Examp
ggtheme = ggplot2::theme_grey(),
ggstatsplot.layer = FALSE
)
#> Note: 99% CI for effect size estimate was computed with 10 bootstrap samples.
-#>
#>
#> # A tibble: 10 x 9
+#>
#>
#> # A tibble: 10 x 9
#> group1 group2 mean.difference se t.value df p.value significance
-#> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <chr>
-#> 1 1 2 -53 19.2 1.95 30.6 0.72 ns
-#> 2 1 3 -64 20.8 2.18 35.3 0.703 ns
-#> 3 1 4 -88.5 19.1 3.27 30.2 0.21 ns
-#> 4 1 5 -77.5 18.7 2.94 28.5 0.235 ns
-#> 5 2 3 -11 15.8 0.492 35.7 0.988 ns
-#> 6 2 4 -35.5 13.6 1.85 38.0 0.72 ns
-#> 7 2 5 -24.5 12.9 1.34 37.5 0.988 ns
-#> 8 3 4 -24.5 15.7 1.10 35.4 0.988 ns
-#> 9 3 5 -13.5 15.2 0.63 33.6 0.988 ns
-#> 10 4 5 11 12.8 0.608 37.6 0.988 ns
-#> # ... with 1 more variable: p.value.label <chr>
+#> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <chr>
+#> 1 1 2 -53 19.2 1.95 30.6 0.72 ns
+#> 2 1 3 -64 20.8 2.18 35.3 0.703 ns
+#> 3 1 4 -88.5 19.1 3.27 30.2 0.21 ns
+#> 4 1 5 -77.5 18.7 2.94 28.5 0.235 ns
+#> 5 2 3 -11 15.8 0.492 35.7 0.988 ns
+#> 6 2 4 -35.5 13.6 1.85 38.0 0.72 ns
+#> 7 2 5 -24.5 12.9 1.34 37.5 0.988 ns
+#> 8 3 4 -24.5 15.7 1.10 35.4 0.988 ns
+#> 9 3 5 -13.5 15.2 0.63 33.6 0.988 ns
+#> 10 4 5 11 12.8 0.608 37.6 0.988 ns
+#> # ... with 1 more variable: p.value.label <chr>
#> Note: Shapiro-Wilk Normality Test for Speed-of-light measurement : p-value = 0.514
#>
#> Note: Bartlett's test for homogeneity of variances for factor The experiment number : p-value = 0.021
#>
#>
# A tibble: 3 x 12
+ggstatsplot::ggcoefstats(x = mod, output = "tidy")#> # A tibble: 3 x 12
#> term estimate conf.low conf.high std.error statistic p.value significance
-#> <fct> <dbl> <dbl> <dbl> <dbl> <chr> <dbl> <chr>
-#> 1 cyl -1.98 -2.89 -1.06 0.449 -4.40 1.41e-4 ***
-#> 2 am 10.2 1.36 19.0 4.30 2.36 2.53e-2 *
-#> 3 cyl:~ -1.31 -2.75 0.143 0.707 -1.85 7.55e-2 ns
-#> # ... with 4 more variables: p.value.formatted <chr>, p.value.formatted2 <chr>,
-#> # df.residual <int>, label <chr>
+#> <fct> <dbl> <dbl> <dbl> <dbl> <chr> <dbl> <chr>
+#> 1 cyl -1.98 -2.89 -1.06 0.449 -4.40 1.41e-4 ***
+#> 2 am 10.2 1.36 19.0 4.30 2.36 2.53e-2 *
+#> 3 cyl:~ -1.31 -2.75 0.143 0.707 -1.85 7.55e-2 ns
+#> # ... with 4 more variables: p.value.formatted <chr>, p.value.formatted2 <chr>,
+#> # df.residual <int>, label <chr>
#>
# A tibble: 1 x 12
+ggstatsplot::ggcoefstats(x = mod, output = "glance")#> # A tibble: 1 x 12
#> r.squared adj.r.squared sigma statistic p.value df logLik AIC BIC
-#> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
-#> 1 0.785 0.762 2.94 34.1 1.73e-9 3 -77.8 166. 173.
-#> # ... with 3 more variables: deviance <dbl>, df.residual <int>, nobs <int>
+#> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
+#> 1 0.785 0.762 2.94 34.1 1.73e-9 3 -77.8 166. 173.
+#> # ... with 3 more variables: deviance <dbl>, df.residual <int>, nobs <int>
#>
# A tibble: 32 x 10
+ggstatsplot::ggcoefstats(x = mod, output = "augment")#> # A tibble: 32 x 10
#> .rownames mpg cyl am .fitted .resid .std.resid .hat .sigma .cooksd
-#> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
-#> 1 Mazda RX4 21 6 1 21.4 0.364 -0.131 0.106 2.99 5.10e-4
-#> 2 Mazda RX4 ~ 21 6 1 21.4 0.364 -0.131 0.106 2.99 5.10e-4
-#> 3 Datsun 710 22.8 4 1 27.9 5.13 -1.86 0.117 2.80 1.14e-1
-#> 4 Hornet 4 D~ 21.4 6 0 19.0 -2.38 0.842 0.0735 2.96 1.41e-2
-#> 5 Hornet Spo~ 18.7 8 0 15.1 -3.63 1.29 0.0784 2.90 3.53e-2
-#> 6 Valiant 18.1 6 0 19.0 0.919 -0.325 0.0735 2.99 2.09e-3
-#> 7 Duster 360 14.3 8 0 15.1 0.768 -0.272 0.0784 2.99 1.57e-3
-#> 8 Merc 240D 24.4 4 0 23.0 -1.43 0.563 0.255 2.98 2.71e-2
-#> 9 Merc 230 22.8 4 0 23.0 0.171 -0.0672 0.255 2.99 3.87e-4
-#> 10 Merc 280 19.2 6 0 19.0 -0.181 0.0639 0.0735 2.99 8.11e-5
-#> # ... with 22 more rows
+#> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
+#> 1 Mazda RX4 21 6 1 21.4 0.364 -0.131 0.106 2.99 5.10e-4
+#> 2 Mazda RX4 ~ 21 6 1 21.4 0.364 -0.131 0.106 2.99 5.10e-4
+#> 3 Datsun 710 22.8 4 1 27.9 5.13 -1.86 0.117 2.80 1.14e-1
+#> 4 Hornet 4 D~ 21.4 6 0 19.0 -2.38 0.842 0.0735 2.96 1.41e-2
+#> 5 Hornet Spo~ 18.7 8 0 15.1 -3.63 1.29 0.0784 2.90 3.53e-2
+#> 6 Valiant 18.1 6 0 19.0 0.919 -0.325 0.0735 2.99 2.09e-3
+#> 7 Duster 360 14.3 8 0 15.1 0.768 -0.272 0.0784 2.99 1.57e-3
+#> 8 Merc 240D 24.4 4 0 23.0 -1.43 0.563 0.255 2.98 2.71e-2
+#> 9 Merc 230 22.8 4 0 23.0 0.171 -0.0672 0.255 2.99 3.87e-4
+#> 10 Merc 280 19.2 6 0 19.0 -0.181 0.0639 0.0735 2.99 8.11e-5
+#> # ... with 22 more rows
# -------------- with custom dataframe -----------------------------------
#> Computing p-values via Kenward-Roger approximation. Use `p.kr = FALSE` if computation takes too long.
#>
# A tibble: 1 x 6
+ggstatsplot::ggcoefstats(x = lmm1, output = "glance")#> Computing p-values via Kenward-Roger approximation. Use `p.kr = FALSE` if computation takes too long.
#> # A tibble: 1 x 6
#> sigma logLik AIC BIC REMLcrit df.residual
-#> <dbl> <dbl> <dbl> <dbl> <dbl> <int>
-#> 1 25.6 -872. 1756. 1775. 1744. 174
+#> <dbl> <dbl> <dbl> <dbl> <dbl> <int>
+#> 1 25.6 -872. 1756. 1775. 1744. 174
#> # A tibble: 228 x 13
+)
#> # A tibble: 228 x 13
#> inst time status age sex ph.ecog ph.karno pat.karno meal.cal wt.loss
-#> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
-#> 1 3 306 2 74 1 1 90 100 1175 NA
-#> 2 3 455 2 68 1 0 90 90 1225 15
-#> 3 3 1010 1 56 1 0 90 90 NA 15
-#> 4 5 210 2 57 1 1 90 60 1150 11
-#> 5 1 883 2 60 1 0 100 90 NA 0
-#> 6 12 1022 1 74 1 1 50 80 513 0
-#> 7 7 310 2 68 2 2 70 60 384 10
-#> 8 11 361 2 71 2 2 60 80 538 1
-#> 9 1 218 2 53 1 1 70 80 825 16
-#> 10 7 166 2 61 1 2 70 70 271 34
-#> # ... with 218 more rows, and 3 more variables: .fitted <dbl>, .se.fit <dbl>,
-#> # .resid <dbl>
+#> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
+#> 1 3 306 2 74 1 1 90 100 1175 NA
+#> 2 3 455 2 68 1 0 90 90 1225 15
+#> 3 3 1010 1 56 1 0 90 90 NA 15
+#> 4 5 210 2 57 1 1 90 60 1150 11
+#> 5 1 883 2 60 1 0 100 90 NA 0
+#> 6 12 1022 1 74 1 1 50 80 513 0
+#> 7 7 310 2 68 2 2 70 60 384 10
+#> 8 11 361 2 71 2 2 60 80 538 1
+#> 9 1 218 2 53 1 1 70 80 825 16
+#> 10 7 166 2 61 1 2 70 70 271 34
+#> # ... with 218 more rows, and 3 more variables: .fitted <dbl>, .se.fit <dbl>,
+#> # .resid <dbl>
#> # A tibble: 4 x 9
+)), statistic = "t")
#> # A tibble: 4 x 9
#> term estimate std.error statistic p.value significance
-#> <chr> <dbl> <dbl> <chr> <dbl> <chr>
-#> 1 (Intercept) 2.98 1.57 1.90 0.0683 ns
-#> 2 cyl 0.478 0.233 2.05 0.0503 ns
-#> 3 mpg -0.00947 0.0660 -0.14 0.887 ns
-#> 4 cyl:mpg -0.0219 0.0120 -1.82 0.0790 ns
+#> <chr> <dbl> <dbl> <chr> <dbl> <chr>
+#> 1 (Intercept) 2.98 1.57 1.90 0.0683 ns
+#> 2 cyl 0.478 0.233 2.05 0.0503 ns
+#> 3 mpg -0.00947 0.0660 -0.14 0.887 ns
+#> 4 cyl:mpg -0.0219 0.0120 -1.82 0.0790 ns
#> p.value.formatted p.value.formatted2
-#> <chr> <chr>
-#> 1 0.068 ==0.068
-#> 2 0.050 ==0.050
-#> 3 0.887 ==0.887
-#> 4 0.079 ==0.079
+#> <chr> <chr>
+#> 1 0.068 ==0.068
+#> 2 0.050 ==0.050
+#> 3 0.887 ==0.887
+#> 4 0.079 ==0.079
#> label
-#> <chr>
-#> 1 list(~italic(beta)==2.98, ~italic(t)==1.90, ~italic(p)==0.068)
-#> 2 list(~italic(beta)==0.48, ~italic(t)==2.05, ~italic(p)==0.050)
-#> 3 list(~italic(beta)==-0.01, ~italic(t)==-0.14, ~italic(p)==0.887)
-#> 4 list(~italic(beta)==-0.02, ~italic(t)==-1.82, ~italic(p)==0.079)
+#> <chr>
+#> 1 list(~italic(beta)==2.98, ~italic(t)==1.90, ~italic(p)==0.068)
+#> 2 list(~italic(beta)==0.48, ~italic(t)==2.05, ~italic(p)==0.050)
+#> 3 list(~italic(beta)==-0.01, ~italic(t)==-0.14, ~italic(p)==0.887)
+#> 4 list(~italic(beta)==-0.02, ~italic(t)==-1.82, ~italic(p)==0.079)
#> # A tibble: 2 x 11
+)
#> # A tibble: 2 x 11
#> term estimate std.error statistic conf.low conf.high p.value
-#> <chr> <dbl> <dbl> <chr> <dbl> <dbl> <dbl>
-#> 1 (Intercept) -0.0166 0.000928 -17.85 -0.0184 -0.0147 0.000000428
-#> 2 log(u) 0.0153 0.000415 36.97 0.0145 0.0162 0.00000000275
+#> <chr> <dbl> <dbl> <chr> <dbl> <dbl> <dbl>
+#> 1 (Intercept) -0.0166 0.000928 -17.85 -0.0184 -0.0147 0.000000428
+#> 2 log(u) 0.0153 0.000415 36.97 0.0145 0.0162 0.00000000275
#> significance p.value.formatted p.value.formatted2
-#> <chr> <chr> <chr>
-#> 1 *** < 0.001 <= 0.001
-#> 2 *** < 0.001 <= 0.001
+#> <chr> <chr> <chr>
+#> 1 *** < 0.001 <= 0.001
+#> 2 *** < 0.001 <= 0.001
#> label
-#> <chr>
-#> 1 list(~italic(beta)==-0.02, ~italic(t)==-17.85, ~italic(p)<= 0.001)
-#> 2 list(~italic(beta)==0.02, ~italic(t)==36.97, ~italic(p)<= 0.001)
+#> <chr>
+#> 1 list(~italic(beta)==-0.02, ~italic(t)==-17.85, ~italic(p)<= 0.001)
+#> 2 list(~italic(beta)==0.02, ~italic(t)==36.97, ~italic(p)<= 0.001)
#> # A tibble: 5 x 9
#> term estimate std.error statistic p.value significance
-#> <chr> <dbl> <dbl> <chr> <dbl> <chr>
-#> 1 (Intercept) 3.04e+ 0 0.171 17.81 5.43e-71 ***
-#> 2 outcome2 -4.54e- 1 0.202 -2.25 2.46e- 2 *
-#> 3 outcome3 -2.93e- 1 0.193 -1.52 1.28e- 1 ns
-#> 4 treatment2 1.34e-15 0.200 0.00 10.00e- 1 ns
-#> 5 treatment3 1.42e-15 0.200 0.00 10.00e- 1 ns
+#> <chr> <dbl> <dbl> <chr> <dbl> <chr>
+#> 1 (Intercept) 3.04e+ 0 0.171 17.81 5.43e-71 ***
+#> 2 outcome2 -4.54e- 1 0.202 -2.25 2.46e- 2 *
+#> 3 outcome3 -2.93e- 1 0.193 -1.52 1.28e- 1 ns
+#> 4 treatment2 1.34e-15 0.200 0.00 10.00e- 1 ns
+#> 5 treatment3 1.42e-15 0.200 0.00 10.00e- 1 ns
#> p.value.formatted p.value.formatted2
-#> <chr> <chr>
-#> 1 < 0.001 <= 0.001
-#> 2 0.025 ==0.025
-#> 3 0.128 ==0.128
-#> 4 1.000 ==1.000
-#> 5 1.000 ==1.000
+#> <chr> <chr>
+#> 1 < 0.001 <= 0.001
+#> 2 0.025 ==0.025
+#> 3 0.128 ==0.128
+#> 4 1.000 ==1.000
+#> 5 1.000 ==1.000
#> label
-#> <chr>
-#> 1 list(~italic(beta)==3.04, ~italic(z)==17.81, ~italic(p)<= 0.001)
-#> 2 list(~italic(beta)==-0.45, ~italic(z)==-2.25, ~italic(p)==0.025)
-#> 3 list(~italic(beta)==-0.29, ~italic(z)==-1.52, ~italic(p)==0.128)
-#> 4 list(~italic(beta)==0.00, ~italic(z)==0.00, ~italic(p)==1.000)
-#> 5 list(~italic(beta)==0.00, ~italic(z)==0.00, ~italic(p)==1.000)
+#> <chr>
+#> 1 list(~italic(beta)==3.04, ~italic(z)==17.81, ~italic(p)<= 0.001)
+#> 2 list(~italic(beta)==-0.45, ~italic(z)==-2.25, ~italic(p)==0.025)
+#> 3 list(~italic(beta)==-0.29, ~italic(z)==-1.52, ~italic(p)==0.128)
+#> 4 list(~italic(beta)==0.00, ~italic(z)==0.00, ~italic(p)==1.000)
+#> 5 list(~italic(beta)==0.00, ~italic(z)==0.00, ~italic(p)==1.000)
#> # A tibble: 7 x 13
+)
#> # A tibble: 7 x 13
#> term statistic df1 df2 estimate conf.low conf.high p.value significance
-#> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <chr>
-#> 1 block 4.45 5 12 0.298 0.0298 0.675 0.0159 *
-#> 2 N 12.26 1 12 0.195 0.0440 0.588 0.00437 **
-#> 3 P 0.54 1 12 -0.00789 NA 0.233 0.475 ns
-#> 4 K 6.17 1 12 0.0894 NA 0.472 0.0288 *
-#> 5 N:P 1.38 1 12 0.00655 NA 0.294 0.263 ns
-#> 6 N:K 2.15 1 12 0.0198 NA 0.336 0.169 ns
-#> 7 P:K 0.03 1 12 -0.0168 NA 0.126 0.863 ns
+#> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <chr>
+#> 1 block 4.45 5 12 0.298 0.0298 0.675 0.0159 *
+#> 2 N 12.26 1 12 0.195 0.0440 0.588 0.00437 **
+#> 3 P 0.54 1 12 -0.00789 NA 0.233 0.475 ns
+#> 4 K 6.17 1 12 0.0894 NA 0.472 0.0288 *
+#> 5 N:P 1.38 1 12 0.00655 NA 0.294 0.263 ns
+#> 6 N:K 2.15 1 12 0.0198 NA 0.336 0.169 ns
+#> 7 P:K 0.03 1 12 -0.0168 NA 0.126 0.863 ns
#> p.value.formatted p.value.formatted2 effsize.text
-#> <chr> <chr> <list>
-#> 1 0.016 ==0.016 <language>
-#> 2 0.004 ==0.004 <language>
-#> 3 0.475 ==0.475 <language>
-#> 4 0.029 ==0.029 <language>
-#> 5 0.263 ==0.263 <language>
-#> 6 0.169 ==0.169 <language>
-#> 7 0.863 ==0.863 <language>
+#> <chr> <chr> <list>
+#> 1 0.016 ==0.016 <language>
+#> 2 0.004 ==0.004 <language>
+#> 3 0.475 ==0.475 <language>
+#> 4 0.029 ==0.029 <language>
+#> 5 0.263 ==0.263 <language>
+#> 6 0.169 ==0.169 <language>
+#> 7 0.863 ==0.863 <language>
#> label
-#> <chr>
-#> 1 "list(~italic(F)(5*\",\"*12)==4.45, ~italic(p)==0.016, ~italic(omega)^2==0.30~
-#> 2 "list(~italic(F)(1*\",\"*12)==12.26, ~italic(p)==0.004, ~italic(omega)^2==0.1~
-#> 3 "list(~italic(F)(1*\",\"*12)==0.54, ~italic(p)==0.475, ~italic(omega)^2==-0.0~
-#> 4 "list(~italic(F)(1*\",\"*12)==6.17, ~italic(p)==0.029, ~italic(omega)^2==0.09~
-#> 5 "list(~italic(F)(1*\",\"*12)==1.38, ~italic(p)==0.263, ~italic(omega)^2==0.01~
-#> 6 "list(~italic(F)(1*\",\"*12)==2.15, ~italic(p)==0.169, ~italic(omega)^2==0.02~
-#> 7 "list(~italic(F)(1*\",\"*12)==0.03, ~italic(p)==0.863, ~italic(omega)^2==-0.0~
+#> <chr>
+#> 1 "list(~italic(F)(5*\",\"*12)==4.45, ~italic(p)==0.016, ~italic(omega)^2==0.30~
+#> 2 "list(~italic(F)(1*\",\"*12)==12.26, ~italic(p)==0.004, ~italic(omega)^2==0.1~
+#> 3 "list(~italic(F)(1*\",\"*12)==0.54, ~italic(p)==0.475, ~italic(omega)^2==-0.0~
+#> 4 "list(~italic(F)(1*\",\"*12)==6.17, ~italic(p)==0.029, ~italic(omega)^2==0.09~
+#> 5 "list(~italic(F)(1*\",\"*12)==1.38, ~italic(p)==0.263, ~italic(omega)^2==0.01~
+#> 6 "list(~italic(F)(1*\",\"*12)==2.15, ~italic(p)==0.169, ~italic(omega)^2==0.02~
+#> 7 "list(~italic(F)(1*\",\"*12)==0.03, ~italic(p)==0.863, ~italic(omega)^2==-0.0~
#> Note: Results from one-sample proportion tests for each
#> level of the variable cyl testing for equal
#> proportions of the variable vs.
-#>
#>
#> # A tibble: 3 x 7
+#>
#>
#> # A tibble: 3 x 7
#> condition `0` `1` `Chi-squared` df `p-value` significance
-#> <fct> <chr> <chr> <dbl> <dbl> <dbl> <chr>
-#> 1 4 9.09% 90.91% 7.36 1 0.007 **
-#> 2 6 42.86% 57.14% 0.143 1 0.705 ns
-#> 3 8 100.00% NA 14 1 0 ***
#> Warning: Chi-squared approximation may be incorrect
#> Note: 95% CI for effect size estimate was computed with 10 bootstrap samples.
+#> <fct> <chr> <chr> <dbl> <dbl> <dbl> <chr>
+#> 1 4 9.09% 90.91% 7.36 1 0.007 **
+#> 2 6 42.86% 57.14% 0.143 1 0.705 ns
+#> 3 8 100.00% NA 14 1 0 ***
#> Warning: Chi-squared approximation may be incorrect
#> Note: 95% CI for effect size estimate was computed with 10 bootstrap samples.
#>
#>
#> Note: Results from one-sample proportion tests for each
#> level of the variable Eye testing for equal
#> proportions of the variable Hair.
-#>
#>
#> # A tibble: 4 x 9
+#>
#>
#> # A tibble: 4 x 9
#> condition Black Brown Red Blond `Chi-squared` df `p-value`
-#> <fct> <chr> <chr> <chr> <chr> <dbl> <dbl> <dbl>
-#> 1 Brown 32.65% 54.08% 10.20% 3.06% 62.9 3 0
-#> 2 Blue 10.89% 49.50% 9.90% 29.70% 42.4 3 0
-#> 3 Hazel 21.28% 53.19% 14.89% 10.64% 21 3 0
-#> 4 Green 9.09% 45.45% 21.21% 24.24% 9.06 3 0.028
+#> <fct> <chr> <chr> <chr> <chr> <dbl> <dbl> <dbl>
+#> 1 Brown 32.65% 54.08% 10.20% 3.06% 62.9 3 0
+#> 2 Blue 10.89% 49.50% 9.90% 29.70% 42.4 3 0
+#> 3 Hazel 21.28% 53.19% 14.89% 10.64% 21 3 0
+#> 4 Green 9.09% 45.45% 21.21% 24.24% 9.06 3 0.028
#> significance
-#> <chr>
-#> 1 ***
-#> 2 ***
-#> 3 ***
-#> 4 *
#> Warning: Chi-squared approximation may be incorrect
#> Note: 95% CI for effect size estimate was computed with 100 bootstrap samples.
+#> <chr>
+#> 1 ***
+#> 2 ***
+#> 3 ***
+#> 4 *
#> Warning: Chi-squared approximation may be incorrect
#> Note: 95% CI for effect size estimate was computed with 100 bootstrap samples.
#>
#>
#> Note: Results from one-sample proportion tests for each
#> level of the variable Eye testing for equal
#> proportions of the variable Hair.
-#>
#>
#> # A tibble: 4 x 9
+#>
#>
#> # A tibble: 4 x 9
#> condition Black Brown Red Blond `Chi-squared` df `p-value`
-#> <fct> <chr> <chr> <chr> <chr> <dbl> <dbl> <dbl>
-#> 1 Brown 29.51% 54.10% 13.11% 3.28% 72.2 3 0
-#> 2 Blue 7.89% 29.82% 6.14% 56.14% 74.8 3 0
-#> 3 Hazel 10.87% 63.04% 15.22% 10.87% 35.7 3 0
-#> 4 Green 6.45% 45.16% 22.58% 25.81% 9.39 3 0.025
+#> <fct> <chr> <chr> <chr> <chr> <dbl> <dbl> <dbl>
+#> 1 Brown 29.51% 54.10% 13.11% 3.28% 72.2 3 0
+#> 2 Blue 7.89% 29.82% 6.14% 56.14% 74.8 3 0
+#> 3 Hazel 10.87% 63.04% 15.22% 10.87% 35.7 3 0
+#> 4 Green 6.45% 45.16% 22.58% 25.81% 9.39 3 0.025
#> significance
-#> <chr>
-#> 1 ***
-#> 2 ***
-#> 3 ***
-#> 4 *
#> Warning: Chi-squared approximation may be incorrect
#> Note: 95% CI for effect size estimate was computed with 100 bootstrap samples.
+#> <chr>
+#> 1 ***
+#> 2 ***
+#> 3 ***
+#> 4 *
#> Warning: Chi-squared approximation may be incorrect
#> Note: 95% CI for effect size estimate was computed with 100 bootstrap samples.
#>
#>
#> Warning: Individual plots in the combined `grouped_` plot
#> can't be further modified with `ggplot2` functions.
#>
#>
#> Note: Shapiro-Wilk Normality Test for hwy : p-value = < 0.001
#>
#> Note: Bartlett's test for homogeneity of variances for factor year : p-value = 0.144
#>
#> Note: Shapiro-Wilk Normality Test for hwy : p-value = 0.033
#>
#> Note: Bartlett's test for homogeneity of variances for factor year : p-value = 0.682
#>
#> Warning: Individual plots in the combined `grouped_` plot
#> can't be further modified with `ggplot2` functions.
-#>
#>
# modifying individual plots using `ggplot.component` argument
+#>
#>
+
+
#> Warning: No. of factor levels is greater than specified palette color count.
#> Try using another color `palette` (and/or `package`).
-#>
#>
-
+#> #>
+
