CRAN Package Check Results for Package lmerTest

Last updated on 2018-04-22 19:46:58 CEST.

Flavor Version Tinstall Tcheck Ttotal Status Flags
r-devel-linux-x86_64-debian-clang 3.0-0 8.27 193.83 202.10 OK
r-devel-linux-x86_64-debian-gcc 3.0-0 6.02 149.27 155.29 OK
r-devel-linux-x86_64-fedora-clang 3.0-0 242.16 OK
r-devel-linux-x86_64-fedora-gcc 3.0-0 130.78 OK
r-devel-windows-ix86+x86_64 3.0-0 19.00 82.00 101.00 OK --no-examples --no-tests --no-vignettes
r-devel-osx-x86_64 3.0-0 OK
r-patched-linux-x86_64 3.0-0 6.02 188.87 194.89 OK
r-patched-solaris-x86 3.0-0 357.30 ERROR
r-release-linux-x86_64 3.0-0 5.08 235.56 240.64 OK
r-release-windows-ix86+x86_64 3.0-0 9.00 117.00 126.00 OK --no-examples --no-tests --no-vignettes
r-release-osx-x86_64 3.0-0 OK
r-oldrel-windows-ix86+x86_64 3.0-0 14.00 120.00 134.00 OK --no-examples --no-tests --no-vignettes

Additional issues

noLD

Check Details

Version: 3.0-0
Check: tests
Result: ERROR
     Running ‘test_a_utils.R’
     Running ‘test_anova.R’ [15s/19s]
     Running ‘test_compare_sas.R’ [9s/14s]
     Running ‘test_contest1D.R’ [9s/14s]
     Running ‘test_contestMD.R’ [10s/14s]
     Running ‘test_drop1.R’ [9s/10s]
     Running ‘test_lmer.R’ [9s/10s]
     Running ‘test_lmerTest_paper.R’ [40s/44s]
     Running ‘test_ls_means.R’
     Running ‘test_ranova_step.R’ [49s/50s]
     Running ‘test_summary.R’
     Running ‘test_zerovar.R’
     Running ‘zlmerTest_zeroDenom.R’
    Running the tests in ‘tests/test_ls_means.R’ failed.
    Complete output:
     > # test_lsmeans.R
     >
     > library(lmerTest)
     Loading required package: lme4
     Loading required package: Matrix
    
     Attaching package: 'lmerTest'
    
     The following object is masked from 'package:lme4':
    
     lmer
    
     The following object is masked from 'package:stats':
    
     step
    
     >
     > ########### Basic model structures:
     >
     > # Factor * covariate:
     > data("cake", package="lme4")
     > model <- lmer(angle ~ recipe * temp + (1|recipe:replicate), cake)
     > (lsm <- ls_means(model))
     Least Squares Means table:
    
     Estimate Std. Error df t value lower upper Pr(>|t|)
     recipeA 33.1222 1.7368 42 19.070 29.6172 36.6273 < 2.2e-16 ***
     recipeB 31.6444 1.7368 42 18.220 28.1394 35.1495 < 2.2e-16 ***
     recipeC 31.6000 1.7368 42 18.194 28.0949 35.1051 < 2.2e-16 ***
     ---
     Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
    
     Confidence level: 95%
     Degrees of freedom method: Satterthwaite
     > stopifnot(
     + nrow(lsm) == 3L,
     + ncol(lsm) == 7L,
     + # Balanced, so LS-means equal raw means:
     + isTRUE(all.equal(c(with(cake, tapply(angle, recipe, mean))), lsm[, "Estimate"],
     + check.attributes=FALSE))
     + )
     >
     > # Pairwise differences of LS-means:
     > plsm <- ls_means(model, pairwise = TRUE)
     > plsm2 <- difflsmeans(model)
     > C <- as.matrix(lmerTest:::get_pairs(rownames(lsm)))
     > stopifnot(
     + isTRUE(all.equal(plsm, plsm2)),
     + isTRUE(all.equal(plsm[, "Estimate"], c(lsm[, "Estimate"] %*% C),
     + check.attributes=FALSE))
     + )
     >
     > # Contrasts vectors:
     > show_tests(lsm)
     $recipe
     (Intercept) recipeB recipeC temp recipeB:temp recipeC:temp
     recipeA 1 0 0 200 0 0
     recipeB 1 1 0 200 200 0
     recipeC 1 0 1 200 0 200
    
     > show_tests(plsm)
     $recipe
     (Intercept) recipeB recipeC temp recipeB:temp recipeC:temp
     recipeA - recipeB 0 -1 0 0 -200 0
     recipeA - recipeC 0 0 -1 0 0 -200
     recipeB - recipeC 0 1 -1 0 200 -200
    
     >
     > # Factor * Ordered:
     > model <- lmer(angle ~ recipe * temperature + (1|recipe:replicate), cake)
     > (lsm2 <- ls_means(model))
     Least Squares Means table:
    
     Estimate Std. Error df t value lower upper
     recipeA 33.1222 1.7368 42.0 19.070 29.6172 36.6273
     recipeB 31.6444 1.7368 42.0 18.220 28.1394 35.1495
     recipeC 31.6000 1.7368 42.0 18.194 28.0949 35.1051
     temperature175 27.9778 1.1767 77.4 23.776 25.6349 30.3207
     temperature185 29.9556 1.1767 77.4 25.457 27.6127 32.2985
     temperature195 31.4222 1.1767 77.4 26.704 29.0793 33.7651
     temperature205 32.1778 1.1767 77.4 27.346 29.8349 34.5207
     temperature215 35.8444 1.1767 77.4 30.462 33.5015 38.1873
     temperature225 35.3556 1.1767 77.4 30.046 33.0127 37.6985
     recipeA:temperature175 29.1333 2.0381 77.4 14.294 25.0753 33.1913
     recipeB:temperature175 26.8667 2.0381 77.4 13.182 22.8087 30.9247
     recipeC:temperature175 27.9333 2.0381 77.4 13.706 23.8753 31.9913
     recipeA:temperature185 31.5333 2.0381 77.4 15.472 27.4753 35.5913
     recipeB:temperature185 29.4000 2.0381 77.4 14.425 25.3420 33.4580
     recipeC:temperature185 28.9333 2.0381 77.4 14.196 24.8753 32.9913
     recipeA:temperature195 30.8000 2.0381 77.4 15.112 26.7420 34.8580
     recipeB:temperature195 31.7333 2.0381 77.4 15.570 27.6753 35.7913
     recipeC:temperature195 31.7333 2.0381 77.4 15.570 27.6753 35.7913
     recipeA:temperature205 33.5333 2.0381 77.4 16.453 29.4753 37.5913
     recipeB:temperature205 32.1333 2.0381 77.4 15.766 28.0753 36.1913
     recipeC:temperature205 30.8667 2.0381 77.4 15.145 26.8087 34.9247
     recipeA:temperature215 38.6667 2.0381 77.4 18.972 34.6087 42.7247
     recipeB:temperature215 34.4667 2.0381 77.4 16.911 30.4087 38.5247
     recipeC:temperature215 34.4000 2.0381 77.4 16.878 30.3420 38.4580
     recipeA:temperature225 35.0667 2.0381 77.4 17.206 31.0087 39.1247
     recipeB:temperature225 35.2667 2.0381 77.4 17.304 31.2087 39.3247
     recipeC:temperature225 35.7333 2.0381 77.4 17.533 31.6753 39.7913
     Pr(>|t|)
     recipeA < 2.2e-16 ***
     recipeB < 2.2e-16 ***
     recipeC < 2.2e-16 ***
     temperature175 < 2.2e-16 ***
     temperature185 < 2.2e-16 ***
     temperature195 < 2.2e-16 ***
     temperature205 < 2.2e-16 ***
     temperature215 < 2.2e-16 ***
     temperature225 < 2.2e-16 ***
     recipeA:temperature175 < 2.2e-16 ***
     recipeB:temperature175 < 2.2e-16 ***
     recipeC:temperature175 < 2.2e-16 ***
     recipeA:temperature185 < 2.2e-16 ***
     recipeB:temperature185 < 2.2e-16 ***
     recipeC:temperature185 < 2.2e-16 ***
     recipeA:temperature195 < 2.2e-16 ***
     recipeB:temperature195 < 2.2e-16 ***
     recipeC:temperature195 < 2.2e-16 ***
     recipeA:temperature205 < 2.2e-16 ***
     recipeB:temperature205 < 2.2e-16 ***
     recipeC:temperature205 < 2.2e-16 ***
     recipeA:temperature215 < 2.2e-16 ***
     recipeB:temperature215 < 2.2e-16 ***
     recipeC:temperature215 < 2.2e-16 ***
     recipeA:temperature225 < 2.2e-16 ***
     recipeB:temperature225 < 2.2e-16 ***
     recipeC:temperature225 < 2.2e-16 ***
     ---
     Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
    
     Confidence level: 95%
     Degrees of freedom method: Satterthwaite
     > stopifnot(
     + nrow(lsm2) == 3 + 6 + 3*6,
     + ncol(lsm) == 7L,
     + # Balanced, so LS-means equal raw means:
     + isTRUE(all.equal(lsm[1:3, ], lsm2[1:3, ],
     + check.attributes=FALSE))
     + )
     >
     >
     > # Factor * Factor:
     > cake2 <- cake
     > cake2$temperature <- factor(cake2$temperature, ordered = FALSE)
     > model <- lmer(angle ~ recipe * temperature + (1|recipe:replicate), cake2)
     > (lsm3 <- ls_means(model))
     Least Squares Means table:
    
     Estimate Std. Error df t value lower upper
     recipeA 33.1222 1.7368 42.0 19.070 29.6172 36.6273
     recipeB 31.6444 1.7368 42.0 18.220 28.1394 35.1495
     recipeC 31.6000 1.7368 42.0 18.194 28.0949 35.1051
     temperature175 27.9778 1.1767 77.4 23.776 25.6349 30.3207
     temperature185 29.9556 1.1767 77.4 25.457 27.6127 32.2985
     temperature195 31.4222 1.1767 77.4 26.704 29.0793 33.7651
     temperature205 32.1778 1.1767 77.4 27.346 29.8349 34.5207
     temperature215 35.8444 1.1767 77.4 30.462 33.5015 38.1873
     temperature225 35.3556 1.1767 77.4 30.046 33.0127 37.6985
     recipeA:temperature175 29.1333 2.0381 77.4 14.294 25.0753 33.1913
     recipeB:temperature175 26.8667 2.0381 77.4 13.182 22.8087 30.9247
     recipeC:temperature175 27.9333 2.0381 77.4 13.706 23.8753 31.9913
     recipeA:temperature185 31.5333 2.0381 77.4 15.472 27.4753 35.5913
     recipeB:temperature185 29.4000 2.0381 77.4 14.425 25.3420 33.4580
     recipeC:temperature185 28.9333 2.0381 77.4 14.196 24.8753 32.9913
     recipeA:temperature195 30.8000 2.0381 77.4 15.112 26.7420 34.8580
     recipeB:temperature195 31.7333 2.0381 77.4 15.570 27.6753 35.7913
     recipeC:temperature195 31.7333 2.0381 77.4 15.570 27.6753 35.7913
     recipeA:temperature205 33.5333 2.0381 77.4 16.453 29.4753 37.5913
     recipeB:temperature205 32.1333 2.0381 77.4 15.766 28.0753 36.1913
     recipeC:temperature205 30.8667 2.0381 77.4 15.145 26.8087 34.9247
     recipeA:temperature215 38.6667 2.0381 77.4 18.972 34.6087 42.7247
     recipeB:temperature215 34.4667 2.0381 77.4 16.911 30.4087 38.5247
     recipeC:temperature215 34.4000 2.0381 77.4 16.878 30.3420 38.4580
     recipeA:temperature225 35.0667 2.0381 77.4 17.206 31.0087 39.1247
     recipeB:temperature225 35.2667 2.0381 77.4 17.304 31.2087 39.3247
     recipeC:temperature225 35.7333 2.0381 77.4 17.533 31.6753 39.7913
     Pr(>|t|)
     recipeA < 2.2e-16 ***
     recipeB < 2.2e-16 ***
     recipeC < 2.2e-16 ***
     temperature175 < 2.2e-16 ***
     temperature185 < 2.2e-16 ***
     temperature195 < 2.2e-16 ***
     temperature205 < 2.2e-16 ***
     temperature215 < 2.2e-16 ***
     temperature225 < 2.2e-16 ***
     recipeA:temperature175 < 2.2e-16 ***
     recipeB:temperature175 < 2.2e-16 ***
     recipeC:temperature175 < 2.2e-16 ***
     recipeA:temperature185 < 2.2e-16 ***
     recipeB:temperature185 < 2.2e-16 ***
     recipeC:temperature185 < 2.2e-16 ***
     recipeA:temperature195 < 2.2e-16 ***
     recipeB:temperature195 < 2.2e-16 ***
     recipeC:temperature195 < 2.2e-16 ***
     recipeA:temperature205 < 2.2e-16 ***
     recipeB:temperature205 < 2.2e-16 ***
     recipeC:temperature205 < 2.2e-16 ***
     recipeA:temperature215 < 2.2e-16 ***
     recipeB:temperature215 < 2.2e-16 ***
     recipeC:temperature215 < 2.2e-16 ***
     recipeA:temperature225 < 2.2e-16 ***
     recipeB:temperature225 < 2.2e-16 ***
     recipeC:temperature225 < 2.2e-16 ***
     ---
     Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
    
     Confidence level: 95%
     Degrees of freedom method: Satterthwaite
     > stopifnot(
     + isTRUE(all.equal(lsm2, lsm3, check.attributes=FALSE))
     + )
     >
     > # Covariate (only):
     > data("sleepstudy", package="lme4")
     > m <- lmer(Reaction ~ Days + (Days | Subject), sleepstudy)
     > (lsm <- ls_means(m))
     Least Squares Means table:
    
     Estimate Std. Error df t value lower upper Pr(>|t|)
    
     Confidence level: 95%
     Degrees of freedom method: Satterthwaite
     > stopifnot(
     + nrow(lsm) == 0L,
     + ncol(lsm) == 7L
     + )
     >
     > # No fixef:
     > m <- lmer(Reaction ~ 0 + (Days | Subject), sleepstudy)
     > (lsm <- ls_means(m))
     Least Squares Means table:
    
     Estimate Std. Error df t value lower upper Pr(>|t|)
    
     Confidence level: 95%
     Degrees of freedom method: Satterthwaite
     > stopifnot(
     + nrow(lsm) == 0L,
     + ncol(lsm) == 7L
     + )
     >
     > ########### Arguments and options:
     >
     > # which
     > model <- lmer(angle ~ recipe * temperature + (1|recipe:replicate), cake2)
     > (lsm4 <- ls_means(model, which = "recipe"))
     Least Squares Means table:
    
     Estimate Std. Error df t value lower upper Pr(>|t|)
     recipeA 33.1222 1.7368 42 19.070 29.6172 36.6273 < 2.2e-16 ***
     recipeB 31.6444 1.7368 42 18.220 28.1394 35.1495 < 2.2e-16 ***
     recipeC 31.6000 1.7368 42 18.194 28.0949 35.1051 < 2.2e-16 ***
     ---
     Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
    
     Confidence level: 95%
     Degrees of freedom method: Satterthwaite
     > stopifnot(
     + nrow(lsm4) == 3L,
     + ncol(lsm4) == 7L,
     + isTRUE(all.equal(lsm3[1:3, ], lsm4, check.attributes=FALSE))
     + )
     >
     > # KR:
     > (lsm5 <- ls_means(model, which = "recipe", ddf = "Kenward-Roger"))
     Least Squares Means table:
    
     Estimate Std. Error df t value lower upper Pr(>|t|)
     recipeA 33.1222 1.7368 42 19.070 29.6172 36.6273 < 2.2e-16 ***
     recipeB 31.6444 1.7368 42 18.220 28.1394 35.1495 < 2.2e-16 ***
     recipeC 31.6000 1.7368 42 18.194 28.0949 35.1051 < 2.2e-16 ***
     ---
     Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
    
     Confidence level: 95%
     Degrees of freedom method: Kenward-Roger
     >
     > # level:
     > (lsm6 <- ls_means(model, which = "recipe", level=0.99))
     Least Squares Means table:
    
     Estimate Std. Error df t value lower upper Pr(>|t|)
     recipeA 33.1222 1.7368 42 19.070 28.4361 37.8083 < 2.2e-16 ***
     recipeB 31.6444 1.7368 42 18.220 26.9584 36.3305 < 2.2e-16 ***
     recipeC 31.6000 1.7368 42 18.194 26.9139 36.2861 < 2.2e-16 ***
     ---
     Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
    
     Confidence level: 99%
     Degrees of freedom method: Satterthwaite
     >
     > stopifnot(
     + all(lsm6[, "lower"] < lsm4[, "lower"]),
     + all(lsm6[, "upper"] > lsm4[, "upper"])
     + )
     >
     >
     >
     > ########### Missing cels -> unestimable contrasts:
     >
     > # Missing cell:
     > cake3 <- cake
     > cake3$temperature <- factor(cake3$temperature, ordered=FALSE)
     > cake3 <- droplevels(subset(cake3, temperature %in% levels(cake3$temperature)[1:3]))
     > cake3 <- droplevels(subset(cake3, !(recipe == "C" & temperature == "195") ))
     > str(cake3)
     'data.frame': 120 obs. of 5 variables:
     $ replicate : Factor w/ 15 levels "1","2","3","4",..: 1 1 1 1 1 1 1 1 2 2 ...
     $ recipe : Factor w/ 3 levels "A","B","C": 1 1 1 2 2 2 3 3 1 1 ...
     $ temperature: Factor w/ 3 levels "175","185","195": 1 2 3 1 2 3 1 2 1 2 ...
     $ angle : int 42 46 47 39 46 51 46 44 47 29 ...
     $ temp : num 175 185 195 175 185 195 175 185 175 185 ...
     > with(cake3, table(recipe, temperature))
     temperature
     recipe 175 185 195
     A 15 15 15
     B 15 15 15
     C 15 15 0
     > model <- lmer(angle ~ recipe * temperature + (1|recipe:replicate), cake3)
     fixed-effect model matrix is rank deficient so dropping 1 column / coefficient
     > (lsm7 <- ls_means(model))
     Least Squares Means table:
    
     Estimate Std. Error df t value lower upper
     recipeA 30.4889 1.7407 40.3 17.515 26.9716 34.0062
     recipeB 29.3333 1.7407 40.3 16.851 25.8160 32.8506
     recipeC NA NA NA NA NA NA
     temperature175 27.9778 1.1016 57.2 25.398 25.7720 30.1835
     temperature185 29.9556 1.1016 57.2 27.193 27.7498 32.1613
     temperature195 NA NA NA NA NA NA
     recipeA:temperature175 29.1333 1.9080 57.2 15.269 25.3129 32.9538
     recipeB:temperature175 26.8667 1.9080 57.2 14.081 23.0462 30.6871
     recipeC:temperature175 27.9333 1.9080 57.2 14.640 24.1129 31.7538
     recipeA:temperature185 31.5333 1.9080 57.2 16.527 27.7129 35.3538
     recipeB:temperature185 29.4000 1.9080 57.2 15.409 25.5795 33.2205
     recipeC:temperature185 28.9333 1.9080 57.2 15.164 25.1129 32.7538
     recipeA:temperature195 30.8000 1.9080 57.2 16.143 26.9795 34.6205
     recipeB:temperature195 31.7333 1.9080 57.2 16.632 27.9129 35.5538
     recipeC:temperature195 NA NA NA NA NA NA
     Pr(>|t|)
     recipeA < 2.2e-16 ***
     recipeB < 2.2e-16 ***
     recipeC NA
     temperature175 < 2.2e-16 ***
     temperature185 < 2.2e-16 ***
     temperature195 NA
     recipeA:temperature175 < 2.2e-16 ***
     recipeB:temperature175 < 2.2e-16 ***
     recipeC:temperature175 < 2.2e-16 ***
     recipeA:temperature185 < 2.2e-16 ***
     recipeB:temperature185 < 2.2e-16 ***
     recipeC:temperature185 < 2.2e-16 ***
     recipeA:temperature195 < 2.2e-16 ***
     recipeB:temperature195 < 2.2e-16 ***
     recipeC:temperature195 NA
     ---
     Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
    
     Confidence level: 95%
     Degrees of freedom method: Satterthwaite
     >
     > # Using show_tests with options:
     > show_tests(lsm7, fractions = TRUE)
     $recipe
     (Intercept) recipeB recipeC temperature185 temperature195
     recipeA 1 0 0 1/3 1/3
     recipeB 1 1 0 1/3 1/3
     recipeC 1 0 1 1/3 1/3
     recipeB:temperature185 recipeC:temperature185 recipeB:temperature195
     recipeA 0 0 0
     recipeB 1/3 0 1/3
     recipeC 0 1/3 0
     recipeC:temperature195
     recipeA 0
     recipeB 0
     recipeC 1/3
    
     $temperature
     (Intercept) recipeB recipeC temperature185 temperature195
     temperature175 1 1/3 1/3 0 0
     temperature185 1 1/3 1/3 1 0
     temperature195 1 1/3 1/3 0 1
     recipeB:temperature185 recipeC:temperature185
     temperature175 0 0
     temperature185 1/3 1/3
     temperature195 0 0
     recipeB:temperature195 recipeC:temperature195
     temperature175 0 0
     temperature185 0 0
     temperature195 1/3 1/3
    
     $`recipe:temperature`
     (Intercept) recipeB recipeC temperature185
     recipeA:temperature175 1 0 0 0
     recipeB:temperature175 1 1 0 0
     recipeC:temperature175 1 0 1 0
     recipeA:temperature185 1 0 0 1
     recipeB:temperature185 1 1 0 1
     recipeC:temperature185 1 0 1 1
     recipeA:temperature195 1 0 0 0
     recipeB:temperature195 1 1 0 0
     recipeC:temperature195 1 0 1 0
     temperature195 recipeB:temperature185
     recipeA:temperature175 0 0
     recipeB:temperature175 0 0
     recipeC:temperature175 0 0
     recipeA:temperature185 0 0
     recipeB:temperature185 0 1
     recipeC:temperature185 0 0
     recipeA:temperature195 1 0
     recipeB:temperature195 1 0
     recipeC:temperature195 1 0
     recipeC:temperature185 recipeB:temperature195
     recipeA:temperature175 0 0
     recipeB:temperature175 0 0
     recipeC:temperature175 0 0
     recipeA:temperature185 0 0
     recipeB:temperature185 0 0
     recipeC:temperature185 1 0
     recipeA:temperature195 0 0
     recipeB:temperature195 0 1
     recipeC:temperature195 0 0
     recipeC:temperature195
     recipeA:temperature175 0
     recipeB:temperature175 0
     recipeC:temperature175 0
     recipeA:temperature185 0
     recipeB:temperature185 0
     recipeC:temperature185 0
     recipeA:temperature195 0
     recipeB:temperature195 0
     recipeC:temperature195 1
    
     > show_tests(lsm7, fractions = TRUE, names = FALSE)
     $recipe
     [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9]
     [1,] 1 0 0 1/3 1/3 0 0 0 0
     [2,] 1 1 0 1/3 1/3 1/3 0 1/3 0
     [3,] 1 0 1 1/3 1/3 0 1/3 0 1/3
    
     $temperature
     [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9]
     [1,] 1 1/3 1/3 0 0 0 0 0 0
     [2,] 1 1/3 1/3 1 0 1/3 1/3 0 0
     [3,] 1 1/3 1/3 0 1 0 0 1/3 1/3
    
     $`recipe:temperature`
     [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9]
     [1,] 1 0 0 0 0 0 0 0 0
     [2,] 1 1 0 0 0 0 0 0 0
     [3,] 1 0 1 0 0 0 0 0 0
     [4,] 1 0 0 1 0 0 0 0 0
     [5,] 1 1 0 1 0 1 0 0 0
     [6,] 1 0 1 1 0 0 1 0 0
     [7,] 1 0 0 0 1 0 0 0 0
     [8,] 1 1 0 0 1 0 0 1 0
     [9,] 1 0 1 0 1 0 0 0 1
    
     >
     > # Missing diagonal:
     > cake4 <- cake
     > cake4$temperature <- factor(cake4$temperature, ordered=FALSE)
     > cake4 <- droplevels(subset(cake4, temperature %in% levels(cake4$temperature)[1:3]))
     > cake4 <- droplevels(subset(cake4, !((recipe == "A" & temperature == "175") |
     + (recipe == "B" & temperature == "185") |
     + (recipe == "C" & temperature == "195") )))
     > # str(cake4)
     > with(cake4, table(recipe, temperature))
     temperature
     recipe 175 185 195
     A 0 15 15
     B 15 0 15
     C 15 15 0
     > model <- lmer(angle ~ recipe * temperature + (1|recipe:replicate), cake4)
     fixed-effect model matrix is rank deficient so dropping 3 columns / coefficients
     > ls_means(model)
     Least Squares Means table:
    
     Estimate Std. Error df t value lower upper
     recipeA NA NA NA NA NA NA
     recipeB NA NA NA NA NA NA
     recipeC NA NA NA NA NA NA
     temperature175 NA NA NA NA NA NA
     temperature185 NA NA NA NA NA NA
     temperature195 NA NA NA NA NA NA
     recipeA:temperature175 NA NA NA NA NA NA
     recipeB:temperature175 26.8667 1.8908 53.5 14.209 23.0750 30.6584
     recipeC:temperature175 27.9333 1.8908 53.5 14.773 24.1416 31.7250
     recipeA:temperature185 31.5333 1.8908 53.5 16.677 27.7416 35.3250
     recipeB:temperature185 NA NA NA NA NA NA
     recipeC:temperature185 28.9333 1.8908 53.5 15.302 25.1416 32.7250
     recipeA:temperature195 30.8000 1.8908 53.5 16.289 27.0083 34.5917
     recipeB:temperature195 31.7333 1.8908 53.5 16.783 27.9416 35.5250
     recipeC:temperature195 NA NA NA NA NA NA
     Pr(>|t|)
     recipeA NA
     recipeB NA
     recipeC NA
     temperature175 NA
     temperature185 NA
     temperature195 NA
     recipeA:temperature175 NA
     recipeB:temperature175 < 2.2e-16 ***
     recipeC:temperature175 < 2.2e-16 ***
     recipeA:temperature185 < 2.2e-16 ***
     recipeB:temperature185 NA
     recipeC:temperature185 < 2.2e-16 ***
     recipeA:temperature195 < 2.2e-16 ***
     recipeB:temperature195 < 2.2e-16 ***
     recipeC:temperature195 NA
     ---
     Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
    
     Confidence level: 95%
     Degrees of freedom method: Satterthwaite
     >
     >
     > ########### Various contrasts codings:
     >
     > model <- lmer(angle ~ recipe * temperature + (1|recipe:replicate), cake3,
     + contrasts = list(recipe="contr.sum", temperature="contr.helmert"))
     fixed-effect model matrix is rank deficient so dropping 1 column / coefficient
     > (lsm8 <- ls_means(model))
     Least Squares Means table:
    
     Estimate Std. Error df t value lower upper
     recipeA 30.4889 1.7407 40.3 17.515 26.9716 34.0062
     recipeB 29.3333 1.7407 40.3 16.851 25.8160 32.8506
     recipeC NA NA NA NA NA NA
     temperature175 27.9778 1.1016 57.2 25.398 25.7720 30.1835
     temperature185 29.9556 1.1016 57.2 27.193 27.7498 32.1613
     temperature195 NA NA NA NA NA NA
     recipeA:temperature175 29.1333 1.9080 57.2 15.269 25.3129 32.9538
     recipeB:temperature175 26.8667 1.9080 57.2 14.081 23.0462 30.6871
     recipeC:temperature175 27.9333 1.9080 57.2 14.640 24.1129 31.7538
     recipeA:temperature185 31.5333 1.9080 57.2 16.527 27.7129 35.3538
     recipeB:temperature185 29.4000 1.9080 57.2 15.409 25.5795 33.2205
     recipeC:temperature185 28.9333 1.9080 57.2 15.164 25.1129 32.7538
     recipeA:temperature195 30.8000 1.9080 57.2 16.143 26.9795 34.6205
     recipeB:temperature195 31.7333 1.9080 57.2 16.632 27.9129 35.5538
     recipeC:temperature195 NA NA NA NA NA NA
     Pr(>|t|)
     recipeA < 2.2e-16 ***
     recipeB < 2.2e-16 ***
     recipeC NA
     temperature175 < 2.2e-16 ***
     temperature185 < 2.2e-16 ***
     temperature195 NA
     recipeA:temperature175 < 2.2e-16 ***
     recipeB:temperature175 < 2.2e-16 ***
     recipeC:temperature175 < 2.2e-16 ***
     recipeA:temperature185 < 2.2e-16 ***
     recipeB:temperature185 < 2.2e-16 ***
     recipeC:temperature185 < 2.2e-16 ***
     recipeA:temperature195 < 2.2e-16 ***
     recipeB:temperature195 < 2.2e-16 ***
     recipeC:temperature195 NA
     ---
     Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
    
     Confidence level: 95%
     Degrees of freedom method: Satterthwaite
     > # show_tests(lsm7)
     > # show_tests(lsm8)
     > stopifnot(
     + isTRUE(all.equal(lsm7, lsm8, check.attributes=FALSE))
     + )
     Error: isTRUE(all.equal(lsm7, lsm8, check.attributes = FALSE)) is not TRUE
     Execution halted
Flavor: r-patched-solaris-x86