docs/RepeatedVignette.md
In cwendorf/easiOrigin: easiOrigin
Repeated Vignette
This vignette analyzes data from a single-factor within-subjects design.
- Data Management
- Data Entry
- Descriptive Statistics
- Analyses of the Means
- Confidence Intervals
- Significance Tests
- Analyses of a Mean Difference
- Confidence Interval
- Significance Test
- Analyses of the Pairwise
Comparisons
- Confidence Intervals
- Significance Tests
- Analyses of a Set of Contrasts
- Confidence Intervals
- Significance Tests
- Analyses of the Effect
- Source Table
- Significance Test
- Effect Size
Data Management
Data Entry
Outcome1 <- c(0,0,3,5)
Outcome2 <- c(4,7,4,9)
Outcome3 <- c(9,6,4,9)
RepeatedData <- data.frame(Outcome1,Outcome2,Outcome3)
Descriptive Statistics
descMeans(RepeatedData)
## $`Descriptive Statistics for the Data`
## N M SD
## Outcome1 4.000 2.000 2.449
## Outcome2 4.000 6.000 2.449
## Outcome3 4.000 7.000 2.449
Analyses of the Means
Confidence Intervals
ciMeans(RepeatedData)
## $`Confidence Intervals for the Means`
## M SE df LL UL
## Outcome1 2.000 1.225 9.000 -0.771 4.771
## Outcome2 6.000 1.225 9.000 3.229 8.771
## Outcome3 7.000 1.225 9.000 4.229 9.771
graphMeans(RepeatedData)
ciMeans(RepeatedData,conf.level=.99)
## $`Confidence Intervals for the Means`
## M SE df LL UL
## Outcome1 2.000 1.225 9.000 -1.980 5.980
## Outcome2 6.000 1.225 9.000 2.020 9.980
## Outcome3 7.000 1.225 9.000 3.020 10.980
graphMeans(RepeatedData,conf.level=.99,mu=5)
Significance Tests
nhstMeans(RepeatedData)
## $`Hypothesis Tests for the Means`
## Diff SE t df p
## Outcome1 2.000 1.225 1.633 9.000 0.137
## Outcome2 6.000 1.225 4.899 9.000 0.001
## Outcome3 7.000 1.225 5.715 9.000 0.000
nhstMeans(RepeatedData,mu=5)
## $`Hypothesis Tests for the Means`
## Diff SE t df p
## Outcome1 -3.000 1.225 -2.449 9.000 0.037
## Outcome2 1.000 1.225 0.816 9.000 0.435
## Outcome3 2.000 1.225 1.633 9.000 0.137
Analyses of a Mean Difference
ComparisonData <- data.frame(Outcome1,Outcome2)
Confidence Interval
ciDifference(ComparisonData)
## $`Confidence Interval for the Mean Difference`
## Diff SE df LL UL
## Comparison -4.000 1.225 3.000 -7.898 -0.102
graphDifference(ComparisonData)
ciDifference(ComparisonData,conf.level=.99)
## $`Confidence Interval for the Mean Difference`
## Diff SE df LL UL
## Comparison -4.000 1.225 3.000 -11.154 3.154
graphDifference(ComparisonData,conf.level=.99)
Significance Test
nhstDifference(ComparisonData)
## $`Hypothesis Tests for the Mean Difference`
## Diff SE t df p
## Comparison -4.000 1.225 -3.266 3.000 0.047
nhstDifference(ComparisonData,mu=-2)
## $`Hypothesis Tests for the Mean Difference`
## Diff SE t df p
## Comparison -2.000 1.225 -1.633 3.000 0.201
Analyses of the Pairwise Comparisons
Confidence Intervals
ciPairwise(RepeatedData)
## $`Confidence Intervals for the Pairwise Comparisons`
## Diff SE df LL UL
## Outcome1 v Outcome2 -4.000 1.225 3.000 -7.898 -0.102
## Outcome1 v Outcome3 -5.000 1.683 3.000 -10.357 0.357
## Outcome2 v Outcome3 -1.000 1.354 3.000 -5.309 3.309
graphPairwise(RepeatedData)
ciPairwise(RepeatedData,conf.level=.99)
## $`Confidence Intervals for the Pairwise Comparisons`
## Diff SE df LL UL
## Outcome1 v Outcome2 -4.000 1.225 3.000 -11.154 3.154
## Outcome1 v Outcome3 -5.000 1.683 3.000 -14.832 4.832
## Outcome2 v Outcome3 -1.000 1.354 3.000 -8.909 6.909
graphPairwise(RepeatedData,mu=-2,conf.level=.99)
Significance Tests
nhstPairwise(RepeatedData)
## $`Hypothesis Tests for the Pairwise Comparisons`
## Diff SE t df p
## Outcome1 v Outcome2 -4.000 1.225 -3.266 3.000 0.047
## Outcome1 v Outcome3 -5.000 1.683 -2.970 3.000 0.059
## Outcome2 v Outcome3 -1.000 1.354 -0.739 3.000 0.514
nhstPairwise(RepeatedData,mu=-2)
## $`Hypothesis Tests for the Pairwise Comparisons`
## Diff SE t df p
## Outcome1 v Outcome2 -2.000 1.225 -1.633 3.000 0.201
## Outcome1 v Outcome3 -3.000 1.683 -1.782 3.000 0.173
## Outcome2 v Outcome3 1.000 1.354 0.739 3.000 0.514
Analyses of a Set of Contrasts
Confidence Intervals
ciContrasts(RepeatedData,contrasts=contr.sum)
## $`Confidence Intervals for the Contrasts`
## Est SE df LL UL
## (Intercept) 5.000 0.585 6.000 3.568 6.432
## Factor1 -3.000 0.828 6.000 -5.025 -0.975
## Factor2 1.000 0.828 6.000 -1.025 3.025
graphContrasts(RepeatedData,contrasts=contr.sum)
ciContrasts(RepeatedData,contrasts=contr.sum,conf.level=.99)
## $`Confidence Intervals for the Contrasts`
## Est SE df LL UL
## (Intercept) 5.000 0.585 6.000 2.830 7.170
## Factor1 -3.000 0.828 6.000 -6.069 0.069
## Factor2 1.000 0.828 6.000 -2.069 4.069
graphContrasts(RepeatedData,contrasts=contr.sum,conf.level=.99)
Significance Tests
nhstContrasts(RepeatedData,contrasts=contr.sum)
## $`Hypothesis Tests for the Contrasts`
## Est SE t df p
## (Intercept) 5.000 0.585 8.542 6.000 0.000
## Factor1 -3.000 0.828 -3.624 6.000 0.011
## Factor2 1.000 0.828 1.208 6.000 0.272
Analyses of the Effect
Source Table
descEffect(RepeatedData)
## $`Source Table for the Effect`
## SS df MS
## Factor 56.000 2.000 28.000
## Subjects 29.333 3.000 9.778
## Residuals 24.667 6.000 4.111
Significance Test
nhstEffect(RepeatedData)
## $`Hypothesis Test for the Effect`
## F df p
## Factor 6.811 2.000 0.029
## Subjects 2.378 3.000 0.169
## Residuals NA 6.000 NA
Effect Size
pvaEffect(RepeatedData)
## $`Proportion of Variance Accounted for by the Effect`
## EtaSq ParEtaSq
## Factor 0.509 0.694
## Subjects 0.267 0.543
## Residuals 0.224 NA
cwendorf/easiOrigin documentation built on Nov. 1, 2023, 10:57 a.m.
R Package Documentation
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Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Repeated Vignette
This vignette analyzes data from a single-factor within-subjects design.
- Data Management
- Data Entry
- Descriptive Statistics
- Analyses of the Means
- Confidence Intervals
- Significance Tests
- Analyses of a Mean Difference
- Confidence Interval
- Significance Test
- Analyses of the Pairwise Comparisons
- Confidence Intervals
- Significance Tests
- Analyses of a Set of Contrasts
- Confidence Intervals
- Significance Tests
- Analyses of the Effect
- Source Table
- Significance Test
- Effect Size
Data Management
Data Entry
Outcome1 <- c(0,0,3,5)
Outcome2 <- c(4,7,4,9)
Outcome3 <- c(9,6,4,9)
RepeatedData <- data.frame(Outcome1,Outcome2,Outcome3)
Descriptive Statistics
descMeans(RepeatedData)
## $`Descriptive Statistics for the Data`
## N M SD
## Outcome1 4.000 2.000 2.449
## Outcome2 4.000 6.000 2.449
## Outcome3 4.000 7.000 2.449
Analyses of the Means
Confidence Intervals
ciMeans(RepeatedData)
## $`Confidence Intervals for the Means`
## M SE df LL UL
## Outcome1 2.000 1.225 9.000 -0.771 4.771
## Outcome2 6.000 1.225 9.000 3.229 8.771
## Outcome3 7.000 1.225 9.000 4.229 9.771
graphMeans(RepeatedData)
ciMeans(RepeatedData,conf.level=.99)
## $`Confidence Intervals for the Means`
## M SE df LL UL
## Outcome1 2.000 1.225 9.000 -1.980 5.980
## Outcome2 6.000 1.225 9.000 2.020 9.980
## Outcome3 7.000 1.225 9.000 3.020 10.980
graphMeans(RepeatedData,conf.level=.99,mu=5)
Significance Tests
nhstMeans(RepeatedData)
## $`Hypothesis Tests for the Means`
## Diff SE t df p
## Outcome1 2.000 1.225 1.633 9.000 0.137
## Outcome2 6.000 1.225 4.899 9.000 0.001
## Outcome3 7.000 1.225 5.715 9.000 0.000
nhstMeans(RepeatedData,mu=5)
## $`Hypothesis Tests for the Means`
## Diff SE t df p
## Outcome1 -3.000 1.225 -2.449 9.000 0.037
## Outcome2 1.000 1.225 0.816 9.000 0.435
## Outcome3 2.000 1.225 1.633 9.000 0.137
Analyses of a Mean Difference
ComparisonData <- data.frame(Outcome1,Outcome2)
Confidence Interval
ciDifference(ComparisonData)
## $`Confidence Interval for the Mean Difference`
## Diff SE df LL UL
## Comparison -4.000 1.225 3.000 -7.898 -0.102
graphDifference(ComparisonData)
ciDifference(ComparisonData,conf.level=.99)
## $`Confidence Interval for the Mean Difference`
## Diff SE df LL UL
## Comparison -4.000 1.225 3.000 -11.154 3.154
graphDifference(ComparisonData,conf.level=.99)
Significance Test
nhstDifference(ComparisonData)
## $`Hypothesis Tests for the Mean Difference`
## Diff SE t df p
## Comparison -4.000 1.225 -3.266 3.000 0.047
nhstDifference(ComparisonData,mu=-2)
## $`Hypothesis Tests for the Mean Difference`
## Diff SE t df p
## Comparison -2.000 1.225 -1.633 3.000 0.201
Analyses of the Pairwise Comparisons
Confidence Intervals
ciPairwise(RepeatedData)
## $`Confidence Intervals for the Pairwise Comparisons`
## Diff SE df LL UL
## Outcome1 v Outcome2 -4.000 1.225 3.000 -7.898 -0.102
## Outcome1 v Outcome3 -5.000 1.683 3.000 -10.357 0.357
## Outcome2 v Outcome3 -1.000 1.354 3.000 -5.309 3.309
graphPairwise(RepeatedData)
ciPairwise(RepeatedData,conf.level=.99)
## $`Confidence Intervals for the Pairwise Comparisons`
## Diff SE df LL UL
## Outcome1 v Outcome2 -4.000 1.225 3.000 -11.154 3.154
## Outcome1 v Outcome3 -5.000 1.683 3.000 -14.832 4.832
## Outcome2 v Outcome3 -1.000 1.354 3.000 -8.909 6.909
graphPairwise(RepeatedData,mu=-2,conf.level=.99)
Significance Tests
nhstPairwise(RepeatedData)
## $`Hypothesis Tests for the Pairwise Comparisons`
## Diff SE t df p
## Outcome1 v Outcome2 -4.000 1.225 -3.266 3.000 0.047
## Outcome1 v Outcome3 -5.000 1.683 -2.970 3.000 0.059
## Outcome2 v Outcome3 -1.000 1.354 -0.739 3.000 0.514
nhstPairwise(RepeatedData,mu=-2)
## $`Hypothesis Tests for the Pairwise Comparisons`
## Diff SE t df p
## Outcome1 v Outcome2 -2.000 1.225 -1.633 3.000 0.201
## Outcome1 v Outcome3 -3.000 1.683 -1.782 3.000 0.173
## Outcome2 v Outcome3 1.000 1.354 0.739 3.000 0.514
Analyses of a Set of Contrasts
Confidence Intervals
ciContrasts(RepeatedData,contrasts=contr.sum)
## $`Confidence Intervals for the Contrasts`
## Est SE df LL UL
## (Intercept) 5.000 0.585 6.000 3.568 6.432
## Factor1 -3.000 0.828 6.000 -5.025 -0.975
## Factor2 1.000 0.828 6.000 -1.025 3.025
graphContrasts(RepeatedData,contrasts=contr.sum)
ciContrasts(RepeatedData,contrasts=contr.sum,conf.level=.99)
## $`Confidence Intervals for the Contrasts`
## Est SE df LL UL
## (Intercept) 5.000 0.585 6.000 2.830 7.170
## Factor1 -3.000 0.828 6.000 -6.069 0.069
## Factor2 1.000 0.828 6.000 -2.069 4.069
graphContrasts(RepeatedData,contrasts=contr.sum,conf.level=.99)
Significance Tests
nhstContrasts(RepeatedData,contrasts=contr.sum)
## $`Hypothesis Tests for the Contrasts`
## Est SE t df p
## (Intercept) 5.000 0.585 8.542 6.000 0.000
## Factor1 -3.000 0.828 -3.624 6.000 0.011
## Factor2 1.000 0.828 1.208 6.000 0.272
Analyses of the Effect
Source Table
descEffect(RepeatedData)
## $`Source Table for the Effect`
## SS df MS
## Factor 56.000 2.000 28.000
## Subjects 29.333 3.000 9.778
## Residuals 24.667 6.000 4.111
Significance Test
nhstEffect(RepeatedData)
## $`Hypothesis Test for the Effect`
## F df p
## Factor 6.811 2.000 0.029
## Subjects 2.378 3.000 0.169
## Residuals NA 6.000 NA
Effect Size
pvaEffect(RepeatedData)
## $`Proportion of Variance Accounted for by the Effect`
## EtaSq ParEtaSq
## Factor 0.509 0.694
## Subjects 0.267 0.543
## Residuals 0.224 NA
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