I recently tried fitting and plotting the conditional effects of a bayesian regression of the below dataset using the following code:
minnow_model <- brms::brm(formula = Num_Chub ~ Depth_bin * veg_cover * set_dur_hr, family = zero_inflated_poisson, data = minnow_glm, init = "random", iter = 10000)
plot(minnow_model)
plot(brms::conditional_effects(minnow_model))
In previous applications, this has worked fine and produced the posterior plots and conditional plots. Despite this, when I attempted to reuse code from before fit to this data, I am running into this error:
Error in tcrossprod(b, X) : "tcrossprod" is not a BUILTIN function
Error: Something went wrong (see the error message above). Perhaps you transformed numeric variables to factors or vice versa within the model formula? If yes, please convert your variables beforehand. Or did you set a predictor variable to NA?
I'm at a loss on this error since I cannot see anywhere that I've created a factor variable or set one to NA
1, 2, 1, 1, 1, 0, 0, 0, 0, 0, 3, 0, 1, 0, 0, 0, 2, 0, 1, 1, 1,
1, 0, 1, 0, 2, 0, 0, 0, 5, 2, 1, 1, 7, 0, 0, 0, 4, 2, 1, 3, 0,
2, 1, 0, 0, 0, 2, 0, 1, 1, 1, 0, 2, 0, 0, 0, 0, 0, 4, 0, 0, 1,
1, 3, 5, 1, 1, 0, 0, 0, 0, 2, 0, 0, 3, 0, 1, 0, 1, 0, 1, 1, 0,
1, 1, 1, 1, 1, 6, 1, 0, 0, 0, 0, 0, 0, 0, 2, 0, 2, 1, 5, 3, 0,
1, 0, 0, 2, 1, 0, 1, 7, 0, 0, 2, 3, 8, 4, 0, 3, 0, 0, 1, 0, 0,
1, 0, 0, 0, 2, 2, 4, 2, 2, 2, 5, 0, 0, 7, 1, 0, 0, 0, 1, 1, 0,
0, 0, 0, 1, 0, 0, 1, 1, 1, 0, 2, 2, 2, 1, 1, 2, 0, 4, 3, 3, 2,
2, 1, 0, 0, 1, 2, 0, 3, 1, 4, 3, 1, 1, 3, 6, 0, 2, 1, 2, 1, 2,
5, 0, 3, 3, 2, 1, 0, 0, 5, 0, 3, 0, 2, 0, 0, 0, 1, 0, 0, 0, 0,
1, 1, 3, 1, 1, 0, 4, 1, 0, 0, 1, 1, 2, 1, 0, 2, 2, 0, 4, 0, 2,
0, 0, 0, 1, 0, 7, 0, 0, 3, 2, 8, 2, 2, 1, 3, 1, 0, 1, 0, 1, 6,
5, 1, 3, 7, 3, 5, 1, 7), Depth_bin = c(0.5, 1.1, 0.7, 0.7, 0.6,
0.5, 0.5, 0.7, 0.6, 0.7, 0.4, 0.3, 0.5, 0.5, 0.5, 0.2, 0.4, 0.7,
0.6, 0.7, 1.1, 0.4, 0.3, 0.5, 0.5, 1, 0.4, 0.5, 0.5, 0.7, 0.6,
0.6, 0.4, 0.5, 0.4, 1.5, 0.6, 0.7, 1.2, 0.9, 0.8, 0.6, 0.6, 0.5,
0.8, 1.4, 1.3, 1.1, 1.2, 1.2, 0.5, 0.7, 0.5, 0.4, 0.7, 0.6, 0.5,
0.7, 0.9, 0.9, 0.9, 0.5, 0.6, 0.4, 0.3, 0.3, 0.3, 0.7, 0.8, 0.5,
0.4, 0.8, 0.9, 0.5, 0.9, 0.7, 0.6, 0.6, 0.7, 0.7, 0.6, 0.7, 0.7,
0.9, 0.9, 0.6, 0.7, 1.1, 1.2, 1, 0.7, 0.6, 0.6, 0.3, 0.6, 0.9,
0.7, 0.6, 0.7, 0.4, 0.6, 0.4, 0.3, 0.5, 0.5, 1, 0.7, 0.5, 0.8,
0.6, 1, 1, 0.8, 0.6, 0.7, 0.6, 0.6, 0.2, 1.3, 1.3, 1.5, 1.2,
1.3, 0.3, 0.6, 1.4, 1.4, 1.3, 1.4, 0.4, 0.7, 0.5, 0.6, 0.7, 0.9,
1.3, 1.3, 1.1, 0.6, 0.7, 0.6, 0.5, 0.9, 0.4, 0.4, 0.9, 0.3, 0.8,
0.2, 0.3, 0.7, 0.8, 0.6, 0.7, 0.4, 0.8, 0.7, 0.9, 0.7, 0.7, 0.5,
0.6, 0.5, 0.4, 0.6, 0.5, 1, 0.9, 0.7, 0.6, 1.1, 1.2, 1.1, 1.2,
0.8, 1.4, 1.3, 1, 0.7, 0.8, 0.7, 0.4, 0.4, 0.4, 0.6, 0.5, 0.7,
1.4, 0.4, 0.8, 0.9, 0.4, 0.7, 0.4, 0.4, 0.8, 0.6, 0.8, 0.8, 0.9,
0.9, 0.8, 0.9, 0.8, 0.3, 1.4, 1.2, 1, 0.8, 1.5, 0.5, 0.4, 0.6,
1.2, 0.5, 0.5, 0.5, 0.5, 1, 1.4, 0.6, 0.3, 0.4, 0.4, 0.3, 0.7,
0.5, 0.6, 0.3, 0.4, 0.4, 0.6, 0.3, 1, 1.3, 0.7, 0.4, 0.4, 0.5,
0.4, 0.4, 1, 1.2, 0.9, 0.5, 0.5, 1.1, 1, 1.1, 0.7, 0.8, 0.5,
0.4, 0.6, 0.7, 0.7, 0.4, 0.5, 0.7, 0.6, 0.6, 0.4, 0.4, 0.4, 0.6,
1.1, 0.8, 0.7, 0.5, 0.2, 0.4), veg_cover = c(0.35, 0.35, 0.35,
0.35, 0.35, 0.35, 0.35, 0.35, 0.35, 0.35, 0.35, 0.35, 0.35, 0.35,
0.35, 0.35, 0.35, 0.35, 0.35, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0.35, 0.35, 0.35, 0.35, 0.35, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0.35, 0.35, 0.35,
0.35, 0.35, 0.35, 0.35, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0.35, 0.35, 0.35, 0.35, 0.35, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 0.35, 0.35, 0.35, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0.5, 1, 1,
1, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.45, 0.45, 1, 0.5, 1, 1, 1,
0.35, 0.35, 0.15, 0.15, 0.45, 0.45, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 0.5, 0.45, 0.5, 0.5, 0.5, 0.5, 1, 1, 1, 1, 0.15,
0.35, 0.35, 0.45, 0.45, 0.35, 0.35, 0.35, 0.35, 0.35, 1, 1, 1,
1, 0.5, 0.5, 0.5, 0.45, 0.5, 1, 1, 1, 0.5, 0.45, 0.45, 1, 1,
1, 1, 1, 1, 1, 0.35, 0.35, 0.35, 0.45, 0.35, 0.35, 0.35, 1, 0.5,
0.5, 0.45, 0.45, 1, 1, 1, 0.35, 0.15, 0.35), set_dur_hr = c(1.11805555555556,
1.085, 1.11722222222222, 1.12972222222222, 1.02527777777778,
0.997222222222222, 0.987777777777778, 1.06083333333333, 1.07,
1.08472222222222, 1.0375, 0.853055555555555, 0.751388888888889,
0.758055555555556, 0.757777777777778, 0.754444444444444, 0.765833333333333,
0.930833333333333, 0.943888888888889, 0.943888888888889, 1.01222222222222,
0.997222222222222, 1.06527777777778, 1.01055555555556, 0.978888888888889,
0.937222222222222, 0.932222222222222, 1.06666666666667, 1.06638888888889,
1.05861111111111, 1.08277777777778, 1.07138888888889, 1.01722222222222,
1.07027777777778, 0.898333333333333, 0.803055555555556, 0.751111111111111,
1.00388888888889, 0.978333333333333, 1.00888888888889, 1.64694444444444,
1.62694444444444, 1.61027777777778, 1.59111111111111, 1.56611111111111,
1.20444444444444, 1.19083333333333, 1.19861111111111, 1.15111111111111,
1.08138888888889, 1.03916666666667, 1.01027777777778, 0.890277777777778,
0.862777777777778, 0.8825, 0.855555555555556, 0.861388888888889,
0.855833333333333, 1.0775, 1.06861111111111, 1.03138888888889,
1.00777777777778, 0.985555555555556, 0.9675, 0.953333333333333,
0.933055555555556, 0.938888888888889, 0.918055555555556, 1.38805555555556,
1.34277777777778, 1.345, 1.40722222222222, 1.24805555555556,
1.20583333333333, 1.19361111111111, 1.21583333333333, 1.11277777777778,
1.18777777777778, 1.06166666666667, 1.0075, 0.9925, 0.974444444444444,
0.964444444444444, 1.14972222222222, 0.971111111111111, 0.966388888888889,
1.02694444444444, 1.02861111111111, 1.03722222222222, 0.993611111111111,
1.08305555555556, 1.12055555555556, 1.14027777777778, 1.1275,
1.2, 1.10638888888889, 1.17305555555556, 1.18611111111111, 1.17666666666667,
1.18194444444444, 1.05916666666667, 1.20388888888889, 1.12777777777778,
1.11305555555556, 1.16083333333333, 1.13027777777778, 1.12944444444444,
1.12555555555556, 1.13972222222222, 1.05472222222222, 1.08583333333333,
1.07361111111111, 1.31083333333333, 1.35666666666667, 1.36916666666667,
1.34527777777778, 1.38833333333333, 1.43611111111111, 1.44194444444444,
1.47805555555556, 1.54083333333333, 1.3775, 1.40611111111111,
1.41388888888889, 1.75527777777778, 1.65944444444444, 1.61416666666667,
1.60138888888889, 1.60416666666667, 1.55305555555556, 1.27666666666667,
1.26361111111111, 1.26833333333333, 1.25388888888889, 1.36138888888889,
1.31194444444444, 1.26555555555556, 1.36861111111111, 1.17638888888889,
1.125, 1.09, 1.275, 1.14611111111111, 1.28666666666667, 1.30305555555556,
1.135, 1.31416666666667, 1.14361111111111, 1.30638888888889,
1.30333333333333, 1.28111111111111, 1.07194444444444, 1.0425,
1.06166666666667, 1.04666666666667, 1.06055555555556, 1.07055555555556,
1.04916666666667, 1.01611111111111, 1.02027777777778, 1.00055555555556,
1.05611111111111, 1.04805555555556, 1.0375, 1.03, 1.01388888888889,
1.02916666666667, 1.00361111111111, 1.00111111111111, 0.975555555555556,
0.8725, 0.923055555555556, 0.92, 16.9977777777778, 16.9888888888889,
17.1847222222222, 16.9525, 17.2447222222222, 17.1047222222222,
16.3511111111111, 16.2858333333333, 16.5088888888889, 16.2911111111111,
16.3136111111111, 16.3413888888889, 16.3663888888889, 16.4336111111111,
16.7961111111111, 17.6608333333333, 17.6461111111111, 17.6663888888889,
18.0411111111111, 18.015, 17.9622222222222, 17.9177777777778,
17.8241666666667, 17.8527777777778, 17.8063888888889, 17.7719444444444,
17.6705555555556, 17.6702777777778, 17.0244444444444, 16.0636111111111,
16.0116666666667, 16.8563888888889, 16.8305555555556, 16.8913888888889,
16.8830555555556, 16.8741666666667, 15.7952777777778, 15.8719444444444,
1.14694444444444, 1.04888888888889, 1.065, 1.01222222222222,
1.09944444444444, 0.941111111111111, 0.910833333333333, 0.820833333333333,
0.777222222222222, 0.735555555555556, 22.3055555555556, 22.3141666666667,
22.2866666666667, 22.2663888888889, 22.2625, 22.2580555555556,
22.2391666666667, 22.2069444444444, 22.2091666666667, 22.04,
22.0244444444444, 21.7861111111111, 21.6363888888889, 21.5886111111111,
21.4666666666667, 21.435, 21.4127777777778, 21.3761111111111,
21.3402777777778, 21.4447222222222, 21.13, 21.0936111111111,
21.12, 21.1280555555556, 21.1180555555556, 21.1008333333333,
21.1086111111111, 21.0369444444444, 20.9561111111111, 20.9358333333333,
20.9086111111111, 20.9216666666667, 21.1127777777778, 21.0152777777778,
21.0411111111111, 20.9283333333333, 20.8433333333333, 20.885,
20.9241666666667, 20.9480555555556, 21.7147222222222, 21.6247222222222,
21.6258333333333, 21.6255555555556, 21.4777777777778, 21.41,
21.3813888888889, 21.2902777777778, 21.2294444444444, 21.1716666666667
)), class = c("tbl_df", "tbl", "data.frame"), row.names = c(NA,
-271L))```