jmv (version 2.5.6)

linReg: Linear Regression

Description

Linear regression is used to explore the relationship between a continuous dependent variable, and one or more continuous and/or categorical explanatory variables. Other statistical methods, such as ANOVA and ANCOVA, are in reality just forms of linear regression.

Usage

linReg(data, dep, covs = NULL, factors = NULL, weights = NULL,
  blocks = list(list()), refLevels = NULL, intercept = "refLevel",
  r = TRUE, r2 = TRUE, r2Adj = FALSE, aic = FALSE, bic = FALSE,
  rmse = FALSE, modelTest = FALSE, anova = FALSE, ci = FALSE,
  ciWidth = 95, stdEst = FALSE, ciStdEst = FALSE,
  ciWidthStdEst = 95, norm = FALSE, qqPlot = FALSE,
  resPlots = FALSE, durbin = FALSE, collin = FALSE, cooks = FALSE,
  emMeans = list(list()), ciEmm = TRUE, ciWidthEmm = 95,
  emmPlots = TRUE, emmTables = FALSE, emmWeights = TRUE)

Value

A results object containing:

results$modelFit					a table
results$modelComp					a table
results$models					an array of model specific results
results$predictOV					an output
results$residsOV					an output
results$cooksOV					an output

Tables can be converted to data frames with asDF or as.data.frame. For example:

results$modelFit$asDF

as.data.frame(results$modelFit)

Arguments

data

the data as a data frame

dep

the dependent variable from data, variable must be numeric

covs

the covariates from data

factors

the fixed factors from data

weights

the (optional) weights from data to be used in the fitting process

blocks

a list containing vectors of strings that name the predictors that are added to the model. The elements are added to the model according to their order in the list

refLevels

a list of lists specifying reference levels of the dependent variable and all the factors

intercept

'refLevel' (default) or 'grandMean', coding of the intercept. Either creates contrast so that the intercept represents the reference level or the grand mean

r

TRUE (default) or FALSE, provide the statistical measure R for the models

r2

TRUE (default) or FALSE, provide the statistical measure R-squared for the models

r2Adj

TRUE or FALSE (default), provide the statistical measure adjusted R-squared for the models

aic

TRUE or FALSE (default), provide Aikaike's Information Criterion (AIC) for the models

bic

TRUE or FALSE (default), provide Bayesian Information Criterion (BIC) for the models

rmse

TRUE or FALSE (default), provide RMSE for the models

modelTest

TRUE (default) or FALSE, provide the model comparison between the models and the NULL model

anova

TRUE or FALSE (default), provide the omnibus ANOVA test for the predictors

ci

TRUE or FALSE (default), provide a confidence interval for the model coefficients

ciWidth

a number between 50 and 99.9 (default: 95) specifying the confidence interval width

stdEst

TRUE or FALSE (default), provide a standardized estimate for the model coefficients

ciStdEst

TRUE or FALSE (default), provide a confidence interval for the model coefficient standardized estimates

ciWidthStdEst

a number between 50 and 99.9 (default: 95) specifying the confidence interval width

norm

TRUE or FALSE (default), perform a Shapiro-Wilk test on the residuals

qqPlot

TRUE or FALSE (default), provide a Q-Q plot of residuals

resPlots

TRUE or FALSE (default), provide residual plots where the dependent variable and each covariate is plotted against the standardized residuals.

durbin

TRUE or FALSE (default), provide results of the Durbin- Watson test for autocorrelation

collin

TRUE or FALSE (default), provide VIF and tolerence collinearity statistics

cooks

TRUE or FALSE (default), provide summary statistics for the Cook's distance

emMeans

a formula containing the terms to estimate marginal means for, supports up to three variables per term

ciEmm

TRUE (default) or FALSE, provide a confidence interval for the estimated marginal means

ciWidthEmm

a number between 50 and 99.9 (default: 95) specifying the confidence interval width for the estimated marginal means

emmPlots

TRUE (default) or FALSE, provide estimated marginal means plots

emmTables

TRUE or FALSE (default), provide estimated marginal means tables

emmWeights

TRUE (default) or FALSE, weigh each cell equally or weigh them according to the cell frequency

Examples

data('Prestige', package='carData')

linReg(data = Prestige, dep = income,
       covs = vars(education, prestige, women),
       blocks = list(list('education', 'prestige', 'women')))

#
#  LINEAR REGRESSION
#
#  Model Fit Measures
#  ---------------------------
#    Model    R        R²
#  ---------------------------
#        1    0.802    0.643
#  ---------------------------
#
#
#  MODEL SPECIFIC RESULTS
#
#  MODEL 1
#
#
#  Model Coefficients
#  --------------------------------------------------------
#    Predictor    Estimate    SE         t         p
#  --------------------------------------------------------
#    Intercept      -253.8    1086.16    -0.234     0.816
#    women           -50.9       8.56    -5.948    < .001
#    prestige        141.4      29.91     4.729    < .001
#    education       177.2     187.63     0.944     0.347
#  --------------------------------------------------------
#