* using log directory ‘/data/localhost/ripley/R/packages/tests-MKL/DImodels.Rcheck’ * using R Under development (unstable) (2026-10-28 r90786) * using platform: x86_64-pc-linux-gnu * R was compiled by gcc (GCC) 16.2.1 20260819 (Red Hat 16.2.1-2) GNU Fortran (GCC) 16.2.1 20260819 (Red Hat 16.2.1-2) * running under: Fedora Linux 44 (Server Edition) * using session charset: UTF-8 * current time: 2026-10-29 11:06:11 UTC * using option ‘--no-stop-on-test-error’ * checking for file ‘DImodels/DESCRIPTION’ ... OK * checking extension type ... Package * this is package ‘DImodels’ version ‘1.3.3’ * checking package namespace information ... OK * checking package dependencies ... OK * checking if this is a source package ... OK * checking if there is a namespace ... OK * checking for executable files ... OK * checking for hidden files and directories ... OK * checking for portable file names ... OK * checking for sufficient/correct file permissions ... OK * checking whether package ‘DImodels’ can be installed ... [10s/26s] OK * checking package directory ... OK * checking ‘build’ directory ... OK * checking DESCRIPTION meta-information ... OK * checking top-level files ... OK * checking for left-over files ... OK * checking index information ... OK * checking package subdirectories ... OK * checking code files for non-ASCII characters ... OK * checking R files for syntax errors ... OK * checking whether the package can be loaded ... OK * checking whether the package can be loaded with stated dependencies ... OK * checking whether the package can be unloaded cleanly ... OK * checking whether the namespace can be loaded with stated dependencies ... OK * checking whether the namespace can be unloaded cleanly ... OK * checking loading without being on the library search path ... OK * checking use of S3 registration ... OK * checking dependencies in R code ... OK * checking S3 generic/method consistency ... OK * checking replacement functions ... OK * checking foreign function calls ... OK * checking R code for possible problems ... [18s/45s] OK * checking Rd files ... OK * checking Rd metadata ... OK * checking Rd line widths ... OK * checking Rd cross-references ... OK * checking for missing documentation entries ... OK * checking for code/documentation mismatches ... OK * checking Rd \usage sections ... OK * checking Rd contents ... OK * checking for unstated dependencies in examples ... OK * checking contents of ‘data’ directory ... OK * checking data for non-ASCII characters ... OK * checking data for ASCII and uncompressed saves ... OK * checking installed files from ‘inst/doc’ ... OK * checking files in ‘vignettes’ ... OK * checking examples ... [23s/57s] OK * checking for unstated dependencies in ‘tests’ ... OK * checking tests ... Running ‘testthat.R’ [34s/103s] [34s/104s] ERROR Running the tests in ‘tests/testthat.R’ failed. Complete output: > # This file is part of the standard setup for testthat. > # It is recommended that you do not modify it. > # > # Where should you do additional test configuration? > # Learn more about the roles of various files in: > # * https://r-pkgs.org/tests.html > # * https://testthat.r-lib.org/reference/test_package.html#special-files > > library(testthat) > library(DImodels) > > test_check("DImodels") Fitted model: Evenness 'E' DImodel Theta estimate: 0.8357 Fitted model: Evenness 'E' DImodel Theta estimate: 0.7603 Fitted model: Evenness 'E' DImodel Theta estimate: 0.7603 Fitted model: Evenness 'E' DImodel Fitted model: Evenness 'E' DImodel Fitted model: Evenness 'E' DImodel Fitted model: Evenness 'E' DImodel Theta estimate: 0.8045 Fitted model: Evenness 'E' DImodel Fitted model: Structural 'STR' DImodel Fitted model: Species identity 'ID' DImodel Fitted model: Average interactions 'AV' DImodel Fitted model: Evenness 'E' DImodel Fitted model: Functional group effects 'FG' DImodel Fitted model: Additive species contributions to interactions 'ADD' DImodel Fitted model: Separate pairwise interactions 'FULL' DImodel Fitted model: Structural 'STR' DImodel Fitted model: Species identity 'ID' DImodel Fitted model: Average interactions 'AV' DImodel Fitted model: Evenness 'E' DImodel Fitted model: Functional group effects 'FG' DImodel Fitted model: Additive species contributions to interactions 'ADD' DImodel Fitted model: Separate pairwise interactions 'FULL' DImodel Fitted model: Structural 'STR' DImodel Fitted model: Species identity 'ID' DImodel Fitted model: Average interactions 'AV' DImodel Fitted model: Evenness 'E' DImodel Fitted model: Functional group effects 'FG' DImodel Fitted model: Additive species contributions to interactions 'ADD' DImodel Fitted model: Separate pairwise interactions 'FULL' DImodel Fitted model: Structural 'STR' DImodel Fitted model: Species identity 'ID' DImodel Fitted model: Average interactions 'AV' DImodel Fitted model: Evenness 'E' DImodel Fitted model: Functional group effects 'FG' DImodel Fitted model: Additive species contributions to interactions 'ADD' DImodel Fitted model: Separate pairwise interactions 'FULL' DImodel Fitted model: Additive species contributions to interactions 'ADD' DImodel Theta estimate: 0.786 Fitted model: Additive species contributions to interactions 'ADD' DImodel Theta estimate: 0.856 Fitted model: Functional group effects 'FG' DImodel Theta estimate: 0.7524 Fitted model: Functional group effects 'FG' DImodel Theta estimate: 0.8299 -------------------------------------------------------------------------------- Investigating richness model and three DI alternatives AIC AICc BIC df 1 333.9517 334.3267 340.6102 2 2 329.5335 329.9085 336.1920 2 3 329.4024 330.0373 338.2804 3 4 277.0934 278.4705 292.6300 5 5 271.7793 273.6460 287.3159 6 Description 1 Model 1: Richness only 2 Model 2: Average interactions 'AV' DImodel with common identity effects and theta = 0.5 3 Model 3: Average interactions 'AV' DImodel with common identity effects and theta estimated 4 Model 4: Average interactions 'AV' DImodel with unique identity effects and theta = 0.5 5 Model 5: Average interactions 'AV' DImodel with unique identity effects and theta estimated -------------------------------------------------------------------------------- Models 1 and 2 are equivalent if all mixtures in the dataset at any given level of richness are equi-proportional. richness_vs_DI is limited in terms of model selection. Only five models are explored. See ?autoDI and ?DI for more options. -------------------------------------------------------------------------------- Fitted model: Average interactions 'AV' DImodel Theta estimate: 0.8357 -------------------------------------------------------------------------------- Investigating richness model and three DI alternatives AIC AICc BIC df 1 335.9073 336.5422 344.7853 3 2 331.4862 332.1211 340.3642 3 3 331.3535 332.3213 342.4511 4 4 278.9786 280.8452 296.7346 6 5 273.6548 276.0955 291.4109 7 Description 1 Model 1: Richness only 2 Model 2: Average interactions 'AV' DImodel with common identity effects and theta = 0.5 3 Model 3: Average interactions 'AV' DImodel with common identity effects and theta estimated 4 Model 4: Average interactions 'AV' DImodel with unique identity effects and theta = 0.5 5 Model 5: Average interactions 'AV' DImodel with unique identity effects and theta estimated -------------------------------------------------------------------------------- Models 1 and 2 are equivalent if all mixtures in the dataset at any given level of richness are equi-proportional. richness_vs_DI is limited in terms of model selection. Only five models are explored. See ?autoDI and ?DI for more options. -------------------------------------------------------------------------------- Fitted model: Average interactions 'AV' DImodel Theta estimate: 0.8357 Fitted model: Functional group effects 'FG' DImodel Fitted model: Custom DI model Fitted model: Average interactions 'AV' DImodel Theta estimate: 0.8357 Fitted model: Custom DI model Fitted model: Functional group effects 'FG' DImodel 'community' is a factor with 25 levels, one for each unique set of proportions. Fitted model: Functional group effects 'FG' DImodel 'community' is a factor with 25 levels, one for each unique set of proportions. Fitted model: Functional group effects 'FG' DImodel Analysis of Deviance Table Model 1: yield ~ 0 + p1_ID + p2_ID + p3_ID + p4_ID + FG_bfg_G_H + FG_wfg_G + FG_wfg_H + nitrogen50 + densityhigh Model 2: yield ~ 0 + p1_ID + p2_ID + p3_ID + p4_ID + FG_bfg_G_H + FG_wfg_G + FG_wfg_H + nitrogen50 + densityhigh + community2 + community3 + community4 + community5 + community6 + community7 + community8 + community9 + community10 + community11 + community12 + community13 + community14 + community15 + community16 + community17 + community18 + community19 Resid. Df Resid. Dev Df Deviance 1 59 136.930 2 41 80.413 18 56.517 Saving _problems/test-DI-343.R Fitted model: Functional group effects 'FG' DImodel Theta estimate: 0.7524 'community' is a factor with 25 levels, one for each unique set of proportions. Fitted model: Functional group effects 'FG' DImodel 'community' is a factor with 25 levels, one for each unique set of proportions. Fitted model: Functional group effects 'FG' DImodel Analysis of Deviance Table Model 1: yield ~ 0 + p1_ID + p2_ID + p3_ID + p4_ID + FG_bfg_G_H + FG_wfg_G + FG_wfg_H + nitrogen50 + densityhigh Model 2: yield ~ 0 + p1_ID + p2_ID + p3_ID + p4_ID + FG_bfg_G_H + FG_wfg_G + FG_wfg_H + nitrogen50 + densityhigh + community2 + community3 + community4 + community5 + community6 + community7 + community8 + community9 + community10 + community11 + community12 + community13 + community14 + community15 + community16 + community17 + community18 + community19 Resid. Df Resid. Dev Df Deviance 1 58 127.083 2 40 80.413 18 46.67 Fitted model: Average interactions 'AV' DImodel Theta estimate: 0.7603 Fitted model: Separate pairwise interactions 'FULL' DImodel Theta estimate: 0.7706 Fitted model: Functional group effects 'FG' DImodel Fitted model: Separate pairwise interactions 'FULL' DImodel Theta estimate: 0.4525 Fitted model: Average interactions 'AV' DImodel Theta estimate: 0.4533 Fitted model: Structural 'STR' DImodel Fitted model: Custom DI model -------------------------------------------------------------------------------- Step 1: Investigating whether theta is equal to 1 or not for the AV model, including all available structures Theta estimate: 0.4533 Selection by AIC Warning: DI Model with the lowest AIC will be selected, even if the difference is very small. Please inspect other models to see differences in AIC. AIC DI_Model treat theta 1 225.4313 AV none FALSE 2 217.4542 AV none TRUE Description 1 Average interactions 'AV' DImodel 2 Average interactions 'AV' DImodel, estimating theta The conclusion of the model selection procedure is that theta is significantly different from 1. -------------------------------------------------------------------------------- Step 2: Investigating the interactions Selection by AIC Warning: DI Model with the lowest AIC will be selected, even if the difference is very small. Please inspect other models to see differences in AIC. AIC DI_Model treat theta 1 360.9133 STR none FALSE 2 365.5893 ID none FALSE 3 217.4542 AV none TRUE 4 220.3987 FG none TRUE 5 221.0360 ADD none TRUE 6 224.3384 FULL none TRUE Description 1 Structural 'STR' DImodel 2 Species identity 'ID' DImodel 3 Average interactions 'AV' DImodel, estimating theta 4 Functional group effects 'FG' DImodel, estimating theta 5 Additive species contributions to interactions 'ADD' DImodel, estimating theta 6 Separate pairwise interactions 'FULL' DImodel, estimating theta Selected model: Average interactions 'AV' DImodel, estimating theta -------------------------------------------------------------------------------- Step 3: No investigation of treatment effect included, since no treatment was specified (argument 'treat' omitted) -------------------------------------------------------------------------------- Step 4: Comparing the final selected model with the reference (community) model 'community' is a factor with 15 levels, one for each unique set of proportions. model Resid. Df Resid. SSq Resid. MSq Df SSq F Pr(>F) DI Model 1 Selected 54 104.3104 1.9317 DI Model 2 Reference 45 98.0794 2.1795 9 6.231 0.3176 0.965 -------------------------------------------------------------------------------- autoDI is limited in terms of model selection. Exercise caution when choosing your final model. -------------------------------------------------------------------------------- -------------------------------------------------------------------------------- Sequential analysis: Investigating only non-diversity experimental design structures model Resid. Df Resid. SSq Resid. MSq Df SSq F Pr(>F) 1 Intercept only 59 1346.175 22.8165 2 treat 56 1296.957 23.1599 3 49.2178 0.7084 0.5511 -------------------------------------------------------------------------------- Step 1: Investigating whether theta is equal to 1 or not for the AV model, including all available structures Theta estimate: 0.4533 Selection by AIC Warning: DI Model with the lowest AIC will be selected, even if the difference is very small. Please inspect other models to see differences in AIC. AIC DI_Model treat theta 1 200.8260 AV 'block' FALSE 2 185.1528 AV 'block' TRUE Description 1 Average interactions 'AV' DImodel with treatment 2 Average interactions 'AV' DImodel with treatment, estimating theta The conclusion of the model selection procedure is that theta is significantly different from 1. -------------------------------------------------------------------------------- Step 2: Investigating the interactions Selection by AIC Warning: DI Model with the lowest AIC will be selected, even if the difference is very small. Please inspect other models to see differences in AIC. AIC DI_Model treat theta 1 364.6785 STR 'block' FALSE 2 369.3037 ID 'block' FALSE 3 185.1528 AV 'block' TRUE 4 187.1385 FG 'block' TRUE 5 186.4887 ADD 'block' TRUE 6 189.1097 FULL 'block' TRUE Description 1 Structural 'STR' DImodel with treatment 2 Species identity 'ID' DImodel with treatment 3 Average interactions 'AV' DImodel with treatment, estimating theta 4 Functional group effects 'FG' DImodel with treatment, estimating theta 5 Additive species contributions to interactions 'ADD' DImodel with treatment, estimating theta 6 Separate pairwise interactions 'FULL' DImodel with treatment, estimating theta Selected model: Average interactions 'AV' DImodel with treatment, estimating theta -------------------------------------------------------------------------------- Step 3: Investigating the treatment effect Selection by AIC Warning: DI Model with the lowest AIC will be selected, even if the difference is very small. Please inspect other models to see differences in AIC. AIC DI_Model treat theta 1 217.4542 AV none FALSE 2 185.1528 AV 'block' FALSE Description 1 Average interactions 'AV' DImodel 2 Average interactions 'AV' DImodel with treatment Selected model: Average interactions 'AV' DImodel with treatment -------------------------------------------------------------------------------- Step 4: Comparing the final selected model with the reference (community) model 'community' is a factor with 15 levels, one for each unique set of proportions. model Resid. Df Resid. SSq Resid. MSq Df SSq F Pr(>F) DI Model 1 Selected 51 55.0925 1.0802 DI Model 2 Reference 42 48.8616 1.1634 9 6.231 0.5951 0.7936 -------------------------------------------------------------------------------- autoDI is limited in terms of model selection. Exercise caution when choosing your final model. -------------------------------------------------------------------------------- -------------------------------------------------------------------------------- Sequential analysis: Investigating only non-diversity experimental design structures model Resid. Df Resid. SSq Resid. MSq Df SSq F Pr(>F) 1 Intercept only 67 680.2884 10.1536 2 density 66 679.9658 10.3025 1 0.3226 0.0324 0.8578 3 density + treat 65 647.9784 9.9689 1 31.9874 3.2087 0.0779 F tests were specified for model selection, however AICc will be used for step 1, since models are not nested in this step -------------------------------------------------------------------------------- Step 1: Investigating whether theta is equal to 1 or not for the AV model, including all available structures Theta estimate: 0.7603 Selection by AICc Warning: DI Model with the lowest AICc will be selected, even if the difference is very small. Please inspect other models to see differences in AICc. AICc DI_Model treat theta 1 275.4202 AV 'nitrogen' FALSE 2 274.3108 AV 'nitrogen' TRUE Description 1 Average interactions 'AV' DImodel with treatment 2 Average interactions 'AV' DImodel with treatment, estimating theta The conclusion of the model selection procedure is that theta is significantly different from 1. -------------------------------------------------------------------------------- Step 2: Investigating the interactions All models include density Selection using F tests Description DI Model 1 Structural 'STR' DImodel with treatment DI Model 2 Species identity 'ID' DImodel with treatment DI Model 3 Average interactions 'AV' DImodel with treatment, estimating theta DI Model 4 Additive species contributions to interactions 'ADD' DImodel with treatment, estimating theta DI Model 5 Separate pairwise interactions 'FULL' DImodel with treatment, estimating theta DI_model treat estimate_theta Resid. Df Resid. SSq Resid. MSq DI Model 1 STR 'nitrogen' FALSE 65 647.9784 9.9689 DI Model 2 ID 'nitrogen' FALSE 62 374.8340 6.0457 DI Model 3 AV 'nitrogen' TRUE 60 164.8882 2.7481 DI Model 4 ADD 'nitrogen' TRUE 57 148.5415 2.6060 DI Model 5 FULL 'nitrogen' TRUE 55 115.5999 2.1018 Df SSq F Pr(>F) DI Model 1 DI Model 2 3 273.1444 43.3188 <0.0001 DI Model 3 2 209.9458 49.9439 <0.0001 DI Model 4 3 16.3467 2.5925 0.0618 DI Model 5 2 32.9416 7.8365 0.001 Functional groups (argument 'FG') were not specified, and therefore not investigated. Selected model: Separate pairwise interactions 'FULL' DImodel with treatment, estimating theta -------------------------------------------------------------------------------- Step 3: Investigating the treatment effect Selection using F tests Description DI Model 1 Separate pairwise interactions 'FULL' DImodel DI Model 2 Separate pairwise interactions 'FULL' DImodel with treatment DI_model treat estimate_theta Resid. Df Resid. SSq Resid. MSq DI Model 1 FULL none FALSE 56 126.5017 2.2590 DI Model 2 FULL 'nitrogen' FALSE 55 115.5853 2.1016 Df SSq F Pr(>F) DI Model 1 DI Model 2 1 10.9164 5.1944 0.0266 Selected model: Separate pairwise interactions 'FULL' DImodel with treatment -------------------------------------------------------------------------------- Step 4: Comparing the final selected model with the reference (community) model 'community' is a factor with 25 levels, one for each unique set of proportions. model Resid. Df Resid. SSq Resid. MSq Df SSq F Pr(>F) DI Model 1 Selected 55 115.5853 2.1016 DI Model 2 Reference 41 80.4127 1.9613 14 35.1727 1.281 0.2602 -------------------------------------------------------------------------------- autoDI is limited in terms of model selection. Exercise caution when choosing your final model. -------------------------------------------------------------------------------- -------------------------------------------------------------------------------- Sequential analysis: Investigating only non-diversity experimental design structures model Resid. Df Resid. SSq Resid. MSq Df SSq F Pr(>F) 1 Intercept only 67 680.2884 10.1536 2 block 66 679.9658 10.3025 1 0.3226 0.0324 0.8578 3 block + treat 65 647.9784 9.9689 1 31.9874 3.2087 0.0779 -------------------------------------------------------------------------------- Step 1: Investigating whether theta is equal to 1 or not for the AV model, including all available structures Theta estimate: 0.7603 Selection by BICc Warning: DI Model with the lowest BICc will be selected, even if the difference is very small. Please inspect other models to see differences in BICc. BICc DI_Model treat theta 1 295.8848 AV 'nitrogen' FALSE 2 297.7304 AV 'nitrogen' TRUE Description 1 Average interactions 'AV' DImodel with treatment 2 Average interactions 'AV' DImodel with treatment, estimating theta The conclusion of the model selection procedure is that theta is not significantly different from 1. -------------------------------------------------------------------------------- Step 2: Investigating the interactions All models include block Selection by BICc Warning: DI Model with the lowest BICc will be selected, even if the difference is very small. Please inspect other models to see differences in BICc. BICc DI_Model treat theta 1 364.4890 STR 'nitrogen' FALSE 2 342.5247 ID 'nitrogen' FALSE 3 295.8848 AV 'nitrogen' FALSE 4 290.9111 FG 'nitrogen' FALSE 5 305.9078 ADD 'nitrogen' FALSE 6 303.0112 FULL 'nitrogen' FALSE Description 1 Structural 'STR' DImodel with treatment 2 Species identity 'ID' DImodel with treatment 3 Average interactions 'AV' DImodel with treatment 4 Functional group effects 'FG' DImodel with treatment 5 Additive species contributions to interactions 'ADD' DImodel with treatment 6 Separate pairwise interactions 'FULL' DImodel with treatment Selected model: Functional group effects 'FG' DImodel with treatment -------------------------------------------------------------------------------- Step 3: Investigating the treatment effect Selection by BICc Warning: DI Model with the lowest BICc will be selected, even if the difference is very small. Please inspect other models to see differences in BICc. BICc DI_Model treat theta 1 288.4666 FG none FALSE 2 290.9111 FG 'nitrogen' FALSE Description 1 Functional group effects 'FG' DImodel 2 Functional group effects 'FG' DImodel with treatment Selected model: Functional group effects 'FG' DImodel -------------------------------------------------------------------------------- Step 4: Comparing the final selected model with the reference (community) model 'community' is a factor with 25 levels, one for each unique set of proportions. model Resid. Df Resid. SSq Resid. MSq Df SSq F Pr(>F) DI Model 1 Selected 60 143.8881 2.3981 DI Model 2 Reference 42 84.6499 2.0155 18 59.2382 1.6329 0.0954 -------------------------------------------------------------------------------- autoDI is limited in terms of model selection. Exercise caution when choosing your final model. -------------------------------------------------------------------------------- -------------------------------------------------------------------------------- Sequential analysis: Investigating only non-diversity experimental design structures model Resid. Df Resid. SSq Resid. MSq Df SSq F Pr(>F) 1 Intercept only 67 680.2884 10.1536 2 block 66 679.9658 10.3025 1 0.3226 0.0324 0.8578 3 block + density 65 647.9784 9.9689 1 31.9874 3.2087 0.0779 -------------------------------------------------------------------------------- Step 1: Investigating whether theta is equal to 1 or not for the AV model, including all available structures Theta estimate: 0.7603 Selection by AICc Warning: DI Model with the lowest AICc will be selected, even if the difference is very small. Please inspect other models to see differences in AICc. AICc DI_Model treat theta 1 275.4202 AV none FALSE 2 274.3108 AV none TRUE Description 1 Average interactions 'AV' DImodel 2 Average interactions 'AV' DImodel, estimating theta The conclusion of the model selection procedure is that theta is significantly different from 1. -------------------------------------------------------------------------------- Step 2: Investigating the interactions All models include block and density Selection by AICc Warning: DI Model with the lowest AICc will be selected, even if the difference is very small. Please inspect other models to see differences in AICc. AICc DI_Model treat theta 1 354.9063 STR none FALSE 2 324.9167 ID none FALSE 3 274.3108 AV none TRUE 4 262.2169 FG none TRUE 5 275.7806 ADD none TRUE 6 264.9828 FULL none TRUE Description 1 Structural 'STR' DImodel 2 Species identity 'ID' DImodel 3 Average interactions 'AV' DImodel, estimating theta 4 Functional group effects 'FG' DImodel, estimating theta 5 Additive species contributions to interactions 'ADD' DImodel, estimating theta 6 Separate pairwise interactions 'FULL' DImodel, estimating theta Selected model: Functional group effects 'FG' DImodel, estimating theta -------------------------------------------------------------------------------- Step 3: No investigation of treatment effect included, since no treatment was specified (argument 'treat' omitted) -------------------------------------------------------------------------------- Step 4: Comparing the final selected model with the reference (community) model 'community' is a factor with 25 levels, one for each unique set of proportions. model Resid. Df Resid. SSq Resid. MSq Df SSq F Pr(>F) DI Model 1 Selected 58 127.0827 2.1911 DI Model 2 Reference 41 80.4127 1.9613 17 46.67 1.3997 0.1865 -------------------------------------------------------------------------------- autoDI is limited in terms of model selection. Exercise caution when choosing your final model. -------------------------------------------------------------------------------- -------------------------------------------------------------------------------- Sequential analysis: Investigating only non-diversity experimental design structures model Resid. Df Resid. SSq Resid. MSq Df SSq F Pr(>F) 1 Intercept only 67 680.2884 10.1536 2 treat 66 648.3010 9.8227 1 31.9874 3.2565 0.0757 -------------------------------------------------------------------------------- Step 1: Investigating whether theta is equal to 1 or not for the AV model, including all available structures Theta estimate: 0.7603 Selection by BIC Warning: DI Model with the lowest BIC will be selected, even if the difference is very small. Please inspect other models to see differences in BIC. BIC DI_Model treat theta 1 286.6419 AV 'nitrogen' FALSE 2 287.0963 AV 'nitrogen' TRUE Description 1 Average interactions 'AV' DImodel with treatment 2 Average interactions 'AV' DImodel with treatment, estimating theta The conclusion of the model selection procedure is that theta is not significantly different from 1. -------------------------------------------------------------------------------- Step 2: Investigating the interactions Selection by BIC Warning: DI Model with the lowest BIC will be selected, even if the difference is very small. Please inspect other models to see differences in BIC. BIC DI_Model treat theta 1 358.9638 STR 'nitrogen' FALSE 2 334.4255 ID 'nitrogen' FALSE 3 286.6419 AV 'nitrogen' FALSE 4 278.7087 FG 'nitrogen' FALSE 5 291.8826 ADD 'nitrogen' FALSE 6 284.7467 FULL 'nitrogen' FALSE Description 1 Structural 'STR' DImodel with treatment 2 Species identity 'ID' DImodel with treatment 3 Average interactions 'AV' DImodel with treatment 4 Functional group effects 'FG' DImodel with treatment 5 Additive species contributions to interactions 'ADD' DImodel with treatment 6 Separate pairwise interactions 'FULL' DImodel with treatment Selected model: Functional group effects 'FG' DImodel with treatment -------------------------------------------------------------------------------- Step 3: Investigating the treatment effect Selection by BIC Warning: DI Model with the lowest BIC will be selected, even if the difference is very small. Please inspect other models to see differences in BIC. BIC DI_Model treat theta 1 277.8519 FG none FALSE 2 278.7087 FG 'nitrogen' FALSE Description 1 Functional group effects 'FG' DImodel 2 Functional group effects 'FG' DImodel with treatment Selected model: Functional group effects 'FG' DImodel -------------------------------------------------------------------------------- Step 4: Comparing the final selected model with the reference (community) model 'community' is a factor with 25 levels, one for each unique set of proportions. model Resid. Df Resid. SSq Resid. MSq Df SSq F Pr(>F) DI Model 1 Selected 61 144.2107 2.3641 DI Model 2 Reference 43 84.9725 1.9761 18 59.2382 1.6654 0.0858 -------------------------------------------------------------------------------- autoDI is limited in terms of model selection. Exercise caution when choosing your final model. -------------------------------------------------------------------------------- -------------------------------------------------------------------------------- Sequential analysis: Investigating only non-diversity experimental design structures model Resid. Df Resid. SSq Resid. MSq Df SSq F Pr(>F) 1 Intercept only 67 680.2884 10.1536 2 density 66 648.3010 9.8227 1 31.9874 3.2565 0.0757 -------------------------------------------------------------------------------- Step 1: Investigating whether theta is equal to 1 or not for the AV model, including all available structures Theta estimate: 0.7603 Selection by BIC Warning: DI Model with the lowest BIC will be selected, even if the difference is very small. Please inspect other models to see differences in BIC. BIC DI_Model treat theta 1 286.6419 AV none FALSE 2 287.0963 AV none TRUE Description 1 Average interactions 'AV' DImodel 2 Average interactions 'AV' DImodel, estimating theta The conclusion of the model selection procedure is that theta is not significantly different from 1. -------------------------------------------------------------------------------- Step 2: Investigating the interactions All models include density Selection by BIC Warning: DI Model with the lowest BIC will be selected, even if the difference is very small. Please inspect other models to see differences in BIC. BIC DI_Model treat theta 1 358.9638 STR none FALSE 2 334.4255 ID none FALSE 3 286.6419 AV none FALSE 4 278.7087 FG none FALSE 5 291.8826 ADD none FALSE 6 284.7467 FULL none FALSE Description 1 Structural 'STR' DImodel 2 Species identity 'ID' DImodel 3 Average interactions 'AV' DImodel 4 Functional group effects 'FG' DImodel 5 Additive species contributions to interactions 'ADD' DImodel 6 Separate pairwise interactions 'FULL' DImodel Selected model: Functional group effects 'FG' DImodel -------------------------------------------------------------------------------- Step 3: No investigation of treatment effect included, since no treatment was specified (argument 'treat' omitted) -------------------------------------------------------------------------------- Step 4: Comparing the final selected model with the reference (community) model 'community' is a factor with 25 levels, one for each unique set of proportions. model Resid. Df Resid. SSq Resid. MSq Df SSq F Pr(>F) DI Model 1 Selected 60 137.2527 2.2875 DI Model 2 Reference 42 80.7353 1.9223 18 56.5175 1.6334 0.0952 -------------------------------------------------------------------------------- autoDI is limited in terms of model selection. Exercise caution when choosing your final model. -------------------------------------------------------------------------------- -------------------------------------------------------------------------------- Sequential analysis: Investigating only non-diversity experimental design structures model Resid. Df Resid. SSq Resid. MSq Df SSq F Pr(>F) 1 Intercept only 67 680.2884 10.1536 2 block 66 679.9658 10.3025 1 0.3226 0.0313 0.8601 -------------------------------------------------------------------------------- Step 1: Investigating whether theta is equal to 1 or not for the AV model, including all available structures Theta estimate: 0.8357 Selection by BIC Warning: DI Model with the lowest BIC will be selected, even if the difference is very small. Please inspect other models to see differences in BIC. BIC DI_Model treat theta 1 289.1779 AV none FALSE 2 291.4109 AV none TRUE Description 1 Average interactions 'AV' DImodel 2 Average interactions 'AV' DImodel, estimating theta The conclusion of the model selection procedure is that theta is not significantly different from 1. -------------------------------------------------------------------------------- Step 2: Investigating the interactions All models include block Selection by BIC Warning: DI Model with the lowest BIC will be selected, even if the difference is very small. Please inspect other models to see differences in BIC. BIC DI_Model treat theta 1 362.2065 STR none FALSE 2 339.9356 ID none FALSE 3 289.1779 AV none FALSE 4 281.9191 FG none FALSE 5 294.7050 ADD none FALSE 6 288.2771 FULL none FALSE Description 1 Structural 'STR' DImodel 2 Species identity 'ID' DImodel 3 Average interactions 'AV' DImodel 4 Functional group effects 'FG' DImodel 5 Additive species contributions to interactions 'ADD' DImodel 6 Separate pairwise interactions 'FULL' DImodel Selected model: Functional group effects 'FG' DImodel -------------------------------------------------------------------------------- Step 3: No investigation of treatment effect included, since no treatment was specified (argument 'treat' omitted) -------------------------------------------------------------------------------- Step 4: Comparing the final selected model with the reference (community) model 'community' is a factor with 25 levels, one for each unique set of proportions. model Resid. Df Resid. SSq Resid. MSq Df SSq F Pr(>F) DI Model 1 Selected 60 143.8881 2.3981 DI Model 2 Reference 42 84.6499 2.0155 18 59.2382 1.6329 0.0954 -------------------------------------------------------------------------------- autoDI is limited in terms of model selection. Exercise caution when choosing your final model. -------------------------------------------------------------------------------- -------------------------------------------------------------------------------- Sequential analysis: Investigating only non-diversity experimental design structures model Resid. Df Resid. SSq Resid. MSq Df SSq F 1 Intercept only 203 2040.865 10.0535 2 block 202 1915.455 9.4825 1 125.4102 13.1363 3 block + density 201 1911.058 9.5078 1 4.3968 0.4605 4 block + density + treat 200 1909.368 9.5468 1 1.6904 0.1771 Pr(>F) 1 2 4e-04 3 0.4982 4 0.6744 -------------------------------------------------------------------------------- Step 1: Investigating whether theta is equal to 1 or not for the AV model, including all available structures Theta estimate: 0.7218 Selection by BIC Warning: DI Model with the lowest BIC will be selected, even if the difference is very small. Please inspect other models to see differences in BIC. BIC DI_Model treat theta 1 818.6809 AV 'nitrogen' FALSE 2 809.1482 AV 'nitrogen' TRUE Description 1 Average interactions 'AV' DImodel with treatment 2 Average interactions 'AV' DImodel with treatment, estimating theta The conclusion of the model selection procedure is that theta is significantly different from 1. -------------------------------------------------------------------------------- Step 2: Investigating the interactions All models include block and density Selection by BIC Warning: DI Model with the lowest BIC will be selected, even if the difference is very small. Please inspect other models to see differences in BIC. BIC DI_Model treat theta 1 1061.7447 STR 'nitrogen' FALSE 2 958.1681 ID 'nitrogen' FALSE 3 809.1482 AV 'nitrogen' TRUE 4 764.8121 FG 'nitrogen' TRUE 5 802.9692 ADD 'nitrogen' TRUE 6 760.8406 FULL 'nitrogen' TRUE Description 1 Structural 'STR' DImodel with treatment 2 Species identity 'ID' DImodel with treatment 3 Average interactions 'AV' DImodel with treatment, estimating theta 4 Functional group effects 'FG' DImodel with treatment, estimating theta 5 Additive species contributions to interactions 'ADD' DImodel with treatment, estimating theta 6 Separate pairwise interactions 'FULL' DImodel with treatment, estimating theta Selected model: Separate pairwise interactions 'FULL' DImodel with treatment, estimating theta -------------------------------------------------------------------------------- Step 3: Investigating the treatment effect Selection by BIC Warning: DI Model with the lowest BIC will be selected, even if the difference is very small. Please inspect other models to see differences in BIC. BIC DI_Model treat theta 1 775.4057 FULL none FALSE 2 760.8309 FULL 'nitrogen' FALSE Description 1 Separate pairwise interactions 'FULL' DImodel 2 Separate pairwise interactions 'FULL' DImodel with treatment Selected model: Separate pairwise interactions 'FULL' DImodel with treatment -------------------------------------------------------------------------------- Step 4: Comparing the final selected model with the reference (community) model 'community' is a factor with 25 levels, one for each unique set of proportions. model Resid. Df Resid. SSq Resid. MSq Df SSq F Pr(>F) DI Model 1 Selected 190 336.5572 1.7714 DI Model 2 Reference 176 228.7338 1.2996 14 107.8234 5.9261 <0.0001 -------------------------------------------------------------------------------- autoDI is limited in terms of model selection. Exercise caution when choosing your final model. -------------------------------------------------------------------------------- -------------------------------------------------------------------------------- Step 1: Investigating whether theta is equal to 1 or not for the AV model, including all available structures Theta estimate: 1.0331 Selection by AIC Warning: DI Model with the lowest AIC will be selected, even if the difference is very small. Please inspect other models to see differences in AIC. AIC DI_Model treat theta 1 80.22723 AV none FALSE 2 82.19915 AV none TRUE Description 1 Average interactions 'AV' DImodel 2 Average interactions 'AV' DImodel, estimating theta The conclusion of the model selection procedure is that theta is not significantly different from 1. -------------------------------------------------------------------------------- Step 2: Investigating the interactions Selection by AIC Warning: DI Model with the lowest AIC will be selected, even if the difference is very small. Please inspect other models to see differences in AIC. AIC DI_Model treat theta Description 1 100.39643 STR none FALSE Structural 'STR' DImodel 2 100.67327 ID none FALSE Species identity 'ID' DImodel 3 80.22723 AV none FALSE Average interactions 'AV' DImodel 4 53.36836 FULL none FALSE Separate pairwise interactions 'FULL' DImodel Functional groups (argument 'FG') were not specified, and therefore not investigated. The 'ADD' variables are only computed for > 3 species cases as the 'ADD' model is not informative for the 2 or 3 species case. Selected model: Separate pairwise interactions 'FULL' DImodel -------------------------------------------------------------------------------- Step 3: No investigation of treatment effect included, since no treatment was specified (argument 'treat' omitted) -------------------------------------------------------------------------------- Step 4: Comparing the final selected model with the reference (community) model Lack-of-fit test is not possible as there are no replicates of communities in the data. Skipping step 4 -------------------------------------------------------------------------------- autoDI is limited in terms of model selection. Exercise caution when choosing your final model. -------------------------------------------------------------------------------- -------------------------------------------------------------------------------- Step 1: Investigating whether theta is equal to 1 or not for the FULL model, including all available structures Theta estimate: 1.4017 Selection by AIC Warning: DI Model with the lowest AIC will be selected, even if the difference is very small. Please inspect other models to see differences in AIC. AIC DI_Model treat theta 1 363.9277 FULL none FALSE 2 365.5317 FULL none TRUE Description 1 Separate pairwise interactions 'FULL' DImodel 2 Separate pairwise interactions 'FULL' DImodel, estimating theta The conclusion of the model selection procedure is that theta is not significantly different from 1. -------------------------------------------------------------------------------- Step 2: Investigating the interactions Selection by AIC Warning: DI Model with the lowest AIC will be selected, even if the difference is very small. Please inspect other models to see differences in AIC. AIC DI_Model treat theta Description 1 385.8681 STR none FALSE Structural 'STR' DImodel 2 379.6018 ID none FALSE Species identity 'ID' DImodel 3 363.9277 FULL none FALSE Separate pairwise interactions 'FULL' DImodel Functional groups (argument 'FG') were not specified, and therefore not investigated. The 'ADD' variables are only computed for > 3 species cases as the 'ADD' model is not informative for the 2 or 3 species case. Selected model: Separate pairwise interactions 'FULL' DImodel -------------------------------------------------------------------------------- Step 3: No investigation of treatment effect included, since no treatment was specified (argument 'treat' omitted) -------------------------------------------------------------------------------- Step 4: Comparing the final selected model with the reference (community) model 'community' is a factor with 7 levels, one for each unique set of proportions. model Resid. Df Resid. SSq Resid. MSq Df SSq F Pr(>F) DI Model 1 Selected 61 974.9396 15.9826 DI Model 2 Reference 57 889.4896 15.6051 4 85.4501 1.3689 0.2561 -------------------------------------------------------------------------------- autoDI is limited in terms of model selection. Exercise caution when choosing your final model. -------------------------------------------------------------------------------- F tests were specified for model selection, however AICc will be used for step 1, since models are not nested in this step -------------------------------------------------------------------------------- Step 1: Investigating whether theta is equal to 1 or not for the AV model, including all available structures Theta estimate: 0.9768 Selection by AICc Warning: DI Model with the lowest AICc will be selected, even if the difference is very small. Please inspect other models to see differences in AICc. AICc DI_Model treat theta 1 287.4519 AV none FALSE 2 289.8320 AV none TRUE Description 1 Average interactions 'AV' DImodel 2 Average interactions 'AV' DImodel, estimating theta The conclusion of the model selection procedure is that theta is not significantly different from 1. -------------------------------------------------------------------------------- Step 2: Investigating the interactions Selection using F tests Description DI Model 1 Structural 'STR' DImodel DI Model 2 Species identity 'ID' DImodel DI Model 3 Average interactions 'AV' DImodel DI Model 4 Functional group effects 'FG' DImodel DI Model 5 Separate pairwise interactions 'FULL' DImodel DI_model treat estimate_theta Resid. Df Resid. SSq Resid. MSq Df DI Model 1 STR none FALSE 63 1462.1967 23.2095 DI Model 2 ID none FALSE 61 1165.7315 19.1104 2 DI Model 3 AV none FALSE 60 281.4708 4.6912 1 DI Model 4 FG none FALSE 59 280.4582 4.7535 1 DI Model 5 FULL none FALSE 58 205.4254 3.5418 1 SSq F Pr(>F) DI Model 1 DI Model 2 296.4652 41.8521 <0.0001 DI Model 3 884.2608 249.663 <0.0001 DI Model 4 1.0126 0.2859 0.5949 DI Model 5 75.0328 21.1848 <0.0001 The 'ADD' variables are only computed for > 3 species cases as the 'ADD' model is not informative for the 2 or 3 species case. Selected model: Separate pairwise interactions 'FULL' DImodel -------------------------------------------------------------------------------- Step 3: No investigation of treatment effect included, since no treatment was specified (argument 'treat' omitted) -------------------------------------------------------------------------------- Step 4: Comparing the final selected model with the reference (community) model 'community' is a factor with 16 levels, one for each unique set of proportions. model Resid. Df Resid. SSq Resid. MSq Df SSq F Pr(>F) DI Model 1 Selected 58 205.4254 3.5418 DI Model 2 Reference 48 179.8704 3.7473 10 25.555 0.682 0.7356 -------------------------------------------------------------------------------- autoDI is limited in terms of model selection. Exercise caution when choosing your final model. -------------------------------------------------------------------------------- Fitted model: Species identity 'ID' DImodel Theta estimate: NA Fitted model: Evenness 'E' DImodel Fitted model: Average interactions 'AV' DImodel Fitted model: Functional group effects 'FG' DImodel Theta estimate: 0.4529 Fitted model: Additive species contributions to interactions 'ADD' DImodel Fitted model: Separate pairwise interactions 'FULL' DImodel Theta estimate: 0.4525 Interaction/Identity effects were specified manually in `contrast_vars` using the values specified by user instead of those calculated internally. Fitted model: Additive species contributions to interactions 'ADD' DImodel Fitted model: Average interactions 'AV' DImodel Theta estimate: 0.7603 Generated contrast matrix: p1_ID p2_ID p3_ID p4_ID AV nitrogen50 densityhigh `Test 1` 0 0 0 0 0 0 0 `nitrogen150:densityhigh` `Test 1` 0 Generated contrast matrix: p1_ID p2_ID p3_ID p4_ID AV nitrogen50 densityhigh `Test 1` 1 1 -1 -1 0 0 0 `nitrogen150:densityhigh` `Test 1` 0 Generated contrast matrix: p1_ID p2_ID p3_ID p4_ID AV nitrogen50 densityhigh `Test 1` 1 1 -1 -1 0 0 0 `nitrogen150:densityhigh` `Test 1` 0 Generated contrast matrix: p1_ID p2_ID p3_ID p4_ID AV nitrogen50 densityhigh `Test 1` 1 1 -1 -1 0 0 0 `nitrogen150:densityhigh` `Test 1` 0 Fitted model: Species identity 'ID' DImodel Theta estimate: NA Fitted model: Average interactions 'AV' DImodel Fitted model: Functional group effects 'FG' DImodel Fitted model: Functional group effects 'FG' DImodel Fitted model: Additive species contributions to interactions 'ADD' DImodel Fitted model: Separate pairwise interactions 'FULL' DImodel Theta estimate: 0.3119 Fitted model: Average interactions 'AV' DImodel Theta estimate: 0.7603 Fitted model: Custom DI model Fitted model: Separate pairwise interactions 'FULL' DImodel Fitted model: Additive species contributions to interactions 'ADD' DImodel Fitted model: Average interactions 'AV' DImodel Theta estimate: 0.8357 Fitted model: Evenness 'E' DImodel Fitted model: Average interactions 'AV' DImodel Fitted model: Additive species contributions to interactions 'ADD' DImodel Fitted model: Functional group effects 'FG' DImodel Fitted model: Separate pairwise interactions 'FULL' DImodel Fitted model: Species identity 'ID' DImodel Fitted model: Functional group effects 'FG' DImodel Fitted model: Evenness 'E' DImodel Fitted model: Average interactions 'AV' DImodel Theta estimate: 0.7603 Fitted model: Additive species contributions to interactions 'ADD' DImodel Theta estimate: 0.786 Fitted model: Functional group effects 'FG' DImodel Theta estimate: 0.7524 Fitted model: Separate pairwise interactions 'FULL' DImodel Theta estimate: 0.602 Fitted model: Species identity 'ID' DImodel Theta estimate: NA Fitted model: Functional group effects 'FG' DImodel Fitted model: Functional group effects 'FG' DImodel Fitted model: Separate pairwise interactions 'FULL' DImodel Fitted model: Functional group effects 'FG' DImodel [ FAIL 1 | WARN 1 | SKIP 0 | PASS 192 ] ══ Failed tests ════════════════════════════════════════════════════════════════ ── Failure ('test-DI.R:342:3'): Interior functions in DI_internal work ───────── Expected `DI_reference(mod_no, paste0("p", 1:4), Switzerland)` to equal `DI_compare(mod_no)`. Differences: attr(attr(attr(attr(actual, 'extra_formula'), '.Environment')$ref_model$model, 'terms'), '.Environment')$fit_to_check$effects[23:29] vs attr(attr(attr(attr(expected, 'extra_formula'), '.Environment')$ref_model$model, 'terms'), '.Environment')$fit_to_check$effects[23:29] -1.311190163 0.808848380 -2.794338588 - 0.197686287 + -0.197686287 2.553291584 -0.839601772 -1.981494056 attr(attr(attr(attr(actual, 'extra_formula'), '.Environment')$ref_model$model, 'terms'), '.Environment')$fit_to_check$effects[30:53] vs attr(attr(attr(attr(expected, 'extra_formula'), '.Environment')$ref_model$model, 'terms'), '.Environment')$fit_to_check$effects[30:53] -2.020068966 -0.474246951 1.109575220 - -1.460406127 + -0.080019711 - 2.548296789 + 2.030460375 - -0.214339238 + -1.076889241 -1.144855483 0.573023384 0.555890336 - 0.797052928 + 2.177439345 and 14 more ... attr(attr(attr(attr(actual, 'extra_formula'), '.Environment')$ref_model$model, 'terms'), '.Environment')$fit_to_check$qr$qr vs attr(attr(attr(attr(expected, 'extra_formula'), '.Environment')$ref_model$model, 'terms'), '.Environment')$fit_to_check$qr$qr [, 1] [, 2] [, 3] [, 4] [, 5] [, 6] [, 7] [, 8] [, 9] [,10] [,11] [,12] [,13] [,14] [,15] [,16] [,17] [,18] [,19] [,20] [,21] [,22] [,23] [,24] [,25] [,26] [,27] [,28] [,29] [,30] [,31] [,32] [,33] [,34] attr(attr(attr(attr(actual, 'extra_formula'), '.Environment')$ref_model$model, 'terms'), '.Environment')$fit_to_check$qr$qr[22, ] 0.12358007 0.006815828 0.101974935 -0.008429949 0.14097773 -0.0041894787 0.002707503 0.03206788 -1.578694e-01 2.209867e-02 1.092975e-01 5.217854e-03 -1.548931e-01 3.855876e-02 -6.603873e-01 7.327123e-02 1.041886e-01 -2.628381e-03 -1.188891e-02 4.102010e-02 8.527681e-04 1.058038e+00 -3.968283e-01 -1.324793e-02 -2.562149e-01 3.252146e-01 -2.291425e-01 -1.158195e-17 3.522870e-01 1.093200e-01 -8.249586e-02 -3.557030e-02 -9.760364e-01 3.177485e-02 attr(attr(attr(attr(actual, 'extra_formula'), '.Environment')$ref_model$model, 'terms'), '.Environment')$fit_to_check$qr$qr[23, ] 0.12358007 0.006815828 0.005795345 0.102277742 0.14059178 -0.0040354244 0.003175855 0.03141465 -1.590375e-01 2.213335e-02 2.187606e-02 -9.091668e-02 -1.510481e-01 2.619697e-02 6.594239e-02 -6.325805e-01 1.270297e-01 9.353118e-02 4.227539e-02 -4.773909e-02 -5.425341e-02 -1.051080e-02 9.808011e-01 -1.965124e-02 2.471611e-01 -1.448109e-01 2.873189e-01 -1.272997e-16 -1.046531e-01 1.621593e-01 -1.223698e-01 -5.276301e-02 -3.606235e-01 -1.040042e+00 attr(attr(attr(attr(actual, 'extra_formula'), '.Environment')$ref_model$model, 'terms'), '.Environment')$fit_to_check$qr$qr[24, ] 0.03089502 0.120139299 0.102151735 -0.008275990 0.14039562 -0.0048283555 0.002500024 0.03113605 -1.594888e-01 1.154255e-01 1.139950e-01 8.717059e-03 -1.586106e-01 4.799994e-02 8.504118e-02 8.336013e-02 7.611923e-01 2.426576e-01 1.139706e-01 -8.616394e-03 -7.780873e-03 1.649969e-02 5.212623e-02 -3.659554e-01 -4.066066e-01 -4.066066e-01 -5.324952e-02 -3.203712e-16 -5.324952e-02 -9.390068e-02 -5.063321e-01 1.061534e-01 1.530586e-01 1.530586e-01 - attr(attr(attr(attr(actual, 'extra_formula'), '.Environment')$ref_model$model, 'terms'), '.Environment')$fit_to_check$qr$qr[25, ] 0.03089502 0.120139299 0.005972144 0.102431701 0.14000967 -0.0046743012 0.002968376 0.03048282 -1.606570e-01 1.154602e-01 2.657359e-02 -8.741748e-02 -1.547656e-01 3.563815e-02 5.184911e-02 1.147898e-01 -1.071780e-02 -5.597208e-01 -1.094502e-01 1.753927e-02 -4.101197e-02 1.540341e-03 5.091169e-03 5.369583e-02 -1.048978e+00 4.766543e-01 4.766543e-01 7.444956e-17 9.566952e-02 8.834952e-18 3.301868e-01 -3.301868e-01 3.301868e-01 -3.301868e-01 + attr(attr(attr(attr(expected, 'extra_formula'), '.Environment')$ref_model$model, 'terms'), '.Environment')$fit_to_check$qr$qr[25, ] 0.03089502 0.120139299 0.005972144 0.102431701 0.14000967 -0.0046743012 0.002968376 0.03048282 -1.606570e-01 1.154602e-01 2.657359e-02 -8.741748e-02 -1.547656e-01 3.563815e-02 5.184911e-02 1.147898e-01 -1.071780e-02 -5.597208e-01 -1.094502e-01 1.753927e-02 -4.101197e-02 1.540341e-03 5.091169e-03 5.369583e-02 -1.048978e+00 4.766543e-01 4.766543e-01 7.444956e-17 9.566952e-02 7.750069e-18 3.301868e-01 -3.301868e-01 3.301868e-01 -3.301868e-01 - attr(attr(attr(attr(actual, 'extra_formula'), '.Environment')$ref_model$model, 'terms'), '.Environment')$fit_to_check$qr$qr[26, ] 0.03089502 0.025391025 0.117769007 0.102554817 0.03043950 -0.0121502153 -0.275245638 0.04128019 -1.287336e-01 1.524116e-02 1.639067e-01 -1.111881e-01 -1.432149e-01 -1.163218e-02 -1.119120e-01 1.069584e-01 3.473109e-03 8.625182e-02 -5.056847e-01 -1.759948e-02 -2.406343e-02 9.910604e-03 -2.096821e-02 3.381124e-02 2.181215e-17 9.344281e-01 -3.505402e-01 2.142580e-17 -5.838879e-01 9.948398e-18 -5.390931e-01 5.390931e-01 2.022347e-01 -2.022347e-01 + attr(attr(attr(attr(expected, 'extra_formula'), '.Environment')$ref_model$model, 'terms'), '.Environment')$fit_to_check$qr$qr[26, ] 0.03089502 0.025391025 0.117769007 0.102554817 0.03043950 -0.0121502153 -0.275245638 0.04128019 -1.287336e-01 1.524116e-02 1.639067e-01 -1.111881e-01 -1.432149e-01 -1.163218e-02 -1.119120e-01 1.069584e-01 3.473109e-03 8.625182e-02 -5.056847e-01 -1.759948e-02 -2.406343e-02 9.910604e-03 -2.096821e-02 3.381124e-02 2.181215e-17 -9.344281e-01 3.505402e-01 -2.142580e-17 5.838879e-01 -4.813640e-18 5.390931e-01 -5.390931e-01 -2.022347e-01 2.022347e-01 - attr(attr(attr(attr(actual, 'extra_formula'), '.Environment')$ref_model$model, 'terms'), '.Environment')$fit_to_check$qr$qr[27, ] 0.30895017 -0.061917325 -0.052646904 -0.045845624 -0.16002215 -0.1566738754 -0.037154482 0.11328986 -1.171581e-01 -4.759027e-02 -3.504651e-02 5.098717e-02 1.334064e-01 -1.139211e-01 -1.173598e-02 6.833623e-02 1.360206e-01 -1.211996e-01 -8.301829e-02 3.132470e-01 -6.690167e-04 -4.192785e-02 -2.518304e-02 6.965412e-02 -8.917302e-17 1.337154e-16 8.661856e-01 -3.767012e-18 -8.661856e-01 7.062356e-18 -1.128019e-16 3.343807e-18 -4.997225e-01 4.997225e-01 + attr(attr(attr(attr(expected, 'extra_formula'), '.Environment')$ref_model$model, 'terms'), '.Environment')$fit_to_check$qr$qr[27, ] 0.30895017 -0.061917325 -0.052646904 -0.045845624 -0.16002215 -0.1566738754 -0.037154482 0.11328986 -1.171581e-01 -4.759027e-02 -3.504651e-02 5.098717e-02 1.334064e-01 -1.139211e-01 -1.173598e-02 6.833623e-02 1.360206e-01 -1.211996e-01 -8.301829e-02 3.132470e-01 -6.690167e-04 -4.192785e-02 -2.518304e-02 6.965412e-02 -8.379369e-17 -1.290705e-16 8.661856e-01 -3.767012e-18 -8.661856e-01 7.331282e-18 -1.139549e-16 -5.005882e-17 -4.997225e-01 4.997225e-01 - attr(attr(attr(attr(actual, 'extra_formula'), '.Environment')$ref_model$model, 'terms'), '.Environment')$fit_to_check$qr$qr[28, ] 0.00000000 0.315827579 -0.052057573 -0.045332427 -0.16196251 -0.1588034647 -0.037846078 0.11018379 -1.225563e-01 2.634992e-01 -1.938808e-02 6.265118e-02 1.210147e-01 -8.245046e-02 -5.871357e-02 8.305804e-03 1.072776e-01 1.596616e-01 1.085970e-02 -1.137848e-01 -2.210473e-01 -6.334251e-02 -4.281300e-02 -3.876626e-02 -6.753921e-17 1.015757e-16 -7.126317e-17 9.490294e-16 3.439778e-16 2.272594e-18 -2.612185e-16 -1.480439e-16 3.817373e-17 -8.748575e-17 + attr(attr(attr(attr(expected, 'extra_formula'), '.Environment')$ref_model$model, 'terms'), '.Environment')$fit_to_check$qr$qr[28, ] 0.00000000 0.315827579 -0.052057573 -0.045332427 -0.16196251 -0.1588034647 -0.037846078 0.11018379 -1.225563e-01 2.634992e-01 -1.938808e-02 6.265118e-02 1.210147e-01 -8.245046e-02 -5.871357e-02 8.305804e-03 1.072776e-01 1.596616e-01 1.085970e-02 -1.137848e-01 -2.210473e-01 -6.334251e-02 -4.281300e-02 -3.876626e-02 -6.775772e-17 -7.363913e-17 -6.324202e-17 9.490294e-16 3.277893e-16 2.974981e-18 -2.381747e-16 -9.793735e-17 4.833701e-17 -1.051435e-16 - attr(attr(attr(attr(actual, 'extra_formula'), '.Environment')$ref_model$model, 'terms'), '.Environment')$fit_to_check$qr$qr[29, ] 0.00000000 0.000000000 0.320598636 -0.044922042 -0.16351415 0.0267550403 0.158028353 0.09621849 -1.574888e-01 -1.610237e-02 2.369682e-01 8.594013e-02 1.519575e-01 2.855429e-02 1.721958e-01 -1.052991e-03 1.163313e-01 -5.359915e-02 9.991078e-02 -3.773262e-02 3.484501e-02 -2.819583e-01 -2.357141e-02 -3.281079e-02 -2.127869e-17 -3.830925e-17 9.049527e-17 1.333610e-01 9.531600e-16 1.500624e-16 4.593297e-16 -3.620246e-16 2.226458e-16 -5.232841e-16 + attr(attr(attr(attr(expected, 'extra_formula'), '.Environment')$ref_model$model, 'terms'), '.Environment')$fit_to_check$qr$qr[29, ] 0.00000000 0.000000000 0.320598636 -0.044922042 -0.16351415 0.0267550403 0.158028353 0.09621849 -1.574888e-01 -1.610237e-02 2.369682e-01 8.594013e-02 1.519575e-01 2.855429e-02 1.721958e-01 -1.052991e-03 1.163313e-01 -5.359915e-02 9.991078e-02 -3.773262e-02 3.484501e-02 -2.819583e-01 -2.357141e-02 -3.281079e-02 -1.589936e-17 1.748148e-17 9.642502e-17 1.333610e-01 9.323396e-16 1.177149e-16 2.394165e-16 -5.716947e-16 2.261183e-16 -4.477886e-16 - attr(attr(attr(attr(actual, 'extra_formula'), '.Environment')$ref_model$model, 'terms'), '.Environment')$fit_to_check$qr$qr[30, ] 0.00000000 0.000000000 0.000000000 0.324103597 -0.16480064 0.0272685548 0.159589526 0.09404106 -1.613826e-01 -1.598679e-02 -5.443643e-02 -2.345083e-01 1.647742e-01 -1.265169e-02 6.155556e-02 1.037124e-01 1.393028e-01 -2.641489e-02 -7.815888e-02 5.829071e-02 3.671293e-02 7.915276e-02 -2.425982e-01 -3.347392e-02 -3.204621e-17 -3.214371e-17 -2.556283e-17 -4.412889e-02 -4.919704e-02 4.537882e-16 -1.052318e-16 5.291390e-17 -3.668335e-16 -1.158579e-16 + attr(attr(attr(attr(expected, 'extra_formula'), '.Environment')$ref_model$model, 'terms'), '.Environment')$fit_to_check$qr$qr[30, ] 0.00000000 0.000000000 0.000000000 0.324103597 -0.16480064 0.0272685548 0.159589526 0.09404106 -1.613826e-01 -1.598679e-02 -5.443643e-02 -2.345083e-01 1.647742e-01 -1.265169e-02 6.155556e-02 1.037124e-01 1.393028e-01 -2.641489e-02 -7.815888e-02 5.829071e-02 3.671293e-02 7.915276e-02 -2.425982e-01 -3.347392e-02 -2.543130e-17 4.864529e-17 -5.056200e-17 -4.412889e-02 -7.655354e-02 4.653512e-16 -7.219220e-17 5.408166e-17 -4.576500e-16 -1.867823e-16 - attr(attr(attr(attr(actual, 'extra_formula'), '.Environment')$ref_model$model, 'terms'), '.Environment')$fit_to_check$qr$qr[31, ] 0.15447508 0.126955127 -0.052352239 -0.045589025 -0.16099233 0.5026476915 -0.006753118 0.04280308 7.490401e-02 1.065872e-01 -5.329031e-02 5.629537e-02 8.146776e-02 5.433482e-01 -7.915986e-02 4.434330e-02 -5.699078e-02 1.489666e-01 -5.961898e-02 1.513639e-01 -8.372141e-02 1.554518e-01 3.964328e-02 2.476147e-01 1.852134e-16 -1.994267e-16 -3.010659e-16 -1.061126e-01 -4.431157e-01 4.023712e-02 -1.312783e-15 -1.542578e-16 5.844848e-17 7.489680e-17 + attr(attr(attr(attr(expected, 'extra_formula'), '.Environment')$ref_model$model, 'terms'), '.Environment')$fit_to_check$qr$qr[31, ] 0.15447508 0.126955127 -0.052352239 -0.045589025 -0.16099233 0.5026476915 -0.006753118 0.04280308 7.490401e-02 1.065872e-01 -5.329031e-02 5.629537e-02 8.146776e-02 5.433482e-01 -7.915986e-02 4.434330e-02 -5.699078e-02 1.489666e-01 -5.961898e-02 1.513639e-01 -8.372141e-02 1.554518e-01 3.964328e-02 2.476147e-01 1.852134e-16 2.292716e-16 -3.150512e-16 -1.061126e-01 -4.857260e-01 8.379573e-03 -1.211013e-15 -1.276632e-17 4.955467e-17 7.073676e-17 and 37 more ... attr(attr(attr(attr(actual, 'extra_formula'), '.Environment')$ref_model$model, 'terms'), '.Environment')$fit_to_check$qr$qraux[26:34] vs attr(attr(attr(attr(expected, 'extra_formula'), '.Environment')$ref_model$model, 'terms'), '.Environment')$fit_to_check$qr$qraux[26:34] 1.0000 1.0000 1.0358 - 1.0450 + 1.0655 - 1.0094 + 1.0056 - 1.0968 + 1.1988 - 1.1906 + 1.1605 - 1.0420 + 1.1059 - 1.1574 + 1.1802 [ FAIL 1 | WARN 1 | SKIP 0 | PASS 192 ] Error: ! Test failures. Execution halted * checking for unstated dependencies in vignettes ... OK * checking package vignettes ... OK * checking re-building of vignette outputs ... [17s/53s] OK * checking PDF version of manual ... [5s/10s] OK * checking HTML version of manual ... OK * checking for non-standard things in the check directory ... OK * checking for detritus in the temp directory ... OK * DONE Status: 1 ERROR See ‘/data/localhost/ripley/R/packages/tests-MKL/DImodels.Rcheck/00check.log’ for details. Command exited with non-zero status 1 Time 7:08.84, 151.72 + 12.35