[1] "FAME" Best Normalizing transformation with 210 Observations Estimated Normality Statistics (Pearson P / df, lower => more normal): - arcsinh(x): 4.3133 - Center+scale: 10.8933 - Double Reversed Log_b(x+a): 11.5102 - Exp(x): 26.595 - Log_b(x+a): 7.4067 - orderNorm (ORQ): 4.3833 - sqrt(x + a): 4.3833 - Yeo-Johnson: 4.4633 Estimation method: Out-of-sample via CV with 10 folds and 5 repeats Based off these, bestNormalize chose: Standardized asinh(x) Transformation with 210 nonmissing obs.: Relevant statistics: - mean (before standardization) = 1.179362 - sd (before standardization) = 1.236941 Analysis of Deviance Table (Type III Wald chisquare tests) Response: asinh(FAME) Chisq Df Pr(>Chisq) (Intercept) 123.499 1 < 2.2e-16 *** Background 19.746 1 8.845e-06 *** Time 21.884 5 0.0005509 *** Background:Time 12.420 5 0.0294678 * --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 [1] 628.0363 DHARMa zero-inflation test via comparison to expected zeros with simulation under H0 = fitted model data: simulationOutput ratioObsSim = 1.3327, p-value = 0.002 alternative hypothesis: two.sided DHARMa outlier test based on exact binomial test with approximate expectations data: simulationOutput1 outliers at both margin(s) = 0, observations = 210, p-value = 1 alternative hypothesis: true probability of success is not equal to 0.001998002 95 percent confidence interval: 0.00000000 0.01741271 sample estimates: frequency of outliers (expected: 0.001998001998002 ) 0 DHARMa nonparametric dispersion test via sd of residuals fitted vs. simulated data: simulationOutput dispersion = 0.98441, p-value = 0.824 alternative hypothesis: two.sided Package `see` required for model diagnostic plots.$VIF # Check for Multicollinearity $QQ Simulated residuals from a model of class `glmmTMB` based on 250 simulations. Use `check_residuals()` to check uniformity of residuals or `residuals()` to extract simulated residuals. It is recommended to refer to `?DHARMa::simulateResiudals` and `vignette("DHARMa")` for more information about different settings in particular situations or for particular models. $HOMOGENEITY x y 1 2.1989891 1.46953224 2 2.1989891 0.57717485 3 2.1989891 1.03012779 4 1.0868597 0.80860985 5 1.0868597 1.20611295 6 1.0868597 0.43503599 7 1.0414021 0.24492717 8 1.0414021 1.18342977 9 1.0414021 0.51642015 10 1.2744628 1.12076663 11 1.2744628 0.60864076 12 1.4435370 0.82592744 13 1.4435370 1.19279433 14 1.4435370 1.19279433 15 1.2698686 1.12830683 16 1.2698686 0.82787980 17 2.1989891 0.72227138 18 2.1989891 1.45129758 19 2.1989891 1.00273166 20 1.0868597 0.57427342 21 1.0868597 1.03499525 22 1.0868597 0.69533585 23 1.0414021 0.70587844 24 1.0414021 0.65386246 25 1.0414021 1.32429945 26 1.2744628 1.12076663 27 1.2744628 1.72398943 28 1.2744628 0.81543398 29 1.4435370 1.19279433 30 1.4435370 0.73991548 31 1.4435370 1.19279433 32 1.2698686 1.75350023 33 1.2698686 0.78270201 34 1.2698686 0.60861067 35 2.1989891 1.47218758 36 2.1989891 0.60202699 37 2.1989891 0.35298636 38 1.0868597 1.03499525 39 1.0868597 1.24010318 40 1.0868597 1.03499525 41 1.0414021 1.01311986 42 1.0414021 0.88736412 43 1.0414021 1.08955354 44 1.2744628 0.69573590 45 1.2744628 1.12076663 46 1.2744628 1.27880764 47 1.4435370 1.19279433 48 1.4435370 1.41369828 49 1.4435370 0.96203941 50 1.2698686 0.50453781 51 1.2698686 1.11874474 52 1.2698686 1.11874474 53 2.1989891 1.43089464 54 2.1989891 0.99245261 55 2.1989891 1.47218758 56 1.0868597 1.03499525 57 1.0868597 0.90339679 58 1.0868597 0.96989537 59 1.0414021 0.67015951 60 1.0414021 0.65657823 61 1.0414021 1.01311986 62 1.2744628 1.00785518 63 1.2744628 0.99765652 64 1.2744628 0.95251090 65 1.4435370 0.31661744 66 1.4435370 0.72757224 67 1.4435370 1.05001749 68 1.2698686 0.65442380 69 1.2698686 0.06197228 70 1.2698686 0.28278918 71 2.1989891 0.83714009 72 2.1989891 0.47809695 73 2.1989891 1.11267844 74 1.0868597 1.49333301 75 1.0868597 1.03499525 76 1.0868597 1.03499525 77 1.0414021 0.99743745 78 1.0414021 0.64122941 79 1.0414021 0.49377712 80 1.2744628 0.64872656 81 1.2744628 0.71165388 82 1.2744628 1.12076663 83 1.4435370 1.61296643 84 1.4435370 0.55647997 85 1.4435370 1.21297775 86 1.2698686 1.11874474 87 1.2698686 1.11874474 88 1.2698686 1.11874474 89 2.1989891 1.47218758 90 2.1989891 1.47218758 91 2.1989891 0.57493398 92 1.0868597 0.35708026 93 1.0868597 0.44160065 94 1.0414021 0.47669583 95 1.0414021 1.01311986 96 1.2744628 1.12076663 97 1.2744628 0.77302433 98 1.2744628 0.75107667 99 1.4435370 0.46784435 100 1.4435370 0.81274962 101 1.2698686 1.59462110 102 1.2698686 1.11874474 103 1.2698686 1.11874474 104 0.7526348 0.86127881 105 0.7526348 1.33500395 106 0.7526348 1.55461490 107 0.7179207 0.84118178 108 0.7179207 0.84118178 109 0.7179207 0.66053261 110 0.9953776 0.66251253 111 0.9953776 0.54548020 112 0.9953776 0.62439139 113 1.0092217 0.99734381 114 1.0092217 0.99734381 115 1.0092217 0.99734381 116 1.0628815 1.02351459 117 1.0628815 1.22703426 118 1.0628815 1.02351459 119 1.1117642 1.23063613 120 1.1117642 1.04678613 121 1.1117642 0.56740495 122 0.7526348 0.86127881 123 0.7526348 0.59513403 124 0.7526348 0.86127881 125 0.7179207 0.84118178 126 0.7179207 0.84118178 127 0.7179207 0.51310192 128 0.9953776 0.99047961 129 0.9953776 1.56432132 130 0.9953776 1.15651264 131 1.0092217 0.79964440 132 1.0092217 0.99734381 133 1.0092217 1.70860295 134 1.0628815 1.02351459 135 1.0628815 0.42409194 136 1.0628815 1.02351459 137 1.1117642 1.04678613 138 1.1117642 1.04678613 139 1.1117642 1.04678613 140 0.7526348 0.49773002 141 0.7526348 0.86127881 142 0.7526348 0.86127881 143 0.7179207 1.48875793 144 0.7179207 0.84118178 145 0.7179207 0.53594444 146 0.9953776 1.14493920 147 0.9953776 0.59632502 148 0.9953776 0.77527295 149 1.0092217 0.40676764 150 1.0092217 0.99734381 151 1.0092217 0.55187551 152 1.0628815 1.02351459 153 1.0628815 0.54931515 154 1.0628815 0.25693745 155 1.1117642 0.10869774 156 1.1117642 1.22062069 157 1.1117642 0.32183644 158 0.7526348 0.86127881 159 0.7526348 0.86127881 160 0.7526348 0.86127881 161 0.7179207 1.22152620 162 0.7179207 0.84118178 163 0.7179207 0.28710219 164 0.9953776 0.79965158 165 0.9953776 0.99047961 166 0.9953776 1.88322917 167 1.0092217 0.99734381 168 1.0092217 1.23717435 169 1.0092217 0.99734381 170 1.0628815 0.14658648 171 1.0628815 1.67241938 172 1.0628815 0.43171108 173 1.1117642 0.51331263 174 1.1117642 1.04678613 175 1.1117642 1.04678613 176 0.7526348 0.86127881 177 0.7526348 0.86127881 178 0.7526348 0.86127881 179 0.7179207 0.84118178 180 0.7179207 0.84118178 181 0.7179207 0.84118178 182 0.9953776 0.38571817 183 0.9953776 0.99047961 184 0.9953776 0.99047961 185 1.0092217 0.78620190 186 1.0092217 0.41239120 187 1.0092217 0.99734381 188 1.0628815 0.98781791 189 1.0628815 1.02351458 190 1.0628815 0.63466164 191 1.1117642 1.31390487 192 1.1117642 1.04678613 193 1.1117642 1.29691306 194 0.7526348 0.58070624 195 0.7526348 1.09272418 196 0.7526348 0.78443377 197 0.7179207 0.84118178 198 0.7179207 0.85026030 199 0.7179207 0.70083165 200 0.9953776 0.99047961 201 0.9953776 0.99047961 202 0.9953776 0.83421468 203 1.0092217 0.99734380 204 1.0092217 0.63794313 205 1.0092217 1.04400723 206 1.0628815 0.75588397 207 1.0628815 1.02351458 208 1.1117642 0.33779638 209 1.1117642 1.04678613 210 1.1117642 0.91532919 $REQQ $REQQ$Hive x y conf.low conf.high facet 1 -1.2815516 -3.226637e-09 -6.630701e-05 6.630055e-05 (Intercept) 2 -0.6433454 -1.328483e-09 -5.711377e-05 5.711112e-05 (Intercept) 3 -0.2018935 -9.012148e-10 -5.600843e-05 5.600663e-05 (Intercept) 4 0.2018935 6.417211e-10 -5.553597e-05 5.553725e-05 (Intercept) 5 0.6433454 2.125509e-09 -6.018926e-05 6.019351e-05 (Intercept) 6 1.2815516 2.689107e-09 -6.307665e-05 6.308203e-05 (Intercept) $PP_CHECK Warning: Maximum value of original data is not included in the replicated data. Model may not capture the variation of the data. attr(,"class") [1] "check_model" "see_check_model" attr(,"panel") [1] TRUE attr(,"dot_size") [1] 2 attr(,"line_size") [1] 0.8 attr(,"check") [1] "all" attr(,"alpha") [1] 0.2 attr(,"dot_alpha") [1] 0.8 attr(,"show_dots") [1] TRUE attr(,"detrend") [1] FALSE attr(,"colors") [1] "#3aaf85" "#1b6ca8" "#cd201f" attr(,"theme") [1] "see::theme_lucid" attr(,"model_info") attr(,"model_info")$is_binomial [1] FALSE attr(,"model_info")$is_bernoulli [1] FALSE attr(,"model_info")$is_count [1] FALSE attr(,"model_info")$is_poisson [1] FALSE attr(,"model_info")$is_negbin [1] FALSE attr(,"model_info")$is_beta [1] FALSE attr(,"model_info")$is_betabinomial [1] FALSE attr(,"model_info")$is_orderedbeta [1] FALSE attr(,"model_info")$is_dirichlet [1] FALSE attr(,"model_info")$is_exponential [1] FALSE attr(,"model_info")$is_logit [1] FALSE attr(,"model_info")$is_probit [1] FALSE attr(,"model_info")$is_censored [1] FALSE attr(,"model_info")$is_truncated [1] FALSE attr(,"model_info")$is_survival [1] FALSE attr(,"model_info")$is_linear [1] FALSE attr(,"model_info")$is_tweedie [1] FALSE attr(,"model_info")$is_zeroinf [1] TRUE attr(,"model_info")$is_zero_inflated [1] TRUE attr(,"model_info")$is_dispersion [1] FALSE attr(,"model_info")$is_hurdle [1] FALSE attr(,"model_info")$is_ordinal [1] FALSE attr(,"model_info")$is_cumulative [1] FALSE attr(,"model_info")$is_multinomial [1] FALSE attr(,"model_info")$is_categorical [1] FALSE attr(,"model_info")$is_mixed [1] TRUE attr(,"model_info")$is_multivariate [1] FALSE attr(,"model_info")$is_trial [1] FALSE attr(,"model_info")$is_bayesian [1] FALSE attr(,"model_info")$is_gam [1] FALSE attr(,"model_info")$is_anova [1] FALSE attr(,"model_info")$is_timeseries [1] FALSE attr(,"model_info")$is_ttest [1] FALSE attr(,"model_info")$is_correlation [1] FALSE attr(,"model_info")$is_onewaytest [1] FALSE attr(,"model_info")$is_chi2test [1] FALSE attr(,"model_info")$is_ranktest [1] FALSE attr(,"model_info")$is_levenetest [1] FALSE attr(,"model_info")$is_variancetest [1] FALSE attr(,"model_info")$is_xtab [1] FALSE attr(,"model_info")$is_proptest [1] FALSE attr(,"model_info")$is_binomtest [1] FALSE attr(,"model_info")$is_ftest [1] FALSE attr(,"model_info")$is_meta [1] FALSE attr(,"model_info")$link_function [1] "identity" attr(,"model_info")$family [1] "gaussian" attr(,"model_info")$n_obs [1] 210 attr(,"model_info")$n_grouplevels Hive 6 attr(,"bandwidth") [1] "nrd" attr(,"type") [1] "density" attr(,"model_class") [1] "glmmTMB" contrast estimate SE df t.ratio p.value Gotland 00H vs Aland 00H -2.0353 0.458 195 -4.444 0.0002 Gotland 00H vs Gotland 06H 0.0488 0.490 195 0.100 0.9673 Aland 00H vs Aland 06H 1.5649 0.429 195 3.644 0.0028 Gotland 06H vs Aland 06H -0.5192 0.467 195 -1.112 0.9673 Gotland 06H vs Gotland 12H -0.3904 0.453 195 -0.862 0.9673 Aland 06H vs Aland 12H 0.0640 0.429 195 0.149 0.9673 Gotland 12H vs Aland 12H -0.0648 0.413 195 -0.157 0.9673 Gotland 12H vs Gotland 18H -0.0195 0.475 195 -0.041 0.9673 Aland 12H vs Aland 18H -0.3280 0.421 195 -0.779 0.9673 Gotland 18H vs Aland 18H -0.3732 0.482 195 -0.774 0.9673 Gotland 18H vs Gotland 24H -0.0755 0.500 195 -0.151 0.9673 Aland 18H vs Aland 24H -0.2379 0.447 195 -0.533 0.9673 Gotland 24H vs Aland 24H -0.5356 0.468 195 -1.145 0.9673 Gotland 24H vs Gotland 36H -0.0688 0.492 195 -0.140 0.9673 Aland 24H vs Aland 36H 0.2444 0.480 195 0.509 0.9673 Aland 36H vs Gotland 36H 0.2225 0.503 195 0.442 0.9673 Note: contrasts are still on the asinh scale P value adjustment: fdr method for 16 tests [1] "FAEE" Best Normalizing transformation with 210 Observations Estimated Normality Statistics (Pearson P / df, lower => more normal): - arcsinh(x): 6.6933 - Center+scale: 7.4033 - Double Reversed Log_b(x+a): 8.99 - Exp(x): 11.49 - Log_b(x+a): 8.4867 - orderNorm (ORQ): 6.3867 - sqrt(x + a): 6.2067 - Yeo-Johnson: 6.47 Estimation method: Out-of-sample via CV with 10 folds and 5 repeats Based off these, bestNormalize chose: Standardized sqrt(x + a) Transformation with 210 nonmissing obs.: Relevant statistics: - a = 0 - mean (before standardization) = 0.3860871 - sd (before standardization) = 0.4322159 Analysis of Deviance Table (Type III Wald chisquare tests) Response: sqrt(FAEE) Chisq Df Pr(>Chisq) (Intercept) 72.768 1 < 2.2e-16 *** Background 14.519 1 0.0001387 *** Time 19.291 5 0.0016964 ** Background:Time 10.578 5 0.0604198 . --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 [1] 245.2251 DHARMa zero-inflation test via comparison to expected zeros with simulation under H0 = fitted model data: simulationOutput ratioObsSim = Inf, p-value < 2.2e-16 alternative hypothesis: two.sided DHARMa outlier test based on exact binomial test with approximate expectations data: simulationOutput1 outliers at both margin(s) = 1, observations = 210, p-value = 0.343 alternative hypothesis: true probability of success is not equal to 0.001998002 95 percent confidence interval: 0.0001205537 0.0262446357 sample estimates: frequency of outliers (expected: 0.001998001998002 ) 0.004761905 DHARMa nonparametric dispersion test via sd of residuals fitted vs. simulated data: simulationOutput dispersion = 0.99444, p-value = 0.976 alternative hypothesis: two.sided Package `see` required for model diagnostic plots.$VIF # Check for Multicollinearity $QQ Simulated residuals from a model of class `glmmTMB` based on 250 simulations. Use `check_residuals()` to check uniformity of residuals or `residuals()` to extract simulated residuals. It is recommended to refer to `?DHARMa::simulateResiudals` and `vignette("DHARMa")` for more information about different settings in particular situations or for particular models. $HOMOGENEITY x y 1 0.8121718 1.3065172 2 0.8121718 0.4720660 3 0.8121718 0.7792832 4 0.2993222 0.1285212 5 0.2993222 0.8324824 6 0.2993222 0.2550172 7 0.4692811 0.8592871 8 0.4692811 0.9199306 9 0.4692811 0.2273932 10 0.3985882 0.7919249 11 0.3985882 0.6607082 12 0.4492513 0.5973452 13 0.4492513 0.8407491 14 0.4492513 0.8407491 15 0.3049276 1.1726217 16 0.3049276 0.7508023 17 0.8121718 0.8135390 18 0.8121718 0.8158695 19 0.8121718 1.0973585 20 0.2993222 0.7655274 21 0.2993222 0.6862640 22 0.2993222 0.6862640 23 0.4692811 0.1277196 24 0.4692811 0.3255132 25 0.4692811 0.9812478 26 0.3985882 0.7919249 27 0.3985882 0.9396856 28 0.3985882 0.7919249 29 0.4492513 0.8407491 30 0.4492513 0.6738509 31 0.4492513 0.8407491 32 0.3049276 0.7151289 33 0.3049276 0.6926600 34 0.3049276 0.5426166 35 0.8121718 1.1304353 36 0.8121718 0.3390906 37 0.8121718 0.1774743 38 0.2993222 0.6862640 39 0.2993222 0.9355711 40 0.2993222 0.6862640 41 0.4692811 0.8592871 42 0.4692811 0.6168925 43 0.4692811 0.5846790 44 0.3985882 0.2219593 45 0.3985882 0.7919249 46 0.3985882 0.8503250 47 0.4492513 0.8407491 48 0.4492513 1.0550904 49 0.4492513 0.6216263 50 0.3049276 0.8280881 51 0.3049276 0.6926600 52 0.3049276 0.6926600 53 0.8121718 0.9890044 54 0.8121718 0.6570057 55 0.8121718 1.1304353 56 0.2993222 0.6862640 57 0.2993222 0.7346550 58 0.2993222 0.9355711 59 0.4692811 0.4424991 60 0.4692811 0.2983045 61 0.4692811 0.8592871 62 0.3985882 0.8401600 63 0.3985882 0.6694344 64 0.3985882 0.9065774 65 0.4492513 0.4606092 66 0.4492513 0.8407491 67 0.4492513 0.6839170 68 0.3049276 0.6926600 69 0.3049276 0.3499284 70 0.3049276 0.8875649 71 0.8121718 0.3926942 72 0.8121718 0.5558770 73 0.8121718 0.4464337 74 0.2993222 0.7666216 75 0.2993222 0.6862640 76 0.2993222 0.6862640 77 0.4692811 0.7107487 78 0.4692811 0.8592870 79 0.4692811 0.8198805 80 0.3985882 0.3120397 81 0.3985882 0.7919249 82 0.3985882 0.7919249 83 0.4492513 1.2524861 84 0.4492513 0.8407491 85 0.4492513 0.8593557 86 0.3049276 0.6926600 87 0.3049276 0.6926600 88 0.3049276 0.6926600 89 0.8121718 1.1304353 90 0.8121718 1.1304353 91 0.8121718 0.2869362 92 0.2993222 0.6862640 93 0.2993222 0.6862640 94 0.4692811 0.2270602 95 0.4692811 0.8592870 96 0.3985882 0.7919249 97 0.3985882 0.5739726 98 0.3985882 0.3233496 99 0.4492513 0.3035632 100 0.4492513 0.1321513 101 0.3049276 0.4715520 102 0.3049276 0.6926600 103 0.3049276 0.6926600 104 0.2991142 0.9177559 105 0.2991142 1.0822097 106 0.2991142 1.3820515 107 0.2133486 0.5793842 108 0.2133486 0.5793842 109 0.2133486 0.6569796 110 0.3424056 0.7339934 111 0.3424056 0.9410601 112 0.3424056 0.7339934 113 0.2565323 0.6353203 114 0.2565323 0.6353203 115 0.2565323 0.6353203 116 0.4403518 0.8323800 117 0.4403518 0.7193312 118 0.4403518 0.8323800 119 0.3502621 0.7159417 120 0.3502621 0.7423664 121 0.3502621 0.5866814 122 0.2991142 0.6860255 123 0.2991142 0.6860255 124 0.2991142 0.6860255 125 0.2133486 0.5793841 126 0.2133486 0.5793841 127 0.2133486 0.5699186 128 0.3424056 0.7339934 129 0.3424056 0.9735174 130 0.3424056 1.0221452 131 0.2565323 0.4760624 132 0.2565323 0.6353203 133 0.2565323 0.9708860 134 0.4403518 0.8323799 135 0.4403518 0.1196498 136 0.4403518 0.8323799 137 0.3502620 0.7423664 138 0.3502620 0.7423664 139 0.3502620 0.7423664 140 0.2991142 0.7239669 141 0.2991142 0.6860255 142 0.2991142 0.6860255 143 0.2133486 0.9092715 144 0.2133486 0.5793841 145 0.2133486 0.6903125 146 0.3424056 0.7339934 147 0.3424056 0.7799728 148 0.3424056 0.7339934 149 0.2565323 0.7026020 150 0.2565323 0.6353203 151 0.2565323 0.7385887 152 0.4403518 0.8323799 153 0.4403518 0.6611459 154 0.4403518 0.4085142 155 0.3502620 0.7065056 156 0.3502620 0.8154954 157 0.3502620 0.2593905 158 0.2991142 0.6860255 159 0.2991142 0.6860255 160 0.2991142 0.6860255 161 0.2133486 0.4512255 162 0.2133486 0.5793842 163 0.2133486 0.1075843 164 0.3424056 0.3368100 165 0.3424056 0.7339934 166 0.3424056 1.1879215 167 0.2565323 0.6353203 168 0.2565323 1.0348296 169 0.2565323 0.6353203 170 0.4403518 0.6478554 171 0.4403518 1.4899153 172 0.4403518 0.2257900 173 0.3502621 0.7423664 174 0.3502621 0.7423664 175 0.3502621 0.7423664 176 0.2991142 0.6860255 177 0.2991142 0.6860255 178 0.2991142 0.6860255 179 0.2133486 0.5793841 180 0.2133486 0.5793841 181 0.2133486 0.5793841 182 0.3424056 0.3814245 183 0.3424056 0.7339934 184 0.3424056 0.7339934 185 0.2565323 0.7217376 186 0.2565323 0.2957506 187 0.2565323 0.6353203 188 0.4403518 0.9098515 189 0.4403518 0.8323799 190 0.4403518 0.5892547 191 0.3502620 0.9805065 192 0.3502620 0.7423664 193 0.3502620 1.0839513 194 0.2991142 0.2352479 195 0.2991142 0.6708664 196 0.2991142 0.4731699 197 0.2133486 0.5793841 198 0.2133486 0.6516572 199 0.2133486 0.8107334 200 0.3424056 0.7339934 201 0.3424056 0.7339934 202 0.3424056 0.6766046 203 0.2565323 0.6353203 204 0.2565323 0.6353203 205 0.2565323 0.3855585 206 0.4403517 0.4271005 207 0.4403517 0.8323799 208 0.3502620 0.4934070 209 0.3502620 0.7423664 210 0.3502620 0.7915235 $REQQ $REQQ$Hive x y conf.low conf.high facet 1 -1.2815516 -3.713554e-09 -7.892059e-05 7.891317e-05 (Intercept) 2 -0.6433454 -1.391570e-09 -3.813764e-05 3.813485e-05 (Intercept) 3 -0.2018935 -1.138229e-09 -3.458854e-05 3.458627e-05 (Intercept) 4 0.2018935 5.323610e-10 -2.808331e-05 2.808438e-05 (Intercept) 5 0.6433454 1.688369e-09 -4.268885e-05 4.269223e-05 (Intercept) 6 1.2815516 4.022623e-09 -8.479404e-05 8.480209e-05 (Intercept) $PP_CHECK Warning: Minimum value of original data is not included in the replicated data. Model may not capture the variation of the data. attr(,"class") [1] "check_model" "see_check_model" attr(,"panel") [1] TRUE attr(,"dot_size") [1] 2 attr(,"line_size") [1] 0.8 attr(,"check") [1] "all" attr(,"alpha") [1] 0.2 attr(,"dot_alpha") [1] 0.8 attr(,"show_dots") [1] TRUE attr(,"detrend") [1] FALSE attr(,"colors") [1] "#3aaf85" "#1b6ca8" "#cd201f" attr(,"theme") [1] "see::theme_lucid" attr(,"model_info") attr(,"model_info")$is_binomial [1] FALSE attr(,"model_info")$is_bernoulli [1] FALSE attr(,"model_info")$is_count [1] FALSE attr(,"model_info")$is_poisson [1] FALSE attr(,"model_info")$is_negbin [1] FALSE attr(,"model_info")$is_beta [1] FALSE attr(,"model_info")$is_betabinomial [1] FALSE attr(,"model_info")$is_orderedbeta [1] FALSE attr(,"model_info")$is_dirichlet [1] FALSE attr(,"model_info")$is_exponential [1] FALSE attr(,"model_info")$is_logit [1] FALSE attr(,"model_info")$is_probit [1] FALSE attr(,"model_info")$is_censored [1] FALSE attr(,"model_info")$is_truncated [1] FALSE attr(,"model_info")$is_survival [1] FALSE attr(,"model_info")$is_linear [1] FALSE attr(,"model_info")$is_tweedie [1] FALSE attr(,"model_info")$is_zeroinf [1] TRUE attr(,"model_info")$is_zero_inflated [1] TRUE attr(,"model_info")$is_dispersion [1] FALSE attr(,"model_info")$is_hurdle [1] FALSE attr(,"model_info")$is_ordinal [1] FALSE attr(,"model_info")$is_cumulative [1] FALSE attr(,"model_info")$is_multinomial [1] FALSE attr(,"model_info")$is_categorical [1] FALSE attr(,"model_info")$is_mixed [1] TRUE attr(,"model_info")$is_multivariate [1] FALSE attr(,"model_info")$is_trial [1] FALSE attr(,"model_info")$is_bayesian [1] FALSE attr(,"model_info")$is_gam [1] FALSE attr(,"model_info")$is_anova [1] FALSE attr(,"model_info")$is_timeseries [1] FALSE attr(,"model_info")$is_ttest [1] FALSE attr(,"model_info")$is_correlation [1] FALSE attr(,"model_info")$is_onewaytest [1] FALSE attr(,"model_info")$is_chi2test [1] FALSE attr(,"model_info")$is_ranktest [1] FALSE attr(,"model_info")$is_levenetest [1] FALSE attr(,"model_info")$is_variancetest [1] FALSE attr(,"model_info")$is_xtab [1] FALSE attr(,"model_info")$is_proptest [1] FALSE attr(,"model_info")$is_binomtest [1] FALSE attr(,"model_info")$is_ftest [1] FALSE attr(,"model_info")$is_meta [1] FALSE attr(,"model_info")$link_function [1] "identity" attr(,"model_info")$family [1] "gaussian" attr(,"model_info")$n_obs [1] 210 attr(,"model_info")$n_grouplevels Hive 6 attr(,"bandwidth") [1] "nrd" attr(,"type") [1] "density" attr(,"model_class") [1] "glmmTMB" contrast estimate SE df t.ratio p.value Gotland 00H vs Aland 00H -0.5131 0.135 195 -3.810 0.0018 Gotland 00H vs Gotland 06H 0.0858 0.135 195 0.637 0.7065 Aland 00H vs Aland 06H 0.5128 0.137 195 3.754 0.0018 Gotland 06H vs Aland 06H -0.0860 0.137 195 -0.629 0.7065 Gotland 06H vs Gotland 12H -0.1291 0.135 195 -0.958 0.7065 Aland 06H vs Aland 12H -0.1700 0.139 195 -1.227 0.7065 Gotland 12H vs Aland 12H -0.1269 0.137 195 -0.929 0.7065 Gotland 12H vs Gotland 18H 0.0859 0.135 195 0.638 0.7065 Aland 12H vs Aland 18H 0.0707 0.139 195 0.510 0.7513 Gotland 18H vs Aland 18H -0.1421 0.137 195 -1.040 0.7065 Gotland 18H vs Gotland 24H -0.1838 0.137 195 -1.346 0.7065 Aland 18H vs Aland 24H -0.0507 0.139 195 -0.366 0.7897 Gotland 24H vs Aland 24H -0.0089 0.139 195 -0.064 0.9488 Gotland 24H vs Gotland 36H 0.0901 0.137 195 0.659 0.7065 Aland 24H vs Aland 36H 0.1443 0.139 195 1.042 0.7065 Aland 36H vs Gotland 36H -0.0453 0.137 195 -0.332 0.7897 Note: contrasts are still on the sqrt scale P value adjustment: fdr method for 16 tests [1] "Methyl Palmitate" Best Normalizing transformation with 210 Observations Estimated Normality Statistics (Pearson P / df, lower => more normal): - arcsinh(x): 4.4633 - Center+scale: 5.3633 - Double Reversed Log_b(x+a): 6.5122 - Exp(x): 20.1967 - Log_b(x+a): 8.11 - orderNorm (ORQ): 4.56 - sqrt(x + a): 4.5767 - Yeo-Johnson: 4.69 Estimation method: Out-of-sample via CV with 10 folds and 5 repeats Based off these, bestNormalize chose: Standardized asinh(x) Transformation with 210 nonmissing obs.: Relevant statistics: - mean (before standardization) = 0.743165 - sd (before standardization) = 0.7904349 [1] "Methyl Palmitate" Best Normalizing transformation with 210 Observations Estimated Normality Statistics (Pearson P / df, lower => more normal): - arcsinh(x): 4.5267 - Center+scale: 5.9567 - Double Reversed Log_b(x+a): 5.9826 - Exp(x): 19.75 - Log_b(x+a): 7.9067 - orderNorm (ORQ): 4.4633 - sqrt(x + a): 4.52 - Yeo-Johnson: 4.7333 Estimation method: Out-of-sample via CV with 10 folds and 5 repeats Based off these, bestNormalize chose: orderNorm Transformation with 210 nonmissing obs and ties - 118 unique values - Original quantiles: 0% 25% 50% 75% 100% 0.000 0.000 0.653 1.676 14.609 Analysis of Deviance Table (Type III Wald chisquare tests) Response: asinh(MPalmitate) Chisq Df Pr(>Chisq) (Intercept) 59.0149 1 1.565e-14 *** Background 14.5624 1 0.0001356 *** Time 11.2030 5 0.0475002 * Background:Time 6.2185 5 0.2855328 --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 [1] 486.9845 DHARMa zero-inflation test via comparison to expected zeros with simulation under H0 = fitted model data: simulationOutput ratioObsSim = 2.3856, p-value < 2.2e-16 alternative hypothesis: two.sided DHARMa outlier test based on exact binomial test with approximate expectations data: simulationOutput1 outliers at both margin(s) = 1, observations = 210, p-value = 0.343 alternative hypothesis: true probability of success is not equal to 0.001998002 95 percent confidence interval: 0.0001205537 0.0262446357 sample estimates: frequency of outliers (expected: 0.001998001998002 ) 0.004761905 DHARMa nonparametric dispersion test via sd of residuals fitted vs. simulated data: simulationOutput dispersion = 1.0089, p-value = 0.944 alternative hypothesis: two.sided Package `see` required for model diagnostic plots.$VIF # Check for Multicollinearity $QQ Simulated residuals from a model of class `glmmTMB` based on 250 simulations. Use `check_residuals()` to check uniformity of residuals or `residuals()` to extract simulated residuals. It is recommended to refer to `?DHARMa::simulateResiudals` and `vignette("DHARMa")` for more information about different settings in particular situations or for particular models. $HOMOGENEITY x y 1 1.4765336 1.51050823 2 1.4765336 0.64308329 3 1.4765336 0.86550594 4 0.8728430 0.43165286 5 0.8728430 1.29547545 6 0.8728430 0.16255070 7 1.0231428 0.30829657 8 1.0231428 1.13369242 9 1.0231428 0.23585342 10 1.1120064 1.15575631 11 1.1120064 0.47443104 12 1.2224584 0.86950626 13 1.2224584 1.21179647 14 1.2224584 1.21179647 15 0.7638821 1.30522021 16 0.7638821 0.97091113 17 1.3679965 0.28708471 18 1.3679965 0.59638758 19 1.3679965 0.49511279 20 0.7643059 0.88848124 21 0.7643059 0.95817808 22 0.7643059 0.44922748 23 0.9146057 0.58580692 24 0.9146057 0.58344503 25 0.9146057 1.13426502 26 1.0034693 1.09790478 27 1.0034693 0.89893900 28 1.0034693 0.69633008 29 1.1139213 1.15675104 30 1.1139213 0.64873636 31 1.1139213 1.15675104 32 0.6553451 1.31464082 33 0.6553451 0.57740009 34 0.6553451 0.87229464 35 1.3886627 1.29154941 36 1.3886627 0.04935072 37 1.3886627 0.45806242 38 0.7849721 0.97104585 39 0.7849721 1.35887373 40 0.7849721 0.97104585 41 0.9352719 1.05994072 42 0.9352719 0.83647658 43 0.9352719 1.05580281 44 1.0241355 0.72104686 45 1.0241355 1.10915270 46 1.0241355 1.31375207 47 1.1345875 1.16743212 48 1.1345875 1.45532216 49 1.1345875 0.47201111 50 0.6760112 0.13922847 51 0.6760112 0.90113446 52 0.6760112 0.90113446 53 1.3815889 0.12279847 54 1.3815889 0.91316748 55 1.3815889 1.28825562 56 0.7778982 0.96666060 57 0.7778982 0.81332112 58 0.7778982 0.98278149 59 0.9281980 0.83584455 60 0.9281980 0.16878109 61 0.9281980 1.05592471 62 1.0170616 1.02003377 63 1.0170616 1.06916749 64 1.0170616 0.70667426 65 1.1275137 0.20574619 66 1.1275137 0.82506349 67 1.1275137 1.14852092 68 0.6689374 0.44709230 69 0.6689374 0.27441993 70 0.6689374 0.30778125 71 1.3124395 0.71221221 72 1.3124395 0.79763684 73 1.3124395 1.08878177 74 0.7087488 0.66291674 75 0.7087488 0.92269635 76 0.7087488 0.92269635 77 0.8590486 0.71847309 78 0.8590486 0.53111234 79 0.8590486 0.71847309 80 0.9479122 0.77051120 81 0.9479122 0.47432739 82 0.9479122 1.06707930 83 1.0583643 1.42715235 84 1.0583643 0.29199885 85 1.0583643 0.99467003 86 0.5997880 0.84881201 87 0.5997880 0.84881201 88 0.5997880 0.84881201 89 1.2847251 1.24227499 90 1.2847251 1.24227499 91 1.2847251 0.43741782 92 0.6810344 0.70163933 93 0.6810344 0.55570529 94 0.8313343 0.61768747 95 0.8313343 0.99931056 96 0.9201979 1.05136434 97 0.9201979 0.74391271 98 0.9201979 0.88838874 99 1.0306499 0.65632301 100 1.0306499 0.95260146 101 0.5720736 0.46362229 102 0.5720736 0.82896958 103 0.5720736 0.82896958 104 0.5717288 0.82871975 105 0.5717288 1.23507473 106 0.5717288 1.73356951 107 0.4157618 0.70669972 108 0.4157618 0.70669972 109 0.4157618 0.40697164 110 0.7936216 0.54091728 111 0.7936216 0.77781647 112 0.7936216 0.48249429 113 0.6323279 0.87153296 114 0.6323279 0.87153296 115 0.6323279 0.87153296 116 0.7922544 0.97553971 117 0.7922544 0.74134234 118 0.7922544 0.97553971 119 0.5929378 0.80993796 120 0.5929378 0.84395094 121 0.5929378 0.40816551 122 0.4631918 0.74592137 123 0.4631918 0.28979784 124 0.4631918 0.74592137 125 0.3072247 0.60749219 126 0.3072247 0.60749219 127 0.3072247 0.73276359 128 0.6850845 0.90716169 129 0.6850845 1.32440850 130 0.6850845 1.12134627 131 0.5237908 0.52244064 132 0.5237908 0.79321622 133 0.5237908 1.24439623 134 0.6837173 0.90625603 135 0.6837173 0.33382765 136 0.6837173 0.90625603 137 0.4844007 0.76280761 138 0.4844007 0.76280761 139 0.4844007 0.76280761 140 0.4838580 0.33743212 141 0.4838580 0.76238015 142 0.4838580 0.76238015 143 0.3278909 0.95029215 144 0.3278909 0.62759188 145 0.3278909 0.59312501 146 0.7057507 1.39483946 147 0.7057507 0.29238915 148 0.7057507 0.62018720 149 0.5444570 0.30274093 150 0.5444570 0.80871304 151 0.5444570 0.54895342 152 0.7043835 0.91985042 153 0.7043835 0.67367837 154 0.7043835 0.24326630 155 0.5050669 0.48650918 156 0.5050669 0.58729449 157 0.5050669 0.15348586 158 0.4767841 0.75678673 159 0.4767841 0.75678673 160 0.4767841 0.75678673 161 0.3208170 0.53290733 162 0.3208170 0.62078517 163 0.3208170 0.39468352 164 0.6986768 0.65032985 165 0.6986768 0.91611670 166 0.6986768 1.14646881 167 0.5373832 0.80344225 168 0.5373832 1.12192200 169 0.5373832 0.80344225 170 0.6973096 0.20620773 171 0.6973096 1.62137665 172 0.6973096 0.09867061 173 0.4979930 0.64509310 174 0.4979930 0.77343579 175 0.4979930 0.77343579 176 0.4076347 0.69975854 177 0.4076347 0.69975854 178 0.4076347 0.69975854 179 0.2516676 0.54982733 180 0.2516676 0.54982733 181 0.2516676 0.54982733 182 0.6295274 0.76284346 183 0.6295274 0.86960086 184 0.6295274 0.86960086 185 0.4682338 0.57949976 186 0.4682338 0.47380481 187 0.4682338 0.74997020 188 0.6281602 1.16388105 189 0.6281602 0.86865604 190 0.6281602 0.76056026 191 0.4288436 0.71669468 192 0.4288436 0.71773169 193 0.4288436 0.75971994 194 0.3799203 0.60251283 195 0.3799203 1.03993684 196 0.3799203 0.53112639 197 0.2239533 0.51867025 198 0.2239533 0.93151312 199 0.2239533 0.67817071 200 0.6018131 0.85024373 201 0.6018131 0.85024373 202 0.6018131 0.68566539 203 0.4405194 0.72743662 204 0.4405194 0.43259203 205 0.4405194 1.18661355 206 0.6004458 0.26411741 207 0.6004458 0.84927737 208 0.4011293 0.67988322 209 0.4011293 0.69415238 210 0.4011293 1.20717973 $REQQ $REQQ$Hive x y conf.low conf.high facet 1 -1.2815516 -0.100887328 -0.3735654 0.1717908 (Intercept) 2 -0.6433454 -0.067847040 -0.2902716 0.1545775 (Intercept) 3 -0.2018935 -0.001613464 -0.1847636 0.1815367 (Intercept) 4 0.2018935 0.014590918 -0.1676317 0.1968135 (Intercept) 5 0.6433454 0.023024194 -0.1651078 0.2111561 (Intercept) 6 1.2815516 0.127781407 -0.1820885 0.4376513 (Intercept) $PP_CHECK Warning: Maximum value of original data is not included in the replicated data. Model may not capture the variation of the data. attr(,"class") [1] "check_model" "see_check_model" attr(,"panel") [1] TRUE attr(,"dot_size") [1] 2 attr(,"line_size") [1] 0.8 attr(,"check") [1] "all" attr(,"alpha") [1] 0.2 attr(,"dot_alpha") [1] 0.8 attr(,"show_dots") [1] TRUE attr(,"detrend") [1] FALSE attr(,"colors") [1] "#3aaf85" "#1b6ca8" "#cd201f" attr(,"theme") [1] "see::theme_lucid" attr(,"model_info") attr(,"model_info")$is_binomial [1] FALSE attr(,"model_info")$is_bernoulli [1] FALSE attr(,"model_info")$is_count [1] FALSE attr(,"model_info")$is_poisson [1] FALSE attr(,"model_info")$is_negbin [1] FALSE attr(,"model_info")$is_beta [1] FALSE attr(,"model_info")$is_betabinomial [1] FALSE attr(,"model_info")$is_orderedbeta [1] FALSE attr(,"model_info")$is_dirichlet [1] FALSE attr(,"model_info")$is_exponential [1] FALSE attr(,"model_info")$is_logit [1] FALSE attr(,"model_info")$is_probit [1] FALSE attr(,"model_info")$is_censored [1] FALSE attr(,"model_info")$is_truncated [1] FALSE attr(,"model_info")$is_survival [1] FALSE attr(,"model_info")$is_linear [1] FALSE attr(,"model_info")$is_tweedie [1] FALSE attr(,"model_info")$is_zeroinf [1] TRUE attr(,"model_info")$is_zero_inflated [1] TRUE attr(,"model_info")$is_dispersion [1] FALSE attr(,"model_info")$is_hurdle [1] FALSE attr(,"model_info")$is_ordinal [1] FALSE attr(,"model_info")$is_cumulative [1] FALSE attr(,"model_info")$is_multinomial [1] FALSE attr(,"model_info")$is_categorical [1] FALSE attr(,"model_info")$is_mixed [1] TRUE attr(,"model_info")$is_multivariate [1] FALSE attr(,"model_info")$is_trial [1] FALSE attr(,"model_info")$is_bayesian [1] FALSE attr(,"model_info")$is_gam [1] FALSE attr(,"model_info")$is_anova [1] FALSE attr(,"model_info")$is_timeseries [1] FALSE attr(,"model_info")$is_ttest [1] FALSE attr(,"model_info")$is_correlation [1] FALSE attr(,"model_info")$is_onewaytest [1] FALSE attr(,"model_info")$is_chi2test [1] FALSE attr(,"model_info")$is_ranktest [1] FALSE attr(,"model_info")$is_levenetest [1] FALSE attr(,"model_info")$is_variancetest [1] FALSE attr(,"model_info")$is_xtab [1] FALSE attr(,"model_info")$is_proptest [1] FALSE attr(,"model_info")$is_binomtest [1] FALSE attr(,"model_info")$is_ftest [1] FALSE attr(,"model_info")$is_meta [1] FALSE attr(,"model_info")$link_function [1] "identity" attr(,"model_info")$family [1] "gaussian" attr(,"model_info")$n_obs [1] 210 attr(,"model_info")$n_grouplevels Hive 6 attr(,"bandwidth") [1] "nrd" attr(,"type") [1] "density" attr(,"model_class") [1] "glmmTMB" contrast estimate SE df t.ratio p.value Gotland 00H vs Aland 00H -1.079 0.283 195 -3.816 0.0029 Gotland 00H vs Gotland 06H 0.186 0.266 195 0.699 0.5915 Aland 00H vs Aland 06H 0.720 0.287 195 2.507 0.1039 Gotland 06H vs Aland 06H -0.545 0.273 195 -1.998 0.1963 Gotland 06H vs Gotland 12H -0.450 0.260 195 -1.730 0.2219 Aland 06H vs Aland 12H -0.179 0.276 195 -0.648 0.5915 Gotland 12H vs Aland 12H -0.274 0.264 195 -1.035 0.5915 Gotland 12H vs Gotland 18H 0.192 0.270 195 0.712 0.5915 Aland 12H vs Aland 18H -0.106 0.290 195 -0.366 0.7148 Gotland 18H vs Aland 18H -0.572 0.289 195 -1.980 0.1963 Gotland 18H vs Gotland 24H -0.191 0.281 195 -0.680 0.5915 Aland 18H vs Aland 24H -0.132 0.312 195 -0.422 0.7148 Gotland 24H vs Aland 24H -0.513 0.308 195 -1.667 0.2219 Gotland 24H vs Gotland 36H 0.238 0.276 195 0.862 0.5915 Aland 24H vs Aland 36H 0.547 0.307 195 1.782 0.2219 Aland 36H vs Gotland 36H 0.204 0.270 195 0.755 0.5915 Note: contrasts are still on the asinh scale P value adjustment: fdr method for 16 tests [1] "Ethyl Palmitate" Best Normalizing transformation with 210 Observations Estimated Normality Statistics (Pearson P / df, lower => more normal): - arcsinh(x): 9.03 - Center+scale: 9.1 - Double Reversed Log_b(x+a): 10.3533 - Exp(x): 9.3633 - Log_b(x+a): 11.2167 - orderNorm (ORQ): 9.4867 - sqrt(x + a): 9.6033 - Yeo-Johnson: 9.4933 Estimation method: Out-of-sample via CV with 10 folds and 5 repeats Based off these, bestNormalize chose: Standardized asinh(x) Transformation with 210 nonmissing obs.: Relevant statistics: - mean (before standardization) = 0.1069032 - sd (before standardization) = 0.1592827 Analysis of Deviance Table (Type III Wald chisquare tests) Response: asinh(EPalmitate) Chisq Df Pr(>Chisq) (Intercept) 38.8489 1 4.579e-10 *** Background 13.5199 1 0.0002361 *** Time 10.5353 5 0.0614126 . Background:Time 7.1657 5 0.2086053 --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 [1] -172.9579 DHARMa zero-inflation test via comparison to expected zeros with simulation under H0 = fitted model data: simulationOutput ratioObsSim = Inf, p-value < 2.2e-16 alternative hypothesis: two.sided DHARMa outlier test based on exact binomial test with approximate expectations data: simulationOutput1 outliers at both margin(s) = 4, observations = 210, p-value = 0.0009046 alternative hypothesis: true probability of success is not equal to 0.001998002 95 percent confidence interval: 0.005213625 0.048048783 sample estimates: frequency of outliers (expected: 0.001998001998002 ) 0.01904762 DHARMa nonparametric dispersion test via sd of residuals fitted vs. simulated data: simulationOutput dispersion = 0.99444, p-value = 0.976 alternative hypothesis: two.sided Package `see` required for model diagnostic plots.$VIF # Check for Multicollinearity $QQ Simulated residuals from a model of class `glmmTMB` based on 250 simulations. Use `check_residuals()` to check uniformity of residuals or `residuals()` to extract simulated residuals. It is recommended to refer to `?DHARMa::simulateResiudals` and `vignette("DHARMa")` for more information about different settings in particular situations or for particular models. $HOMOGENEITY x y 1 0.21925539 0.79674264 2 0.21925539 0.90136243 3 0.21925539 0.60720181 4 0.10044606 0.50990835 5 0.10044606 0.77642484 6 0.10044606 0.19927416 7 0.15269266 0.62868740 8 0.15269266 0.51487175 9 0.15269266 0.27049663 10 0.09364970 0.49235559 11 0.09364970 0.20630776 12 0.17516475 0.48539797 13 0.17516475 0.67336265 14 0.17516475 0.67336265 15 0.09480852 0.66335006 16 0.09480852 0.76535138 17 0.21925539 0.63307228 18 0.21925539 0.57474673 19 0.21925539 0.70687386 20 0.10044606 0.17983736 21 0.10044606 0.50990835 22 0.10044606 0.50990835 23 0.15269266 0.38361397 24 0.15269266 0.58328471 25 0.15269266 0.78117402 26 0.09364970 0.49235559 27 0.09364970 0.63525809 28 0.09364970 0.49235559 29 0.17516475 0.67336265 30 0.17516475 0.44288252 31 0.17516475 0.67336265 32 0.09480852 0.86983960 33 0.09480852 0.49539241 34 0.09480852 0.24080393 35 0.21925539 0.75335697 36 0.21925539 0.17315034 37 0.21925539 0.41506300 38 0.10044606 0.50990835 39 0.10044606 0.81167835 40 0.10044606 0.50990835 41 0.15269266 0.62868740 42 0.15269266 0.66024884 43 0.15269266 0.47488729 44 0.09364970 0.49235559 45 0.09364970 0.49235559 46 0.09364970 0.89682037 47 0.17516475 0.67336265 48 0.17516475 1.21513953 49 0.17516475 0.58819443 50 0.09480852 0.49485077 51 0.09480852 0.49539241 52 0.09480852 0.49539241 53 0.21925539 0.43095640 54 0.21925539 0.99372563 55 0.21925539 0.75335697 56 0.10044606 0.50990835 57 0.10044606 0.47222518 58 0.10044606 0.41014772 59 0.15269266 0.45009424 60 0.15269266 0.23462293 61 0.15269266 0.62868740 62 0.09364970 0.61802548 63 0.09364970 0.58192258 64 0.09364970 0.51228986 65 0.17516475 0.67336265 66 0.17516475 0.67336265 67 0.17516475 0.62315962 68 0.09480852 0.49539241 69 0.09480852 0.49539241 70 0.09480852 0.31959078 71 0.21925539 0.75335696 72 0.21925539 0.33276623 73 0.21925539 0.75335696 74 0.10044606 0.93548838 75 0.10044606 0.50990835 76 0.10044606 0.50990835 77 0.15269266 0.48241501 78 0.15269266 0.62868739 79 0.15269266 0.54530802 80 0.09364970 0.25430098 81 0.09364970 0.49235559 82 0.09364970 0.49235559 83 0.17516475 1.07158617 84 0.17516475 0.67336265 85 0.17516475 0.46657353 86 0.09480852 0.49539241 87 0.09480852 0.49539241 88 0.09480852 0.49539241 89 0.21925539 0.75335696 90 0.21925539 0.75335696 91 0.21925539 0.28003600 92 0.10044606 0.50990835 93 0.10044606 0.50990835 94 0.15269266 0.31498442 95 0.15269266 0.62868739 96 0.09364970 0.49235559 97 0.09364970 0.49235559 98 0.09364970 0.34113857 99 0.17516475 0.36277972 100 0.17516475 0.49966124 101 0.09480852 0.51644691 102 0.09480852 0.49539241 103 0.09480852 0.49539241 104 0.03633495 0.30668177 105 0.03633495 0.98128143 106 0.03633495 0.30668177 107 0.04241154 0.33133527 108 0.04241154 0.33133527 109 0.04241154 0.30045902 110 0.12663014 0.57252469 111 0.12663014 0.57252469 112 0.12663014 0.57252469 113 0.04461092 0.33981791 114 0.04461092 0.33981791 115 0.04461092 0.33981791 116 0.13259833 0.58586115 117 0.13259833 0.68578324 118 0.13259833 0.58586115 119 0.07023254 0.34339258 120 0.07023254 0.42637806 121 0.07023254 0.33249023 122 0.03633495 0.30668177 123 0.03633495 0.30668177 124 0.03633495 0.30668177 125 0.04241154 0.33133527 126 0.04241154 0.33133527 127 0.04241154 0.58654207 128 0.12663014 0.57252469 129 0.12663014 0.49234896 130 0.12663014 0.93339092 131 0.04461092 0.33981791 132 0.04461092 0.33981791 133 0.04461092 0.33981791 134 0.13259833 0.58586115 135 0.13259833 0.15844708 136 0.13259833 0.58586115 137 0.07023254 0.42637806 138 0.07023254 0.42637806 139 0.07023254 0.42637806 140 0.03633495 0.54548113 141 0.03633495 0.30668177 142 0.03633495 0.30668177 143 0.04241154 0.65405093 144 0.04241154 0.33133527 145 0.04241154 0.38168981 146 0.12663014 0.57252469 147 0.12663014 0.93339092 148 0.12663014 0.57252469 149 0.04461092 0.44191518 150 0.04461092 0.33981791 151 0.04461092 0.64688409 152 0.13259833 0.58586115 153 0.13259833 0.31562756 154 0.13259833 0.58586115 155 0.07023254 0.14818117 156 0.07023254 0.49271767 157 0.07023254 0.42637806 158 0.03633495 0.30668177 159 0.03633495 0.30668177 160 0.03633495 0.30668177 161 0.04241154 0.33133527 162 0.04241154 0.33133527 163 0.04241154 0.33133527 164 0.12663014 0.37258547 165 0.12663014 0.57252469 166 0.12663014 1.29685643 167 0.04461092 0.33981791 168 0.04461092 0.85427683 169 0.04461092 0.33981791 170 0.13259833 0.31562756 171 0.13259833 1.28210526 172 0.13259833 0.19013224 173 0.07023254 0.42637806 174 0.07023254 0.42637806 175 0.07023254 0.42637806 176 0.03633495 0.30668177 177 0.03633495 0.30668177 178 0.03633495 0.30668177 179 0.04241153 0.33133527 180 0.04241153 0.33133527 181 0.04241153 0.33133527 182 0.12663014 0.40127088 183 0.12663014 0.57252469 184 0.12663014 0.57252469 185 0.04461092 0.44606124 186 0.04461092 0.20326525 187 0.04461092 0.33981791 188 0.13259833 0.56946200 189 0.13259833 0.58586115 190 0.13259833 0.41537252 191 0.07023254 0.36922833 192 0.07023254 0.42637806 193 0.07023254 0.55213157 194 0.03633495 0.30668177 195 0.03633495 0.30668177 196 0.03633495 0.38774050 197 0.04241153 0.33133527 198 0.04241153 0.47617207 199 0.04241153 0.28788879 200 0.12663014 0.57252469 201 0.12663014 0.57252469 202 0.12663014 0.48865802 203 0.04461092 0.33981791 204 0.04461092 0.33981791 205 0.04461092 0.33981791 206 0.13259833 0.05507597 207 0.13259833 0.58586115 208 0.07023254 0.55869056 209 0.07023254 0.42637806 210 0.07023254 0.75595760 $REQQ $REQQ$Hive x y conf.low conf.high facet 1 -1.2815516 -6.402568e-10 -1.662124e-05 1.661996e-05 (Intercept) 2 -0.6433454 -3.182227e-10 -9.927385e-06 9.926749e-06 (Intercept) 3 -0.2018935 -8.235040e-11 -6.644783e-06 6.644618e-06 (Intercept) 4 0.2018935 2.621221e-10 -8.932998e-06 8.933522e-06 (Intercept) 5 0.6433454 3.381731e-10 -1.029945e-05 1.030013e-05 (Intercept) 6 1.2815516 4.405347e-10 -1.232736e-05 1.232824e-05 (Intercept) $PP_CHECK Warning: Maximum value of original data is not included in the replicated data. Model may not capture the variation of the data. attr(,"class") [1] "check_model" "see_check_model" attr(,"panel") [1] TRUE attr(,"dot_size") [1] 2 attr(,"line_size") [1] 0.8 attr(,"check") [1] "all" attr(,"alpha") [1] 0.2 attr(,"dot_alpha") [1] 0.8 attr(,"show_dots") [1] TRUE attr(,"detrend") [1] FALSE attr(,"colors") [1] "#3aaf85" "#1b6ca8" "#cd201f" attr(,"theme") [1] "see::theme_lucid" attr(,"model_info") attr(,"model_info")$is_binomial [1] FALSE attr(,"model_info")$is_bernoulli [1] FALSE attr(,"model_info")$is_count [1] FALSE attr(,"model_info")$is_poisson [1] FALSE attr(,"model_info")$is_negbin [1] FALSE attr(,"model_info")$is_beta [1] FALSE attr(,"model_info")$is_betabinomial [1] FALSE attr(,"model_info")$is_orderedbeta [1] FALSE attr(,"model_info")$is_dirichlet [1] FALSE attr(,"model_info")$is_exponential [1] FALSE attr(,"model_info")$is_logit [1] FALSE attr(,"model_info")$is_probit [1] FALSE attr(,"model_info")$is_censored [1] FALSE attr(,"model_info")$is_truncated [1] FALSE attr(,"model_info")$is_survival [1] FALSE attr(,"model_info")$is_linear [1] FALSE attr(,"model_info")$is_tweedie [1] FALSE attr(,"model_info")$is_zeroinf [1] TRUE attr(,"model_info")$is_zero_inflated [1] TRUE attr(,"model_info")$is_dispersion [1] FALSE attr(,"model_info")$is_hurdle [1] FALSE attr(,"model_info")$is_ordinal [1] FALSE attr(,"model_info")$is_cumulative [1] FALSE attr(,"model_info")$is_multinomial [1] FALSE attr(,"model_info")$is_categorical [1] FALSE attr(,"model_info")$is_mixed [1] TRUE attr(,"model_info")$is_multivariate [1] FALSE attr(,"model_info")$is_trial [1] FALSE attr(,"model_info")$is_bayesian [1] FALSE attr(,"model_info")$is_gam [1] FALSE attr(,"model_info")$is_anova [1] FALSE attr(,"model_info")$is_timeseries [1] FALSE attr(,"model_info")$is_ttest [1] FALSE attr(,"model_info")$is_correlation [1] FALSE attr(,"model_info")$is_onewaytest [1] FALSE attr(,"model_info")$is_chi2test [1] FALSE attr(,"model_info")$is_ranktest [1] FALSE attr(,"model_info")$is_levenetest [1] FALSE attr(,"model_info")$is_variancetest [1] FALSE attr(,"model_info")$is_xtab [1] FALSE attr(,"model_info")$is_proptest [1] FALSE attr(,"model_info")$is_binomtest [1] FALSE attr(,"model_info")$is_ftest [1] FALSE attr(,"model_info")$is_meta [1] FALSE attr(,"model_info")$link_function [1] "identity" attr(,"model_info")$family [1] "gaussian" attr(,"model_info")$n_obs [1] 210 attr(,"model_info")$n_grouplevels Hive 6 attr(,"bandwidth") [1] "nrd" attr(,"type") [1] "density" attr(,"model_class") [1] "glmmTMB" contrast estimate SE df t.ratio p.value Gotland 00H vs Aland 00H -0.18292 0.0497 195 -3.677 0.0049 Gotland 00H vs Gotland 06H -0.00608 0.0497 195 -0.122 0.9029 Aland 00H vs Aland 06H 0.11881 0.0505 195 2.354 0.1566 Gotland 06H vs Aland 06H -0.05803 0.0505 195 -1.150 0.4026 Gotland 06H vs Gotland 12H -0.08422 0.0497 195 -1.693 0.2699 Aland 06H vs Aland 12H -0.05225 0.0512 195 -1.021 0.4433 Gotland 12H vs Aland 12H -0.02606 0.0505 195 -0.516 0.6687 Gotland 12H vs Gotland 18H 0.08202 0.0497 195 1.649 0.2699 Aland 12H vs Aland 18H 0.05904 0.0512 195 1.153 0.4026 Gotland 18H vs Aland 18H -0.04904 0.0505 195 -0.972 0.4433 Gotland 18H vs Gotland 24H -0.08799 0.0505 195 -1.743 0.2699 Aland 18H vs Aland 24H -0.08152 0.0512 195 -1.592 0.2699 Gotland 24H vs Aland 24H -0.04257 0.0512 195 -0.832 0.5005 Gotland 24H vs Gotland 36H 0.06237 0.0505 195 1.236 0.4026 Aland 24H vs Aland 36H 0.08036 0.0512 195 1.570 0.2699 Aland 36H vs Gotland 36H 0.02458 0.0505 195 0.487 0.6687 Note: contrasts are still on the asinh scale P value adjustment: fdr method for 16 tests [1] "Methyl Linoloate" Best Normalizing transformation with 210 Observations Estimated Normality Statistics (Pearson P / df, lower => more normal): - arcsinh(x): 13.3067 - Center+scale: 14.4767 - Double Reversed Log_b(x+a): 14.6874 - Exp(x): 18.98 - Log_b(x+a): 13.27 - orderNorm (ORQ): 12.5767 - sqrt(x + a): 12.2267 - Yeo-Johnson: 12.72 Estimation method: Out-of-sample via CV with 10 folds and 5 repeats Based off these, bestNormalize chose: Standardized sqrt(x + a) Transformation with 210 nonmissing obs.: Relevant statistics: - a = 0 - mean (before standardization) = 0.3016079 - sd (before standardization) = 0.5008493 Analysis of Deviance Table (Type III Wald chisquare tests) Response: sqrt(MLinoloate) Chisq Df Pr(>Chisq) (Intercept) NaN 1 NaN Background NaN 1 NaN Time NaN 5 NaN Background:Time NaN 5 NaN [1] NA DHARMa zero-inflation test via comparison to expected zeros with simulation under H0 = fitted model data: simulationOutput ratioObsSim = Inf, p-value < 2.2e-16 alternative hypothesis: two.sided DHARMa outlier test based on exact binomial test with approximate expectations data: simulationOutput1 outliers at both margin(s) = 3, observations = 210, p-value = 0.008926 alternative hypothesis: true probability of success is not equal to 0.001998002 95 percent confidence interval: 0.002955803 0.041179059 sample estimates: frequency of outliers (expected: 0.001998001998002 ) 0.01428571 DHARMa nonparametric dispersion test via sd of residuals fitted vs. simulated data: simulationOutput dispersion = 0.99444, p-value = 0.976 alternative hypothesis: two.sided contrast estimate SE df t.ratio p.value Gotland 00H vs Aland 00H -0.6163 NaN 195 NaN NaN Gotland 00H vs Gotland 06H 0.1098 NaN 195 NaN NaN Aland 00H vs Aland 06H 0.6907 NaN 195 NaN NaN Gotland 06H vs Aland 06H -0.0355 NaN 195 NaN NaN Gotland 06H vs Gotland 12H -0.0919 NaN 195 NaN NaN Aland 06H vs Aland 12H -0.1313 NaN 195 NaN NaN Gotland 12H vs Aland 12H -0.0749 NaN 195 NaN NaN Gotland 12H vs Gotland 18H -0.0783 NaN 195 NaN NaN Aland 12H vs Aland 18H -0.0609 NaN 195 NaN NaN Gotland 18H vs Aland 18H -0.0576 NaN 195 NaN NaN Gotland 18H vs Gotland 24H 0.1118 NaN 195 NaN NaN Aland 18H vs Aland 24H -0.0216 NaN 195 NaN NaN Gotland 24H vs Aland 24H -0.1910 NaN 195 NaN NaN Gotland 24H vs Gotland 36H -0.0717 NaN 195 NaN NaN Aland 24H vs Aland 36H 0.0102 NaN 195 NaN NaN Aland 36H vs Gotland 36H 0.1090 NaN 195 NaN NaN Note: contrasts are still on the sqrt scale P value adjustment: fdr method for 1 tests [1] "Ethyl Linoloate" Best Normalizing transformation with 210 Observations Estimated Normality Statistics (Pearson P / df, lower => more normal): - arcsinh(x): 8.17 - Center+scale: 9.5933 - Double Reversed Log_b(x+a): 9.3568 - Exp(x): 11.1833 - Log_b(x+a): 8.71 - orderNorm (ORQ): 7.31 - sqrt(x + a): 7.14 - Yeo-Johnson: 7.3167 Estimation method: Out-of-sample via CV with 10 folds and 5 repeats Based off these, bestNormalize chose: Standardized sqrt(x + a) Transformation with 210 nonmissing obs.: Relevant statistics: - a = 0 - mean (before standardization) = 0.2649134 - sd (before standardization) = 0.3305043 Analysis of Deviance Table (Type III Wald chisquare tests) Response: sqrt(ELinoloate) Chisq Df Pr(>Chisq) (Intercept) 72.124 1 < 2.2e-16 *** Background 12.658 1 0.0003740 *** Time 24.873 5 0.0001474 *** Background:Time 10.839 5 0.0546682 . --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 [1] 129.5656 DHARMa zero-inflation test via comparison to expected zeros with simulation under H0 = fitted model data: simulationOutput ratioObsSim = Inf, p-value < 2.2e-16 alternative hypothesis: two.sided DHARMa outlier test based on exact binomial test with approximate expectations data: simulationOutput1 outliers at both margin(s) = 2, observations = 210, p-value = 0.06672 alternative hypothesis: true probability of success is not equal to 0.001998002 95 percent confidence interval: 0.001155466 0.033977955 sample estimates: frequency of outliers (expected: 0.001998001998002 ) 0.00952381 DHARMa nonparametric dispersion test via sd of residuals fitted vs. simulated data: simulationOutput dispersion = 0.99444, p-value = 0.976 alternative hypothesis: two.sided Package `see` required for model diagnostic plots.$VIF # Check for Multicollinearity $QQ Simulated residuals from a model of class `glmmTMB` based on 250 simulations. Use `check_residuals()` to check uniformity of residuals or `residuals()` to extract simulated residuals. It is recommended to refer to `?DHARMa::simulateResiudals` and `vignette("DHARMa")` for more information about different settings in particular situations or for particular models. $HOMOGENEITY x y 1 0.6139316 1.2808643 2 0.6139316 0.1969265 3 0.6139316 0.6606202 4 0.1772493 0.4892658 5 0.1772493 0.7091827 6 0.1772493 0.5657349 7 0.2951375 0.7300170 8 0.2951375 0.9740552 9 0.2951375 0.2334568 10 0.2833635 0.7153073 11 0.2833635 0.6346212 12 0.3087773 0.4740286 13 0.3087773 0.7466953 14 0.3087773 0.7466953 15 0.1641597 1.1837490 16 0.1641597 0.5577213 17 0.6139316 0.8126441 18 0.6139316 0.5526205 19 0.6139316 1.1561396 20 0.1772493 0.8543400 21 0.1772493 0.5657349 22 0.1772493 0.5657349 23 0.2951375 0.2799667 24 0.2951375 0.7300170 25 0.2951375 0.9104184 26 0.2833635 0.7153073 27 0.2833635 0.8673387 28 0.2833635 0.7153073 29 0.3087773 0.7466953 30 0.3087773 0.6451576 31 0.3087773 0.7466953 32 0.1641597 0.5444450 33 0.1641597 0.5444450 34 0.1641597 0.5840117 35 0.6139316 1.0528845 36 0.6139316 0.4079390 37 0.6139316 0.3863709 38 0.1772493 0.5657349 39 0.1772493 0.8818985 40 0.1772493 0.5657349 41 0.2951375 0.7300170 42 0.2951375 0.4808408 43 0.2951375 0.5758408 44 0.2833635 0.5143909 45 0.2833635 0.7153073 46 0.2833635 0.6406050 47 0.3087773 0.7466953 48 0.3087773 0.7875923 49 0.3087773 0.4299708 50 0.1641597 0.6748235 51 0.1641597 0.5444450 52 0.1641597 0.5444450 53 0.6139316 1.1346586 54 0.6139316 0.4711591 55 0.6139316 1.0528845 56 0.1772493 0.5657349 57 0.1772493 0.7323375 58 0.1772493 0.8569343 59 0.2951375 0.3903713 60 0.2951375 0.1751729 61 0.2951375 0.7300170 62 0.2833635 0.6244057 63 0.2833635 0.4529409 64 0.2833635 0.7711121 65 0.3087773 0.1008541 66 0.3087773 0.7466953 67 0.3087773 0.5491297 68 0.1641597 0.5444450 69 0.1641597 0.3371294 70 0.1641597 0.7988733 71 0.6139316 0.2220150 72 0.6139316 0.3261612 73 0.6139316 0.4348138 74 0.1772493 0.5657349 75 0.1772493 0.5657349 76 0.1772493 0.5657349 77 0.2951375 0.6900676 78 0.2951375 0.7300170 79 0.2951375 0.7585926 80 0.2833635 0.1983919 81 0.2833635 0.7153073 82 0.2833635 0.7153073 83 0.3087773 1.1979401 84 0.3087773 0.7466953 85 0.3087773 0.8036190 86 0.1641597 0.5444450 87 0.1641597 0.5444450 88 0.1641597 0.5444450 89 0.6139316 1.0528845 90 0.6139316 1.0528845 91 0.6139316 0.2807631 92 0.1772493 0.5657349 93 0.1772493 0.5657349 94 0.2951375 0.1880363 95 0.2951375 0.7300170 96 0.2833635 0.7153073 97 0.2833635 0.7655955 98 0.2833635 0.2519706 99 0.3087773 0.1194601 100 0.3087773 0.2234270 101 0.1641597 0.5444450 102 0.1641597 0.5444450 103 0.1641597 0.5444450 104 0.2502029 0.8390771 105 0.2502029 0.9346085 106 0.2502029 1.5100821 107 0.1376781 0.4986011 108 0.1376781 0.4986011 109 0.1376781 0.4742496 110 0.1932506 0.5907194 111 0.1932506 0.8579111 112 0.1932506 0.5907194 113 0.1964333 0.5955638 114 0.1964333 0.5955638 115 0.1964333 0.5955638 116 0.3154043 0.7546656 117 0.3154043 0.4769963 118 0.3154043 0.7546656 119 0.2408514 0.4500985 120 0.2408514 0.6594705 121 0.2408514 0.6274959 122 0.2502029 0.6721512 123 0.2502029 0.6721512 124 0.2502029 0.6721512 125 0.1376781 0.4986011 126 0.1376781 0.4986011 127 0.1376781 0.4986011 128 0.1932506 0.5907194 129 0.1932506 1.0628278 130 0.1932506 0.7685403 131 0.1964333 0.6071334 132 0.1964333 0.5955638 133 0.1964333 1.0910035 134 0.3154043 0.7546656 135 0.3154043 0.2503037 136 0.3154043 0.7546656 137 0.2408514 0.6594705 138 0.2408514 0.6594705 139 0.2408514 0.6594705 140 0.2502029 0.4848532 141 0.2502029 0.6721512 142 0.2502029 0.6721512 143 0.1376781 0.7999352 144 0.1376781 0.4986011 145 0.1376781 0.6407050 146 0.1932506 0.5907194 147 0.1932506 0.2472393 148 0.1932506 0.5907194 149 0.1964333 0.5283176 150 0.1964333 0.5955638 151 0.1964333 0.5065668 152 0.3154043 0.7546656 153 0.3154043 0.6994949 154 0.3154043 0.1846517 155 0.2408514 0.5086609 156 0.2408514 0.6274959 157 0.2408514 0.3469078 158 0.2502029 0.6721512 159 0.2502029 0.6721512 160 0.2502029 0.6721512 161 0.1376781 0.6085206 162 0.1376781 0.4986011 163 0.1376781 0.3871948 164 0.1932506 0.5907194 165 0.1932506 0.5907194 166 0.1932506 0.8604117 167 0.1964333 0.5955638 168 0.1964333 0.9083725 169 0.1964333 0.5955638 170 0.3154043 0.4948915 171 0.3154043 1.5270134 172 0.3154043 0.1201215 173 0.2408514 0.6594705 174 0.2408514 0.6594705 175 0.2408514 0.6594705 176 0.2502029 0.6721512 177 0.2502029 0.6721512 178 0.2502029 0.6721512 179 0.1376781 0.4986011 180 0.1376781 0.4986011 181 0.1376781 0.4986011 182 0.1932506 0.4031204 183 0.1932506 0.5907194 184 0.1932506 0.5907194 185 0.1964333 0.4788039 186 0.1964333 0.3447021 187 0.1964333 0.5955638 188 0.3154043 0.8442549 189 0.3154043 0.7546656 190 0.3154043 0.1889975 191 0.2408514 0.9750326 192 0.2408514 0.6594705 193 0.2408514 1.0397541 194 0.2502029 0.3896528 195 0.2502029 0.7777004 196 0.2502029 0.3464641 197 0.1376781 0.4986011 198 0.1376781 0.5756152 199 0.1376781 0.7795968 200 0.1932506 0.5907194 201 0.1932506 0.5907194 202 0.1932506 0.6467099 203 0.1964333 0.5955638 204 0.1964333 0.5955638 205 0.1964333 0.5283176 206 0.3154043 0.1738230 207 0.3154043 0.7546656 208 0.2408514 0.1358508 209 0.2408514 0.6594705 210 0.2408514 0.7033535 $REQQ $REQQ$Hive x y conf.low conf.high facet 1 -1.2815516 -3.197437e-11 -5.009268e-06 5.009204e-06 (Intercept) 2 -0.6433454 -1.473893e-11 -3.085569e-06 3.085540e-06 (Intercept) 3 -0.2018935 -1.437645e-11 -3.052314e-06 3.052285e-06 (Intercept) 4 0.2018935 -2.006882e-12 -2.323150e-06 2.323146e-06 (Intercept) 5 0.6433454 1.952864e-11 -3.562999e-06 3.563038e-06 (Intercept) 6 1.2815516 4.356800e-11 -6.483116e-06 6.483203e-06 (Intercept) $PP_CHECK Warning: Minimum value of original data is not included in the replicated data. Model may not capture the variation of the data.Warning: Maximum value of original data is not included in the replicated data. Model may not capture the variation of the data. attr(,"class") [1] "check_model" "see_check_model" attr(,"panel") [1] TRUE attr(,"dot_size") [1] 2 attr(,"line_size") [1] 0.8 attr(,"check") [1] "all" attr(,"alpha") [1] 0.2 attr(,"dot_alpha") [1] 0.8 attr(,"show_dots") [1] TRUE attr(,"detrend") [1] FALSE attr(,"colors") [1] "#3aaf85" "#1b6ca8" "#cd201f" attr(,"theme") [1] "see::theme_lucid" attr(,"model_info") attr(,"model_info")$is_binomial [1] FALSE attr(,"model_info")$is_bernoulli [1] FALSE attr(,"model_info")$is_count [1] FALSE attr(,"model_info")$is_poisson [1] FALSE attr(,"model_info")$is_negbin [1] FALSE attr(,"model_info")$is_beta [1] FALSE attr(,"model_info")$is_betabinomial [1] FALSE attr(,"model_info")$is_orderedbeta [1] FALSE attr(,"model_info")$is_dirichlet [1] FALSE attr(,"model_info")$is_exponential [1] FALSE attr(,"model_info")$is_logit [1] FALSE attr(,"model_info")$is_probit [1] FALSE attr(,"model_info")$is_censored [1] FALSE attr(,"model_info")$is_truncated [1] FALSE attr(,"model_info")$is_survival [1] FALSE attr(,"model_info")$is_linear [1] FALSE attr(,"model_info")$is_tweedie [1] FALSE attr(,"model_info")$is_zeroinf [1] TRUE attr(,"model_info")$is_zero_inflated [1] TRUE attr(,"model_info")$is_dispersion [1] FALSE attr(,"model_info")$is_hurdle [1] FALSE attr(,"model_info")$is_ordinal [1] FALSE attr(,"model_info")$is_cumulative [1] FALSE attr(,"model_info")$is_multinomial [1] FALSE attr(,"model_info")$is_categorical [1] FALSE attr(,"model_info")$is_mixed [1] TRUE attr(,"model_info")$is_multivariate [1] FALSE attr(,"model_info")$is_trial [1] FALSE attr(,"model_info")$is_bayesian [1] FALSE attr(,"model_info")$is_gam [1] FALSE attr(,"model_info")$is_anova [1] FALSE attr(,"model_info")$is_timeseries [1] FALSE attr(,"model_info")$is_ttest [1] FALSE attr(,"model_info")$is_correlation [1] FALSE attr(,"model_info")$is_onewaytest [1] FALSE attr(,"model_info")$is_chi2test [1] FALSE attr(,"model_info")$is_ranktest [1] FALSE attr(,"model_info")$is_levenetest [1] FALSE attr(,"model_info")$is_variancetest [1] FALSE attr(,"model_info")$is_xtab [1] FALSE attr(,"model_info")$is_proptest [1] FALSE attr(,"model_info")$is_binomtest [1] FALSE attr(,"model_info")$is_ftest [1] FALSE attr(,"model_info")$is_meta [1] FALSE attr(,"model_info")$link_function [1] "identity" attr(,"model_info")$family [1] "gaussian" attr(,"model_info")$n_obs [1] 210 attr(,"model_info")$n_grouplevels Hive 6 attr(,"bandwidth") [1] "nrd" attr(,"type") [1] "density" attr(,"model_class") [1] "glmmTMB" contrast estimate SE df t.ratio p.value Gotland 00H vs Aland 00H -0.36373 0.102 195 -3.558 0.0038 Gotland 00H vs Gotland 06H 0.11252 0.102 195 1.101 0.7264 Aland 00H vs Aland 06H 0.43668 0.104 195 4.210 0.0006 Gotland 06H vs Aland 06H -0.03957 0.104 195 -0.381 0.9377 Gotland 06H vs Gotland 12H -0.05557 0.102 195 -0.544 0.8543 Aland 06H vs Aland 12H -0.11789 0.105 195 -1.121 0.7264 Gotland 12H vs Aland 12H -0.10189 0.104 195 -0.982 0.7479 Gotland 12H vs Gotland 18H -0.00318 0.102 195 -0.031 0.9752 Aland 12H vs Aland 18H 0.01177 0.105 195 0.112 0.9752 Gotland 18H vs Aland 18H -0.08693 0.104 195 -0.838 0.7571 Gotland 18H vs Gotland 24H -0.11897 0.104 195 -1.147 0.7264 Aland 18H vs Aland 24H -0.02541 0.105 195 -0.242 0.9752 Gotland 24H vs Aland 24H 0.00663 0.105 195 0.063 0.9752 Gotland 24H vs Gotland 36H 0.07455 0.104 195 0.719 0.7571 Aland 24H vs Aland 36H 0.14462 0.105 195 1.375 0.7264 Aland 36H vs Gotland 36H -0.07669 0.104 195 -0.739 0.7571 Note: contrasts are still on the sqrt scale P value adjustment: fdr method for 16 tests [1] "Methyl Stearate" Best Normalizing transformation with 210 Observations Estimated Normality Statistics (Pearson P / df, lower => more normal): - arcsinh(x): 7.14 - Center+scale: 14.69 - Double Reversed Log_b(x+a): 14.7285 - Exp(x): 26.1133 - Log_b(x+a): 8.74 - orderNorm (ORQ): 7.0033 - sqrt(x + a): 7.5167 - Yeo-Johnson: 7.0933 Estimation method: Out-of-sample via CV with 10 folds and 5 repeats Based off these, bestNormalize chose: orderNorm Transformation with 210 nonmissing obs and ties - 102 unique values - Original quantiles: 0% 25% 50% 75% 100% 0.000 0.000 0.252 1.006 44.505 Analysis of Deviance Table (Type III Wald chisquare tests) Response: asinh(MStearate) Chisq Df Pr(>Chisq) (Intercept) 84.552 1 < 2.2e-16 *** Background 25.176 1 5.232e-07 *** Time 28.960 5 2.361e-05 *** Background:Time 21.992 5 0.0005253 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 [1] 567.1533 DHARMa zero-inflation test via comparison to expected zeros with simulation under H0 = fitted model data: simulationOutput ratioObsSim = 1.4901, p-value < 2.2e-16 alternative hypothesis: two.sided DHARMa outlier test based on exact binomial test with approximate expectations data: simulationOutput1 outliers at both margin(s) = 3, observations = 210, p-value = 0.008926 alternative hypothesis: true probability of success is not equal to 0.001998002 95 percent confidence interval: 0.002955803 0.041179059 sample estimates: frequency of outliers (expected: 0.001998001998002 ) 0.01428571 DHARMa nonparametric dispersion test via sd of residuals fitted vs. simulated data: simulationOutput dispersion = 0.98572, p-value = 0.88 alternative hypothesis: two.sided Package `see` required for model diagnostic plots.$VIF # Check for Multicollinearity $QQ Simulated residuals from a model of class `glmmTMB` based on 250 simulations. Use `check_residuals()` to check uniformity of residuals or `residuals()` to extract simulated residuals. It is recommended to refer to `?DHARMa::simulateResiudals` and `vignette("DHARMa")` for more information about different settings in particular situations or for particular models. $HOMOGENEITY x y 1 1.6838011 1.47415069 2 1.6838011 0.46651040 3 1.6838011 1.04897431 4 0.6100170 0.78974336 5 0.6100170 0.76795790 6 0.6100170 0.78069716 7 0.4221229 0.64942812 8 0.4221229 0.63056415 9 0.4221229 0.11503195 10 0.6269144 0.79143587 11 0.6269144 0.06493331 12 0.8972868 0.31917566 13 0.8972868 0.94684144 14 0.8972868 0.94684144 15 0.6793754 0.30005939 16 0.6793754 0.36665650 17 1.6838011 0.42194633 18 1.6838011 1.58834472 19 1.6838011 0.95145990 20 0.6100171 0.78069716 21 0.6100171 0.78069716 22 0.6100171 0.78069716 23 0.4221229 0.43762339 24 0.4221229 0.34203442 25 0.4221229 1.18710238 26 0.6269144 0.79143588 27 0.6269144 1.87871365 28 0.6269144 0.79143588 29 0.8972868 0.94684144 30 0.8972868 0.94684144 31 0.8972868 0.94684144 32 0.6793754 1.89804974 33 0.6793754 0.61310173 34 0.6793754 0.26146328 35 1.6838011 1.29705085 36 1.6838011 1.06769955 37 1.6838011 0.49635929 38 0.6100170 0.78069716 39 0.6100170 0.90564041 40 0.6100170 0.78069716 41 0.4221229 0.64942812 42 0.4221229 0.62072223 43 0.4221229 0.82735581 44 0.6269144 0.33332435 45 0.6269144 0.79143587 46 0.6269144 1.13598571 47 0.8972868 0.94684144 48 0.8972868 1.29269677 49 0.8972868 1.08793726 50 0.6793754 0.48213101 51 0.6793754 0.82388486 52 0.6793754 0.82388486 53 1.6838011 1.57996922 54 1.6838011 0.81397244 55 1.6838011 1.29705085 56 0.6100170 0.78069716 57 0.6100170 0.73524051 58 0.6100170 0.19557325 59 0.4221229 0.39851050 60 0.4221229 0.49632690 61 0.4221229 0.64942812 62 0.6269144 0.29715880 63 0.6269144 0.51172146 64 0.6269144 0.08751917 65 0.8972868 0.27774212 66 0.8972868 0.68213895 67 0.8972868 0.47385291 68 0.6793754 0.82388486 69 0.6793754 0.30117323 70 0.6793754 0.58338462 71 1.6838011 0.87716003 72 1.6838011 0.51824366 73 1.6838011 1.12122870 74 0.6100170 1.60259133 75 0.6100170 0.78069716 76 0.6100170 0.78069716 77 0.4221229 0.76945270 78 0.4221229 0.64942812 79 0.4221229 0.64942812 80 0.6269144 0.36262256 81 0.6269144 0.79143587 82 0.6269144 0.79143587 83 0.8972868 1.67224856 84 0.8972868 0.94684144 85 0.8972868 1.22437850 86 0.6793754 0.82388486 87 0.6793754 0.82388486 88 0.6793754 0.82388486 89 1.6838011 1.29705085 90 1.6838011 1.29705085 91 1.6838011 0.64729266 92 0.6100170 0.38790133 93 0.6100170 0.25203526 94 0.4221229 0.25244655 95 0.4221229 0.64942812 96 0.6269144 0.79143587 97 0.6269144 0.68915956 98 0.6269144 0.52922122 99 0.8972868 0.62881077 100 0.8972868 0.59206624 101 0.6793754 1.67808145 102 0.6793754 0.82388486 103 0.6793754 0.82388486 104 0.2760340 0.52516149 105 0.2760340 1.22666478 106 0.2760340 0.52516149 107 0.4489202 0.66972453 108 0.4489202 0.66972453 109 0.4489202 0.28166003 110 0.8115744 0.90048357 111 0.8115744 0.90048357 112 0.8115744 0.90048357 113 0.6452485 0.80292522 114 0.6452485 0.80292522 115 0.6452485 0.80292522 116 0.5929441 0.76969469 117 0.5929441 1.28340548 118 0.5929441 0.76969469 119 0.7431908 1.20476314 120 0.7431908 0.86171127 121 0.7431908 0.36192838 122 0.2760340 0.52516149 123 0.2760340 0.52516149 124 0.2760340 0.52516149 125 0.4489202 0.66972453 126 0.4489202 0.66972453 127 0.4489202 0.36026003 128 0.8115744 0.90048357 129 0.8115744 1.50511688 130 0.8115744 0.90778446 131 0.6452485 0.81103742 132 0.6452485 0.80292522 133 0.6452485 1.69720397 134 0.5929441 0.76969469 135 0.5929441 0.11776417 136 0.5929441 0.76969469 137 0.7431908 0.86171127 138 0.7431908 0.86171127 139 0.7431908 0.86171127 140 0.2760340 0.51629836 141 0.2760340 0.52516149 142 0.2760340 0.52516149 143 0.4489202 1.51389638 144 0.4489202 0.66972453 145 0.4489202 0.28074633 146 0.8115744 0.90048356 147 0.8115744 0.90048356 148 0.8115744 0.90048356 149 0.6452485 0.29654414 150 0.6452485 0.80292522 151 0.6452485 0.20863654 152 0.5929441 0.76969469 153 0.5929441 0.76969469 154 0.5929441 0.37725806 155 0.7431908 0.44337347 156 0.7431908 1.32036978 157 0.7431908 0.37848019 158 0.2760340 0.52516149 159 0.2760340 0.52516149 160 0.2760340 0.52516149 161 0.4489202 1.28405250 162 0.4489202 0.66972453 163 0.4489202 0.19077095 164 0.8115744 0.90048357 165 0.8115744 0.90048357 166 0.8115744 1.91679717 167 0.6452485 0.80292522 168 0.6452485 1.14138247 169 0.6452485 0.80292522 170 0.5929441 0.29411126 171 0.5929441 1.69036510 172 0.5929441 0.32520596 173 0.7431908 0.86171127 174 0.7431908 0.86171127 175 0.7431908 0.86171127 176 0.2760340 0.52516149 177 0.2760340 0.52516149 178 0.2760340 0.52516149 179 0.4489202 0.66972453 180 0.4489202 0.66972453 181 0.4489202 0.66972453 182 0.8115744 0.44842237 183 0.8115744 0.90048357 184 0.8115744 0.90048357 185 0.6452485 0.66264298 186 0.6452485 0.28422084 187 0.6452485 0.80292522 188 0.5929441 0.22015229 189 0.5929441 0.76969469 190 0.5929441 0.26250180 191 0.7431908 1.36887048 192 0.7431908 0.86171127 193 0.7431908 1.20209804 194 0.2760340 0.20263447 195 0.2760340 0.56877453 196 0.2760340 0.95251281 197 0.4489202 0.66972453 198 0.4489202 0.52531412 199 0.4489202 0.59617724 200 0.8115744 0.90048356 201 0.8115744 0.90048356 202 0.8115744 0.41641767 203 0.6452485 0.80292522 204 0.6452485 0.80292522 205 0.6452485 0.80185547 206 0.5929441 0.79626796 207 0.5929441 0.76969469 208 0.7431908 0.38308851 209 0.7431908 0.86171127 210 0.7431908 0.43457041 $REQQ $REQQ$Hive x y conf.low conf.high facet 1 -1.2815516 -2.585477e-09 -6.489842e-05 6.489325e-05 (Intercept) 2 -0.6433454 -2.367333e-09 -6.340990e-05 6.340516e-05 (Intercept) 3 -0.2018935 -1.457119e-09 -5.835614e-05 5.835323e-05 (Intercept) 4 0.2018935 4.432228e-10 -5.535961e-05 5.536049e-05 (Intercept) 5 0.6433454 1.002954e-09 -5.663645e-05 5.663846e-05 (Intercept) 6 1.2815516 4.963746e-09 -8.592919e-05 8.593911e-05 (Intercept) $PP_CHECK Warning: Maximum value of original data is not included in the replicated data. Model may not capture the variation of the data. attr(,"class") [1] "check_model" "see_check_model" attr(,"panel") [1] TRUE attr(,"dot_size") [1] 2 attr(,"line_size") [1] 0.8 attr(,"check") [1] "all" attr(,"alpha") [1] 0.2 attr(,"dot_alpha") [1] 0.8 attr(,"show_dots") [1] TRUE attr(,"detrend") [1] FALSE attr(,"colors") [1] "#3aaf85" "#1b6ca8" "#cd201f" attr(,"theme") [1] "see::theme_lucid" attr(,"model_info") attr(,"model_info")$is_binomial [1] FALSE attr(,"model_info")$is_bernoulli [1] FALSE attr(,"model_info")$is_count [1] FALSE attr(,"model_info")$is_poisson [1] FALSE attr(,"model_info")$is_negbin [1] FALSE attr(,"model_info")$is_beta [1] FALSE attr(,"model_info")$is_betabinomial [1] FALSE attr(,"model_info")$is_orderedbeta [1] FALSE attr(,"model_info")$is_dirichlet [1] FALSE attr(,"model_info")$is_exponential [1] FALSE attr(,"model_info")$is_logit [1] FALSE attr(,"model_info")$is_probit [1] FALSE attr(,"model_info")$is_censored [1] FALSE attr(,"model_info")$is_truncated [1] FALSE attr(,"model_info")$is_survival [1] FALSE attr(,"model_info")$is_linear [1] FALSE attr(,"model_info")$is_tweedie [1] FALSE attr(,"model_info")$is_zeroinf [1] TRUE attr(,"model_info")$is_zero_inflated [1] TRUE attr(,"model_info")$is_dispersion [1] FALSE attr(,"model_info")$is_hurdle [1] FALSE attr(,"model_info")$is_ordinal [1] FALSE attr(,"model_info")$is_cumulative [1] FALSE attr(,"model_info")$is_multinomial [1] FALSE attr(,"model_info")$is_categorical [1] FALSE attr(,"model_info")$is_mixed [1] TRUE attr(,"model_info")$is_multivariate [1] FALSE attr(,"model_info")$is_trial [1] FALSE attr(,"model_info")$is_bayesian [1] FALSE attr(,"model_info")$is_gam [1] FALSE attr(,"model_info")$is_anova [1] FALSE attr(,"model_info")$is_timeseries [1] FALSE attr(,"model_info")$is_ttest [1] FALSE attr(,"model_info")$is_correlation [1] FALSE attr(,"model_info")$is_onewaytest [1] FALSE attr(,"model_info")$is_chi2test [1] FALSE attr(,"model_info")$is_ranktest [1] FALSE attr(,"model_info")$is_levenetest [1] FALSE attr(,"model_info")$is_variancetest [1] FALSE attr(,"model_info")$is_xtab [1] FALSE attr(,"model_info")$is_proptest [1] FALSE attr(,"model_info")$is_binomtest [1] FALSE attr(,"model_info")$is_ftest [1] FALSE attr(,"model_info")$is_meta [1] FALSE attr(,"model_info")$link_function [1] "identity" attr(,"model_info")$family [1] "gaussian" attr(,"model_info")$n_obs [1] 210 attr(,"model_info")$n_grouplevels Hive 6 attr(,"bandwidth") [1] "nrd" attr(,"type") [1] "density" attr(,"model_class") [1] "glmmTMB" contrast estimate SE df t.ratio p.value Gotland 00H vs Aland 00H -2.1101 0.421 195 -5.018 <.0001 Gotland 00H vs Gotland 06H -0.2591 0.440 195 -0.589 0.8177 Aland 00H vs Aland 06H 1.6095 0.428 195 3.757 0.0018 Gotland 06H vs Aland 06H -0.2415 0.447 195 -0.541 0.8177 Gotland 06H vs Gotland 12H -0.5436 0.564 195 -0.964 0.8177 Aland 06H vs Aland 12H 0.2816 0.431 195 0.653 0.8177 Gotland 12H vs Aland 12H 0.5838 0.555 195 1.052 0.8177 Gotland 12H vs Gotland 18H 0.2493 0.574 195 0.435 0.8177 Aland 12H vs Aland 18H -0.3070 0.412 195 -0.745 0.8177 Gotland 18H vs Aland 18H 0.0275 0.447 195 0.061 0.9511 Gotland 18H vs Gotland 24H 0.0784 0.453 195 0.173 0.9204 Aland 18H vs Aland 24H -0.4053 0.445 195 -0.910 0.8177 Gotland 24H vs Aland 24H -0.4562 0.452 195 -1.010 0.8177 Gotland 24H vs Gotland 36H -0.2252 0.452 195 -0.498 0.8177 Aland 24H vs Aland 36H 0.3266 0.458 195 0.713 0.8177 Aland 36H vs Gotland 36H -0.0957 0.458 195 -0.209 0.9204 Note: contrasts are still on the asinh scale P value adjustment: fdr method for 16 tests [1] "Ethyl Stearate" Best Normalizing transformation with 210 Observations Estimated Normality Statistics (Pearson P / df, lower => more normal): - arcsinh(x): 16.9067 - Center+scale: 16.9433 - Double Reversed Log_b(x+a): 17.0165 - Exp(x): 16.94 - Log_b(x+a): 17.5267 - orderNorm (ORQ): 17.01 - sqrt(x + a): 16.8833 - Yeo-Johnson: 16.7067 Estimation method: Out-of-sample via CV with 10 folds and 5 repeats Based off these, bestNormalize chose: Standardized Yeo-Johnson Transformation with 210 nonmissing obs.: Estimated statistics: - lambda = -4.99994 - mean (before standardization) = 0.0256665 - sd (before standardization) = 0.04821188 Analysis of Deviance Table (Type III Wald chisquare tests) Response: asinh(EStearate) Chisq Df Pr(>Chisq) (Intercept) 43.436 1 4.381e-11 *** Background 15.532 1 8.113e-05 *** Time 25.677 5 0.0001031 *** Background:Time 16.508 5 0.0055339 ** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 [1] -366.5939 DHARMa zero-inflation test via comparison to expected zeros with simulation under H0 = fitted model data: simulationOutput ratioObsSim = Inf, p-value < 2.2e-16 alternative hypothesis: two.sided DHARMa outlier test based on exact binomial test with approximate expectations data: simulationOutput1 outliers at both margin(s) = 3, observations = 210, p-value = 0.008926 alternative hypothesis: true probability of success is not equal to 0.001998002 95 percent confidence interval: 0.002955803 0.041179059 sample estimates: frequency of outliers (expected: 0.001998001998002 ) 0.01428571 DHARMa nonparametric dispersion test via sd of residuals fitted vs. simulated data: simulationOutput dispersion = 0.99896, p-value = 0.968 alternative hypothesis: two.sided Package `see` required for model diagnostic plots.$VIF # Check for Multicollinearity $QQ Simulated residuals from a model of class `glmmTMB` based on 250 simulations. Use `check_residuals()` to check uniformity of residuals or `residuals()` to extract simulated residuals. It is recommended to refer to `?DHARMa::simulateResiudals` and `vignette("DHARMa")` for more information about different settings in particular situations or for particular models. $HOMOGENEITY x y 1 1.685215e-01 1.35897614 2 1.685215e-01 0.74304602 3 1.685215e-01 0.72261561 4 2.942436e-02 0.31048569 5 2.942436e-02 0.31048569 6 2.942436e-02 0.31048569 7 5.005281e-02 0.40495088 8 5.005281e-02 0.32854298 9 5.005281e-02 0.40495088 10 7.189841e-02 0.48534185 11 7.189841e-02 0.24443829 12 4.448896e-02 0.38178092 13 4.448896e-02 0.38178092 14 4.448896e-02 0.38178092 15 5.753504e-02 0.76387251 16 5.753504e-02 0.43416450 17 1.482545e-01 0.62972006 18 1.482545e-01 1.12290947 19 1.482545e-01 0.51325628 20 9.157358e-03 0.17321010 21 9.157358e-03 0.17321010 22 9.157358e-03 0.17321010 23 2.978581e-02 0.31238692 24 2.978581e-02 0.31238692 25 2.978581e-02 0.74820723 26 5.163141e-02 0.41128711 27 5.163141e-02 0.64379431 28 5.163141e-02 0.41128711 29 2.422196e-02 0.28170383 30 2.422196e-02 0.28170383 31 2.422196e-02 0.28170383 32 3.726804e-02 0.34942694 33 3.726804e-02 0.34942694 34 3.726804e-02 0.34942694 35 1.478523e-01 0.69598862 36 1.478523e-01 0.48566383 37 1.478523e-01 0.37518372 38 8.755187e-03 0.16936389 39 8.755187e-03 0.16936389 40 8.755187e-03 0.16936389 41 2.938364e-02 0.31027082 42 2.938364e-02 0.31027082 43 2.938364e-02 0.31027082 44 5.122924e-02 0.40968216 45 5.122924e-02 0.40968216 46 5.122924e-02 0.17082048 47 2.381979e-02 0.27935539 48 2.381979e-02 0.48575867 49 2.381979e-02 0.27935539 50 3.686587e-02 0.69377112 51 3.686587e-02 0.34753643 52 3.686587e-02 0.34753643 53 1.516952e-01 0.70497558 54 1.516952e-01 0.70497558 55 1.516952e-01 0.70497558 56 1.259813e-02 0.20316136 57 1.259813e-02 0.20316136 58 1.259813e-02 0.83040302 59 3.322658e-02 0.32993694 60 3.322658e-02 0.33403880 61 3.322658e-02 0.32993694 62 5.507218e-02 0.74189880 63 5.507218e-02 0.30248276 64 5.507218e-02 0.84516404 65 2.766273e-02 0.30104791 66 2.766273e-02 0.30104791 67 2.766273e-02 0.30104791 68 4.070881e-02 0.36520129 69 4.070881e-02 0.36520129 70 4.070881e-02 0.81608802 71 1.589383e-01 0.44427056 72 1.589383e-01 0.66796557 73 1.589383e-01 0.40529096 74 1.984117e-02 0.25495980 75 1.984117e-02 0.25495980 76 1.984117e-02 0.25495980 77 4.046962e-02 0.29639831 78 4.046962e-02 0.36412683 79 4.046962e-02 0.61398247 80 6.231522e-02 0.45184051 81 6.231522e-02 0.45184051 82 6.231522e-02 0.45184051 83 3.490577e-02 0.76445700 84 3.490577e-02 0.33817127 85 3.490577e-02 0.57864706 86 4.795185e-02 0.39636088 87 4.795185e-02 0.39636088 88 4.795185e-02 0.39636088 89 1.309697e-01 0.65504858 90 1.309697e-01 0.65504858 91 1.309697e-01 0.65504858 92 -8.127414e-03 0.16317902 93 -8.127414e-03 0.16317902 94 1.250104e-02 0.20237702 95 1.250104e-02 0.20237702 96 3.434664e-02 0.33545187 97 3.434664e-02 0.33545187 98 3.434664e-02 0.33545187 99 6.937187e-03 0.29190583 100 6.937187e-03 0.15075780 101 1.998327e-02 0.25587117 102 1.998327e-02 0.25587117 103 1.998327e-02 0.25587117 104 4.613433e-02 0.88046105 105 4.613433e-02 0.52419835 106 4.613433e-02 0.38877671 107 3.520783e-02 0.33963132 108 3.520783e-02 0.33963132 109 3.520783e-02 0.42968804 110 7.623817e-02 0.49977479 111 7.623817e-02 1.09728944 112 7.623817e-02 0.49977479 113 3.750357e-02 0.35052936 114 3.750357e-02 0.35052936 115 3.750357e-02 0.35052936 116 5.074963e-02 0.40775993 117 5.074963e-02 0.29486042 118 5.074963e-02 0.40775993 119 8.235129e-02 0.65843734 120 8.235129e-02 0.51942554 121 8.235129e-02 0.51942554 122 2.586734e-02 0.29111458 123 2.586734e-02 0.29111458 124 2.586734e-02 0.29111458 125 1.494083e-02 0.22124603 126 1.494083e-02 0.22124603 127 1.494083e-02 0.22124603 128 5.597118e-02 0.42822333 129 5.597118e-02 0.42822333 130 5.597118e-02 0.81610954 131 1.723657e-02 0.23763671 132 1.723657e-02 0.23763671 133 1.723657e-02 0.23763671 134 3.048263e-02 0.31601984 135 3.048263e-02 0.31601984 136 3.048263e-02 0.31601984 137 6.208429e-02 0.45100251 138 6.208429e-02 0.45100251 139 6.208429e-02 0.45100251 140 2.546517e-02 0.50390882 141 2.546517e-02 0.28884267 142 2.546517e-02 0.28884267 143 1.453866e-02 0.51156243 144 1.453866e-02 0.21824802 145 1.453866e-02 0.25494281 146 5.556900e-02 0.42668209 147 5.556900e-02 0.42668209 148 5.556900e-02 0.42668209 149 1.683440e-02 0.46053686 150 1.683440e-02 0.23484803 151 1.683440e-02 0.28447546 152 3.008046e-02 0.31392822 153 3.008046e-02 0.31392822 154 3.008046e-02 0.31392822 155 6.168212e-02 0.71444350 156 6.168212e-02 0.72083047 157 6.168212e-02 0.44953938 158 2.930811e-02 0.30987174 159 2.930811e-02 0.30987174 160 2.930811e-02 0.30987174 161 1.838160e-02 0.24540294 162 1.838160e-02 0.24540294 163 1.838160e-02 0.24540294 164 5.941194e-02 0.44118932 165 5.941194e-02 0.44118932 166 5.941194e-02 0.90642356 167 2.067734e-02 0.26027678 168 2.067734e-02 0.56650303 169 2.067734e-02 0.26027678 170 3.392340e-02 0.54421106 171 3.392340e-02 0.33337867 172 3.392340e-02 0.33337867 173 6.552506e-02 0.46333148 174 6.552506e-02 0.46333148 175 6.552506e-02 0.46333148 176 3.655114e-02 0.34604979 177 3.655114e-02 0.34604979 178 3.655114e-02 0.34604979 179 2.562464e-02 0.28974569 180 2.562464e-02 0.28974569 181 2.562464e-02 0.28974569 182 6.665498e-02 0.27003563 183 6.665498e-02 0.46730930 184 6.665498e-02 0.46730930 185 2.792038e-02 0.54515660 186 2.792038e-02 0.30244663 187 2.792038e-02 0.30244663 188 4.116644e-02 0.64528091 189 4.116644e-02 0.36724828 190 4.116644e-02 0.59286700 191 7.276810e-02 0.67169012 192 7.276810e-02 0.48826839 193 7.276810e-02 0.87173641 194 8.582563e-03 0.16768593 195 8.582563e-03 0.16768593 196 8.582563e-03 0.16768593 197 -2.343941e-03 0.08763174 198 -2.343941e-03 0.08763174 199 -2.343941e-03 0.57534644 200 3.868640e-02 0.35601417 201 3.868640e-02 0.35601417 202 3.868640e-02 0.35601417 203 -4.820261e-05 0.01256676 204 -4.820261e-05 0.01256676 205 -4.820261e-05 0.01256676 206 1.319786e-02 0.42095237 207 1.319786e-02 0.20794088 208 4.479951e-02 0.38311112 209 4.479951e-02 0.38311112 210 4.479951e-02 0.38311112 $REQQ $REQQ$Hive x y conf.low conf.high facet 1 -1.2815516 -0.0200688790 -0.05403056 0.01389280 (Intercept) 2 -0.6433454 -0.0031862773 -0.02721298 0.02084043 (Intercept) 3 -0.2018935 -0.0027841062 -0.02675047 0.02118225 (Intercept) 4 0.2018935 0.0006566628 -0.02313923 0.02445255 (Intercept) 5 0.6433454 0.0078997018 -0.01752582 0.03332522 (Intercept) 6 1.2815516 0.0174828920 -0.01398621 0.04895199 (Intercept) $PP_CHECK Warning: Maximum value of original data is not included in the replicated data. Model may not capture the variation of the data. attr(,"class") [1] "check_model" "see_check_model" attr(,"panel") [1] TRUE attr(,"dot_size") [1] 2 attr(,"line_size") [1] 0.8 attr(,"check") [1] "all" attr(,"alpha") [1] 0.2 attr(,"dot_alpha") [1] 0.8 attr(,"show_dots") [1] TRUE attr(,"detrend") [1] FALSE attr(,"colors") [1] "#3aaf85" "#1b6ca8" "#cd201f" attr(,"theme") [1] "see::theme_lucid" attr(,"model_info") attr(,"model_info")$is_binomial [1] FALSE attr(,"model_info")$is_bernoulli [1] FALSE attr(,"model_info")$is_count [1] FALSE attr(,"model_info")$is_poisson [1] FALSE attr(,"model_info")$is_negbin [1] FALSE attr(,"model_info")$is_beta [1] FALSE attr(,"model_info")$is_betabinomial [1] FALSE attr(,"model_info")$is_orderedbeta [1] FALSE attr(,"model_info")$is_dirichlet [1] FALSE attr(,"model_info")$is_exponential [1] FALSE attr(,"model_info")$is_logit [1] FALSE attr(,"model_info")$is_probit [1] FALSE attr(,"model_info")$is_censored [1] FALSE attr(,"model_info")$is_truncated [1] FALSE attr(,"model_info")$is_survival [1] FALSE attr(,"model_info")$is_linear [1] FALSE attr(,"model_info")$is_tweedie [1] FALSE attr(,"model_info")$is_zeroinf [1] TRUE attr(,"model_info")$is_zero_inflated [1] TRUE attr(,"model_info")$is_dispersion [1] FALSE attr(,"model_info")$is_hurdle [1] FALSE attr(,"model_info")$is_ordinal [1] FALSE attr(,"model_info")$is_cumulative [1] FALSE attr(,"model_info")$is_multinomial [1] FALSE attr(,"model_info")$is_categorical [1] FALSE attr(,"model_info")$is_mixed [1] TRUE attr(,"model_info")$is_multivariate [1] FALSE attr(,"model_info")$is_trial [1] FALSE attr(,"model_info")$is_bayesian [1] FALSE attr(,"model_info")$is_gam [1] FALSE attr(,"model_info")$is_anova [1] FALSE attr(,"model_info")$is_timeseries [1] FALSE attr(,"model_info")$is_ttest [1] FALSE attr(,"model_info")$is_correlation [1] FALSE attr(,"model_info")$is_onewaytest [1] FALSE attr(,"model_info")$is_chi2test [1] FALSE attr(,"model_info")$is_ranktest [1] FALSE attr(,"model_info")$is_levenetest [1] FALSE attr(,"model_info")$is_variancetest [1] FALSE attr(,"model_info")$is_xtab [1] FALSE attr(,"model_info")$is_proptest [1] FALSE attr(,"model_info")$is_binomtest [1] FALSE attr(,"model_info")$is_ftest [1] FALSE attr(,"model_info")$is_meta [1] FALSE attr(,"model_info")$link_function [1] "identity" attr(,"model_info")$family [1] "gaussian" attr(,"model_info")$n_obs [1] 210 attr(,"model_info")$n_grouplevels Hive 6 attr(,"bandwidth") [1] "nrd" attr(,"type") [1] "density" attr(,"model_class") [1] "glmmTMB" contrast estimate SE df t.ratio p.value Gotland 00H vs Aland 00H -0.12239 0.0311 195 -3.941 0.0009 Gotland 00H vs Gotland 06H 0.01093 0.0311 195 0.352 0.8290 Aland 00H vs Aland 06H 0.13910 0.0315 195 4.413 0.0003 Gotland 06H vs Aland 06H 0.00578 0.0315 195 0.183 0.8546 Gotland 06H vs Gotland 12H -0.04103 0.0311 195 -1.321 0.7554 Aland 06H vs Aland 12H -0.02063 0.0320 195 -0.646 0.7554 Gotland 12H vs Aland 12H 0.02619 0.0315 195 0.831 0.7554 Gotland 12H vs Gotland 18H 0.03873 0.0311 195 1.247 0.7554 Aland 12H vs Aland 18H -0.02185 0.0320 195 -0.683 0.7554 Gotland 18H vs Aland 18H -0.03439 0.0315 195 -1.091 0.7554 Gotland 18H vs Gotland 24H -0.01325 0.0315 195 -0.420 0.8290 Aland 18H vs Aland 24H 0.02741 0.0320 195 0.857 0.7554 Gotland 24H vs Aland 24H 0.00626 0.0320 195 0.196 0.8546 Gotland 24H vs Gotland 36H -0.03160 0.0315 195 -1.003 0.7554 Aland 24H vs Aland 36H -0.01305 0.0320 195 -0.408 0.8290 Aland 36H vs Gotland 36H -0.02482 0.0315 195 -0.787 0.7554 Note: contrasts are still on the asinh scale P value adjustment: fdr method for 16 tests [1] "E-b-Ocimene" Best Normalizing transformation with 210 Observations Estimated Normality Statistics (Pearson P / df, lower => more normal): - arcsinh(x): 1.3167 - Center+scale: 3.52 - Double Reversed Log_b(x+a): 3.9675 - Exp(x): 23.1233 - Log_b(x+a): 3.9133 - orderNorm (ORQ): 1.13 - sqrt(x + a): 1.2033 - Yeo-Johnson: 1.25 Estimation method: Out-of-sample via CV with 10 folds and 5 repeats Based off these, bestNormalize chose: orderNorm Transformation with 210 nonmissing obs and ties - 179 unique values - Original quantiles: 0% 25% 50% 75% 100% 0.000 0.574 1.789 3.197 23.901 Analysis of Deviance Table (Type III Wald chisquare tests) Response: sqrt(EOcimene) Chisq Df Pr(>Chisq) (Intercept) 25.3027 1 4.900e-07 *** Background 1.2289 1 0.2676 Time 81.8071 5 3.513e-16 *** Background:Time 3.7870 5 0.5805 --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 [1] 440.4271 DHARMa zero-inflation test via comparison to expected zeros with simulation under H0 = fitted model data: simulationOutput ratioObsSim = 3.3464, p-value < 2.2e-16 alternative hypothesis: two.sided DHARMa outlier test based on exact binomial test with approximate expectations data: simulationOutput1 outliers at both margin(s) = 2, observations = 210, p-value = 0.06672 alternative hypothesis: true probability of success is not equal to 0.001998002 95 percent confidence interval: 0.001155466 0.033977955 sample estimates: frequency of outliers (expected: 0.001998001998002 ) 0.00952381 DHARMa nonparametric dispersion test via sd of residuals fitted vs. simulated data: simulationOutput dispersion = 1.0394, p-value = 0.704 alternative hypothesis: two.sided Package `see` required for model diagnostic plots.$VIF # Check for Multicollinearity $QQ Simulated residuals from a model of class `glmmTMB` based on 250 simulations. Use `check_residuals()` to check uniformity of residuals or `residuals()` to extract simulated residuals. It is recommended to refer to `?DHARMa::simulateResiudals` and `vignette("DHARMa")` for more information about different settings in particular situations or for particular models. $HOMOGENEITY x y 1 0.6728934 0.42853546 2 0.6728934 0.15638368 3 0.6728934 0.62257766 4 0.8441974 0.28163430 5 0.8441974 0.40191084 6 0.8441974 0.69135314 7 1.3228543 0.66497813 8 1.3228543 0.81997067 9 1.3228543 0.50859881 10 1.9797912 0.13002937 11 1.9797912 0.27964912 12 2.0663957 0.24819533 13 2.0663957 1.63731841 14 2.0663957 1.35235827 15 1.4772056 0.68415252 16 1.4772056 0.53716634 17 0.6973988 0.90294724 18 0.6973988 0.17057231 19 0.6973988 0.37001570 20 0.8687028 0.99222560 21 0.8687028 0.57919726 22 0.8687028 1.00892784 23 1.3473597 0.64762737 24 1.3473597 0.76037098 25 1.3473597 0.98066560 26 2.0042966 1.07077435 27 2.0042966 0.78511517 28 2.0042966 1.06321870 29 2.0909011 1.33019371 30 2.0909011 0.78971013 31 2.0909011 0.77039706 32 1.5017110 0.61727309 33 1.5017110 0.68807082 34 1.5017110 0.29892945 35 0.7081336 0.97395644 36 0.7081336 0.32837211 37 0.7081336 0.43057519 38 0.8794377 0.42269478 39 0.8794377 0.68979075 40 0.8794377 0.24846568 41 1.3580945 0.71209940 42 1.3580945 0.47317516 43 1.3580945 0.52853317 44 2.0150315 1.04367690 45 2.0150315 0.65466921 46 2.0150315 0.79425434 47 2.1016359 1.00207391 48 2.1016359 1.40187009 49 2.1016359 1.73054147 50 1.5124459 1.32424870 51 1.5124459 0.29154672 52 1.5124459 0.68549666 53 0.7070975 0.22355710 54 0.7070975 0.57002071 55 0.7070975 0.71301575 56 0.8784016 0.17190326 57 0.8784016 0.37476037 58 0.8784016 0.41648245 59 1.3570584 0.96142356 60 1.3570584 0.53534045 61 1.3570584 0.44111722 62 2.0139954 0.65869360 63 2.0139954 0.55970750 64 2.0139954 0.34305392 65 2.1005998 1.06347372 66 2.1005998 0.91437493 67 2.1005998 0.43993429 68 1.5114097 0.28362970 69 1.5114097 0.61836670 70 1.5114097 0.39259055 71 0.6858026 0.59123846 72 0.6858026 0.49002039 73 0.6858026 0.59123846 74 0.8571067 0.36035764 75 0.8571067 1.05449335 76 0.8571067 1.05449335 77 1.3357635 0.42457887 78 1.3357635 0.62127800 79 1.3357635 0.57246580 80 1.9927005 0.58931345 81 1.9927005 0.60785579 82 1.9927005 0.72102440 83 2.0793049 0.97875892 84 2.0793049 0.79659758 85 2.0793049 0.40083446 86 1.4901149 0.51536530 87 1.4901149 0.58286197 88 1.4901149 0.33711478 89 0.6946045 0.94928191 90 0.6946045 0.94928191 91 0.6946045 0.25477307 92 0.8659086 0.47109292 93 0.8659086 0.85381143 94 1.3445654 0.68245075 95 1.3445654 0.35931640 96 2.0015024 0.56730961 97 2.0015024 0.44498242 98 2.0015024 0.64584548 99 2.0881068 0.95258744 100 2.0881068 1.07127344 101 1.4989167 0.44118575 102 1.4989167 0.97296971 103 1.4989167 0.69850736 104 0.4571271 0.47815771 105 0.4571271 0.46563012 106 0.4571271 0.62570196 107 0.5663534 0.85717629 108 0.5663534 0.70134839 109 0.5663534 0.37334376 110 1.5270424 0.56715220 111 1.5270424 0.47138056 112 1.5270424 0.14974478 113 1.8652329 0.73285769 114 1.8652329 0.99323207 115 1.8652329 0.43528955 116 2.0236306 1.20838225 117 2.0236306 0.96471607 118 2.0236306 0.05918880 119 1.2634425 0.33952119 120 1.2634425 1.28027757 121 1.2634425 0.80224672 122 0.4816325 0.27970698 123 0.4816325 0.32681397 124 0.4816325 0.79046796 125 0.5908588 0.87552439 126 0.5908588 0.87552439 127 0.5908588 0.39143656 128 1.5515478 0.85194279 129 1.5515478 1.50822482 130 1.5515478 0.55396889 131 1.8897383 1.12877481 132 1.8897383 0.28872880 133 1.8897383 0.84956890 134 2.0481360 0.64621454 135 2.0481360 1.07872817 136 2.0481360 0.78402045 137 1.2879479 0.33438927 138 1.2879479 1.29263391 139 1.2879479 1.29263391 140 0.4923673 1.06670295 141 0.4923673 0.45862543 142 0.4923673 0.79922858 143 0.6015936 0.71764012 144 0.6015936 0.88344195 145 0.6015936 0.46047695 146 1.5622826 1.15202844 147 1.5622826 0.89122563 148 1.5622826 0.40802619 149 1.9004732 0.07008597 150 1.9004732 1.57020831 151 1.9004732 0.19862665 152 2.0588708 1.63433452 153 2.0588708 0.37124297 154 2.0588708 1.18441908 155 1.2986827 0.50953592 156 1.2986827 0.44267389 157 1.2986827 0.90567524 158 0.4913312 0.79838720 159 0.4913312 0.76888414 160 0.4913312 0.33486131 161 0.6005575 0.57598462 162 0.6005575 0.88268085 163 0.6005575 1.02837563 164 1.5612465 0.40015030 165 1.5612465 1.42318758 166 1.5612465 0.97889863 167 1.8994370 0.65959145 168 1.8994370 1.01071677 169 1.8994370 0.52744911 170 2.0578347 0.72179078 171 2.0578347 1.91646018 172 2.0578347 0.70746843 173 1.2976466 0.73987445 174 1.2976466 0.76411106 175 1.2976466 0.71707787 176 0.4700363 0.78089404 177 0.4700363 0.78089404 178 0.4700363 0.78089404 179 0.5792626 0.30079112 180 0.5792626 0.59144728 181 0.5792626 0.86834888 182 1.5399516 0.59633639 183 1.5399516 0.13804243 184 1.5399516 0.31593981 185 1.8781422 0.90672458 186 1.8781422 0.56462358 187 1.8781422 0.63784190 188 2.0365398 1.23134611 189 2.0365398 0.60342173 190 2.0365398 0.70972148 191 1.2763517 1.18609000 192 1.2763517 0.40782960 193 1.2763517 0.53022908 194 0.4788382 0.14462291 195 0.4788382 0.31433281 196 0.4788382 0.80963799 197 0.5880645 0.98368648 198 0.5880645 0.70747480 199 0.5880645 0.29357412 200 1.5487535 0.74437715 201 1.5487535 0.28966426 202 1.5487535 1.04375168 203 1.8869440 1.56460932 204 1.8869440 0.49559743 205 1.8869440 1.26412714 206 2.0453417 0.66000500 207 2.0453417 1.20094861 208 1.2851536 0.32546255 209 1.2851536 0.97115776 210 1.2851536 0.85576952 $REQQ $REQQ$Hive x y conf.low conf.high facet 1 -1.2815516 -0.0221490000 -0.23708667 0.19278867 (Intercept) 2 -0.6433454 -0.0088080723 -0.11468930 0.09707316 (Intercept) 3 -0.2018935 0.0002881268 -0.06977949 0.07035575 (Intercept) 4 0.2018935 0.0031758328 -0.07250985 0.07886152 (Intercept) 5 0.6433454 0.0131988755 -0.12586785 0.15226560 (Intercept) 6 1.2815516 0.0142696458 -0.13359922 0.16213851 (Intercept) $PP_CHECK Warning: Maximum value of original data is not included in the replicated data. Model may not capture the variation of the data. attr(,"class") [1] "check_model" "see_check_model" attr(,"panel") [1] TRUE attr(,"dot_size") [1] 2 attr(,"line_size") [1] 0.8 attr(,"check") [1] "all" attr(,"alpha") [1] 0.2 attr(,"dot_alpha") [1] 0.8 attr(,"show_dots") [1] TRUE attr(,"detrend") [1] FALSE attr(,"colors") [1] "#3aaf85" "#1b6ca8" "#cd201f" attr(,"theme") [1] "see::theme_lucid" attr(,"model_info") attr(,"model_info")$is_binomial [1] FALSE attr(,"model_info")$is_bernoulli [1] FALSE attr(,"model_info")$is_count [1] FALSE attr(,"model_info")$is_poisson [1] FALSE attr(,"model_info")$is_negbin [1] FALSE attr(,"model_info")$is_beta [1] FALSE attr(,"model_info")$is_betabinomial [1] FALSE attr(,"model_info")$is_orderedbeta [1] FALSE attr(,"model_info")$is_dirichlet [1] FALSE attr(,"model_info")$is_exponential [1] FALSE attr(,"model_info")$is_logit [1] FALSE attr(,"model_info")$is_probit [1] FALSE attr(,"model_info")$is_censored [1] FALSE attr(,"model_info")$is_truncated [1] FALSE attr(,"model_info")$is_survival [1] FALSE attr(,"model_info")$is_linear [1] FALSE attr(,"model_info")$is_tweedie [1] FALSE attr(,"model_info")$is_zeroinf [1] TRUE attr(,"model_info")$is_zero_inflated [1] TRUE attr(,"model_info")$is_dispersion [1] FALSE attr(,"model_info")$is_hurdle [1] FALSE attr(,"model_info")$is_ordinal [1] FALSE attr(,"model_info")$is_cumulative [1] FALSE attr(,"model_info")$is_multinomial [1] FALSE attr(,"model_info")$is_categorical [1] FALSE attr(,"model_info")$is_mixed [1] TRUE attr(,"model_info")$is_multivariate [1] FALSE attr(,"model_info")$is_trial [1] FALSE attr(,"model_info")$is_bayesian [1] FALSE attr(,"model_info")$is_gam [1] FALSE attr(,"model_info")$is_anova [1] FALSE attr(,"model_info")$is_timeseries [1] FALSE attr(,"model_info")$is_ttest [1] FALSE attr(,"model_info")$is_correlation [1] FALSE attr(,"model_info")$is_onewaytest [1] FALSE attr(,"model_info")$is_chi2test [1] FALSE attr(,"model_info")$is_ranktest [1] FALSE attr(,"model_info")$is_levenetest [1] FALSE attr(,"model_info")$is_variancetest [1] FALSE attr(,"model_info")$is_xtab [1] FALSE attr(,"model_info")$is_proptest [1] FALSE attr(,"model_info")$is_binomtest [1] FALSE attr(,"model_info")$is_ftest [1] FALSE attr(,"model_info")$is_meta [1] FALSE attr(,"model_info")$link_function [1] "identity" attr(,"model_info")$family [1] "gaussian" attr(,"model_info")$n_obs [1] 210 attr(,"model_info")$n_grouplevels Hive 6 attr(,"bandwidth") [1] "nrd" attr(,"type") [1] "density" attr(,"model_class") [1] "glmmTMB" contrast estimate SE df t.ratio p.value Gotland 00H vs Aland 00H -0.2230 0.201 195 -1.109 0.5100 Gotland 00H vs Gotland 06H -0.1129 0.202 195 -0.559 0.6628 Aland 00H vs Aland 06H -0.1770 0.205 195 -0.865 0.5644 Gotland 06H vs Aland 06H -0.2871 0.206 195 -1.397 0.3751 Gotland 06H vs Gotland 12H -0.9928 0.208 195 -4.777 0.0001 Aland 06H vs Aland 12H -0.4947 0.206 195 -2.397 0.0558 Gotland 12H vs Aland 12H 0.2110 0.209 195 1.010 0.5100 Gotland 12H vs Gotland 18H -0.3495 0.216 195 -1.615 0.2878 Aland 12H vs Aland 18H -0.6789 0.204 195 -3.329 0.0055 Gotland 18H vs Aland 18H -0.1184 0.214 195 -0.554 0.6628 Gotland 18H vs Gotland 24H -0.1637 0.217 195 -0.755 0.6017 Aland 18H vs Aland 24H -0.0895 0.208 195 -0.430 0.7123 Gotland 24H vs Aland 24H -0.0442 0.213 195 -0.207 0.8359 Gotland 24H vs Gotland 36H 0.7856 0.225 195 3.498 0.0046 Aland 24H vs Aland 36H 0.6089 0.208 195 2.924 0.0154 Aland 36H vs Gotland 36H 0.2209 0.221 195 1.000 0.5100 Note: contrasts are still on the sqrt scale P value adjustment: fdr method for 16 tests R version 4.4.0 (2024-04-24 ucrt) Platform: x86_64-w64-mingw32/x64 Running under: Windows 10 x64 (build 19045) Matrix products: default locale: [1] LC_COLLATE=English_United States.utf8 LC_CTYPE=English_United States.utf8 [3] LC_MONETARY=English_United States.utf8 LC_NUMERIC=C [5] LC_TIME=English_United States.utf8 time zone: Europe/Stockholm tzcode source: internal attached base packages: [1] stats graphics grDevices utils datasets methods base other attached packages: [1] readxl_1.4.3 nlme_3.1-164 emmeans_1.10.2 bestNormalize_1.9.1 [5] effects_4.2-2 RVAideMemoire_0.9-83-7 performance_0.11.0 DHARMa_0.4.6 [9] glmmTMB_1.1.9 moments_0.14.1 car_3.1-2 carData_3.0-5 [13] lme4_1.1-35.3 Matrix_1.7-0 gridExtra_2.3 lubridate_1.9.3 [17] forcats_1.0.0 stringr_1.5.1 purrr_1.0.2 readr_2.1.5 [21] tidyr_1.3.1 tibble_3.2.1 ggplot2_3.5.1 tidyverse_2.0.0 [25] dplyr_1.1.4 loaded via a namespace (and not attached): [1] tidyselect_1.2.1 timeDate_4032.109 butcher_0.3.4 digest_0.6.35 rpart_4.1.23 [6] estimability_1.5.1 timechange_0.3.0 lifecycle_1.0.4 survival_3.5-8 magrittr_2.0.3 [11] compiler_4.4.0 rlang_1.1.3 rngtools_1.5.2 tools_4.4.0 utf8_1.2.4 [16] data.table_1.15.4 doRNG_1.8.6 bit_4.0.5 gap.datasets_0.0.6 abind_1.4-5 [21] withr_3.0.0 numDeriv_2016.8-1.1 datawizard_0.10.0 nnet_7.3-19 grid_4.4.0 [26] fansi_1.0.6 future_1.33.2 xtable_1.8-4 colorspace_2.1-0 globals_0.16.3 [31] scales_1.3.0 iterators_1.0.14 MASS_7.3-60.2 insight_0.19.11 cli_3.6.2 [36] mvtnorm_1.2-5 survey_4.4-2 crayon_1.5.2 generics_0.1.3 rstudioapi_0.16.0 [41] future.apply_1.11.2 tzdb_0.4.0 minqa_1.2.7 DBI_1.2.2 splines_4.4.0 [46] parallel_4.4.0 cellranger_1.1.0 mitools_2.4 vctrs_0.6.5 hardhat_1.3.1 [51] boot_1.3-30 hms_1.1.3 bit64_4.0.5 listenv_0.9.1 nortest_1.0-4 [56] foreach_1.5.2 gower_1.0.1 recipes_1.0.10 gap_1.5-3 parallelly_1.37.1 [61] glue_1.7.0 nloptr_2.0.3 codetools_0.2-20 stringi_1.8.4 gtable_0.3.5 [66] munsell_0.5.1 pillar_1.9.0 ipred_0.9-14 lava_1.8.0 R6_2.5.1 [71] TMB_1.9.11 Rdpack_2.6 doParallel_1.0.17 vroom_1.6.5 lattice_0.22-6 [76] rbibutils_2.2.16 class_7.3-22 Rcpp_1.0.12 prodlim_2023.08.28 mgcv_1.9-1 [81] pkgconfig_2.0.3 $citation To cite RStudio in publications use: Posit team (2024). RStudio: Integrated Development Environment for R. Posit Software, PBC, Boston, MA. URL http://www.posit.co/. A BibTeX entry for LaTeX users is @Manual{, title = {RStudio: Integrated Development Environment for R}, author = {{Posit team}}, organization = {Posit Software, PBC}, address = {Boston, MA}, year = {2024}, url = {http://www.posit.co/}, } $mode [1] "desktop" $version [1] ‘2024.4.1.748’ $long_version [1] "2024.04.1+748" $release_name [1] "Chocolate Cosmos"