## Linear Regression

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.

### 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 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

### Returns

A results object containing:

 results\$modelFit a table results\$modelComp a table results\$models an array of groups

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

results\$modelFit\$asDF

as.data.frame(results\$modelFit)

Elements in arrays can be accessed with [[n]]. For example:

results\$models[] # accesses the first element