reghelper (version 0.3.3)

cell_means_q.lm: Estimated values of a linear model.

Description

cell_means_q.lm calculates the predicted values at specific points, given a fitted regression model.

Usage

cell_means_q.lm(model, vars = NULL, levels = NULL)

Arguments

model

A fitted linear model of type 'lm'.

vars

A vector or list with variable names to be added to the table.

levels

A list with element names corresponding to some or all of the variables in the model. Each list element should be a vector with the names of factor levels (for categorical variables) or numeric points (for continuous variables) at which to test that variable.

Value

A data frame with a row for each predicted value. The first few columns identify the level at which each variable in your model was set. After columns for each variable, the data frame has columns for the predicted value, the standard error of the predicted mean, and the 95 confidence interval.

Details

By default, this function will provide means at -1SD, the mean, and +1SD for continuous variables, and at each levele of categorical variables. This can be overridden with the levels parameter.

If there are additional covariates in the model other than what are selected in the function call, these variables will be set to their respective means. In the case of a categorical covariate, the results will be averaged across all its levels.

Note that in most cases it is easier to use cell_means.lm and pass variable names in directly instead of strings of variable names. cell_means_q.lm uses standard evaluation in cases where such evaluation is easier.

See Also

cell_means.lm

Examples

# NOT RUN {
# iris data
model <- lm(Sepal.Length ~ Petal.Length + Petal.Width, iris)
summary(model)
cell_means_q.lm(model, 'Petal.Length')
# }