Introduction

In this document, we compare various DescrTab2 tests and descriptive statistics with their SAS equivalents.

These comparisons are not automated. The user may check if the results presented within this document are according to the users preferences.

The datasets used in these comparisons are taken mostly from the ?help pages of the underlying test functions used within DescrTab2. For the SAS comparisons, these datasets are then written to .csv format and read into SAS with help of the ?foreign package.

The origin of these datasets is described in the respective sections.

A note about the layout

This document is created by including in the .html SAS output. Unfortunately, this has ugly side effects for the formatting of this document, but everything should still be readable.

Wilcoxon one-sample signed-rank test

Dataset origin: ?wilcox.test (accessed on R-version 4.0.3).


x <- c(1.83,  0.50,  1.62,  2.48, 1.68, 1.88, 1.55, 3.06, 1.30)
y <- c(0.878, 0.647, 0.598, 2.05, 1.06, 1.29, 1.06, 3.14, 1.29)
dat_wilcox.test_1_sample <- tibble(diff = x-y)

descr(dat_wilcox.test_1_sample, test_options = c(nonparametric=TRUE))

Variables	Total	p
	(N=9)
diff
N	9	0.039^Wil1
mean	0.43
sd	0.43
median	0.49
Q1 - Q3	0.01 – 0.62
min - max	-0.15 – 1
^Wil1 Wilcoxon’s one-sample signed-rank test

Mann-Whitney U test

Dataset origin: ?wilcox.test (accessed on R-version 4.0.3).


x <- c(0.80, 0.83, 1.89, 1.04, 1.45, 1.38, 1.91, 1.64, 0.73, 1.46)
y <- c(1.15, 0.88, 0.90, 0.74, 1.21)
group <- c(rep("Trt", length(x)), rep("Ctrl", length(y)))
dat_wilcox.test_2_sample <- tibble(var=c(x,y), group=group)

descr(dat_wilcox.test_2_sample, "group", test_options = c(nonparametric=TRUE))

Variables	Trt	Ctrl	Total	p	CI
	(N=10)	(N=5)	(N=15)
var
N	10	5	15	0.254^MWU	[-0.15, 0.76]^HL
mean	1.3	0.98	1.2
sd	0.44	0.2	0.4
median	1.4	0.9	1.1
Q1 - Q3	0.83 – 1.6	0.88 – 1.1	0.83 – 1.5
min - max	0.73 – 1.9	0.74 – 1.2	0.73 – 1.9
^MWU Mann-Whitney’s U test
^HL CI for the Hodges-Lehmann estimator

Kruskal-Wallis one-way ANOVA

Dataset origin: ?kruskal.test (accessed on R-version 4.0.3).


x <- c(2.9, 3.0, 2.5, 2.6, 3.2) # normal subjects
y <- c(3.8, 2.7, 4.0, 2.4)      # with obstructive airway disease
z <- c(2.8, 3.4, 3.7, 2.2, 2.0) # with asbestosis
group <- c(rep("Trt", length(x)), rep("Ctrl", length(y)), rep("Placebo", length(z)))
dat_kruskal.test <- tibble(var=c(x,y,z), group=group)

descr(dat_kruskal.test, "group", test_options = c(nonparametric=TRUE))

Variables	Trt	Ctrl	Placebo	Total	p
	(N=5)	(N=4)	(N=5)	(N=14)
var
N	5	4	5	14	0.680^KW
mean	2.8	3.2	2.8	2.9
sd	0.29	0.79	0.74	0.61
median	2.9	3.2	2.8	2.8
Q1 - Q3	2.6 – 3	2.5 – 3.9	2.2 – 3.4	2.5 – 3.4
min - max	2.5 – 3.2	2.4 – 4	2 – 3.7	2 – 4
^KW Kruskal-Wallis’s one-way ANOVA

SAS Output

The SAS System

The NPAR1WAY Procedure

Kruskal-Wallis Test
Chi-Square	DF	Pr > ChiSq
0.7714	2	0.6800

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.

Friedman test

Dataset origin: ?friedman.test (accessed on R-version 4.0.3).


RoundingTimes <-
matrix(c(5.40, 5.50, 5.55,
         5.85, 5.70, 5.75,
         5.20, 5.60, 5.50,
         5.55, 5.50, 5.40,
         5.90, 5.85, 5.70,
         5.45, 5.55, 5.60,
         5.40, 5.40, 5.35,
         5.45, 5.50, 5.35,
         5.25, 5.15, 5.00,
         5.85, 5.80, 5.70,
         5.25, 5.20, 5.10,
         5.65, 5.55, 5.45,
         5.60, 5.35, 5.45,
         5.05, 5.00, 4.95,
         5.50, 5.50, 5.40,
         5.45, 5.55, 5.50,
         5.55, 5.55, 5.35,
         5.45, 5.50, 5.55,
         5.50, 5.45, 5.25,
         5.65, 5.60, 5.40,
         5.70, 5.65, 5.55,
         6.30, 6.30, 6.25),
       nrow = 22,
       byrow = TRUE,
       dimnames = list(1 : 22,
                       c("Round Out", "Narrow Angle", "Wide Angle")))

idx <- rep(1:22, 3)
dat <- tibble(var = c(RoundingTimes[,1], RoundingTimes[,2], RoundingTimes[,3]),
              group = c(rep("Round Out", 22), rep("Narrow Angle", 22), rep("Wide Angle", 22)))


descr(dat, "group", test_options = list(nonparametric=TRUE, indices=idx, paired=TRUE))
#> You specified paired tests and did not explicitly
#> specify format_options$print_Total. print_Total is set to FALSE.

Variables	Round Out	Narrow Angle	Wide Angle	p
	(N=22)	(N=22)	(N=22)
var
N	22	22	22	0.004^Frie
mean	5.5	5.5	5.5
sd	0.27	0.26	0.27
median	5.5	5.5	5.4
Q1 - Q3	5.4 – 5.6	5.4 – 5.6	5.3 – 5.5
min - max	5 – 6.3	5 – 6.3	5 – 6.2
^Frie Friedman test

SAS Output

The SAS System

The FREQ Procedure

Summary Statistics for group by var
Controlling for indices

Cochran-Mantel-Haenszel Statistics (Based on Rank Scores)
Statistic	Alternative Hypothesis	DF	Value	Prob
1	Nonzero Correlation	1	5.3571	0.0206
2	Row Mean Scores Differ	2	11.1429	0.0038

Total Sample Size = 66

Cochrans Q test

Dataset origin: This is a replicate of the dataset from the SAS documentation of the Cochrane Q test from proc freq.


d.frm <- DescTools::Untable(xtabs(c(6,2,2,6,16,4,4,6) ~ ., 
    expand.grid(rep(list(c("F","U")), times=3))), 
    colnames = LETTERS[1:3])

# rearrange to long shape    
d.long <- reshape(d.frm, varying=1:3, times=names(d.frm)[c(1:3)], 
                  v.names="resp", direction="long")
idx <- d.long$id
dat <- d.long[, 1:2] %>% mutate(time=as.character(time), resp=as.character(resp))

descr(dat, "time", test_options = list(indices=idx, paired=TRUE))
#> You specified paired tests and did not explicitly
#> specify format_options$print_Total. print_Total is set to FALSE.

Variables	A	B	C	p
	(N=46)	(N=46)	(N=46)
resp
F	28 (61%)	28 (61%)	16 (35%)	0.014^CocQ
U	18 (39%)	18 (39%)	30 (65%)
^CocQ Cochran’s Q test

SAS Output

The SAS System

The FREQ Procedure

Summary Statistics for B by C

Controlling for A

Cochran's Q, for A by B by C
Chi-Square	DF	Pr > ChiSq
8.4706	2	0.0145

McNemars test

Dataset origin: ?mcnemar.test (accessed on R-version 4.0.3). Note that this dataset is not explicitly defined in ?mcnemar.test. It is constructed to reflect the cross table defined there.

dat <- tibble::tibble(var = c(rep("Approve", 794), rep("Approve", 150), rep("Disapprove", 86), rep("Disapprove", 570),
                      rep("Approve", 794), rep("Disapprove", 150), rep("Approve", 86), rep("Disapprove", 570)),
              group= c(rep("first", 1600), rep("second",1600)))
              
descr(dat, "group", test_options = list(paired=TRUE, indices=c(1:1600, 1:1600)))
#> You specified paired tests and did not explicitly
#> specify format_options$print_Total. print_Total is set to FALSE.
#> Warning in sig_test(var, group, test_options, test_override, var_name): Confidence intervals for differences in proportions ignore the paired structure of the data.
#> Use Exact McNemar's test if you want confidence intervals which use the test statistic of the
#> exact McNemar's test.

Variables	first	second	p	CI
	(N=1600)	(N=1600)
var
Approve	944 (59%)	880 (55%)	<0.001^McN	[0.0057, 0.074]^PWa
Disapprove	656 (41%)	720 (45%)
^McN McNemar’s test
^PWa CI for difference in proportions derived from a normal (“Wald”) approximation

descr(dat, "group", test_options = list(paired=TRUE, exact=TRUE, indices=c(1:1600, 1:1600)))
#> You specified paired tests and did not explicitly
#> specify format_options$print_Total. print_Total is set to FALSE.

Variables	first	second	p	CI
	(N=1600)	(N=1600)
var
Approve	944 (59%)	880 (55%)	<0.001^eMcN	[0.021, 0.059]^PMcN
Disapprove	656 (41%)	720 (45%)
^eMcN Exact McNemar’s test
^PMcN CI for difference in proportions derived from an exact McNemar’s test


dat <-
  tibble::tibble(x = c(
    rep("Approve", 794),
    rep("Approve", 150),
    rep("Disapprove", 86),
    rep("Disapprove", 570)
  ),
  y = c(
    rep("Approve", 794),
    rep("Disapprove", 150),
    rep("Approve", 86),
    rep("Disapprove", 570)
  ))

mcnemar.test(dat$x, dat$y, correct = FALSE)
#> 
#>  McNemar's Chi-squared test
#> 
#> data:  dat$x and dat$y
#> McNemar's chi-squared = 17.356, df = 1, p-value = 3.099e-05

SAS Output

The SAS System

The FREQ Procedure

Approve

794

49.63

84.11

90.23

150

9.38

15.89

20.83

944

59.00

Disapprove

86

5.38

13.11

9.77

570

35.63

86.89

79.17

656

41.00

Total

880

55.00

720

45.00

1600

100.00

Statistics for Table of x by y

McNemar's Test
Chi-Square	DF	Pr > ChiSq	Exact Pr >= ChiSq
17.3559	1	<.0001	.000037159

Simple Kappa Coefficient
Estimate	Standard Error	95% Confidence Limits
0.6996	0.0180	0.6644	0.7348

Sample Size = 1600

Chi-squared test

Dataset origin: “Gender-Party dataset”:?chisq.test (accessed on R-version 4.0.3). “a-b dataset”: selfmade.


dat <- tibble(gender=c(rep("F",sum(c(762, 327, 468)) ), rep("M", sum( c(484, 239, 477)))),
              party=c(rep("Democrat", 762), rep("Independent", 327), rep("Republican", 468),
                      rep("Democrat", 484), rep("Independent", 239), rep("Republican", 477)))
              
descr(dat, "gender")

Variables	F	M	Total	p
	(N=1557)	(N=1200)	(N=2757)
party
Democrat	762 (49%)	484 (40%)	1246 (45%)	<0.001^chi2
Independent	327 (21%)	239 (20%)	566 (21%)
Republican	468 (30%)	477 (40%)	945 (34%)
^chi2 Pearson’s chi-squared test

descr(dat)

Variables	Total	p
	(N=2757)
gender
F	1557 (56%)	<0.001^chi1
M	1200 (44%)
party
Democrat	1246 (45%)	<0.001^chi1
Independent	566 (21%)
Republican	945 (34%)
^chi1 Chi-squared goodness-of-fit test

chisq.test(dat$gender, dat$party)
#> 
#>  Pearson's Chi-squared test
#> 
#> data:  dat$gender and dat$party
#> X-squared = 30.07, df = 2, p-value = 2.954e-07
chisq.test(table(dat$gender))
#> 
#>  Chi-squared test for given probabilities
#> 
#> data:  table(dat$gender)
#> X-squared = 46.227, df = 1, p-value = 1.053e-11
chisq.test(table(dat$party))
#> 
#>  Chi-squared test for given probabilities
#> 
#> data:  table(dat$party)
#> X-squared = 252.68, df = 2, p-value < 2.2e-16

dat <- tibble(
  
  a = factor(c(0,
               0,
               1,
               1,
               0,
               0,
               0,
               0,
               0,
               0,
               1)),
  b = factor(c(1,
               1,
               1,
               1,
               1,
               1,
               1,
               0,
               0,
               1,
               0))

)
descr(dat, "b")
#> Warning in (function (x, y = NULL, correct = TRUE, p = rep(1/length(x), : Chi-
#> squared approximation may be incorrect
#> Warning in stats::prop.test(table(group, var), correct = FALSE): Chi-squared
#> approximation may be incorrect

Variables	0	1	Total	p	CI
	(N=3)	(N=8)	(N=11)
a
0	2 (67%)	6 (75%)	8 (73%)	0.782^chi2	[-0.7, 0.53]^PWa
1	1 (33%)	2 (25%)	3 (27%)
^chi2 Pearson’s chi-squared test
^PWa CI for difference in proportions derived from a normal (“Wald”) approximation

SAS Output

The SAS System

The FREQ Procedure

gender	Frequency	Percent	Cumulative Frequency	Cumulative Percent
F	1557	56.47	1557	56.47
M	1200	43.53	2757	100.00

Chi-Square Test for Equal Proportions
Chi-Square	46.2274
DF	1
Pr > ChiSq	<.0001

Sample Size = 2757

party	Frequency	Percent	Cumulative Frequency	Cumulative Percent
Democrat	1246	45.19	1246	45.19
Independent	566	20.53	1812	65.72
Republican	945	34.28	2757	100.00

Chi-Square Test for Equal Proportions
Chi-Square	252.6812
DF	2
Pr > ChiSq	<.0001

Sample Size = 2757

F

762

27.64

48.94

61.16

327

11.86

21.00

57.77

468

16.97

30.06

49.52

1557

56.47

M

484

17.56

40.33

38.84

239

8.67

19.92

42.23

477

17.30

39.75

50.48

1200

43.53

Total

1246

45.19

566

20.53

945

34.28

2757

100.00

Statistics for Table of gender by party

Statistic	DF	Value	Prob
Chi-Square	2	30.0701	<.0001
Likelihood Ratio Chi-Square	2	30.0167	<.0001
Mantel-Haenszel Chi-Square	1	28.9797	<.0001
Phi Coefficient		0.1044
Contingency Coefficient		0.1039
Cramer's V		0.1044

Sample Size = 2757

The SAS System

The FREQ Procedure

0

2

18.18

25.00

66.67

6

54.55

75.00

8

72.73

1

9.09

33.33

2

18.18

66.67

25.00

3

27.27

Total

3

27.27

8

72.73

11

100.00

Statistics for Table of a by b

Statistic	DF	Value	Prob
Chi-Square	1	0.0764	0.7823
Likelihood Ratio Chi-Square	1	0.0745	0.7849
Continuity Adj. Chi-Square	1	0.0000	1.0000
Mantel-Haenszel Chi-Square	1	0.0694	0.7921
Phi Coefficient		-0.0833
Contingency Coefficient		0.0830
Cramer's V		-0.0833
WARNING: 75% of the cells have expected counts less than 5. Chi-Square may not be a valid test.

Fisher's Exact Test
Cell (1,1) Frequency (F)	2
Left-sided Pr <= F	0.6606
Right-sided Pr >= F	0.8485

Table Probability (P)	0.5091
Two-sided Pr <= P	1.0000

Sample Size = 11

t-test

Dataset origin: ?t.test, which references ?sleep (accessed on R-version 4.0.3).


dat <- sleep[, c("extra", "group")]
              

descr(dat[, "extra"])

Variables	Total	p
	(N=20)
value
N	20	0.003^tt1
mean	1.5
sd	2
median	0.95
Q1 - Q3	-0.05 – 3.4
min - max	-1.6 – 5.5
^tt1 Student’s one-sample t-test

descr(dat, "group")

Variables	1	2	Total	p	CI
	(N=10)	(N=10)	(N=20)
extra
N	10	10	20	0.079^tt2	[-3.4, 0.21]^t
mean	0.75	2.3	1.5
sd	1.8	2	2
median	0.35	1.8	0.95
Q1 - Q3	-0.2 – 2	0.8 – 4.4	-0.05 – 3.4
min - max	-1.6 – 3.7	-0.1 – 5.5	-1.6 – 5.5
^tt2 Welch’s two-sample t-test
^t CI for difference in means derived from the t-distribution

descr(dat, "group", test_options = list(paired=TRUE, indices=rep(1:10, 2)))
#> You specified paired tests and did not explicitly
#> specify format_options$print_Total. print_Total is set to FALSE.

Variables	1	2	p	CI
	(N=10)	(N=10)
extra
N	10	10	0.003^tpar	[-2.5, -0.7]^t
mean	0.75	2.3
sd	1.8	2
median	0.35	1.8
Q1 - Q3	-0.2 – 2	0.8 – 4.4
min - max	-1.6 – 3.7	-0.1 – 5.5
^tpar Student’s paired t-test
^t CI for difference in means derived from the t-distribution

SAS Output

The SAS System

The UNIVARIATE Procedure

Variable: extra

Moments
N	20	Sum Weights	20
Mean	1.54	Sum Observations	30.8
Std Deviation	2.01791972	Variance	4.072
Skewness	0.45185739	Kurtosis	-0.7747436
Uncorrected SS	124.8	Corrected SS	77.368
Coeff Variation	131.033748	Std Error Mean	0.45122057

Basic Statistical Measures
Location		Variability
Mean	1.54000	Std Deviation	2.01792
Median	0.95000	Variance	4.07200
Mode	-0.10000	Range	7.10000
		Interquartile Range	3.45000

Note: The mode displayed is the smallest of 3 modes with a count of 2.

Tests for Location: Mu0=0
Test	Statistic		p Value
Student's t	t	3.412965	Pr > \|t\|	0.0029
Sign	M	4.5	Pr >= \|M\|	0.0636
Signed Rank	S	67.5	Pr >= \|S\|	0.0048

Quantiles (Definition 5)
Level	Quantile
100% Max	5.50
99%	5.50
95%	5.05
90%	4.50
75% Q3	3.40
50% Median	0.95
25% Q1	-0.05
10%	-0.70
5%	-1.40
1%	-1.60
0% Min	-1.60

Extreme Observations
Lowest		Highest
Value	Obs	Value	Obs
-1.6	2	3.4	20
-1.2	4	3.7	7
-0.2	3	4.4	16
-0.1	15	4.6	19
-0.1	5	5.5	17

The SAS System

The TTEST Procedure

Variable: extra

group	Method	N	Mean	Std Dev	Std Err	Minimum	Maximum
1		10	0.7500	1.7890	0.5657	-1.6000	3.7000
2		10	2.3300	2.0022	0.6332	-0.1000	5.5000
Diff (1-2)	Pooled		-1.5800	1.8986	0.8491
Diff (1-2)	Satterthwaite		-1.5800		0.8491

group	Method	Mean	95% CL Mean		Std Dev	95% CL Std Dev
1		0.7500	-0.5298	2.0298	1.7890	1.2305	3.2660
2		2.3300	0.8977	3.7623	2.0022	1.3772	3.6553
Diff (1-2)	Pooled	-1.5800	-3.3639	0.2039	1.8986	1.4346	2.8077
Diff (1-2)	Satterthwaite	-1.5800	-3.3655	0.2055

Method	Variances	DF	t Value	Pr > \|t\|
Pooled	Equal	18	-1.86	0.0792
Satterthwaite	Unequal	17.776	-1.86	0.0794

Equality of Variances
Method	Num DF	Den DF	F Value	Pr > F
Folded F	9	9	1.25	0.7427

F-test

Dataset origin: Modified version of ?datasets::npk. npk is used in ?aov (accessed on R-version 4.0.3).


dat <- data.frame(
  y = npk$yield,
  P = ordered(gl(3, 24)),
  N = ordered(gl(3, 1, 24))
)
              
descr(dat[, c("y", "P")], "P")

Variables	1	2	3	Total	p
	(N=24)	(N=24)	(N=24)	(N=72)
y
N	24	24	24	72	>0.999^F
mean	55	55	55	55
sd	6.2	6.2	6.2	6.1
median	56	56	56	56
Q1 - Q3	50 – 59	50 – 59	50 – 59	50 – 59
min - max	44 – 70	44 – 70	44 – 70	44 – 70
^F F-test (ANOVA)

descr(dat[, c("y", "N")], "N")

Variables	1	2	3	Total	p
	(N=24)	(N=24)	(N=24)	(N=72)
y
N	24	24	24	72	0.346^F
mean	54	56	54	55
sd	5.6	6.9	5.6	6.1
median	56	55	56	56
Q1 - Q3	50 – 58	50 – 61	49 – 58	50 – 59
min - max	44 – 62	49 – 70	46 – 63	44 – 70
^F F-test (ANOVA)

SAS Output

The SAS System

The ANOVA Procedure

Class Level Information
Class	Levels	Values
P	3	1 2 3

Number of Observations Read	72
Number of Observations Used	72

The SAS System

The ANOVA Procedure

Dependent Variable: y

Source	DF	Sum of Squares	Mean Square	F Value	Pr > F
Model	2	0.000000	0.000000	0.00	1.0000
Error	69	2629.095000	38.102826
Corrected Total	71	2629.095000

R-Square	Coeff Var	Root MSE	y Mean
0.000000	11.24874	6.172749	54.87500

Source	DF	Anova SS	Mean Square	F Value	Pr > F
P	2	0	0	0.00	1.0000

The SAS System

The ANOVA Procedure

Class Level Information
Class	Levels	Values
N	3	1 2 3

Number of Observations Read	72
Number of Observations Used	72

The SAS System

The ANOVA Procedure

Dependent Variable: y

Source	DF	Sum of Squares	Mean Square	F Value	Pr > F
Model	2	79.702500	39.851250	1.08	0.3457
Error	69	2549.392500	36.947717
Corrected Total	71	2629.095000

R-Square	Coeff Var	Root MSE	y Mean
0.030316	11.07693	6.078463	54.87500

Source	DF	Anova SS	Mean Square	F Value	Pr > F
N	2	79.70250000	39.85125000	1.08	0.3457

Mixed model ANOVA

Dataset origin: Modified version of ?nlme::Orthodont. Orthodont is used in ?lme (accessed on R-version 4.0.3).


dat <- nlme::Orthodont
dat2 <- nlme::Orthodont[1:64,]
dat2$Sex <- "Divers"
dat2$distance <- dat2$distance + c(rep(0.1*c(1,4,3,2), 10), 0.1*rep(c(0.4,2,1.5, 2.3), 6) )
dat2$Subject <- str_replace_all(dat2$Subject, "M", "D")
dat <- bind_rows(dat, dat2)
dat <- as_tibble(dat)


descr(dat[, c("Sex", "distance")], "Sex", test_options = list(paired=TRUE, indices=dat$Subject))
#> You specified paired tests and did not explicitly
#> specify format_options$print_Total. print_Total is set to FALSE.

Variables	Male	Female	Divers	p
	(N=64)	(N=44)	(N=64)
distance
N	64	44	64	0.003^MiAn
mean	25	23	25
sd	2.9	2.4	2.9
median	25	23	25
Q1 - Q3	23 – 26	21 – 24	23 – 27
min - max	17 – 32	16 – 28	17 – 32
^MiAn Mixed model ANOVA

SAS Output

The SAS System

The Mixed Procedure

Model Information
Data Set	WORK.RDATA
Dependent Variable	distance
Covariance Structure	Variance Components
Subject Effect	Subject
Estimation Method	REML
Residual Variance Method	Profile
Fixed Effects SE Method	Model-Based
Degrees of Freedom Method	Satterthwaite

Class Level Information
Class	Levels	Values
Sex	3	Divers Female Male
Subject	43	D01 D02 D03 D04 D05 D06 D07 D08 D09 D10 D11 D12 D13 D14 D15 D16 F01 F02 F03 F04 F05 F06 F07 F08 F09 F10 F11 M01 M02 M03 M04 M05 M06 M07 M08 M09 M10 M11 M12 M13 M14 M15 M16

Dimensions
Covariance Parameters	2
Columns in X	4
Columns in Z per Subject	1
Subjects	43
Max Obs per Subject	4

Number of Observations
Number of Observations Read	172
Number of Observations Used	172
Number of Observations Not Used	0

Iteration History
Iteration	Evaluations	-2 Res Log Like	Criterion
0	1	838.60158118
1	1	823.17375628	0.00000000

Convergence criteria met.

Covariance Parameter Estimates
Cov Parm	Subject	Estimate
Intercept	Subject	2.2123
Residual		5.6941

Fit Statistics
-2 Res Log Likelihood	823.2
AIC (Smaller is Better)	827.2
AICC (Smaller is Better)	827.2
BIC (Smaller is Better)	830.7

Type 3 Tests of Fixed Effects
Effect	Num DF	Den DF	F Value	Pr > F
Sex	2	40	6.69	0.0031

Boschloos test

Dataset origin: selfmade.

DescrTab2 uses the exact2x2::boschloo with option tsmethod=central to calculate p-values. There is no comparison for this option readily available.

dat <- tibble(gender=factor(c("M", "M", "M", "M", "M", "M", "F", "F", "F", "F", "F")),
              party=factor(c("A", "A", "B", "B", "B", "B", "A", "A", "A", "B", "B")))
descr(dat, "gender", test_options = c(exact=TRUE))

Variables	F	M	Total	p	CI
	(N=5)	(N=6)	(N=11)
party
A	3 (60%)	2 (33%)	5 (45%)	0.491^Bolo	[-0.76, 0.37]^PUnc
B	2 (40%)	4 (67%)	6 (55%)
^Bolo Boschloo’s test
^PUnc CI for difference in proportions derived from an unconditional exact test

exact2x2::boschloo(3, 5, 2, 6, tsmethod="central")
#> 
#>  Boschloo's test
#> 
#> data:  x1/n1=(3/5) and x2/n2= (2/6)
#> proportion 1 = 0.6, proportion 2 = 0.33333, p-value = 0.4909
#> alternative hypothesis: true p2(1-p1)/[p1(1-p2)] is less than 1
#>  percent confidence interval:
#>  NA NA
#> sample estimates:
#> p2(1-p1)/[p1(1-p2)] 
#>           0.3333333

However, we can compare the exact2x2::boschloo with tsmethod=minlike to Exact::exact.test:

exact2x2::boschloo(3, 5, 2, 6, tsmethod="minlike")
#> 
#>  Boschloo's test
#> 
#> data:  x1/n1=(3/5) and x2/n2= (2/6)
#> proportion 1 = 0.6, proportion 2 = 0.33333, p-value = 0.5488
#> alternative hypothesis: true p2(1-p1)/[p1(1-p2)] is not equal to 1
#> 95 percent confidence interval:
#>  NA NA
#> sample estimates:
#> p2(1-p1)/[p1(1-p2)] 
#>           0.3333333
Exact::exact.test(table(dat), method="boschloo", to.plot = FALSE)
#> 
#>  Boschloo's Exact Test
#> 
#> data:  3 out of 5 vs. 2 out of 6
#> test statistic = 0.5671, first sample size = 5, second sample size = 6,
#> p-value = 0.5488
#> alternative hypothesis: true difference in proportion is not equal to 0
#> sample estimates:
#> difference in proportion 
#>                0.2666667

Comparison with other software

2022-09-06

Introduction

A note about the layout

Wilcoxon one-sample signed-rank test

Mann-Whitney U test

Kruskal-Wallis one-way ANOVA

Friedman test

Cochrans Q test

McNemars test

Chi-squared test

t-test

F-test

Mixed model ANOVA

Boschloos test