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tests/testthat/_snaps/estimate_mdiff_paired.md
In esci: Estimation Statistics with Confidence Intervals
Compare estimate_mdiff_paired to ESCI_Data_paired, Thomasaon1
Code
estimate
Output
Analysis of raw data:
Data frame = structure(list("Participant ID" = structure(1:12, levels = c("1",
Analysis of raw data:
Data frame = "2", "3", "4", "5", "6", "7", "8", "9", "10", "11", "12"), class = "factor"),
Analysis of raw data:
Data frame = Pretest = c(13, 12, 12, 9, 14, 17, 14, 9, 6, 7, 11, 15),
Analysis of raw data:
Data frame = Posttest = c(14, 13, 16, 12, 15, 18, 13, 10, 10, 8, 14, 16
Analysis of raw data:
Data frame = )), removedRows = list(), addedRows = list(list(start = 0L,
Analysis of raw data:
Data frame = end = 11L)), transforms = list(), row.names = c(NA, 12L), class = "data.frame")
---Overview---
outcome_variable_name mean mean_LL mean_UL median median_LL median_UL
1 Pretest 11.58333 9.476776 13.68989 12.0 9 14
2 Posttest 13.25000 11.410019 15.08998 13.5 10 16
sd min max q1 q3 n missing df mean_SE median_SE
1 3.315483 6 17 9.0 14.00 12 0 11 0.9570974 1.208488
2 2.895922 8 18 11.5 15.25 12 0 11 0.8359806 1.450186
-- es_mean_difference --
type comparison_measure_name reference_measure_name effect
1 Comparison Posttest Pretest Posttest
2 Reference Posttest Pretest Pretest
11 Difference Posttest Pretest Posttest ‒ Pretest
effect_size LL UL SE df ta_LL ta_UL
1 13.250000 11.410019 15.089981 0.8359806 11 11.748675 14.751325
2 11.583333 9.476776 13.689890 0.9570974 11 9.864497 13.302170
11 1.666667 0.715218 2.618115 0.4322831 11 0.890336 2.442997
-- es_smd --
comparison_measure_name reference_measure_name effect effect_size
1 Posttest Pretest Posttest - Pretest 0.5105098
LL UL numerator denominator SE d_biased df
1 0.1806393 0.8902152 1.666667 3.112779 0.1810176 0.5105098 11
This standardized mean difference is called d_average because the standardizer used was s_average. d_average has been corrected for bias. Correction for bias can be important when df < 50.
-- es_r --
x_variable_name y_variable_name effect effect_size LL
1 Pretest Posttest Pretest and Posttest 0.8923908 0.62894
UL SE n df ta_LL ta_UL
1 0.967157 0.06139936 12 10 0.6882891 0.9596343
-- es_median_difference --
type comparison_measure_name reference_measure_name effect
Comparison Posttest Pretest Posttest
1 Reference Posttest Pretest Pretest
2 Difference Posttest Pretest Posttest ‒ Pretest
effect_size LL UL SE ta_LL ta_UL
13.5 10.000000 16.000000 1.450186 10.000000 16.000000
1 12.0 9.000000 14.000000 1.208488 9.000000 14.000000
2 1.5 -1.589665 4.589665 1.576389 -1.589665 4.589665
-- es_mean_ratio --
comparison_measure_name reference_measure_name effect effect_size
1 Posttest Pretest Posttest / Pretest 1.143885
LL UL comparison_mean reference_mean
1 1.05031 1.245797 13.25 11.58333
[1] "This effect-size measure is appropriate only for true ratio scales."
-- es_median_ratio --
comparison_measure_name reference_measure_name effect effect_size
1 Posttest Pretest Posttest / Pretest 1.125
LL UL comparison_median reference_median
1 0.8723319 1.450853 13.5 12
[1] "This effect-size measure is appropriate only for true ratio scales."
Note: LL and UL are lower and upper boundaries of confidence intervals with 95% expected coverage.
Code
mytest
Output
$properties
$properties$effect_size_name
[1] "mean"
$properties$alpha
[1] 0.05
$properties$interval_null
[1] FALSE
$properties$rope
[1] 0 0
$properties$rope_units
[1] "raw"
$point_null
test_type outcome_variable_name effect null_words
1 Nil Hypothesis Test Posttest - Pretest Posttest ‒ Pretest 0.00
confidence LL UL CI
1 95 0.715218 2.618115 95% CI [0.715218, 2.618115]
CI_compare t df p p_result
1 The 95% CI does not contain H_0 3.855498 11 0.002674001 p < 0.05
null_decision conclusion
1 Reject H_0 At α = 0.05, 0.00 is not a plausible value of μ_diff
significant
1 TRUE
Code
estimate_99
Output
Analysis of raw data:
Data frame = structure(list("Participant ID" = structure(1:12, levels = c("1",
Analysis of raw data:
Data frame = "2", "3", "4", "5", "6", "7", "8", "9", "10", "11", "12"), class = "factor"),
Analysis of raw data:
Data frame = Pretest = c(13, 12, 12, 9, 14, 17, 14, 9, 6, 7, 11, 15),
Analysis of raw data:
Data frame = Posttest = c(14, 13, 16, 12, 15, 18, 13, 10, 10, 8, 14, 16
Analysis of raw data:
Data frame = )), removedRows = list(), addedRows = list(list(start = 0L,
Analysis of raw data:
Data frame = end = 11L)), transforms = list(), row.names = c(NA, 12L), class = "data.frame")
---Overview---
outcome_variable_name mean mean_LL mean_UL median median_LL median_UL
1 Pretest 11.58333 8.610774 14.55589 12.0 7 15
2 Posttest 13.25000 10.653606 15.84639 13.5 10 16
sd min max q1 q3 n missing df mean_SE median_SE
1 3.315483 6 17 9.0 14.00 12 0 11 0.9570974 1.208488
2 2.895922 8 18 11.5 15.25 12 0 11 0.8359806 1.450186
-- es_mean_difference --
type comparison_measure_name reference_measure_name effect
1 Comparison Posttest Pretest Posttest
2 Reference Posttest Pretest Pretest
11 Difference Posttest Pretest Posttest ‒ Pretest
effect_size LL UL SE df ta_LL ta_UL
1 13.250000 10.653606 15.846394 0.8359806 11 10.9777384 15.522262
2 11.583333 8.610774 14.555893 0.9570974 11 8.9818669 14.184800
11 1.666667 0.324079 3.009254 0.4322831 11 0.4916869 2.841646
-- es_smd --
comparison_measure_name reference_measure_name effect effect_size
1 Posttest Pretest Posttest - Pretest 0.5105098
LL UL numerator denominator SE d_biased df
1 0.06915683 1.001698 1.666667 3.112779 0.1810176 0.5105098 11
This standardized mean difference is called d_average because the standardizer used was s_average. d_average has been corrected for bias. Correction for bias can be important when df < 50.
-- es_r --
x_variable_name y_variable_name effect effect_size LL
1 Pretest Posttest Pretest and Posttest 0.8923908 0.4887161
UL SE n df ta_LL ta_UL
1 0.9780952 0.06139936 12 10 0.5494077 0.9741826
-- es_median_difference --
type comparison_measure_name reference_measure_name effect
Comparison Posttest Pretest Posttest
1 Reference Posttest Pretest Pretest
2 Difference Posttest Pretest Posttest ‒ Pretest
effect_size LL UL SE ta_LL ta_UL
13.5 10.000000 16.000000 1.450186 10.000000 16.000000
1 12.0 7.000000 15.000000 1.208488 7.000000 15.000000
2 1.5 -2.560508 5.560508 1.576389 -2.560508 5.560508
-- es_mean_ratio --
comparison_measure_name reference_measure_name effect effect_size
1 Posttest Pretest Posttest / Pretest 1.143885
LL UL comparison_mean reference_mean
1 1.014099 1.290281 13.25 11.58333
[1] "This effect-size measure is appropriate only for true ratio scales."
-- es_median_ratio --
comparison_measure_name reference_measure_name effect effect_size
1 Posttest Pretest Posttest / Pretest 1.125
LL UL comparison_median reference_median
1 0.8053215 1.571577 13.5 12
[1] "This effect-size measure is appropriate only for true ratio scales."
Note: LL and UL are lower and upper boundaries of confidence intervals with 99% expected coverage.
Compare estimate_mdiff_paired to ESCI_Summary_paired, penlaptop1
Code
estimate
Output
Analysis of raw data:
---Overview---
outcome_variable_name mean mean_LL mean_UL sd n df mean_SE
1 Before 12.88 11.06827 14.69173 3.40 16 15 0.85
2 After 14.25 11.96935 16.53065 4.28 16 15 1.07
-- es_mean_difference --
type comparison_measure_name reference_measure_name effect
1 Comparison After Before After
2 Reference After Before Before
11 Difference After Before After ‒ Before
effect_size LL UL SE df ta_LL ta_UL
1 14.25 11.9693490 16.530651 1.0700 15 12.3742361 16.125764
2 12.88 11.0682679 14.691732 0.8500 15 11.3899072 14.370093
11 1.37 0.2350031 2.504997 0.5325 15 0.4365007 2.303499
-- es_smd --
comparison_measure_name reference_measure_name effect effect_size
1 After Before After - Before 0.3424327
LL UL numerator denominator SE d_biased df
1 0.05110995 0.6577932 1.37 3.865126 0.154769 0.3424327 15
This standardized mean difference is called d_average because the standardizer used was s_average. d_average has been corrected for bias. Correction for bias can be important when df < 50.
-- es_r --
x_variable_name y_variable_name effect effect_size LL
1 Before After Before and After 0.8707222 0.6430976
UL SE n df ta_LL ta_UL
1 0.9518053 0.06244354 16 14 0.6915049 0.9428632
Note: LL and UL are lower and upper boundaries of confidence intervals with 95% expected coverage.
Code
mytest
Output
$properties
$properties$effect_size_name
[1] "mean"
$properties$alpha
[1] 0.05
$properties$interval_null
[1] FALSE
$properties$rope
[1] 0 0
$properties$rope_units
[1] "raw"
$point_null
test_type outcome_variable_name effect null_words
1 Nil Hypothesis Test After - Before After ‒ Before 0.00
confidence LL UL CI
1 95 0.2350031 2.504997 95% CI [0.2350031, 2.504997]
CI_compare t df p p_result null_decision
1 The 95% CI does not contain H_0 2.57277 15 0.02121729 p < 0.05 Reject H_0
conclusion significant
1 At α = 0.05, 0.00 is not a plausible value of μ_diff TRUE
Code
estimate_99
Output
Analysis of raw data:
---Overview---
outcome_variable_name mean mean_LL mean_UL sd n df mean_SE
1 Before 12.88 10.37529 15.38471 3.40 16 15 0.85
2 After 14.25 11.09702 17.40298 4.28 16 15 1.07
-- es_mean_difference --
type comparison_measure_name reference_measure_name effect
1 Comparison After Before After
2 Reference After Before Before
11 Difference After Before After ‒ Before
effect_size LL UL SE df ta_LL ta_UL
1 14.25 11.0970172 17.402983 1.0700 15 11.46534608 17.034654
2 12.88 10.3752940 15.384706 0.8500 15 10.66789175 15.092108
11 1.37 -0.1991246 2.939125 0.5325 15 -0.01582076 2.755821
-- es_smd --
comparison_measure_name reference_measure_name effect effect_size
1 After Before After - Before 0.3424327
LL UL numerator denominator SE d_biased df
1 -0.0442069 0.75311 1.37 3.865126 0.154769 0.3424327 15
This standardized mean difference is called d_average because the standardizer used was s_average. d_average has been corrected for bias. Correction for bias can be important when df < 50.
-- es_r --
x_variable_name y_variable_name effect effect_size LL
1 Before After Before and After 0.8707222 0.5317828
UL SE n df ta_LL ta_UL
1 0.9655115 0.06244354 16 14 0.5795743 0.9604938
Note: LL and UL are lower and upper boundaries of confidence intervals with 99% expected coverage.
Ensure negative values processed correctly
Code
estimate
Output
Analysis of raw data:
---Overview---
outcome_variable_name mean mean_LL mean_UL median median_LL median_UL
1 y2 21.6 4.167124 39.03288 28 14 40
2 y1 11.2 3.954738 18.44526 12 6 19
sd min max q1 q3 n missing df mean_SE median_SE
1 31.47970 -67 52 17 40.0 15 0 14 8.128023 6.171216
2 13.08325 -21 35 7 18.5 15 0 14 3.378081 3.085608
-- es_mean_difference --
type comparison_measure_name reference_measure_name effect
1 Comparison y1 y2 y1
2 Reference y1 y2 y2
11 Difference y1 y2 y1 ‒ y2
effect_size LL UL SE df ta_LL ta_UL
1 11.2 3.954738 18.4452623 3.378081 14 5.250152 17.149848
2 21.6 4.167124 39.0328761 8.128023 14 7.284030 35.915970
11 -10.4 -21.379887 0.5798875 5.119338 14 -19.416741 -1.383259
-- es_smd --
comparison_measure_name reference_measure_name effect effect_size LL
1 y1 y2 y1 - y2 -0.4157442 -0.8900751
UL numerator denominator SE d_biased df
1 0.02719875 -10.4 24.10542 0.2340027 -0.4157442 14
This standardized mean difference is called d_average because the standardizer used was s_average. d_average has been corrected for bias. Correction for bias can be important when df < 50.
-- es_r --
x_variable_name y_variable_name effect effect_size LL UL
1 y2 y1 y2 and y1 0.9336117 0.7956616 0.9766029
SE n df ta_LL ta_UL
1 0.03430812 15 13 0.8267061 0.9719999
-- es_median_difference --
type comparison_measure_name reference_measure_name effect effect_size
Comparison y1 y2 y1 12
1 Reference y1 y2 y2 28
2 Difference y1 y2 y1 ‒ y2 -16
LL UL SE ta_LL ta_UL
6.00000 19.000000 3.085608 6.00000 19.000000
1 14.00000 40.000000 6.171216 14.00000 40.000000
2 -25.23798 -6.762016 4.713344 -25.23798 -6.762016
-- es_mean_ratio --
comparison_measure_name reference_measure_name effect effect_size LL
1 y1 y2 y1 / y2 0.5185185 0.3809957
UL comparison_mean reference_mean
1 0.7056811 11.2 21.6
[1] "WARNING! Your dataset includes negative values. This effect-size measure is appropriate only for true ratio scales."
-- es_median_ratio --
comparison_measure_name reference_measure_name effect effect_size LL UL
1 y1 y2 y1 / y2 NA NA NA
comparison_median reference_median
1 NA NA
[1] "WARNING! Your dataset includes negative values. This effect-size measure is appropriate only for true ratio scales."
Note: LL and UL are lower and upper boundaries of confidence intervals with 95% expected coverage.
Warnings:
* The ratio between group effect size is appropriate only for true ratio scales where values < 0 are impossible. Your data include at least one negative value, so the requested ratio effect size is not reported.
Test different types of calls to estimate_mdiff_paired
Code
from_vector
Output
Analysis of raw data:
---Overview---
outcome_variable_name mean mean_LL mean_UL median median_LL median_UL
1 bk_wrapper 2.266667 1.757755 2.775578 2 1 3
2 wc_wrapper 1.833333 1.506871 2.159795 2 1 2
sd min max q1 q3 n missing df mean_SE median_SE
1 1.3628908 1 5 1 3 30 0 29 0.2488287 0.4936052
2 0.8742813 1 4 1 2 30 0 29 0.1596212 0.2468026
-- es_mean_difference --
type comparison_measure_name reference_measure_name
1 Comparison wc_wrapper bk_wrapper
2 Reference wc_wrapper bk_wrapper
11 Difference wc_wrapper bk_wrapper
effect effect_size LL UL SE df
1 wc_wrapper 1.8333333 1.5068713 2.15979534 0.1596212 29
2 bk_wrapper 2.2666667 1.7577549 2.77557845 0.2488287 29
11 wc_wrapper ‒ bk_wrapper -0.4333333 -0.9398777 0.07321103 0.2476711 29
ta_LL ta_UL
1 1.5621166 2.1045500
2 1.8438751 2.6894582
11 -0.8541581 -0.0125086
-- es_smd --
comparison_measure_name reference_measure_name effect
1 wc_wrapper bk_wrapper wc_wrapper - bk_wrapper
effect_size LL UL numerator denominator SE d_biased
1 -0.3718897 -0.8165806 0.059636 -0.4333333 1.144954 0.2235287 -0.3718897
df
1 29
This standardized mean difference is called d_average because the standardizer used was s_average. d_average has been corrected for bias. Correction for bias can be important when df < 50.
-- es_r --
x_variable_name y_variable_name effect effect_size
1 bk_wrapper wc_wrapper bk_wrapper and wc_wrapper 0.32798
LL UL SE n df ta_LL ta_UL
1 -0.04226176 0.6119942 0.1657199 30 28 0.01835401 0.5726524
-- es_median_difference --
type comparison_measure_name reference_measure_name
Comparison wc_wrapper bk_wrapper
1 Reference wc_wrapper bk_wrapper
2 Difference wc_wrapper bk_wrapper
effect effect_size LL UL SE ta_LL
wc_wrapper 2 1.000000 2.000000 0.2468026 1.000000
1 bk_wrapper 2 1.000000 3.000000 0.4936052 1.000000
2 wc_wrapper ‒ bk_wrapper 0 -1.109202 1.109202 0.5659300 -1.109202
ta_UL
2.000000
1 3.000000
2 1.109202
-- es_mean_ratio --
comparison_measure_name reference_measure_name effect
1 wc_wrapper bk_wrapper wc_wrapper / bk_wrapper
effect_size LL UL comparison_mean reference_mean
1 0.8088235 0.6385273 1.024538 1.833333 2.266667
[1] "This effect-size measure is appropriate only for true ratio scales."
-- es_median_ratio --
comparison_measure_name reference_measure_name effect
1 wc_wrapper bk_wrapper wc_wrapper / bk_wrapper
effect_size LL UL comparison_median reference_median
1 1 0.5239318 1.908646 2 2
[1] "This effect-size measure is appropriate only for true ratio scales."
Note: LL and UL are lower and upper boundaries of confidence intervals with 95% expected coverage.
Code
from_df_strings
Output
Analysis of raw data:
Data frame = structure(list(wc = c(2, 3, 2, 1, 1, 2, 1, 1, 3, 2, 1, 1, 2,
Analysis of raw data:
Data frame = 4, 1, 1, 4, 2, 2, 1, 2, 2, 1, 3, 2, 2, 1, 1, 2, 2), bk = c(4,
Analysis of raw data:
Data frame = 4, 3, 2, 2, 5, 1, 1, 3, 1, 1, 2, 4, 3, 1, 1, 1, 3, 1, 1, 1, 5,
Analysis of raw data:
Data frame = 1, 4, 1, 3, 2, 4, 2, 1)), row.names = c(NA, 30L), class = "data.frame")
---Overview---
outcome_variable_name mean mean_LL mean_UL median median_LL median_UL
1 bk 2.266667 1.757755 2.775578 2 1 3
2 wc 1.833333 1.506871 2.159795 2 1 2
sd min max q1 q3 n missing df mean_SE median_SE
1 1.3628908 1 5 1 3 30 0 29 0.2488287 0.4936052
2 0.8742813 1 4 1 2 30 0 29 0.1596212 0.2468026
-- es_mean_difference --
type comparison_measure_name reference_measure_name effect
1 Comparison wc bk wc
2 Reference wc bk bk
11 Difference wc bk wc ‒ bk
effect_size LL UL SE df ta_LL ta_UL
1 1.8333333 1.5068713 2.15979534 0.1596212 29 1.5621166 2.1045500
2 2.2666667 1.7577549 2.77557845 0.2488287 29 1.8438751 2.6894582
11 -0.4333333 -0.9398777 0.07321103 0.2476711 29 -0.8541581 -0.0125086
-- es_smd --
comparison_measure_name reference_measure_name effect effect_size LL
1 wc bk wc - bk -0.3718897 -0.8165806
UL numerator denominator SE d_biased df
1 0.059636 -0.4333333 1.144954 0.2235287 -0.3718897 29
This standardized mean difference is called d_average because the standardizer used was s_average. d_average has been corrected for bias. Correction for bias can be important when df < 50.
-- es_r --
x_variable_name y_variable_name effect effect_size LL UL
1 bk wc bk and wc 0.32798 -0.04226176 0.6119942
SE n df ta_LL ta_UL
1 0.1657199 30 28 0.01835401 0.5726524
-- es_median_difference --
type comparison_measure_name reference_measure_name effect effect_size
Comparison wc bk wc 2
1 Reference wc bk bk 2
2 Difference wc bk wc ‒ bk 0
LL UL SE ta_LL ta_UL
1.000000 2.000000 0.2468026 1.000000 2.000000
1 1.000000 3.000000 0.4936052 1.000000 3.000000
2 -1.109202 1.109202 0.5659300 -1.109202 1.109202
-- es_mean_ratio --
comparison_measure_name reference_measure_name effect effect_size LL
1 wc bk wc / bk 0.8088235 0.6385273
UL comparison_mean reference_mean
1 1.024538 1.833333 2.266667
[1] "This effect-size measure is appropriate only for true ratio scales."
-- es_median_ratio --
comparison_measure_name reference_measure_name effect effect_size LL
1 wc bk wc / bk 1 0.5239318
UL comparison_median reference_median
1 1.908646 2 2
[1] "This effect-size measure is appropriate only for true ratio scales."
Note: LL and UL are lower and upper boundaries of confidence intervals with 95% expected coverage.
Code
from_df_tidy
Output
Analysis of raw data:
Data frame = structure(list(wc = c(2, 3, 2, 1, 1, 2, 1, 1, 3, 2, 1, 1, 2,
Analysis of raw data:
Data frame = 4, 1, 1, 4, 2, 2, 1, 2, 2, 1, 3, 2, 2, 1, 1, 2, 2), bk = c(4,
Analysis of raw data:
Data frame = 4, 3, 2, 2, 5, 1, 1, 3, 1, 1, 2, 4, 3, 1, 1, 1, 3, 1, 1, 1, 5,
Analysis of raw data:
Data frame = 1, 4, 1, 3, 2, 4, 2, 1)), row.names = c(NA, 30L), class = "data.frame")
---Overview---
outcome_variable_name mean mean_LL mean_UL median median_LL median_UL
1 bk 2.266667 1.757755 2.775578 2 1 3
2 wc 1.833333 1.506871 2.159795 2 1 2
sd min max q1 q3 n missing df mean_SE median_SE
1 1.3628908 1 5 1 3 30 0 29 0.2488287 0.4936052
2 0.8742813 1 4 1 2 30 0 29 0.1596212 0.2468026
-- es_mean_difference --
type comparison_measure_name reference_measure_name effect
1 Comparison wc bk wc
2 Reference wc bk bk
11 Difference wc bk wc ‒ bk
effect_size LL UL SE df ta_LL ta_UL
1 1.8333333 1.5068713 2.15979534 0.1596212 29 1.5621166 2.1045500
2 2.2666667 1.7577549 2.77557845 0.2488287 29 1.8438751 2.6894582
11 -0.4333333 -0.9398777 0.07321103 0.2476711 29 -0.8541581 -0.0125086
-- es_smd --
comparison_measure_name reference_measure_name effect effect_size LL
1 wc bk wc - bk -0.3718897 -0.8165806
UL numerator denominator SE d_biased df
1 0.059636 -0.4333333 1.144954 0.2235287 -0.3718897 29
This standardized mean difference is called d_average because the standardizer used was s_average. d_average has been corrected for bias. Correction for bias can be important when df < 50.
-- es_r --
x_variable_name y_variable_name effect effect_size LL UL
1 bk wc bk and wc 0.32798 -0.04226176 0.6119942
SE n df ta_LL ta_UL
1 0.1657199 30 28 0.01835401 0.5726524
-- es_median_difference --
type comparison_measure_name reference_measure_name effect effect_size
Comparison wc bk wc 2
1 Reference wc bk bk 2
2 Difference wc bk wc ‒ bk 0
LL UL SE ta_LL ta_UL
1.000000 2.000000 0.2468026 1.000000 2.000000
1 1.000000 3.000000 0.4936052 1.000000 3.000000
2 -1.109202 1.109202 0.5659300 -1.109202 1.109202
-- es_mean_ratio --
comparison_measure_name reference_measure_name effect effect_size LL
1 wc bk wc / bk 0.8088235 0.6385273
UL comparison_mean reference_mean
1 1.024538 1.833333 2.266667
[1] "This effect-size measure is appropriate only for true ratio scales."
-- es_median_ratio --
comparison_measure_name reference_measure_name effect effect_size LL
1 wc bk wc / bk 1 0.5239318
UL comparison_median reference_median
1 1.908646 2 2
[1] "This effect-size measure is appropriate only for true ratio scales."
Note: LL and UL are lower and upper boundaries of confidence intervals with 95% expected coverage.
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Compare estimate_mdiff_paired to ESCI_Data_paired, Thomasaon1
Code
estimate
Output
Analysis of raw data:
Data frame = structure(list("Participant ID" = structure(1:12, levels = c("1",
Analysis of raw data:
Data frame = "2", "3", "4", "5", "6", "7", "8", "9", "10", "11", "12"), class = "factor"),
Analysis of raw data:
Data frame = Pretest = c(13, 12, 12, 9, 14, 17, 14, 9, 6, 7, 11, 15),
Analysis of raw data:
Data frame = Posttest = c(14, 13, 16, 12, 15, 18, 13, 10, 10, 8, 14, 16
Analysis of raw data:
Data frame = )), removedRows = list(), addedRows = list(list(start = 0L,
Analysis of raw data:
Data frame = end = 11L)), transforms = list(), row.names = c(NA, 12L), class = "data.frame")
---Overview---
outcome_variable_name mean mean_LL mean_UL median median_LL median_UL
1 Pretest 11.58333 9.476776 13.68989 12.0 9 14
2 Posttest 13.25000 11.410019 15.08998 13.5 10 16
sd min max q1 q3 n missing df mean_SE median_SE
1 3.315483 6 17 9.0 14.00 12 0 11 0.9570974 1.208488
2 2.895922 8 18 11.5 15.25 12 0 11 0.8359806 1.450186
-- es_mean_difference --
type comparison_measure_name reference_measure_name effect
1 Comparison Posttest Pretest Posttest
2 Reference Posttest Pretest Pretest
11 Difference Posttest Pretest Posttest ‒ Pretest
effect_size LL UL SE df ta_LL ta_UL
1 13.250000 11.410019 15.089981 0.8359806 11 11.748675 14.751325
2 11.583333 9.476776 13.689890 0.9570974 11 9.864497 13.302170
11 1.666667 0.715218 2.618115 0.4322831 11 0.890336 2.442997
-- es_smd --
comparison_measure_name reference_measure_name effect effect_size
1 Posttest Pretest Posttest - Pretest 0.5105098
LL UL numerator denominator SE d_biased df
1 0.1806393 0.8902152 1.666667 3.112779 0.1810176 0.5105098 11
This standardized mean difference is called d_average because the standardizer used was s_average. d_average has been corrected for bias. Correction for bias can be important when df < 50.
-- es_r --
x_variable_name y_variable_name effect effect_size LL
1 Pretest Posttest Pretest and Posttest 0.8923908 0.62894
UL SE n df ta_LL ta_UL
1 0.967157 0.06139936 12 10 0.6882891 0.9596343
-- es_median_difference --
type comparison_measure_name reference_measure_name effect
Comparison Posttest Pretest Posttest
1 Reference Posttest Pretest Pretest
2 Difference Posttest Pretest Posttest ‒ Pretest
effect_size LL UL SE ta_LL ta_UL
13.5 10.000000 16.000000 1.450186 10.000000 16.000000
1 12.0 9.000000 14.000000 1.208488 9.000000 14.000000
2 1.5 -1.589665 4.589665 1.576389 -1.589665 4.589665
-- es_mean_ratio --
comparison_measure_name reference_measure_name effect effect_size
1 Posttest Pretest Posttest / Pretest 1.143885
LL UL comparison_mean reference_mean
1 1.05031 1.245797 13.25 11.58333
[1] "This effect-size measure is appropriate only for true ratio scales."
-- es_median_ratio --
comparison_measure_name reference_measure_name effect effect_size
1 Posttest Pretest Posttest / Pretest 1.125
LL UL comparison_median reference_median
1 0.8723319 1.450853 13.5 12
[1] "This effect-size measure is appropriate only for true ratio scales."
Note: LL and UL are lower and upper boundaries of confidence intervals with 95% expected coverage.
Code
mytest
Output
$properties
$properties$effect_size_name
[1] "mean"
$properties$alpha
[1] 0.05
$properties$interval_null
[1] FALSE
$properties$rope
[1] 0 0
$properties$rope_units
[1] "raw"
$point_null
test_type outcome_variable_name effect null_words
1 Nil Hypothesis Test Posttest - Pretest Posttest ‒ Pretest 0.00
confidence LL UL CI
1 95 0.715218 2.618115 95% CI [0.715218, 2.618115]
CI_compare t df p p_result
1 The 95% CI does not contain H_0 3.855498 11 0.002674001 p < 0.05
null_decision conclusion
1 Reject H_0 At α = 0.05, 0.00 is not a plausible value of μ_diff
significant
1 TRUE
Code
estimate_99
Output
Analysis of raw data:
Data frame = structure(list("Participant ID" = structure(1:12, levels = c("1",
Analysis of raw data:
Data frame = "2", "3", "4", "5", "6", "7", "8", "9", "10", "11", "12"), class = "factor"),
Analysis of raw data:
Data frame = Pretest = c(13, 12, 12, 9, 14, 17, 14, 9, 6, 7, 11, 15),
Analysis of raw data:
Data frame = Posttest = c(14, 13, 16, 12, 15, 18, 13, 10, 10, 8, 14, 16
Analysis of raw data:
Data frame = )), removedRows = list(), addedRows = list(list(start = 0L,
Analysis of raw data:
Data frame = end = 11L)), transforms = list(), row.names = c(NA, 12L), class = "data.frame")
---Overview---
outcome_variable_name mean mean_LL mean_UL median median_LL median_UL
1 Pretest 11.58333 8.610774 14.55589 12.0 7 15
2 Posttest 13.25000 10.653606 15.84639 13.5 10 16
sd min max q1 q3 n missing df mean_SE median_SE
1 3.315483 6 17 9.0 14.00 12 0 11 0.9570974 1.208488
2 2.895922 8 18 11.5 15.25 12 0 11 0.8359806 1.450186
-- es_mean_difference --
type comparison_measure_name reference_measure_name effect
1 Comparison Posttest Pretest Posttest
2 Reference Posttest Pretest Pretest
11 Difference Posttest Pretest Posttest ‒ Pretest
effect_size LL UL SE df ta_LL ta_UL
1 13.250000 10.653606 15.846394 0.8359806 11 10.9777384 15.522262
2 11.583333 8.610774 14.555893 0.9570974 11 8.9818669 14.184800
11 1.666667 0.324079 3.009254 0.4322831 11 0.4916869 2.841646
-- es_smd --
comparison_measure_name reference_measure_name effect effect_size
1 Posttest Pretest Posttest - Pretest 0.5105098
LL UL numerator denominator SE d_biased df
1 0.06915683 1.001698 1.666667 3.112779 0.1810176 0.5105098 11
This standardized mean difference is called d_average because the standardizer used was s_average. d_average has been corrected for bias. Correction for bias can be important when df < 50.
-- es_r --
x_variable_name y_variable_name effect effect_size LL
1 Pretest Posttest Pretest and Posttest 0.8923908 0.4887161
UL SE n df ta_LL ta_UL
1 0.9780952 0.06139936 12 10 0.5494077 0.9741826
-- es_median_difference --
type comparison_measure_name reference_measure_name effect
Comparison Posttest Pretest Posttest
1 Reference Posttest Pretest Pretest
2 Difference Posttest Pretest Posttest ‒ Pretest
effect_size LL UL SE ta_LL ta_UL
13.5 10.000000 16.000000 1.450186 10.000000 16.000000
1 12.0 7.000000 15.000000 1.208488 7.000000 15.000000
2 1.5 -2.560508 5.560508 1.576389 -2.560508 5.560508
-- es_mean_ratio --
comparison_measure_name reference_measure_name effect effect_size
1 Posttest Pretest Posttest / Pretest 1.143885
LL UL comparison_mean reference_mean
1 1.014099 1.290281 13.25 11.58333
[1] "This effect-size measure is appropriate only for true ratio scales."
-- es_median_ratio --
comparison_measure_name reference_measure_name effect effect_size
1 Posttest Pretest Posttest / Pretest 1.125
LL UL comparison_median reference_median
1 0.8053215 1.571577 13.5 12
[1] "This effect-size measure is appropriate only for true ratio scales."
Note: LL and UL are lower and upper boundaries of confidence intervals with 99% expected coverage.
Compare estimate_mdiff_paired to ESCI_Summary_paired, penlaptop1
Code
estimate
Output
Analysis of raw data:
---Overview---
outcome_variable_name mean mean_LL mean_UL sd n df mean_SE
1 Before 12.88 11.06827 14.69173 3.40 16 15 0.85
2 After 14.25 11.96935 16.53065 4.28 16 15 1.07
-- es_mean_difference --
type comparison_measure_name reference_measure_name effect
1 Comparison After Before After
2 Reference After Before Before
11 Difference After Before After ‒ Before
effect_size LL UL SE df ta_LL ta_UL
1 14.25 11.9693490 16.530651 1.0700 15 12.3742361 16.125764
2 12.88 11.0682679 14.691732 0.8500 15 11.3899072 14.370093
11 1.37 0.2350031 2.504997 0.5325 15 0.4365007 2.303499
-- es_smd --
comparison_measure_name reference_measure_name effect effect_size
1 After Before After - Before 0.3424327
LL UL numerator denominator SE d_biased df
1 0.05110995 0.6577932 1.37 3.865126 0.154769 0.3424327 15
This standardized mean difference is called d_average because the standardizer used was s_average. d_average has been corrected for bias. Correction for bias can be important when df < 50.
-- es_r --
x_variable_name y_variable_name effect effect_size LL
1 Before After Before and After 0.8707222 0.6430976
UL SE n df ta_LL ta_UL
1 0.9518053 0.06244354 16 14 0.6915049 0.9428632
Note: LL and UL are lower and upper boundaries of confidence intervals with 95% expected coverage.
Code
mytest
Output
$properties
$properties$effect_size_name
[1] "mean"
$properties$alpha
[1] 0.05
$properties$interval_null
[1] FALSE
$properties$rope
[1] 0 0
$properties$rope_units
[1] "raw"
$point_null
test_type outcome_variable_name effect null_words
1 Nil Hypothesis Test After - Before After ‒ Before 0.00
confidence LL UL CI
1 95 0.2350031 2.504997 95% CI [0.2350031, 2.504997]
CI_compare t df p p_result null_decision
1 The 95% CI does not contain H_0 2.57277 15 0.02121729 p < 0.05 Reject H_0
conclusion significant
1 At α = 0.05, 0.00 is not a plausible value of μ_diff TRUE
Code
estimate_99
Output
Analysis of raw data:
---Overview---
outcome_variable_name mean mean_LL mean_UL sd n df mean_SE
1 Before 12.88 10.37529 15.38471 3.40 16 15 0.85
2 After 14.25 11.09702 17.40298 4.28 16 15 1.07
-- es_mean_difference --
type comparison_measure_name reference_measure_name effect
1 Comparison After Before After
2 Reference After Before Before
11 Difference After Before After ‒ Before
effect_size LL UL SE df ta_LL ta_UL
1 14.25 11.0970172 17.402983 1.0700 15 11.46534608 17.034654
2 12.88 10.3752940 15.384706 0.8500 15 10.66789175 15.092108
11 1.37 -0.1991246 2.939125 0.5325 15 -0.01582076 2.755821
-- es_smd --
comparison_measure_name reference_measure_name effect effect_size
1 After Before After - Before 0.3424327
LL UL numerator denominator SE d_biased df
1 -0.0442069 0.75311 1.37 3.865126 0.154769 0.3424327 15
This standardized mean difference is called d_average because the standardizer used was s_average. d_average has been corrected for bias. Correction for bias can be important when df < 50.
-- es_r --
x_variable_name y_variable_name effect effect_size LL
1 Before After Before and After 0.8707222 0.5317828
UL SE n df ta_LL ta_UL
1 0.9655115 0.06244354 16 14 0.5795743 0.9604938
Note: LL and UL are lower and upper boundaries of confidence intervals with 99% expected coverage.
Ensure negative values processed correctly
Code
estimate
Output
Analysis of raw data:
---Overview---
outcome_variable_name mean mean_LL mean_UL median median_LL median_UL
1 y2 21.6 4.167124 39.03288 28 14 40
2 y1 11.2 3.954738 18.44526 12 6 19
sd min max q1 q3 n missing df mean_SE median_SE
1 31.47970 -67 52 17 40.0 15 0 14 8.128023 6.171216
2 13.08325 -21 35 7 18.5 15 0 14 3.378081 3.085608
-- es_mean_difference --
type comparison_measure_name reference_measure_name effect
1 Comparison y1 y2 y1
2 Reference y1 y2 y2
11 Difference y1 y2 y1 ‒ y2
effect_size LL UL SE df ta_LL ta_UL
1 11.2 3.954738 18.4452623 3.378081 14 5.250152 17.149848
2 21.6 4.167124 39.0328761 8.128023 14 7.284030 35.915970
11 -10.4 -21.379887 0.5798875 5.119338 14 -19.416741 -1.383259
-- es_smd --
comparison_measure_name reference_measure_name effect effect_size LL
1 y1 y2 y1 - y2 -0.4157442 -0.8900751
UL numerator denominator SE d_biased df
1 0.02719875 -10.4 24.10542 0.2340027 -0.4157442 14
This standardized mean difference is called d_average because the standardizer used was s_average. d_average has been corrected for bias. Correction for bias can be important when df < 50.
-- es_r --
x_variable_name y_variable_name effect effect_size LL UL
1 y2 y1 y2 and y1 0.9336117 0.7956616 0.9766029
SE n df ta_LL ta_UL
1 0.03430812 15 13 0.8267061 0.9719999
-- es_median_difference --
type comparison_measure_name reference_measure_name effect effect_size
Comparison y1 y2 y1 12
1 Reference y1 y2 y2 28
2 Difference y1 y2 y1 ‒ y2 -16
LL UL SE ta_LL ta_UL
6.00000 19.000000 3.085608 6.00000 19.000000
1 14.00000 40.000000 6.171216 14.00000 40.000000
2 -25.23798 -6.762016 4.713344 -25.23798 -6.762016
-- es_mean_ratio --
comparison_measure_name reference_measure_name effect effect_size LL
1 y1 y2 y1 / y2 0.5185185 0.3809957
UL comparison_mean reference_mean
1 0.7056811 11.2 21.6
[1] "WARNING! Your dataset includes negative values. This effect-size measure is appropriate only for true ratio scales."
-- es_median_ratio --
comparison_measure_name reference_measure_name effect effect_size LL UL
1 y1 y2 y1 / y2 NA NA NA
comparison_median reference_median
1 NA NA
[1] "WARNING! Your dataset includes negative values. This effect-size measure is appropriate only for true ratio scales."
Note: LL and UL are lower and upper boundaries of confidence intervals with 95% expected coverage.
Warnings:
* The ratio between group effect size is appropriate only for true ratio scales where values < 0 are impossible. Your data include at least one negative value, so the requested ratio effect size is not reported.
Test different types of calls to estimate_mdiff_paired
Code
from_vector
Output
Analysis of raw data:
---Overview---
outcome_variable_name mean mean_LL mean_UL median median_LL median_UL
1 bk_wrapper 2.266667 1.757755 2.775578 2 1 3
2 wc_wrapper 1.833333 1.506871 2.159795 2 1 2
sd min max q1 q3 n missing df mean_SE median_SE
1 1.3628908 1 5 1 3 30 0 29 0.2488287 0.4936052
2 0.8742813 1 4 1 2 30 0 29 0.1596212 0.2468026
-- es_mean_difference --
type comparison_measure_name reference_measure_name
1 Comparison wc_wrapper bk_wrapper
2 Reference wc_wrapper bk_wrapper
11 Difference wc_wrapper bk_wrapper
effect effect_size LL UL SE df
1 wc_wrapper 1.8333333 1.5068713 2.15979534 0.1596212 29
2 bk_wrapper 2.2666667 1.7577549 2.77557845 0.2488287 29
11 wc_wrapper ‒ bk_wrapper -0.4333333 -0.9398777 0.07321103 0.2476711 29
ta_LL ta_UL
1 1.5621166 2.1045500
2 1.8438751 2.6894582
11 -0.8541581 -0.0125086
-- es_smd --
comparison_measure_name reference_measure_name effect
1 wc_wrapper bk_wrapper wc_wrapper - bk_wrapper
effect_size LL UL numerator denominator SE d_biased
1 -0.3718897 -0.8165806 0.059636 -0.4333333 1.144954 0.2235287 -0.3718897
df
1 29
This standardized mean difference is called d_average because the standardizer used was s_average. d_average has been corrected for bias. Correction for bias can be important when df < 50.
-- es_r --
x_variable_name y_variable_name effect effect_size
1 bk_wrapper wc_wrapper bk_wrapper and wc_wrapper 0.32798
LL UL SE n df ta_LL ta_UL
1 -0.04226176 0.6119942 0.1657199 30 28 0.01835401 0.5726524
-- es_median_difference --
type comparison_measure_name reference_measure_name
Comparison wc_wrapper bk_wrapper
1 Reference wc_wrapper bk_wrapper
2 Difference wc_wrapper bk_wrapper
effect effect_size LL UL SE ta_LL
wc_wrapper 2 1.000000 2.000000 0.2468026 1.000000
1 bk_wrapper 2 1.000000 3.000000 0.4936052 1.000000
2 wc_wrapper ‒ bk_wrapper 0 -1.109202 1.109202 0.5659300 -1.109202
ta_UL
2.000000
1 3.000000
2 1.109202
-- es_mean_ratio --
comparison_measure_name reference_measure_name effect
1 wc_wrapper bk_wrapper wc_wrapper / bk_wrapper
effect_size LL UL comparison_mean reference_mean
1 0.8088235 0.6385273 1.024538 1.833333 2.266667
[1] "This effect-size measure is appropriate only for true ratio scales."
-- es_median_ratio --
comparison_measure_name reference_measure_name effect
1 wc_wrapper bk_wrapper wc_wrapper / bk_wrapper
effect_size LL UL comparison_median reference_median
1 1 0.5239318 1.908646 2 2
[1] "This effect-size measure is appropriate only for true ratio scales."
Note: LL and UL are lower and upper boundaries of confidence intervals with 95% expected coverage.
Code
from_df_strings
Output
Analysis of raw data:
Data frame = structure(list(wc = c(2, 3, 2, 1, 1, 2, 1, 1, 3, 2, 1, 1, 2,
Analysis of raw data:
Data frame = 4, 1, 1, 4, 2, 2, 1, 2, 2, 1, 3, 2, 2, 1, 1, 2, 2), bk = c(4,
Analysis of raw data:
Data frame = 4, 3, 2, 2, 5, 1, 1, 3, 1, 1, 2, 4, 3, 1, 1, 1, 3, 1, 1, 1, 5,
Analysis of raw data:
Data frame = 1, 4, 1, 3, 2, 4, 2, 1)), row.names = c(NA, 30L), class = "data.frame")
---Overview---
outcome_variable_name mean mean_LL mean_UL median median_LL median_UL
1 bk 2.266667 1.757755 2.775578 2 1 3
2 wc 1.833333 1.506871 2.159795 2 1 2
sd min max q1 q3 n missing df mean_SE median_SE
1 1.3628908 1 5 1 3 30 0 29 0.2488287 0.4936052
2 0.8742813 1 4 1 2 30 0 29 0.1596212 0.2468026
-- es_mean_difference --
type comparison_measure_name reference_measure_name effect
1 Comparison wc bk wc
2 Reference wc bk bk
11 Difference wc bk wc ‒ bk
effect_size LL UL SE df ta_LL ta_UL
1 1.8333333 1.5068713 2.15979534 0.1596212 29 1.5621166 2.1045500
2 2.2666667 1.7577549 2.77557845 0.2488287 29 1.8438751 2.6894582
11 -0.4333333 -0.9398777 0.07321103 0.2476711 29 -0.8541581 -0.0125086
-- es_smd --
comparison_measure_name reference_measure_name effect effect_size LL
1 wc bk wc - bk -0.3718897 -0.8165806
UL numerator denominator SE d_biased df
1 0.059636 -0.4333333 1.144954 0.2235287 -0.3718897 29
This standardized mean difference is called d_average because the standardizer used was s_average. d_average has been corrected for bias. Correction for bias can be important when df < 50.
-- es_r --
x_variable_name y_variable_name effect effect_size LL UL
1 bk wc bk and wc 0.32798 -0.04226176 0.6119942
SE n df ta_LL ta_UL
1 0.1657199 30 28 0.01835401 0.5726524
-- es_median_difference --
type comparison_measure_name reference_measure_name effect effect_size
Comparison wc bk wc 2
1 Reference wc bk bk 2
2 Difference wc bk wc ‒ bk 0
LL UL SE ta_LL ta_UL
1.000000 2.000000 0.2468026 1.000000 2.000000
1 1.000000 3.000000 0.4936052 1.000000 3.000000
2 -1.109202 1.109202 0.5659300 -1.109202 1.109202
-- es_mean_ratio --
comparison_measure_name reference_measure_name effect effect_size LL
1 wc bk wc / bk 0.8088235 0.6385273
UL comparison_mean reference_mean
1 1.024538 1.833333 2.266667
[1] "This effect-size measure is appropriate only for true ratio scales."
-- es_median_ratio --
comparison_measure_name reference_measure_name effect effect_size LL
1 wc bk wc / bk 1 0.5239318
UL comparison_median reference_median
1 1.908646 2 2
[1] "This effect-size measure is appropriate only for true ratio scales."
Note: LL and UL are lower and upper boundaries of confidence intervals with 95% expected coverage.
Code
from_df_tidy
Output
Analysis of raw data:
Data frame = structure(list(wc = c(2, 3, 2, 1, 1, 2, 1, 1, 3, 2, 1, 1, 2,
Analysis of raw data:
Data frame = 4, 1, 1, 4, 2, 2, 1, 2, 2, 1, 3, 2, 2, 1, 1, 2, 2), bk = c(4,
Analysis of raw data:
Data frame = 4, 3, 2, 2, 5, 1, 1, 3, 1, 1, 2, 4, 3, 1, 1, 1, 3, 1, 1, 1, 5,
Analysis of raw data:
Data frame = 1, 4, 1, 3, 2, 4, 2, 1)), row.names = c(NA, 30L), class = "data.frame")
---Overview---
outcome_variable_name mean mean_LL mean_UL median median_LL median_UL
1 bk 2.266667 1.757755 2.775578 2 1 3
2 wc 1.833333 1.506871 2.159795 2 1 2
sd min max q1 q3 n missing df mean_SE median_SE
1 1.3628908 1 5 1 3 30 0 29 0.2488287 0.4936052
2 0.8742813 1 4 1 2 30 0 29 0.1596212 0.2468026
-- es_mean_difference --
type comparison_measure_name reference_measure_name effect
1 Comparison wc bk wc
2 Reference wc bk bk
11 Difference wc bk wc ‒ bk
effect_size LL UL SE df ta_LL ta_UL
1 1.8333333 1.5068713 2.15979534 0.1596212 29 1.5621166 2.1045500
2 2.2666667 1.7577549 2.77557845 0.2488287 29 1.8438751 2.6894582
11 -0.4333333 -0.9398777 0.07321103 0.2476711 29 -0.8541581 -0.0125086
-- es_smd --
comparison_measure_name reference_measure_name effect effect_size LL
1 wc bk wc - bk -0.3718897 -0.8165806
UL numerator denominator SE d_biased df
1 0.059636 -0.4333333 1.144954 0.2235287 -0.3718897 29
This standardized mean difference is called d_average because the standardizer used was s_average. d_average has been corrected for bias. Correction for bias can be important when df < 50.
-- es_r --
x_variable_name y_variable_name effect effect_size LL UL
1 bk wc bk and wc 0.32798 -0.04226176 0.6119942
SE n df ta_LL ta_UL
1 0.1657199 30 28 0.01835401 0.5726524
-- es_median_difference --
type comparison_measure_name reference_measure_name effect effect_size
Comparison wc bk wc 2
1 Reference wc bk bk 2
2 Difference wc bk wc ‒ bk 0
LL UL SE ta_LL ta_UL
1.000000 2.000000 0.2468026 1.000000 2.000000
1 1.000000 3.000000 0.4936052 1.000000 3.000000
2 -1.109202 1.109202 0.5659300 -1.109202 1.109202
-- es_mean_ratio --
comparison_measure_name reference_measure_name effect effect_size LL
1 wc bk wc / bk 0.8088235 0.6385273
UL comparison_mean reference_mean
1 1.024538 1.833333 2.266667
[1] "This effect-size measure is appropriate only for true ratio scales."
-- es_median_ratio --
comparison_measure_name reference_measure_name effect effect_size LL
1 wc bk wc / bk 1 0.5239318
UL comparison_median reference_median
1 1.908646 2 2
[1] "This effect-size measure is appropriate only for true ratio scales."
Note: LL and UL are lower and upper boundaries of confidence intervals with 95% expected coverage.
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