Compare sample means
compare_means( dataset, var1, var2, samples = "independent", alternative = "two.sided", conf_lev = 0.95, comb = "", adjust = "none", test = "t", data_filter = "", envir = parent.frame() )
| dataset | Dataset |
|---|---|
| var1 | A numeric variable or factor selected for comparison |
| var2 | One or more numeric variables for comparison. If var1 is a factor only one variable can be selected and the mean of this variable is compared across (factor) levels of var1 |
| samples | Are samples independent ("independent") or not ("paired") |
| alternative | The alternative hypothesis ("two.sided", "greater" or "less") |
| conf_lev | Span of the confidence interval |
| comb | Combinations to evaluate |
| adjust | Adjustment for multiple comparisons ("none" or "bonf" for Bonferroni) |
| test | t-test ("t") or Wilcox ("wilcox") |
| data_filter | Expression entered in, e.g., Data > View to filter the dataset in Radiant. The expression should be a string (e.g., "price > 10000") |
| envir | Environment to extract data from |
A list of all variables defined in the function as an object of class compare_means
See https://radiant-rstats.github.io/docs/basics/compare_means.html for an example in Radiant
summary.compare_means to summarize results
plot.compare_means to plot results
#> List of 18 #> $ dat_summary: tibble [5 × 7] (S3: tbl_df/tbl/data.frame) #> ..$ cut : Factor w/ 5 levels "Fair","Good",..: 1 2 3 4 5 #> ..$ mean : num [1:5] 4505 4130 3960 4369 3470 #> ..$ n : int [1:5] 101 275 677 771 1176 #> ..$ n_missing: int [1:5] 0 0 0 0 0 #> ..$ sd : num [1:5] 3750 3730 3896 4237 3827 #> ..$ se : num [1:5] 373 225 150 153 112 #> ..$ me : num [1:5] 740 443 294 300 219 #> $ res :'data.frame': 10 obs. of 10 variables: #> ..$ group1 : chr [1:10] "Fair" "Fair" "Fair" "Fair" ... #> ..$ group2 : chr [1:10] "Good" "Very Good" "Premium" "Ideal" ... #> ..$ t.value : num [1:10] 0.86 1.356 0.337 2.658 0.631 ... #> ..$ p.value : num [1:10] 0.39078 0.17723 0.73666 0.00895 0.5283 ... #> ..$ df : num [1:10] 177 134 136 119 529 ... #> ..$ ci_low : num [1:10] -485 -250 -661 264 -360 ... #> ..$ ci_high : num [1:10] 1235 1340 933 1806 701 ... #> ..$ cis_low : num [1:10] 0 0 0 0 0 0 0 0 0 0 #> ..$ cis_high: num [1:10] 0 0 0 0 0 0 0 0 0 0 #> ..$ sig_star: chr [1:10] "" "" "" "**" ... #> $ cmb :'data.frame': 10 obs. of 2 variables: #> ..$ group1: chr [1:10] "Fair" "Fair" "Fair" "Fair" ... #> ..$ group2: chr [1:10] "Good" "Very Good" "Premium" "Ideal" ... #> $ levs : chr [1:5] "Fair" "Good" "Very Good" "Premium" ... #> $ not_vary : chr(0) #> $ cname : chr "cut" #> $ df_name : chr "diamonds" #> $ vars : chr "cut, price" #> $ dataset : tibble [3,000 × 2] (S3: tbl_df/tbl/data.frame) #> ..$ variable: Factor w/ 5 levels "Fair","Good",..: 5 3 3 5 4 5 5 4 3 2 ... #> ..$ values : int [1:3000] 580 650 630 706 1080 3082 3328 4229 1895 3546 ... #> ..- attr(*, "description")= chr "## Diamond prices\n\nPrices of 3,000 round cut diamonds\n\n### Description\n\nA dataset containing the prices a"| __truncated__ #> $ var1 : chr "cut" #> $ var2 : chr "price" #> $ samples : chr "independent" #> $ alternative: chr "two.sided" #> $ conf_lev : num 0.95 #> $ comb : chr "" #> $ adjust : chr "none" #> $ test : chr "t" #> $ data_filter: chr "" #> - attr(*, "class")= chr [1:2] "compare_means" "list"