docs/RegressionVignette.md

In cwendorf/easiOrigin: easiOrigin

Regression Vignette

This vignette analyzes data from a multiple-variable design.

• Data Management
• Data Entry
• Descriptive Statistics
• Analyses of the Regression Coefficients
• Confidence Intervals
• Significance Tests
• Analyses of the Model
• Source Table
• Significance Test
• Effect Size

Data Management

Data Entry

Predictor1 <- c(0,0,3,5)
Predictor2 <- c(4,7,4,9)
Criterion <- c(9,6,4,9)
RegressionData <- data.frame(Predictor1,Predictor2,Criterion)

Descriptive Statistics

descMeans(RegressionData)

## $`Descriptive Statistics for the Data`
##                  N       M      SD
## Predictor1   4.000   2.000   2.449
## Predictor2   4.000   6.000   2.449
## Criterion    4.000   7.000   2.449

Analyses of the Regression Coefficients

Confidence Intervals

ciCoefficients(Criterion~Predictor1+Predictor2)

## $`Confidence Intervals for the Coefficients`
##                 Est      SE      df      LL      UL
## (Intercept)   4.481   5.868   1.000 -70.076  79.039
## Predictor1   -0.185   1.048   1.000 -13.496  13.125
## Predictor2    0.481   1.048   1.000 -12.829  13.792

graphCoefficients(Criterion~Predictor1+Predictor2)

ciCoefficients(Criterion~Predictor1+Predictor2,conf.level=.99)

## $`Confidence Intervals for the Coefficients`
##                 Est      SE      df       LL      UL
## (Intercept)   4.481   5.868   1.000 -369.042 378.005
## Predictor1   -0.185   1.048   1.000  -66.870  66.499
## Predictor2    0.481   1.048   1.000  -66.203  67.166

graphCoefficients(Criterion~Predictor1+Predictor2,conf.level=.99)

Significance Tests

nhstCoefficients(Criterion~Predictor1+Predictor2)

## $`Hypothesis Tests for the Coefficients`
##                 Est      SE       t      df       p
## (Intercept)   4.481   5.868   0.764   1.000   0.585
## Predictor1   -0.185   1.048  -0.177   1.000   0.889
## Predictor2    0.481   1.048   0.460   1.000   0.726

Analyses of the Model

Source Table

descModel(Criterion~Predictor1+Predictor2)

## $`Source Table for the Model`
##                SS      df      MS
## Model       3.185   2.000   1.593
## Residuals  14.815   1.000  14.815

Significance Test

nhstModel(Criterion~Predictor1+Predictor2)

## $`Hypothesis Test for the Model`
##             F     df1     df2       p
## Model   0.107   2.000   1.000   0.907

Effect Size

pvaModel(Criterion~Predictor1+Predictor2)

## $`Proportion of Variance Accounted for by the Model`
##             R     RSq  AdjRSq
## Model   0.421   0.177  -1.469

cwendorf/easiOrigin documentation built on Nov. 1, 2023, 10:57 a.m.