radiant (version 0.1.95)

plot.glm_predict: Plot method for the predict.glm_reg function

Description

Plot method for the predict.glm_reg function

Usage

"plot"(x, glm_xvar = "", glm_facet_row = ".", glm_facet_col = ".", glm_color = "none", glm_conf_level = 0.95, ...)

Arguments

x

Return value from predict.glm_reg.

glm_xvar

Variable to display along the X-axis of the plot

glm_facet_row

Create vertically arranged subplots for each level of the selected factor variable

glm_facet_col

Create horizontally arranged subplots for each level of the selected factor variable

glm_color

Adds color to a scatter plot to generate a heat map. For a line plot one line is created for each group and each is assigned a different colour

glm_conf_level

Confidence level to use for prediction intervals (.95 is the default). Note that the error bars for predicitions are approximations at this point.

...

further arguments passed to or from other methods

Details

See http://vnijs.github.io/radiant/quant/glm_reg.html for an example in Radiant

See Also

glm_reg to generate the result

summary.glm_reg to summarize results

plot.glm_reg to plot results

predict.glm_reg to generate predictions

Examples

result <- glm_reg("titanic", "survived", c("pclass","sex","age"), glm_levels = "Yes")
pred <- predict(result, glm_predict_cmd = "pclass = levels(pclass)")
plot(pred, glm_xvar = "pclass")
pred <- predict(result, glm_predict_cmd = "age = 0:100")
plot(pred, glm_xvar = "age")
pred <- predict(result, glm_predict_cmd = "pclass = levels(pclass), sex = levels(sex)")
plot(pred, glm_xvar = "pclass", glm_color = "sex")
pred <- predict(result, glm_predict_cmd = "pclass = levels(pclass), age = seq(0,100,20)")
plot(pred, glm_xvar = "pclass", glm_color = "age")
plot(pred, glm_xvar = "age", glm_color = "pclass")
pred <- predict(result, glm_predict_cmd="pclass=levels(pclass), sex=levels(sex), age=seq(0,100,20)")
plot(pred, glm_xvar = "age", glm_color = "sex", glm_facet_col = "pclass")
plot(pred, glm_xvar = "age", glm_color = "pclass", glm_facet_col = "sex")
pred <- predict(result, glm_predict_cmd="pclass=levels(pclass), sex=levels(sex), age=seq(0,100,5)")
plot(pred, glm_xvar = "age", glm_color = "sex", glm_facet_col = "pclass")
plot(pred, glm_xvar = "age", glm_color = "pclass", glm_facet_col = "sex")