Compare Probability of an Event with Benchmark
data <- data.frame(task_1 = c("y", "y", "y", "y", "n", "n", "n", NA, NA, NA, NA, NA, NA, NA),
task_2 = c(0, 1, 1, 0, 1, 0, 1, 0, 0, 0, 1, 1, 1, 1))
## with dataframe column
benchmark_event(data,
column = task_1,
benchmark = 0.8,
event = "y")
#>
#> ── Compare Event Rate with a Benchmark ─────────────────────────────────────────
#> Based on the event rate of 58%, the probability that this rate exceeds a
#> benchmark of 80% is 3%
#> term result
#> count 4
#> total 7
#> benchmark 0.8
#> result 0.033344
#> probability 0.033
benchmark_event(data,
column = task_2,
benchmark = 0.3,
event = 1,
event_type = "success")
#>
#> ── Compare Event Rate with a Benchmark ─────────────────────────────────────────
#> Based on the success rate of 42%, the probability that this rate exceeds a
#> benchmark of 30% is 78%
#> term result
#> count 6
#> total 14
#> benchmark 0.3
#> result 0.7805158
#> probability 0.781
## pipeable
data |>
benchmark_event(column = task_2,
benchmark = 0.3,
event = 1,
event_type = "success")
#>
#> ── Compare Event Rate with a Benchmark ─────────────────────────────────────────
#> Based on the success rate of 42%, the probability that this rate exceeds a
#> benchmark of 30% is 78%
#> term result
#> count 6
#> total 14
#> benchmark 0.3
#> result 0.7805158
#> probability 0.781
# specify `input = "values` to use with direct values
benchmark_event(benchmark = 0.8,
count = 9,
total = 11,
input = "values")
#>
#> ── Compare Event Rate with a Benchmark ─────────────────────────────────────────
#> Based on the event rate of 82%, the probability that this rate exceeds a
#> benchmark of 80% is 38%
#> term result
#> count 9
#> total 11
#> benchmark 0.8
#> result 0.3825985
#> probability 0.383
# get confidence intervals
# test_wald_adj(10, 12)Compare Score with a Benchmark
scores <- 80 + 23 * scale(rnorm(172)) # 80 = mean, 23 = sd
data <- data.frame(scores = scores)
# with dataframe column
benchmark_score(data, scores, 67)
#>
#> ── Compare Score with a Benchmark ──────────────────────────────────────────────
#> We can be 100% confident that the true score is between 67 and 93
#> term result
#> mean 80
#> sd 23
#> se 1.753734
#> n 172
#> df 171
#> probability 2.732705e-12
#> tail one
#> confidence 1
#> margin_of_error 13
#> t 7.412757
#> lower_ci 67
#> upper_ci 93
# pipeable
data |> benchmark_score(scores, 67)
#>
#> ── Compare Score with a Benchmark ──────────────────────────────────────────────
#> We can be 100% confident that the true score is between 67 and 93
#> term result
#> mean 80
#> sd 23
#> se 1.753734
#> n 172
#> df 171
#> probability 2.732705e-12
#> tail one
#> confidence 1
#> margin_of_error 13
#> t 7.412757
#> lower_ci 67
#> upper_ci 93
# specify `input = "values` to use with direct values
benchmark_score(mean = 80,
sd = 23,
n = 172,
benchmark = 67,
input = "values")
#>
#> ── Compare Score with a Benchmark ──────────────────────────────────────────────
#> We can be 100% confident that the true score is between 67 and 93
#> term result
#> mean 80
#> sd 23
#> se 1.753734
#> n 172
#> df 171
#> probability 2.732705e-12
#> tail one
#> confidence 1
#> margin_of_error 13
#> t 7.412757
#> lower_ci 67
#> upper_ci 93Compare Time with a Benchmark
data <- data.frame(time = c(60, 53, 70, 42, 62, 43, 81))
benchmark_time(data, column = time, benchmark = 60, alpha = 0.05)
#>
#> ── Compare Time with a Benchmark ───────────────────────────────────────────────
#> term t.result_table.
#> lower_ci 45.8
#> upper_ci 71.7
#> t 0.509
#> probability 0.314Compare Means Between Groups
# Wide data - default
data_wide <- data.frame(A = c(4, 2, 5, 3, 6, 2, 5),
B = c(5, 2, 1, 2, 1, 3, 2))
compare_means_between_groups(data_wide, var1 = A, var2 = B)
#>
#> ── Welch Two Sample t-test ─────────────────────────────────────────────────────
#> term value
#> mean_of_A 3.86
#> mean_of_B 2.29
#> t 1.99
#> df 11.8
#> p_value 0.0707
#> ci_level 0.95
#> lower_ci -0.156
#> upper_ci 3.3
# Long data
data_long <- data_wide |> tibble::rowid_to_column("id") |>
tidyr::pivot_longer(cols = -id, names_to = "group", values_to = "variable")
compare_means_between_groups(data_long,
variable = variable,
grouping_variable = group,
groups = c("A", "B"),
input = "long")
#>
#> ── Welch Two Sample t-test ─────────────────────────────────────────────────────
#> term value
#> mean_of_A 3.86
#> mean_of_B 2.29
#> t 1.99
#> df 11.8
#> p_value 0.0707
#> ci_level 0.95
#> lower_ci -0.156
#> upper_ci 3.3
A <- 51.6 + 4.07 * scale(rnorm(11))
A <- c(A, NA)
B <- 49.6 + 4.63 * scale(rnorm(12))
data <- data.frame(A, B)
compare_means_between_groups(data, A, B)
#>
#> ── Welch Two Sample t-test ─────────────────────────────────────────────────────
#> term value
#> mean_of_A 51.6
#> mean_of_B 49.6
#> t 1.1
#> df 21
#> p_value 0.283
#> ci_level 0.95
#> lower_ci -1.77
#> upper_ci 5.77Compare Means Within Groups
data <- data.frame(id = c(1:7), task1 = c(4, 1, 2, 3, 8, 4, 4), task2 = c(7, 13, 9, 7, 18, 8, 10))
compare_means_within_groups(data, task1, task2)
#>
#> ── Compare Means Within Groups ─────────────────────────────────────────────────
#> X. result.estimate
#> mean.difference -6.571429
#> t -5.18
#> p 0.002
#> df 6
#> ci_level 0.95
#> lower_ci -9.68
#> upper_ci -3.46Compare Rates Between Groups
design = c("A","B")
complete = c(10, 4)
incomplete = c(2, 9)
data <- data.frame(design, complete, incomplete)
data <- data |> tidyr::pivot_longer(!design, names_to = "rate", values_to = "n") |>
tidyr::uncount(n)
compare_rates_between_groups(data, group = design, event = rate)
#>
#> ── Compare Rates Between Groups ────────────────────────────────────────────────
#> → N-1 Two Proportions test
#> term value
#> a 0.526
#> b 0.98
#> c 0.246
#> d 0.16
#> den 0.199
#> num 0.515
#> z 2.59
#> p_value 0.00955
#> n 25
#> lower_ci 0.133
#> upper_ci 0.776Compare Rates Within Groups
A <- c(1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1)
B <- c(0, 1, 1, 0, 0, 1, 1, 1, 0, 1, 0, 1, 0, 1, 0)
data <- data.frame(A, B)
compare_rates_within_groups(data, A, B, input = "wide")
#>
#> ── Compare Rates Within Groups ─────────────────────────────────────────────────
#> → McNemar's Test
#> p_value lower_ci upper_ci
#> 0.125 0.000569 0.59