mydata, such as provided by the rad function included in this package for reading and processing data in preparation for analysis. The default analysis provides the model's parameter estimates and corresponding inferential analyses, goodness of fit, the ANOVA table, correlation matrix of the model's variables, analysis of residuals, and confidence and prediction intervals. By default the residual analysis lists the data and fitted value for each observation as well as the residual, Studentized residual and Cook's distance, with the first 25 observations listed and sorted by Cook's distance. The output for the confidence and prediction intervals also provides the data and fitted value for each observations, as well as the lower and upper bounds for each of the two intervals. The observations are sorted by the lower bound of each prediction interval. Also, for models with a single predictor variable, a scatterplot of the data is produced, along with the regression line and corresponding confidence and prediction intervals.
Overriding the default settings can turn off features and reduce the number of provided analyses.
reg(my.formula, dframe=mydata, graph=TRUE, cor=TRUE,
res.rows=NULL, res.sort=c("cooks","rstudent","off"),
pred=TRUE, pred.sort=c("predint", "off"), sig.digits=NULL)formula for specifying a model. For
example, for a response variable named Y and two predictor variables, X1 and X2, specify the corresponding linear model as Y ~ X1 + X2.mydata, the name of the data frame that contains the data.
The default name is consistent with the name given by the rad function for reading the data, also available in this packTRUE. If there is one predictor variable in the model, a
scatterplot with regression line is produced. If prediction intervals are requested, both the confidence and prediction intervals are added to the graph.TRUE, which prints a correlation matrix of the model variables."all"."cooks", for specifying Cook's distance as the sort
criterion for the display of the rows of data and associated residuals. Other values are "rstudent" for Studentized residuals, and "off" to not prTRUE, which, produces confidence and prediction intervals
for each row of data."predint", which sorts the rows of data and associated
intervals by the lower bound of each prediction interval. Turn off this sort by specifying a value of "off".options
function regarding the digits option. The distinction is that this value applies selectively to portions of the output, the different type of rlm, summary and confint. The residual analysis invokes fitted, resid, rstudent, and cooks.distance. The option for prediction intervals calls the standard R function predict, once with the argument interval="confidence" and once with interval="prediction". If there is only one predictor variable in the model, a scatterplot of the data with regression line is produced, along with the plotted confidence and prediction intervals. The output for the residual analysis displays by default just the first 25 observations with the largest values of Cook's distance, sorted by this criterion. The output of the prediction intervals is re-organized so that each row's computed fitted value and prediction interval are listed adjacent to the corresponding values of the predictor variables and response variable. Each row of information, the data and corresponding intervals, is by default sorted by the lower bound of the prediction interval.
The options function is called to turn off the stars for different significance levels (show.signif.stars=FALSE) and to turn off scientific notation for the output (scipen=30).
The purpose of reg is to combine these function calls into one, and provide ancillary analyses such as sorting where appropriate to assist in interpretation.
formula, lm, summary.lm, anova, confint, fitted, resid, rstudent, cooks.distance# Generate random data
X1 <- rnorm(20)
X2 <- rnorm(20)
Y <- .7*X1 + .2*X2 + .6*rnorm(20)
mydata <- data.frame(cbind(X1, X2, Y))
attach(mydata)
# Call reg for a one-predictor regression
# Provide all default analyses including scatterplot etc.
reg(Y ~ X1)
# Call reg according to a multiple regression model
# Provide the full range of default analyses
reg(Y ~ X1 + X2)
# Call reg and modify the default settings as specified
reg(Y ~ X1 + X2, res.row=8, res.sort="rstudent", sig.digits=8, pred=FALSE)Run the code above in your browser using DataLab