The package **RcmdrPlugin.UCA** adds some options to the Rcmdr menu that are useful for new users in R. Namely:

- Test and confidence interval for variance
- Randomness test for numerical and dichotomous variables
- Prediction of values using the active model

In the following sections, each of these options will be described in detail.

Within the menu “Statistics” -> “Variances”, a new entry is provided to calculate confidence intervals and make contrasts on the variance in a normal population.This option uses the `sigma.test`

function of the TeachingDemos package.

Two options are provided within the “Non-parametric Test” menu to perform the randomness test according to the type of variable.

Within the menu “Statistics” -> “Non-parametric tests” -> “Randomness test for a factor of two levels…”, a new entry is provided to test the randomness of a factor-type variable with two levels. This option uses the `runs.test`

function of the tseries package, although to avoid conflicts it has been renamed as `twolevelfactor.runs.test`

.

Within the “Statistics” menu -> “Non-parametric tests” -> “Randomness test for a numeric variable …”, a new entry is provided to test the randomness of a numeric variable. This option uses the `runs.test`

function of the randtest package, although to avoid conflicts it has been renamed as `numeric.runs.test`

.

The menu entry “Predict using active model”, in models menu, has two options to predict data using active model depending on how the data for predictor variables will be provided.

If you select “Input data and predict”, a new data set, as a data.frame, will be created and the editor will be invoked. Then you can entry the values of the predictor variable that you want to use for prediction, the values for non predictor variables are not required. When you close the data editor the predicted values for predicted variable are shown.

If you select “Models” -> “Predict using active model” -> “Add predictions to existing dataset…” the predictions are added to the selected data set using the active model and the selected data set for the values of the explanatory variables. After selecting this option, the user can select an existing data set using dialog box.

If the data set does not provides the values for all predicting variables an error will occur and no predicted values will be provided.

Unlike the menu option “Add observation statistics to data…”, this option can be used with a different data set than the one used to construct the model, if that dataset provides the values for all the predictor variables.