Hi,

I’m trying to find the right estimate for a dependent and an independent variable, but I’m not sure what is happening here. The dependent variable is a specific sound made by a type of bird. The values represent how many times a bird makes that specific sound in a minute. The independent variable is how much a bird sings with birds different from oneself. Zero values indicate that a bird never sings with other types of birds. Higher variables represent increased entropy (the bird sings with an increased number of types of other birds). Almost half of the birds only sing with their kind, so the independent variable is ‘zero-inflated.’

See below for three brms suggestions. The first is more intuitive to me. The dependent variable looks normally distributed to me, and I assume that the distribution of the independent variable is not a big concern (?). In the second model (model_2), the dependent and the independent variable is swapped in the formula, but the standardized output looks identical to model_1. In the third model, model_3, ‘family = hurdle_lognormal() is added, and the output is more conservative than in model_1 and model_2. The tail in the pp_check(model_3) is longer than in the density plot, so hurdle_lognormal() is maybe not entirely appropriate.

Which model shows the correlation between the dependent and the independent variables, if any? And, why does model_1 and model_2 produce the same standardized estimates (with the effectsize::standardize() package) if distribution family matters in brms? (Added that the standardized output, not the unstandardized, is similar after jsocolar’s response.)

model_1:

model_2:

model_3:

Density plot of independent variable:

```
data <- tribble(
~independent, ~dependent,
0.475104950825432,24.33385976553,
0.931752350036424,13.4535568661822,
0,35.5570188879926,
0.978876249602107,19.0955559671331,
0,22.4963311249353,
1.48092077315076,17.8123502749233,
0,19.3291471951513,
0,23.2441762977182,
0,23.2927636346998,
0,24.3954700801138,
0,20.5882384242946,
0.864789009883719,22.3505775686187,
0.47019083324118,19.5663614624022,
1.14237424676109,16.5588026482227,
1.17147105221271,12.1286866240021,
0,16.3087052536411,
0,25.0383445586907,
0,27.0993725140426,
0.910750569086581,19.4271473202162,
0,21.539334714812,
0,16.1585341270091,
0,14.8117535496131,
0,23.7749241592805,
0.729393530556281,15.7854703429959,
0,19.6348331819269,
0,18.7496761710538,
0.469795349397395,19.9684431411922,
0,17.5952606880194,
0.926879666280559,18.014171155283,
0,26.6404232174919,
0,34.6939701259565,
0.468784998340705,23.4421951670494,
0.873477067021566,14.8475658012473,
0,24.8547255261819,
0.925798574658323,19.1606375746935,
1.3758660136893,14.2614635077128,
0.907496382286009,5.20771720976394,
0,11.7335616128042,
0,11.0442051544141,
0.585840348633798,22.3243545533164,
0.476637505556964,23.8691125356285,
0,23.1696709382993,
0,21.3636514620346,
0,32.1265174635231,
0.925929124062113,27.3552238941316,
0,26.9585939107343,
0,20.6204570296389,
0,12.704953586671,
0,25.0766825202277,
0.464895284954346,18.5166531201006,
0,15.1937439281183,
0.591470236948633,23.3379526159775,
0,27.0617666242848,
0,19.3919112587532,
0,29.6596276319073,
0,34.3326412449171,
0,24.9433341755981,
0.727810081716114,15.3767806419392,
0.471772729519587,24.5938346005064,
0,18.6690857279866,
0.81491944165422,20.7457489790098,
0,24.6863561432963,
0,27.26502689043,
0,23.153980266219,
0,22.1294972993748,
0.47630755819978,25.1068688975237,
0,18.0092791211805,
1.37170237143627,13.8415553147245,
0,22.1942321244425,
0,27.667486169116,
0,33.0707348516833,
0.469377824759326,23.4354194391814,
0,35.1490587595687,
0,22.4715336148491,
0,17.0661503945461,
0,22.2264237949063,
0,13.7640418567295,
0,24.4250051967047,
0,14.9715091586532,
0.72951998261547,14.9108687758242,
0,11.674792236118,
0,21.0497395321024,
0,26.3728197700917,
0.475776553536671,17.6718281712596,
0,16.1805340750521,
0,25.5407940439696,
0.586691374392858,15.07947223304,
0,23.6832147587178,
0,11.8085277685732,
0.461258342759425,14.8125126693847,
0,19.4737144352526,
0,16.0046843086061,
0.91764819052779,10.8793686697501,
0.921983886262004,7.28807984606729,
1.00998273481615,9.25967267187909,
0,20.1824607743782,
0,30.8862120518263,
0,20.6327939396891,
1.29706386830051,13.3517691018069,
0,21.9623659882357
)
#model_1
prior <- get_prior(data = data,
dependent ~ independent)
prior
prior$prior[1] <- "normal(0,5)"
model_1 <- brm(data = data_2,
dependent ~ independent,
prior = prior)
#model_2
prior <- get_prior(data = data,
independent ~ dependent)
prior
prior$prior[1] <- "normal(0,5)"
model_2 <- brm(data = data,
independent ~ dependent,
prior = prior)
#model_3
prior <- get_prior(data = data,
independent ~ dependent,
family = hurdle_lognormal())
prior
prior$prior[1] <- "normal(0,5)"
model_3 <- brm(data = data,
independent ~ dependent,
family = hurdle_lognormal(),
prior = prior)
pp_check(model_3)
data %>%
ggplot(aes(independent)) +
geom_density()```
* Operating System: macOS Monterey
* brms Version: 2.16.3
```