docs/OneWayVignette.md
In cwendorf/easiOrigin: easiOrigin
OneWay Vignette
This vignette analyzes data from a one-way between-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
Factor <- c(rep(1,4),rep(2,4),rep(3,4))
Outcome <- c(0,0,3,5,4,7,4,9,9,6,4,9)
Factor <- factor(Factor,levels=c(1,2,3),labels=c("Level1","Level2","Level3"))
OneWayData <- data.frame(Factor,Outcome)
Descriptive Statistics
descMeans(Outcome~Factor)
## $`Descriptive Statistics for the Data`
## N M SD
## Level1 4.000 2.000 2.449
## Level2 4.000 6.000 2.449
## Level3 4.000 7.000 2.449
Analyses of the Means
Confidence Intervals
ciMeans(Outcome~Factor)
## $`Confidence Intervals for the Means`
## M SE df LL UL
## Level1 2.000 1.225 9.000 -0.771 4.771
## Level2 6.000 1.225 9.000 3.229 8.771
## Level3 7.000 1.225 9.000 4.229 9.771
graphMeans(Outcome~Factor)
ciMeans(Outcome~Factor,conf.level=.99)
## $`Confidence Intervals for the Means`
## M SE df LL UL
## Level1 2.000 1.225 9.000 -1.980 5.980
## Level2 6.000 1.225 9.000 2.020 9.980
## Level3 7.000 1.225 9.000 3.020 10.980
graphMeans(Outcome~Factor,conf.level=.99,mu=5)
Significance Tests
nhstMeans(Outcome~Factor)
## $`Hypothesis Tests for the Means`
## Diff SE t df p
## Level1 2.000 1.225 1.633 9.000 0.137
## Level2 6.000 1.225 4.899 9.000 0.001
## Level3 7.000 1.225 5.715 9.000 0.000
nhstMeans(Outcome~Factor,mu=5)
## $`Hypothesis Tests for the Means`
## Diff SE t df p
## Level1 -3.000 1.225 -2.449 9.000 0.037
## Level2 1.000 1.225 0.816 9.000 0.435
## Level3 2.000 1.225 1.633 9.000 0.137
Analyses of a Mean Difference
Comparison=factor(Factor,c("Level1","Level2"))
Confidence Interval
ciDifference(Outcome~Comparison)
## $`Confidence Interval for the Mean Difference`
## Diff SE df LL UL
## Comparison -4.000 1.732 6.000 -8.238 0.238
graphDifference(Outcome~Comparison)
ciDifference(Outcome~Comparison,conf.level=.99)
## $`Confidence Interval for the Mean Difference`
## Diff SE df LL UL
## Comparison -4.000 1.732 6.000 -10.421 2.421
graphDifference(Outcome~Comparison,conf.level=.99)
Significance Test
nhstDifference(Outcome~Comparison)
## $`Hypothesis Tests for the Mean Difference`
## Diff SE t df p
## Comparison -4.000 1.732 -2.309 6.000 0.060
nhstDifference(Outcome~Comparison,mu=-2)
## $`Hypothesis Tests for the Mean Difference`
## Diff SE t df p
## Comparison -2.000 1.732 -1.155 6.000 0.292
Analyses of the Pairwise Comparisons
Confidence Intervals
ciPairwise(Outcome~Factor)
## $`Confidence Intervals for the Pairwise Comparisons`
## Diff SE df LL UL
## Level1 v Level2 -4.000 1.732 6.000 -8.238 0.238
## Level1 v Level3 -5.000 1.732 6.000 -9.238 -0.762
## Level2 v Level3 -1.000 1.732 6.000 -5.238 3.238
graphPairwise(Outcome~Factor)
ciPairwise(Outcome~Factor,conf.level=.99)
## $`Confidence Intervals for the Pairwise Comparisons`
## Diff SE df LL UL
## Level1 v Level2 -4.000 1.732 6.000 -10.421 2.421
## Level1 v Level3 -5.000 1.732 6.000 -11.421 1.421
## Level2 v Level3 -1.000 1.732 6.000 -7.421 5.421
graphPairwise(Outcome~Factor,mu=-2,conf.level=.99)
Significance Tests
nhstPairwise(Outcome~Factor)
## $`Hypothesis Tests for the Pairwise Comparisons`
## Diff SE t df p
## Level1 v Level2 -4.000 1.732 -2.309 6.000 0.060
## Level1 v Level3 -5.000 1.732 -2.887 6.000 0.028
## Level2 v Level3 -1.000 1.732 -0.577 6.000 0.585
nhstPairwise(Outcome~Factor,mu=-2)
## $`Hypothesis Tests for the Pairwise Comparisons`
## Diff SE t df p
## Level1 v Level2 -2.000 1.732 -1.155 6.000 0.292
## Level1 v Level3 -3.000 1.732 -1.732 6.000 0.134
## Level2 v Level3 1.000 1.732 0.577 6.000 0.585
Analyses of a Set of Contrasts
Confidence Intervals
ciContrasts(Outcome~Factor,contrasts=contr.sum)
## $`Confidence Intervals for the Contrasts`
## Est SE df LL UL
## (Intercept) 5.000 0.707 9.000 3.400 6.600
## x1 -3.000 1.000 9.000 -5.262 -0.738
## x2 1.000 1.000 9.000 -1.262 3.262
graphContrasts(Outcome~Factor,contrasts=contr.sum)
ciContrasts(Outcome~Factor,contrasts=contr.sum,conf.level=.99)
## $`Confidence Intervals for the Contrasts`
## Est SE df LL UL
## (Intercept) 5.000 0.707 9.000 2.702 7.298
## x1 -3.000 1.000 9.000 -6.250 0.250
## x2 1.000 1.000 9.000 -2.250 4.250
graphContrasts(Outcome~Factor,contrasts=contr.sum,conf.level=.99)
Significance Tests
nhstContrasts(Outcome~Factor,contrasts=contr.sum)
## $`Hypothesis Tests for the Contrasts`
## Est SE t df p
## (Intercept) 5.000 0.707 7.071 9.000 0.000
## x1 -3.000 1.000 -3.000 9.000 0.015
## x2 1.000 1.000 1.000 9.000 0.343
Analyses of the Effect
Source Table
descEffect(Outcome~Factor)
## $`Source Table for the Effect`
## SS df MS
## Factor 56.000 2.000 28.000
## Residuals 54.000 9.000 6.000
Significance Test
nhstEffect(Outcome~Factor)
## $`Hypothesis Test for the Effect`
## F df p
## Factor 4.667 2.000 0.041
## Residuals NA 9.000 NA
Effect Size
pvaEffect(Outcome~Factor)
## $`Proportion of Variance Accounted for by the Effect`
## EtaSq ParEtaSq
## Factor 0.509 0.509
## Residuals 0.491 NA
cwendorf/easiOrigin documentation built on Nov. 1, 2023, 10:57 a.m.
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OneWay Vignette
This vignette analyzes data from a one-way between-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
Factor <- c(rep(1,4),rep(2,4),rep(3,4))
Outcome <- c(0,0,3,5,4,7,4,9,9,6,4,9)
Factor <- factor(Factor,levels=c(1,2,3),labels=c("Level1","Level2","Level3"))
OneWayData <- data.frame(Factor,Outcome)
Descriptive Statistics
descMeans(Outcome~Factor)
## $`Descriptive Statistics for the Data`
## N M SD
## Level1 4.000 2.000 2.449
## Level2 4.000 6.000 2.449
## Level3 4.000 7.000 2.449
Analyses of the Means
Confidence Intervals
ciMeans(Outcome~Factor)
## $`Confidence Intervals for the Means`
## M SE df LL UL
## Level1 2.000 1.225 9.000 -0.771 4.771
## Level2 6.000 1.225 9.000 3.229 8.771
## Level3 7.000 1.225 9.000 4.229 9.771
graphMeans(Outcome~Factor)
ciMeans(Outcome~Factor,conf.level=.99)
## $`Confidence Intervals for the Means`
## M SE df LL UL
## Level1 2.000 1.225 9.000 -1.980 5.980
## Level2 6.000 1.225 9.000 2.020 9.980
## Level3 7.000 1.225 9.000 3.020 10.980
graphMeans(Outcome~Factor,conf.level=.99,mu=5)
Significance Tests
nhstMeans(Outcome~Factor)
## $`Hypothesis Tests for the Means`
## Diff SE t df p
## Level1 2.000 1.225 1.633 9.000 0.137
## Level2 6.000 1.225 4.899 9.000 0.001
## Level3 7.000 1.225 5.715 9.000 0.000
nhstMeans(Outcome~Factor,mu=5)
## $`Hypothesis Tests for the Means`
## Diff SE t df p
## Level1 -3.000 1.225 -2.449 9.000 0.037
## Level2 1.000 1.225 0.816 9.000 0.435
## Level3 2.000 1.225 1.633 9.000 0.137
Analyses of a Mean Difference
Comparison=factor(Factor,c("Level1","Level2"))
Confidence Interval
ciDifference(Outcome~Comparison)
## $`Confidence Interval for the Mean Difference`
## Diff SE df LL UL
## Comparison -4.000 1.732 6.000 -8.238 0.238
graphDifference(Outcome~Comparison)
ciDifference(Outcome~Comparison,conf.level=.99)
## $`Confidence Interval for the Mean Difference`
## Diff SE df LL UL
## Comparison -4.000 1.732 6.000 -10.421 2.421
graphDifference(Outcome~Comparison,conf.level=.99)
Significance Test
nhstDifference(Outcome~Comparison)
## $`Hypothesis Tests for the Mean Difference`
## Diff SE t df p
## Comparison -4.000 1.732 -2.309 6.000 0.060
nhstDifference(Outcome~Comparison,mu=-2)
## $`Hypothesis Tests for the Mean Difference`
## Diff SE t df p
## Comparison -2.000 1.732 -1.155 6.000 0.292
Analyses of the Pairwise Comparisons
Confidence Intervals
ciPairwise(Outcome~Factor)
## $`Confidence Intervals for the Pairwise Comparisons`
## Diff SE df LL UL
## Level1 v Level2 -4.000 1.732 6.000 -8.238 0.238
## Level1 v Level3 -5.000 1.732 6.000 -9.238 -0.762
## Level2 v Level3 -1.000 1.732 6.000 -5.238 3.238
graphPairwise(Outcome~Factor)
ciPairwise(Outcome~Factor,conf.level=.99)
## $`Confidence Intervals for the Pairwise Comparisons`
## Diff SE df LL UL
## Level1 v Level2 -4.000 1.732 6.000 -10.421 2.421
## Level1 v Level3 -5.000 1.732 6.000 -11.421 1.421
## Level2 v Level3 -1.000 1.732 6.000 -7.421 5.421
graphPairwise(Outcome~Factor,mu=-2,conf.level=.99)
Significance Tests
nhstPairwise(Outcome~Factor)
## $`Hypothesis Tests for the Pairwise Comparisons`
## Diff SE t df p
## Level1 v Level2 -4.000 1.732 -2.309 6.000 0.060
## Level1 v Level3 -5.000 1.732 -2.887 6.000 0.028
## Level2 v Level3 -1.000 1.732 -0.577 6.000 0.585
nhstPairwise(Outcome~Factor,mu=-2)
## $`Hypothesis Tests for the Pairwise Comparisons`
## Diff SE t df p
## Level1 v Level2 -2.000 1.732 -1.155 6.000 0.292
## Level1 v Level3 -3.000 1.732 -1.732 6.000 0.134
## Level2 v Level3 1.000 1.732 0.577 6.000 0.585
Analyses of a Set of Contrasts
Confidence Intervals
ciContrasts(Outcome~Factor,contrasts=contr.sum)
## $`Confidence Intervals for the Contrasts`
## Est SE df LL UL
## (Intercept) 5.000 0.707 9.000 3.400 6.600
## x1 -3.000 1.000 9.000 -5.262 -0.738
## x2 1.000 1.000 9.000 -1.262 3.262
graphContrasts(Outcome~Factor,contrasts=contr.sum)
ciContrasts(Outcome~Factor,contrasts=contr.sum,conf.level=.99)
## $`Confidence Intervals for the Contrasts`
## Est SE df LL UL
## (Intercept) 5.000 0.707 9.000 2.702 7.298
## x1 -3.000 1.000 9.000 -6.250 0.250
## x2 1.000 1.000 9.000 -2.250 4.250
graphContrasts(Outcome~Factor,contrasts=contr.sum,conf.level=.99)
Significance Tests
nhstContrasts(Outcome~Factor,contrasts=contr.sum)
## $`Hypothesis Tests for the Contrasts`
## Est SE t df p
## (Intercept) 5.000 0.707 7.071 9.000 0.000
## x1 -3.000 1.000 -3.000 9.000 0.015
## x2 1.000 1.000 1.000 9.000 0.343
Analyses of the Effect
Source Table
descEffect(Outcome~Factor)
## $`Source Table for the Effect`
## SS df MS
## Factor 56.000 2.000 28.000
## Residuals 54.000 9.000 6.000
Significance Test
nhstEffect(Outcome~Factor)
## $`Hypothesis Test for the Effect`
## F df p
## Factor 4.667 2.000 0.041
## Residuals NA 9.000 NA
Effect Size
pvaEffect(Outcome~Factor)
## $`Proportion of Variance Accounted for by the Effect`
## EtaSq ParEtaSq
## Factor 0.509 0.509
## Residuals 0.491 NA
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