# How to control the size of the uncertainty intervals when plotting conditional effects?

When I try to plot my model I cannot control the size of the uncertainty intervals around the conditional effects. I have large uncertainty and would like to be able to see a plot without uncertainty intervals. I see you can control this with the argument `prob = 0` but it still prints a figure with default intervals using this code:

``````plot(conditional_effects(nbglm_awb_default),
points = T,
prob = 0, offset = T)
``````

Here’s my model:

``````nbglm_awb_default<-
brm(
AWB ~  0 + Intercept + # this allows control of prior on the intercept
ndate * NP + Season + CarcassPres +
(1 | NP / StandardTransect) +
offset(log(Tlength)),
family = negbinomial,
data = mydata,
chains = 4,
#control = list(adapt_delta = 0.999, max_treedepth = 15),
prior = newprior_awb,
save_pars = save_pars(all = TRUE)
)
``````

Here’s my data:

``````mydata <- structure(list(AWB = c(7, 66, 15, 44, 22, 60, 45, 32, 30, 33,
14, 0, 45, 39, 39, 24, 37, 66, 37, 60, 18, 3, 25, 13, 34, 38,
58, 0, 12, 6, 33, 2, 34, 18, 75, 20, 4, 9, 15, 4, 0, 21, 50,
24, 21, 9, 5, 87, 13, 43, 1, 19, 13, 1, 28, 56, 18, 42, 13, 2,
53, 16, 37, 51, 79, 5, 49, 11, 34, 91, 30, 2, 0, 15, 3, 57, 5,
18, 31, 14, 56, 72, 35, 94, 10, 45, 8, 29, 33, 34, 8, 53, 54,
24, 5, 21, 11, 27, 83, 23, 24, 4, 10, 13, 17, 11, 51, 6), ndate = c(0,
0, 0, 0, 0, 14, 14, 14, 14, 14, 14, 19, 19, 19, 19, 20, 20, 25,
25, 25, 25, 25, 25, 32, 32, 33, 33, 33, 33, 33, 38, 38, 38, 38,
38, 38, 39, 39, 39, 39, 39, 41, 41, 42, 42, 42, 42, 43, 43, 43,
44, 46, 46, 46, 46, 51, 51, 51, 51, 51, 51, 54, 54, 54, 55, 56,
56, 56, 58, 58, 61, 61, 61, 61, 61, 63, 63, 66, 66, 67, 67, 67,
68, 68, 68, 68, 68, 71, 72, 73, 78, 78, 79, 79, 89, 89, 89, 90,
90, 91, 91, 91, 96, 96, 96, 96, 97, 97), nyear = c(2013, 2013,
2013, 2013, 2013, 2014, 2014, 2014, 2014, 2014, 2014, 2015, 2015,
2015, 2015, 2015, 2015, 2015, 2015, 2015, 2015, 2015, 2015, 2016,
2016, 2016, 2016, 2016, 2016, 2016, 2016, 2016, 2016, 2016, 2016,
2016, 2016, 2016, 2016, 2016, 2016, 2017, 2017, 2017, 2017, 2017,
2017, 2017, 2017, 2017, 2017, 2017, 2017, 2017, 2017, 2017, 2017,
2017, 2017, 2017, 2017, 2018, 2018, 2018, 2018, 2018, 2018, 2018,
2018, 2018, 2018, 2018, 2018, 2018, 2018, 2018, 2018, 2019, 2019,
2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019,
2020, 2020, 2020, 2020, 2021, 2021, 2021, 2021, 2021, 2021, 2021,
2021, 2021, 2021, 2021, 2021, 2021, 2021), NP = structure(c(1L,
1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 2L, 2L, 2L, 2L, 1L, 1L,
1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 2L, 2L, 2L, 2L, 1L, 1L, 3L, 3L,
2L, 2L, 2L, 3L, 3L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 3L, 3L,
3L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 1L, 1L, 3L, 3L, 2L, 2L, 2L,
1L, 1L, 3L, 3L, 3L, 2L, 2L, 2L, 3L, 3L, 1L), .Label = c("Katavi",
"Ruaha", "Selous"), class = "factor"), Season = c("Dry", "Dry",
"Dry", "Dry", "Dry", "Dry", "Dry", "Dry", "Dry", "Dry", "Dry",
"Wet", "Wet", "Wet", "Wet", "Wet", "Wet", "Dry", "Dry", "Dry",
"Dry", "Dry", "Dry", "Wet", "Wet", "Wet", "Wet", "Wet", "Wet",
"Wet", "Wet", "Dry", "Dry", "Dry", "Dry", "Dry", "Dry", "Dry",
"Dry", "Dry", "Dry", "Wet", "Wet", "Wet", "Wet", "Wet", "Wet",
"Wet", "Wet", "Wet", "Wet", "Dry", "Dry", "Dry", "Dry", "Dry",
"Dry", "Dry", "Dry", "Dry", "Dry", "Wet", "Wet", "Wet", "Wet",
"Wet", "Wet", "Wet", "Dry", "Dry", "Dry", "Wet", "Dry", "Dry",
"Dry", "Dry", "Dry", "Wet", "Wet", "Wet", "Wet", "Wet", "Wet",
"Wet", "Wet", "Wet", "Wet", "Dry", "Dry", "Dry", "Wet", "Wet",
"Wet", "Wet", "Wet", "Wet", "Wet", "Wet", "Wet", "Wet", "Wet",
"Wet", "Dry", "Dry", "Dry", "Dry", "Dry", "Dry"), CarcassPres = structure(c(1L,
1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L), .Label = c("0",
"1"), class = "factor"), StandardTransect = structure(c(4L, 3L,
1L, 5L, 7L, 5L, 1L, 8L, 6L, 4L, 3L, 8L, 5L, 6L, 1L, 4L, 3L, 4L,
3L, 5L, 1L, 8L, 6L, 8L, 6L, 5L, 1L, 8L, 6L, 1L, 5L, 5L, 1L, 6L,
4L, 3L, 8L, 6L, 5L, 1L, 8L, 4L, 3L, 5L, 1L, 8L, 6L, 5L, 1L, 6L,
8L, 5L, 1L, 8L, 6L, 4L, 3L, 5L, 1L, 8L, 6L, 3L, 4L, 10L, 2L,
1L, 5L, 6L, 2L, 10L, 5L, 8L, 8L, 1L, 6L, 3L, 4L, 4L, 3L, 9L,
2L, 10L, 5L, 1L, 7L, 5L, 7L, 2L, 10L, 9L, 4L, 3L, 10L, 2L, 5L,
1L, 7L, 4L, 3L, 2L, 10L, 9L, 5L, 7L, 1L, 2L, 9L, 4L), .Label = c("Jongomero",
"Kidai", "LakeChada", "LakeKatavi", "Lunda", "Magangwe", "Mbagi-Mdonya",
"Mpululu", "Msolwa", "Mtemere"), class = "factor"), Tlength = c(35.2,
86.7, 93, 75, 27.2, 74.4, 93, 10.3, 45.8, 35.2, 78.2, 10.3, 71,
45.8, 93, 35.2, 63.9, 35.2, 77.9, 86.6, 93, 10.3, 45.8, 10.3,
45.8, 68.9, 93, 10.3, 45.8, 93, 86.7, 90.5, 93, 45.8, 35.2, 81.6,
10.3, 45.8, 88.2, 93, 10.3, 35.2, 64.6, 82.3, 93, 10.3, 45.8,
77.9, 93, 45.8, 10.3, 90.3, 93, 10.3, 45.8, 35.2, 77.4, 87.5,
93, 10.3, 45.8, 66, 35.2, 71.2, 85.7, 93, 87.5, 45.8, 85.5, 69.6,
97.8, 10.3, 10.3, 93, 45.8, 86.6, 35.2, 35.2, 71.9, 77.9, 88.5,
80, 85.2, 93, 56.1, 85.5, 56.1, 97.6, 81.8, 79.7, 35.2, 71.1,
81.9, 53.8, 86.5, 68.7, 60.7, 78.7, 56.6, 66.9, 71.8, 79.2, 82.6,
71.8, 92.4, 85.6, 78.5, 77.3)), row.names = c(NA, -108L), class = "data.frame")
``````

Here’s my session info:

``````R version 4.0.3 (2020-10-10)
Platform: x86_64-w64-mingw32/x64 (64-bit)
Running under: Windows 10 x64 (build 19044)

Matrix products: default

locale:
[1] LC_COLLATE=English_Ireland.1252  LC_CTYPE=English_Ireland.1252
[3] LC_MONETARY=English_Ireland.1252 LC_NUMERIC=C
[5] LC_TIME=English_Ireland.1252

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base

other attached packages:
[1] sjPlot_2.8.6       sjstats_0.18.1     interactions_1.1.5 tidybayes_3.0.0    readxl_1.3.1
[6] brms_2.16.3        Rcpp_1.0.8.3       forcats_0.5.2      stringr_1.4.0      dplyr_1.0.9
[11] purrr_0.3.4        readr_2.1.2        tidyr_1.2.0        tibble_3.1.6       ggplot2_3.3.5
[16] tidyverse_1.3.2

loaded via a namespace (and not attached):
[1] backports_1.4.1      jtools_2.1.0         plyr_1.8.6           igraph_1.2.6
[5] splines_4.0.3        svUnit_1.0.6         crosstalk_1.1.0.1    rstantools_2.1.1
[9] inline_0.3.16        digest_0.6.27        htmltools_0.5.1.1    rsconnect_0.8.16
[17] tzdb_0.1.2           modelr_0.1.8         RcppParallel_5.0.2   matrixStats_0.57.0
[21] xts_0.12.1           prettyunits_1.1.1    colorspace_1.4-1     rvest_1.0.3
[25] ggdist_3.0.0         xfun_0.31            haven_2.5.1          callr_3.7.0
[29] crayon_1.5.1         jsonlite_1.7.2       lme4_1.1-25          zoo_1.8-8
[33] glue_1.6.2           gtable_0.3.0         gargle_1.2.0         emmeans_1.5.2-1
[37] sjmisc_2.8.5         V8_3.3.1             distributional_0.2.2 pkgbuild_1.3.1
[41] rstan_2.21.2         abind_1.4-5          scales_1.1.1         mvtnorm_1.1-3
[45] DBI_1.1.0            ggeffects_0.16.0     miniUI_0.1.1.1       performance_0.7.1
[53] DT_0.16              htmlwidgets_1.5.3    httr_1.4.2           threejs_0.3.3
[57] arrayhelpers_1.1-0   posterior_1.1.0      ellipsis_0.3.2       pkgconfig_2.0.3
[61] loo_2.4.1            farver_2.0.3         dbplyr_2.2.1         utf8_1.1.4
[65] labeling_0.4.2       effectsize_0.4.0     tidyselect_1.1.2     rlang_1.0.4
[69] reshape2_1.4.4       later_1.1.0.1        munsell_0.5.0        cellranger_1.1.0
[73] tools_4.0.3          cli_3.3.0            generics_0.1.0       sjlabelled_1.1.7
[77] broom_1.0.0          ggridges_0.5.2       fastmap_1.0.1        knitr_1.39
[81] processx_3.5.0       fs_1.5.0             pander_0.6.3         nlme_3.1-149
[85] mime_0.11            projpred_2.0.2       xml2_1.3.3           compiler_4.0.3
[89] bayesplot_1.7.2      shinythemes_1.1.2    rstudioapi_0.13      curl_4.3.2
[93] gamm4_0.2-6          reprex_2.0.2         statmod_1.4.35       stringi_1.5.3
[97] parameters_0.13.0    ps_1.4.0             Brobdingnag_1.2-6    lattice_0.20-41
[101] Matrix_1.2-18        nloptr_1.2.2.2       markdown_1.1         shinyjs_2.0.0
[105] tensorA_0.36.2       vctrs_0.4.1          pillar_1.6.4         lifecycle_1.0.1
[109] bridgesampling_1.0-0 estimability_1.3     insight_0.13.2       httpuv_1.5.4
[113] R6_2.5.0             promises_1.1.1       gridExtra_2.3        codetools_0.2-16
[117] boot_1.3-25          colourpicker_1.1.0   MASS_7.3-53          gtools_3.8.2
[121] assertthat_0.2.1     withr_2.4.3          shinystan_2.5.0      bayestestR_0.9.0
[125] mgcv_1.8-33          parallel_4.0.3       hms_1.1.2            grid_4.0.3
``````plot(conditional_effects(nbglm_awb_default, prob = 0),