chpt 2 models
Sarah Kincy
2026-05-05
Reading in data
library(readr)
chpt2_r_data <- read_csv("~/Downloads/chapter 2 /chpt2.r.data.csv",
col_types = cols(block = col_character(),
patch = col_character(), position = col_character(),
max.temp = col_number(), ros = col_number(),
rcd10 = col_number(), rcd20 = col_number(),
mortality1 = col_number(), mortality2 = col_number(),
mortality3 = col_number(), mortality10 = col_number(),
mortality20 = col_number(), mortality100 = col_number(),
mortality200 = col_number(), green.needles = col_number(),
tree.char = col_number(), tree.scorch = col_number(),
rcd1.avg = col_number(), rcd2.avg = col_number(),
rcd3.avg = col_number(), rcd4.avg = col_number(),
difn = col_number()))## New names:
## • `` -> `...27`
## • `` -> `...28`
## • `` -> `...29`## Warning: One or more parsing issues, call `problems()` on your data frame for details,
## e.g.:
## dat <- vroom(...)
## problems(dat)chpt2_r_data <- chpt2_r_data[-(37:52), ]
chpt2_r_data <- chpt2_r_data[, !(names(chpt2_r_data) %in% c("...27", "...28", "...29"))]Library
library(glmmTMB)
library(DHARMa)## This is DHARMa 0.4.7. For overview type '?DHARMa'. For recent changes, type news(package = 'DHARMa')library(performance)
library(emmeans)## Welcome to emmeans.
## Caution: You lose important information if you filter this package's results.
## See '? untidy'library(multcomp)## Loading required package: mvtnorm## Loading required package: survival## Loading required package: TH.data## Loading required package: MASS##
## Attaching package: 'TH.data'## The following object is masked from 'package:MASS':
##
## geyserModel 5: severity (tree scorch), first order fire effect
chpt2_r_data$tree.scorch_prop <- chpt2_r_data$tree.scorch / 100
chpt2_r_data$tree.scorch_beta <- pmin(
pmax(chpt2_r_data$tree.scorch_prop, 0.001),
0.999
)
model5 <- glmmTMB(
tree.scorch_beta ~ season * location + ros + max.temp + (1 | patch) + (1 | block),
data = chpt2_r_data,
family = beta_family(link = "logit")
)## Warning in (function (start, objective, gradient = NULL, hessian = NULL, :
## NA/NaN function evaluation
## Warning in (function (start, objective, gradient = NULL, hessian = NULL, :
## NA/NaN function evaluationsummary(model5)## Family: beta ( logit )
## Formula: tree.scorch_beta ~ season * location + ros + max.temp + (1 |
## patch) + (1 | block)
## Data: chpt2_r_data
##
## AIC BIC logLik -2*log(L) df.resid
## -1.1 16.4 11.5 -23.1 25
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## patch (Intercept) 0.4064 0.6375
## block (Intercept) 0.5410 0.7355
## Number of obs: 36, groups: patch, 12; block, 4
##
## Dispersion parameter for beta family (): 5.51
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.204818 1.056560 -1.140 0.254
## seasongrowing 0.505530 1.123349 0.450 0.653
## locationinside 0.132397 0.446411 0.297 0.767
## locationoutside -0.462199 0.446083 -1.036 0.300
## ros -2.672361 2.734752 -0.977 0.328
## max.temp 0.002531 0.001553 1.630 0.103
## seasongrowing:locationinside 0.488643 0.843691 0.579 0.562
## seasongrowing:locationoutside 1.585017 0.747550 2.120 0.034 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1emm <- emmeans(model5, ~ season * location, type = "response")
pairs(emm, adjust = "FDR")## contrast odds.ratio SE df null z.ratio p.value
## dormant edge / growing edge 0.603 0.678 Inf 1 -0.450 0.7531
## dormant edge / dormant inside 0.876 0.391 Inf 1 -0.297 0.7668
## dormant edge / growing inside 0.324 0.358 Inf 1 -1.020 0.5557
## dormant edge / dormant outside 1.588 0.708 Inf 1 1.036 0.5557
## dormant edge / growing outside 0.196 0.217 Inf 1 -1.472 0.4592
## growing edge / dormant inside 1.452 1.660 Inf 1 0.327 0.7668
## growing edge / growing inside 0.537 0.403 Inf 1 -0.828 0.5557
## growing edge / dormant outside 2.632 2.970 Inf 1 0.859 0.5557
## growing edge / growing outside 0.325 0.200 Inf 1 -1.825 0.4592
## dormant inside / growing inside 0.370 0.407 Inf 1 -0.904 0.5557
## dormant inside / dormant outside 1.812 0.810 Inf 1 1.330 0.4592
## dormant inside / growing outside 0.224 0.249 Inf 1 -1.347 0.4592
## growing inside / dormant outside 4.898 5.380 Inf 1 1.446 0.4592
## growing inside / growing outside 0.605 0.414 Inf 1 -0.734 0.5786
## dormant outside / growing outside 0.124 0.136 Inf 1 -1.898 0.4592
##
## P value adjustment: fdr method for 15 tests
## Tests are performed on the log odds ratio scalecld(emm, adjust = "FDR", Letters = letters)## season location response SE df asymp.LCL asymp.UCL .group
## dormant outside 0.414 0.141 Inf 0.132 0.766 a
## dormant edge 0.528 0.146 Inf 0.193 0.840 a
## dormant inside 0.561 0.146 Inf 0.211 0.860 a
## growing edge 0.650 0.213 Inf 0.135 0.957 a
## growing inside 0.776 0.164 Inf 0.223 0.977 a
## growing outside 0.851 0.118 Inf 0.331 0.985 a
##
## Confidence level used: 0.95
## Conf-level adjustment: bonferroni method for 6 estimates
## Intervals are back-transformed from the logit scale
## P value adjustment: fdr method for 15 tests
## Tests are performed on the log odds ratio scale
## significance level used: alpha = 0.05
## NOTE: If two or more means share the same grouping symbol,
## then we cannot show them to be different.
## But we also did not show them to be the same.sim_res <- simulateResiduals(model5)
plot(sim_res)## Warning in newton(lsp = lsp, X = G$X, y = G$y, Eb = G$Eb, UrS = G$UrS, L = G$L,
## : Fitting terminated with step failure - check results carefully
testDispersion(sim_res)
##
## DHARMa nonparametric dispersion test via sd of residuals fitted vs.
## simulated
##
## data: simulationOutput
## dispersion = 1.2106, p-value = 0.376
## alternative hypothesis: two.sidedtestZeroInflation(sim_res)
##
## DHARMa zero-inflation test via comparison to expected zeros with
## simulation under H0 = fitted model
##
## data: simulationOutput
## ratioObsSim = NaN, p-value = 1
## alternative hypothesis: two.sidedtestOutliers(sim_res)
##
## DHARMa outlier test based on exact binomial test with approximate
## expectations
##
## data: sim_res
## outliers at both margin(s) = 0, observations = 36, p-value = 1
## alternative hypothesis: true probability of success is not equal to 0.007968127
## 95 percent confidence interval:
## 0.00000000 0.09739376
## sample estimates:
## frequency of outliers (expected: 0.00796812749003984 )
## 0check_model(model5)## `check_outliers()` does not yet support models of class `glmmTMB`.
model 5, same as above but with no max temp
chpt2_r_data$tree.scorch_prop <- chpt2_r_data$tree.scorch / 100
chpt2_r_data$tree.scorch_beta <- pmin(
pmax(chpt2_r_data$tree.scorch_prop, 0.001),
0.999
)
model5.1 <- glmmTMB(
tree.scorch_beta ~ season * location + max.temp + (1 | patch) + (1 | block),
data = chpt2_r_data,
family = beta_family(link = "logit")
)## Warning in (function (start, objective, gradient = NULL, hessian = NULL, :
## NA/NaN function evaluation
## Warning in (function (start, objective, gradient = NULL, hessian = NULL, :
## NA/NaN function evaluationsummary(model5.1)## Family: beta ( logit )
## Formula:
## tree.scorch_beta ~ season * location + max.temp + (1 | patch) +
## (1 | block)
## Data: chpt2_r_data
##
## AIC BIC logLik -2*log(L) df.resid
## -2.2 13.7 11.1 -22.2 26
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## patch (Intercept) 0.3646 0.6038
## block (Intercept) 0.4340 0.6588
## Number of obs: 36, groups: patch, 12; block, 4
##
## Dispersion parameter for beta family (): 5.11
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.041253 1.018344 -1.022 0.3065
## seasongrowing 0.500746 1.053434 0.475 0.6345
## locationinside 0.199183 0.450830 0.442 0.6586
## locationoutside -0.314924 0.433852 -0.726 0.4679
## max.temp 0.001936 0.001414 1.369 0.1708
## seasongrowing:locationinside 0.537207 0.839295 0.640 0.5221
## seasongrowing:locationoutside 1.516843 0.762577 1.989 0.0467 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1emm <- emmeans(model5.1, ~ season * location, type = "response")
pairs(emm, adjust = "FDR")## contrast odds.ratio SE df null z.ratio p.value
## dormant edge / growing edge 0.606 0.638 Inf 1 -0.475 0.7057
## dormant edge / dormant inside 0.819 0.369 Inf 1 -0.442 0.7057
## dormant edge / growing inside 0.290 0.299 Inf 1 -1.200 0.5340
## dormant edge / dormant outside 1.370 0.594 Inf 1 0.726 0.6293
## dormant edge / growing outside 0.182 0.189 Inf 1 -1.644 0.4524
## growing edge / dormant inside 1.352 1.460 Inf 1 0.280 0.7795
## growing edge / growing inside 0.479 0.358 Inf 1 -0.986 0.5405
## growing edge / dormant outside 2.261 2.360 Inf 1 0.782 0.6293
## growing edge / growing outside 0.301 0.186 Inf 1 -1.943 0.3903
## dormant inside / growing inside 0.354 0.364 Inf 1 -1.010 0.5405
## dormant inside / dormant outside 1.672 0.746 Inf 1 1.152 0.5340
## dormant inside / growing outside 0.222 0.233 Inf 1 -1.437 0.4524
## growing inside / dormant outside 4.721 4.870 Inf 1 1.505 0.4524
## growing inside / growing outside 0.628 0.437 Inf 1 -0.669 0.6293
## dormant outside / growing outside 0.133 0.137 Inf 1 -1.958 0.3903
##
## P value adjustment: fdr method for 15 tests
## Tests are performed on the log odds ratio scalecld(emm, adjust = "FDR", Letters = letters)## season location response SE df asymp.LCL asymp.UCL .group
## dormant outside 0.434 0.133 Inf 0.155 0.762 a
## dormant edge 0.512 0.138 Inf 0.197 0.818 a
## dormant inside 0.562 0.138 Inf 0.226 0.849 a
## growing edge 0.634 0.203 Inf 0.147 0.946 a
## growing inside 0.784 0.150 Inf 0.261 0.974 a
## growing outside 0.852 0.109 Inf 0.369 0.983 a
##
## Confidence level used: 0.95
## Conf-level adjustment: bonferroni method for 6 estimates
## Intervals are back-transformed from the logit scale
## P value adjustment: fdr method for 15 tests
## Tests are performed on the log odds ratio scale
## significance level used: alpha = 0.05
## NOTE: If two or more means share the same grouping symbol,
## then we cannot show them to be different.
## But we also did not show them to be the same.sim_res <- simulateResiduals(model5.1)
plot(sim_res)
testDispersion(sim_res)
##
## DHARMa nonparametric dispersion test via sd of residuals fitted vs.
## simulated
##
## data: simulationOutput
## dispersion = 1.0622, p-value = 0.704
## alternative hypothesis: two.sidedtestZeroInflation(sim_res)
##
## DHARMa zero-inflation test via comparison to expected zeros with
## simulation under H0 = fitted model
##
## data: simulationOutput
## ratioObsSim = NaN, p-value = 1
## alternative hypothesis: two.sidedtestOutliers(sim_res)
##
## DHARMa outlier test based on exact binomial test with approximate
## expectations
##
## data: sim_res
## outliers at both margin(s) = 0, observations = 36, p-value = 1
## alternative hypothesis: true probability of success is not equal to 0.007968127
## 95 percent confidence interval:
## 0.00000000 0.09739376
## sample estimates:
## frequency of outliers (expected: 0.00796812749003984 )
## 0check_model(model5.1)## `check_outliers()` does not yet support models of class `glmmTMB`.
Model 6: impacts on severity (char height), first order fire effect
model6 <- glmmTMB(
tree.char ~ season * location + ros + max.temp + (1 | patch ) + (1 | block),
data = chpt2_r_data,
family = gaussian
)
summary(model6)## Family: gaussian ( identity )
## Formula:
## tree.char ~ season * location + ros + max.temp + (1 | patch) + (1 | block)
## Data: chpt2_r_data
##
## AIC BIC logLik -2*log(L) df.resid
## 104.1 121.5 -41.0 82.1 25
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## patch (Intercept) 2.990e-01 5.468e-01
## block (Intercept) 2.662e-10 1.632e-05
## Residual 3.830e-01 6.188e-01
## Number of obs: 36, groups: patch, 12; block, 4
##
## Dispersion estimate for gaussian family (sigma^2): 0.383
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 1.483263 0.637565 2.326 0.0200 *
## seasongrowing -1.244890 0.564735 -2.204 0.0275 *
## locationinside -0.064884 0.327828 -0.198 0.8431
## locationoutside -0.511361 0.326098 -1.568 0.1169
## ros -1.334118 1.894395 -0.704 0.4813
## max.temp 0.001395 0.001016 1.374 0.1696
## seasongrowing:locationinside 0.426288 0.568412 0.750 0.4533
## seasongrowing:locationoutside 1.081040 0.555466 1.946 0.0516 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1emm <- emmeans(model6, ~ season * location, type = "response")
pairs(emm, adjust = "FDR")## contrast estimate SE df t.ratio p.value
## dormant edge - growing edge 1.2449 0.565 25 2.204 0.3814
## dormant edge - dormant inside 0.0649 0.328 25 0.198 0.8447
## dormant edge - growing inside 0.8835 0.521 25 1.696 0.3814
## dormant edge - dormant outside 0.5114 0.326 25 1.568 0.3814
## dormant edge - growing outside 0.6752 0.527 25 1.281 0.3814
## growing edge - dormant inside -1.1800 0.594 25 -1.985 0.3814
## growing edge - growing inside -0.3614 0.511 25 -0.708 0.6072
## growing edge - dormant outside -0.7335 0.566 25 -1.297 0.3814
## growing edge - growing outside -0.5697 0.462 25 -1.234 0.3814
## dormant inside - growing inside 0.8186 0.510 25 1.604 0.3814
## dormant inside - dormant outside 0.4465 0.316 25 1.413 0.3814
## dormant inside - growing outside 0.6103 0.534 25 1.142 0.3963
## growing inside - dormant outside -0.3721 0.506 25 -0.735 0.6072
## growing inside - growing outside -0.2083 0.453 25 -0.460 0.7495
## dormant outside - growing outside 0.1638 0.517 25 0.317 0.8080
##
## P value adjustment: fdr method for 15 testscld(emm, adjust = "FDR", Letters = letters)## season location emmean SE df lower.CL upper.CL .group
## growing edge 0.971 0.469 25 -0.374 2.32 a
## growing inside 1.332 0.417 25 0.137 2.53 a
## growing outside 1.540 0.422 25 0.330 2.75 a
## dormant outside 1.704 0.295 25 0.860 2.55 a
## dormant inside 2.151 0.305 25 1.276 3.03 a
## dormant edge 2.215 0.302 25 1.350 3.08 a
##
## Confidence level used: 0.95
## Conf-level adjustment: bonferroni method for 6 estimates
## P value adjustment: fdr method for 15 tests
## significance level used: alpha = 0.05
## NOTE: If two or more means share the same grouping symbol,
## then we cannot show them to be different.
## But we also did not show them to be the same.sim_res <- simulateResiduals(model6)
plot(sim_res)
testDispersion(sim_res)
##
## DHARMa nonparametric dispersion test via sd of residuals fitted vs.
## simulated
##
## data: simulationOutput
## dispersion = 1.0588, p-value = 0.768
## alternative hypothesis: two.sidedtestZeroInflation(sim_res)
##
## DHARMa zero-inflation test via comparison to expected zeros with
## simulation under H0 = fitted model
##
## data: simulationOutput
## ratioObsSim = NaN, p-value = 1
## alternative hypothesis: two.sidedtestOutliers(sim_res)
##
## DHARMa outlier test based on exact binomial test with approximate
## expectations
##
## data: sim_res
## outliers at both margin(s) = 1, observations = 36, p-value = 0.2502
## alternative hypothesis: true probability of success is not equal to 0.007968127
## 95 percent confidence interval:
## 0.0007030252 0.1452892647
## sample estimates:
## frequency of outliers (expected: 0.00796812749003984 )
## 0.02777778check_model(model6)## `check_outliers()` does not yet support models of class `glmmTMB`.
Model 6, same as above but with no interaction
model6.1 <- glmmTMB(
tree.char ~ season + location + ros + max.temp + (1 | patch ) + (1 | block),
data = chpt2_r_data,
family = gaussian
)
summary(model6.1)## Family: gaussian ( identity )
## Formula:
## tree.char ~ season + location + ros + max.temp + (1 | patch) + (1 | block)
## Data: chpt2_r_data
##
## AIC BIC logLik -2*log(L) df.resid
## 103.6 117.9 -42.8 85.6 27
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## patch (Intercept) 2.554e-01 0.5053561
## block (Intercept) 3.960e-10 0.0000199
## Residual 4.547e-01 0.6743317
## Number of obs: 36, groups: patch, 12; block, 4
##
## Dispersion estimate for gaussian family (sigma^2): 0.455
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 1.065515 0.606655 1.756 0.0790 .
## seasongrowing -0.671269 0.415960 -1.614 0.1066
## locationinside 0.030617 0.317010 0.097 0.9231
## locationoutside -0.158768 0.294083 -0.540 0.5893
## ros -1.058873 2.008341 -0.527 0.5980
## max.temp 0.001810 0.001041 1.739 0.0821 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1emm <- emmeans(model6.1, ~ location, type = "response")
pairs(emm, adjust = "FDR")## contrast estimate SE df t.ratio p.value
## edge - inside -0.0306 0.317 27 -0.097 0.9238
## edge - outside 0.1588 0.294 27 0.540 0.8906
## inside - outside 0.1894 0.287 27 0.660 0.8906
##
## Results are averaged over the levels of: season
## P value adjustment: fdr method for 3 testscld(emm, adjust = "FDR", Letters = letters)## location emmean SE df lower.CL upper.CL .group
## outside 1.55 0.257 27 0.893 2.2 a
## edge 1.71 0.271 27 1.016 2.4 a
## inside 1.74 0.258 27 1.078 2.4 a
##
## Results are averaged over the levels of: season
## Confidence level used: 0.95
## Conf-level adjustment: bonferroni method for 3 estimates
## P value adjustment: fdr method for 3 tests
## significance level used: alpha = 0.05
## NOTE: If two or more means share the same grouping symbol,
## then we cannot show them to be different.
## But we also did not show them to be the same.sim_res <- simulateResiduals(model6)
plot(sim_res)
testDispersion(sim_res)
##
## DHARMa nonparametric dispersion test via sd of residuals fitted vs.
## simulated
##
## data: simulationOutput
## dispersion = 1.0588, p-value = 0.768
## alternative hypothesis: two.sidedtestZeroInflation(sim_res)
##
## DHARMa zero-inflation test via comparison to expected zeros with
## simulation under H0 = fitted model
##
## data: simulationOutput
## ratioObsSim = NaN, p-value = 1
## alternative hypothesis: two.sidedtestOutliers(sim_res)
##
## DHARMa outlier test based on exact binomial test with approximate
## expectations
##
## data: sim_res
## outliers at both margin(s) = 1, observations = 36, p-value = 0.2502
## alternative hypothesis: true probability of success is not equal to 0.007968127
## 95 percent confidence interval:
## 0.0007030252 0.1452892647
## sample estimates:
## frequency of outliers (expected: 0.00796812749003984 )
## 0.02777778check_model(model6)## `check_outliers()` does not yet support models of class `glmmTMB`.
Model 7: impacts on delayed planted seedling mortality: 2nd order fire effect
mortality_data <- subset(chpt2_r_data, !is.na(mortality3))
mortality_data$mortality3_beta <- pmin(pmax(mortality_data$mortality3, 0.001), 0.999)
model7 <- glmmTMB(
mortality3_beta ~ location + season + difn + ros + max.temp,
data = mortality_data,
family = beta_family(link = "logit")
)
summary(model7)## Family: beta ( logit )
## Formula: mortality3_beta ~ location + season + difn + ros + max.temp
## Data: mortality_data
##
## AIC BIC logLik -2*log(L) df.resid
## -117.0 -105.6 66.5 -133.0 23
##
##
## Dispersion parameter for beta family (): 0.951
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.344e+00 1.453e+00 -0.925 0.355
## locationinside -1.059e+00 6.151e-01 -1.722 0.085 .
## locationoutside -8.637e-01 5.933e-01 -1.456 0.145
## seasongrowing -5.969e-01 5.100e-01 -1.170 0.242
## difn 1.654e-02 1.701e-02 0.973 0.331
## ros -1.316e+00 3.363e+00 -0.391 0.696
## max.temp 3.856e-05 1.592e-03 0.024 0.981
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1#emmeans(model6, pairwise ~ location | season)
sim_res <- simulateResiduals(model7)
plot(sim_res)
testDispersion(sim_res)
##
## DHARMa nonparametric dispersion test via sd of residuals fitted vs.
## simulated
##
## data: simulationOutput
## dispersion = 0.65463, p-value = 0.152
## alternative hypothesis: two.sidedtestZeroInflation(sim_res)
##
## DHARMa zero-inflation test via comparison to expected zeros with
## simulation under H0 = fitted model
##
## data: simulationOutput
## ratioObsSim = NaN, p-value = 1
## alternative hypothesis: two.sidedtestOutliers(sim_res)
##
## DHARMa outlier test based on exact binomial test with approximate
## expectations
##
## data: sim_res
## outliers at both margin(s) = 0, observations = 31, p-value = 1
## alternative hypothesis: true probability of success is not equal to 0.007968127
## 95 percent confidence interval:
## 0.0000000 0.1121887
## sample estimates:
## frequency of outliers (expected: 0.00796812749003984 )
## 0check_model(model7)## `check_outliers()` does not yet support models of class `glmmTMB`.
model 9: planted seedling growth post fire, 2nd order fire effect
model9 <- glmmTMB(
rcd20 ~ season + location + max.temp +
+ (1 | patch ) + (1 | block),
data = chpt2_r_data,
family = tweedie(link = "log")
)## Warning in (function (start, objective, gradient = NULL, hessian = NULL, :
## NA/NaN function evaluationsummary(model9)## Family: tweedie ( log )
## Formula:
## rcd20 ~ season + location + max.temp + +(1 | patch) + (1 | block)
## Data: chpt2_r_data
##
## AIC BIC logLik -2*log(L) df.resid
## 139.5 151.8 -60.8 121.5 20
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## patch (Intercept) 0.06155 0.2481
## block (Intercept) 1.90584 1.3805
## Number of obs: 29, groups: patch, 12; block, 4
##
## Dispersion parameter for tweedie family (): 0.538
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.6701421 0.9493271 -0.706 0.4802
## seasongrowing 1.1460729 1.6439544 0.697 0.4857
## locationinside 0.0421320 0.2028110 0.208 0.8354
## locationoutside 0.0281369 0.2036509 0.138 0.8901
## max.temp 0.0012851 0.0005559 2.312 0.0208 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1#emmeans(model9, pairwise ~ location | season)
sim_res <- simulateResiduals(model9)
plot(sim_res)
testDispersion(sim_res)
##
## DHARMa nonparametric dispersion test via sd of residuals fitted vs.
## simulated
##
## data: simulationOutput
## dispersion = 0.11097, p-value = 0.744
## alternative hypothesis: two.sidedtestZeroInflation(sim_res)
##
## DHARMa zero-inflation test via comparison to expected zeros with
## simulation under H0 = fitted model
##
## data: simulationOutput
## ratioObsSim = 0.53079, p-value = 0.816
## alternative hypothesis: two.sidedtestOutliers(sim_res)
##
## DHARMa outlier test based on exact binomial test with approximate
## expectations
##
## data: sim_res
## outliers at both margin(s) = 0, observations = 29, p-value = 1
## alternative hypothesis: true probability of success is not equal to 0.007968127
## 95 percent confidence interval:
## 0.0000000 0.1194449
## sample estimates:
## frequency of outliers (expected: 0.00796812749003984 )
## 0check_model(model9)## `check_outliers()` does not yet support models of class `glmmTMB`.