gamma(1,.5)[sd]
## Economic_pvrty 1.008 0.054 0.906 1.118 1.000 gamma(1,.5)[sd]
## Land_est 1.005 0.054 0.903 1.116 1.000 gamma(1,.5)[sd]
## Forest_est.1 1.006 0.055 0.905 1.119 1.000 gamma(1,.5)[sd]
## Health 0.852 0.046 0.768 0.946 1.000 gamma(1,.5)[sd]
## FR.dist 1.005 0.054 0.904 1.115 1.001 gamma(1,.5)[sd]
## DOBB 1.021 0.055 0.919 1.134 1.000 gamma(1,.5)[sd]
## SEX 0.247 0.013 0.222 0.274 1.000 gamma(1,.5)[sd]
## EDUCAT 1.622 0.088 1.459 1.806 1.000 gamma(1,.5)[sd]
## Soci_est.1 1.026 0.057 0.921 1.144 1.000 gamma(1,.5)[sd]
## MSTATUS_Div.wd 0.144 0.008 0.129 0.16 1.000 gamma(1,.5)[sd]
## MSTATUS_Single 0.063 0.003 0.057 0.07 1.000 gamma(1,.5)[sd]
## Alcohol 1.545 0.083 1.391 1.716 1.000 gamma(1,.5)[sd]
## SMOKING 0.100 0.005 0.09 0.111 1.000 gamma(1,.5)[sd]
## LOCB_EWAF 0.057 0.003 0.051 0.063 1.000 gamma(1,.5)[sd]
## LOCB_KADONE 0.070 0.004 0.063 0.078 1.000 gamma(1,.5)[sd]
## LOCB_KADTWO 0.075 0.004 0.067 0.083 1.000 gamma(1,.5)[sd]
## LOCB_KARO 0.177 0.010 0.159 0.196 1.000 gamma(1,.5)[sd]
## LOCB_KYEM 0.057 0.003 0.051 0.064 1.000 gamma(1,.5)[sd]
## LOCB_MARA 0.107 0.006 0.096 0.119 1.000 gamma(1,.5)[sd]
## LOCB_NONE 0.069 0.004 0.062 0.076 1.000 gamma(1,.5)[sd]
## LOCB_NTWO 0.082 0.004 0.074 0.091 1.000 gamma(1,.5)[sd]
## LOCB_NYAB 0.051 0.003 0.045 0.056 1.000 gamma(1,.5)[sd]Extra) Looking posteriors on the same scale as the priors
# Extract the variable names
summary_DF_names <- paste0(fit_main_1@ParTable$lhs, fit_main_1@ParTable$op , fit_main_1@ParTable$rhs)
# Create a DF from the MCMC draws
fit_1_MCMCbinded_DF <- data.frame(fit_1_MCMCbinded)
# Rename the columns
colnames(fit_1_MCMCbinded_DF) <- make.unique(summary_DF_names)# Function for creating prior DF
prior.posterior.plot <- function(type="normal", mean=0, variance=1, shape.a=1, shape.b=1, sec.min=-6, sec.max=6, step=.01) {
x <- seq(sec.min, sec.max, by = step)
# For a normally distributed prior
if (type == "normal") {
prior.d <- dnorm(x,mean = mean, sd = sqrt(variance))
}
# For a beta distributed prior
if (type == "beta") {
prior.d <- dbeta(x, shape1 = shape.a, shape2 = shape.b)
}
# Plot
df <- data.frame(x = x, prior.d = prior.d)
return(df)
}
### Depression ~ Food insecurity ###
Dep_FI_DF <- prior.posterior.plot(type = "normal", mean = 1, variance = 1)
# Plot the prior (red) and posterior (black)
ggplot(data=Dep_FI_DF, aes(x=x, y=prior.d, group=1)) +
geom_line(size = .5, color = "red") +
xlab("Depression ~ Food insecurity") +
ylab("Prob. den.") +
geom_density(data = fit_1_MCMCbinded_DF,
aes(x = `PHQ8_est~FIES_est`),
inherit.aes = FALSE,
size = .5)
### Depression ~ Economic poverty ###
Dep_EP_DF <- prior.posterior.plot(type = "normal", mean = 1, variance = 1)
# Plot the prior (red) and posterior (black)
ggplot(data=Dep_EP_DF, aes(x=x, y=prior.d, group=1)) +
geom_line(size = .5, color = "red") +
xlab("Depression ~ Economic poverty") +
ylab("Prob. den.") +
geom_density(data = fit_1_MCMCbinded_DF,
aes(x = `PHQ8_est~Economic_poverty`),
inherit.aes = FALSE,
size = .5)
# Food insecurity ~ Forest dependence (covariance)
# Unclear how to convert between a prior on the beta scale and posterior on the linear scale
# Food insecurity ~ Farm size
FI_Farm_DF <- prior.posterior.plot(type = "normal", mean = -0.5, variance = 4)
# Plot the prior (red) and posterior (black)
ggplot(data=FI_Farm_DF, aes(x=x, y=prior.d, group=1)) +
geom_line(size = .5, color = "red") +
xlab("Food insecurity ~ Farm size") +
ylab("Prob. den.") +
geom_density(data = fit_1_MCMCbinded_DF,
aes(x = `FIES_est~Land_est`),
inherit.aes = FALSE,
size = .5)
# Food insecurity ~ Economic poverty
FI_EP_DF <- prior.posterior.plot(type = "normal", mean = 1, variance = 1)
# Plot the prior (red) and posterior (black)
ggplot(data=FI_EP_DF, aes(x=x, y=prior.d, group=1)) +
geom_line(size = .5, color = "red") +
xlab("Food insecurity ~ Economic poverty") +
ylab("Prob. den.") +
geom_density(data = fit_1_MCMCbinded_DF,
aes(x = `FIES_est~Economic_poverty`),
inherit.aes = FALSE,
size = .5)
# Food insecurity ~ Distance for forest reserve
FI_Dist_DF <- prior.posterior.plot(type = "normal", mean = -0.5, variance = 4)
# Plot the prior (red) and posterior (black)
ggplot(data=FI_Dist_DF, aes(x=x, y=prior.d, group=1)) +
geom_line(size = .5, color = "red") +
xlab("Food insecurity ~ Distance for forest reserve") +
ylab("Prob. den.") +
geom_density(data = fit_1_MCMCbinded_DF,
aes(x = `FIES_est~FR.dist`),
inherit.aes = FALSE,
size = .5)
# Economic poverty ~ Forest dependence
# Unclear how to convert between a prior on the beta scale and posterior on the linear scale
# Economic poverty ~ Farm size
# Unclear how to convert between a prior on the beta scale and posterior on the linear scale
# Depression ~ Age
De_Age_DF <- prior.posterior.plot(type = "normal", mean = 0.5, variance = 4)
# Plot the prior (red) and posterior (black)
ggplot(data=De_Age_DF, aes(x=x, y=prior.d, group=1)) +
geom_line(size = .5, color = "red") +
xlab("Depression ~ Age") +
ylab("Prob. den.") +
geom_density(data = fit_1_MCMCbinded_DF,
aes(x = `PHQ8_est~DOBB`),
inherit.aes = FALSE,
size = .5)
# Depression ~ Gender
De_Gen_DF <- prior.posterior.plot(type = "normal", mean = 0.5, variance = 4)
# Plot the prior (red) and posterior (black)
ggplot(data=De_Gen_DF, aes(x=x, y=prior.d, group=1)) +
geom_line(size = .5, color = "red") +
xlab("Depression ~ Gender") +
ylab("Prob. den.") +
geom_density(data = fit_1_MCMCbinded_DF,
aes(x = `PHQ8_est~SEX`),
inherit.aes = FALSE,
size = .5)
# Depression ~ Education
De_Edu_DF <- prior.posterior.plot(type = "normal", mean = -0.5, variance = 4)
# Plot the prior (red) and posterior (black)
ggplot(data=De_Edu_DF, aes(x=x, y=prior.d, group=1)) +
geom_line(size = .5, color = "red") +
xlab("Depression ~ Education") +
ylab("Prob. den.") +
geom_density(data = fit_1_MCMCbinded_DF,
aes(x = `PHQ8_est~EDUCAT`),
inherit.aes = FALSE,
size = .5)
# Depression ~ Social support
De_Soc_DF <- prior.posterior.plot(type = "normal", mean = -0.5, variance = 4)
# Plot the prior (red) and posterior (black)
ggplot(data=De_Soc_DF, aes(x=x, y=prior.d, group=1)) +
geom_line(size = .5, color = "red") +
xlab("Depression ~ Social support") +
ylab("Prob. den.") +
geom_density(data = fit_1_MCMCbinded_DF,
aes(x = `PHQ8_est~Soci_est.1`),
inherit.aes = FALSE,
size = .5)
# Depression ~ Divorced or widowed
De_Mar_DF <- prior.posterior.plot(type = "normal", mean = 0.5, variance = 4)
# Plot the prior (red) and posterior (black)
ggplot(data=De_Mar_DF, aes(x=x, y=prior.d, group=1)) +
geom_line(size = .5, color = "red") +
xlab("Depression ~ Divorced or widowed") +
ylab("Prob. den.") +
geom_density(data = fit_1_MCMCbinded_DF,
aes(x = `PHQ8_est~MSTATUS_Div.wid`),
inherit.aes = FALSE,
size = .5)
# Depression ~ Never married
De_Mar2_DF <- prior.posterior.plot(type = "normal", mean = 0, variance = 9, sec.min=-9, sec.max=9)
# Plot the prior (red) and posterior (black)
ggplot(data=De_Mar2_DF, aes(x=x, y=prior.d, group=1)) +
geom_line(size = .5, color = "red") +
xlab("Depression ~ Never married") +
ylab("Prob. den.") +
geom_density(data = fit_1_MCMCbinded_DF,
aes(x = `PHQ8_est~MSTATUS_Single`),
inherit.aes = FALSE,
size = .5)
# Depression ~ General health
# Unclear how to convert between a prior on the beta scale and posterior on the linear scale
# Depression ~ Alcohol consumption
De_Alc_DF <- prior.posterior.plot(type = "normal", mean = 0.5, variance = 4)
# Plot the prior (red) and posterior (black)
ggplot(data=De_Alc_DF, aes(x=x, y=prior.d, group=1)) +
geom_line(size = .5, color = "red") +
xlab("Depression ~ Alcohol consumption") +
ylab("Prob. den.") +
geom_density(data = fit_1_MCMCbinded_DF,
aes(x = `PHQ8_est~Alcohol`),
inherit.aes = FALSE,
size = .5)
# Depression ~ Smoking
De_Smok_DF <- prior.posterior.plot(type = "normal", mean = 0.5, variance = 4)
# Plot the prior (red) and posterior (black)
ggplot(data=De_Smok_DF, aes(x=x, y=prior.d, group=1)) +
geom_line(size = .5, color = "red") +
xlab("Depression ~ Smoking") +
ylab("Prob. den.") +
geom_density(data = fit_1_MCMCbinded_DF,
aes(x = `PHQ8_est~SMOKING`),
inherit.aes = FALSE,
size = .5)
# # Depression ~ Community (all)
# De_COM_DF <- prior.posterior.plot(type = "normal", mean = 0, variance = 9, sec.min=-9, sec.max=9)
#
# # Plot the prior (red) and posterior (black)
# ggplot(data=De_COM_DF, aes(x=x, y=prior.d, group=1)) +
# geom_line(size = .5, color = "red") +
# xlab("Depression ~ Community (all)") +
# ylab("Prob. den.") +
# geom_density(data = fit_1_MCMCbinded_DF,
# aes(x = `PHQ8_est~LOCB_EWAF`),
# inherit.aes = FALSE,
# size = .5) +
# geom_density(data = fit_1_MCMCbinded_DF,
# aes(x = `PHQ8_est~LOCB_KADONE`),
# inherit.aes = FALSE,
# size = .5)+
# geom_density(data = fit_1_MCMCbinded_DF,
# aes(x = `PHQ8_est~LOCB_KADTWO`),
# inherit.aes = FALSE,
# size = .5)+
# geom_density(data = fit_1_MCMCbinded_DF,
# aes(x = `PHQ8_est~LOCB_KARO`),
# inherit.aes = FALSE,
# size = .5)+
# geom_density(data = fit_1_MCMCbinded_DF,
# aes(x = `PHQ8_est~LOCB_KYEM`),
# inherit.aes = FALSE,
# size = .5)+
# geom_density(data = fit_1_MCMCbinded_DF,
# aes(x = `PHQ8_est~LOCB_MARA`),
# inherit.aes = FALSE,
# size = .5)+
# geom_density(data = fit_1_MCMCbinded_DF,
# aes(x = `PHQ8_est~LOCB_NONE`),
# inherit.aes = FALSE,
# size = .5)+
# geom_density(data = fit_1_MCMCbinded_DF,
# aes(x = `PHQ8_est~LOCB_NTWO`),
# inherit.aes = FALSE,
# size = .5)+
# geom_density(data = fit_1_MCMCbinded_DF,
# aes(x = `PHQ8_est~LOCB_NYAB`),
# inherit.aes = FALSE,
# size = .5)+
# geom_density(data = fit_1_MCMCbinded_DF,
# aes(x = `PHQ8_est~LOCB_NYAK`),
# inherit.aes = FALSE,
# size = .5)
Social support instrument
This suggests good model fit.