predictPlot {NCStats} | R Documentation |
Shows the predicted value and interval on a fitted line plot. This function is used to illustrate predictions with SLR or IVR models and to show distinctions between confidence and prediction intervals.
predictPlot(...) predictionPlot(mdl, newdata, interval = "prediction", conf.level = 0.95, lty = 1, lwd = 3, legend = "topright", ...)
... |
Other arguments to the |
mdl |
an |
newdata |
A data frame in which to look for variables with which to predict. This cannot be omitted as it is with |
interval |
a string indicating whether to plot confidence ( |
conf.level |
a decimal numeric indicating the level of confidence to use for confidence and prediction intervals (default is |
lty |
a numeric indicating the type of line used for representing the intervals (see |
lwd |
a numeric indicating the width of line used for representing the intervals (see |
legend |
Controls use and placement of the legend (see details in |
This function produces a fitted line plot with both confidence and prediction bands shown. It then constructs vertical bars representing the predicted values with the corresponding interval (chosen with interval
) for all observations found in newdata
.
This function is only appropriate for SLR and IVR with a single quantitative covariate and two or fewer factors.
The predictPlot()
is just a pass-through to predictionPlot()
.
A data.frame is returned that contains the number of the new observation (for comparison to the graphic that is produced), the values of the variables in newdata
, and the predicted values at those observed values.
predict
specifically predict.lm
and fitPlot
from FSA.
lm1 <- lm(Sepal.Length~Petal.Length*Species,data=iris) lm2 <- lm(Sepal.Length~Petal.Length+Species,data=iris) lm3 <- lm(Sepal.Length~Petal.Length,data=iris) op <- par(mfrow=c(2,2),mar=c(3,3,2,1),mgp=c(2,0.7,0)) newdf <- data.frame(Petal.Length=c(2,4),Species=c("setosa","versicolor")) predictionPlot(lm1,newdf,legend="topleft") predictionPlot(lm2,newdf,legend="topleft") predictionPlot(lm3,newdf) predictionPlot(lm3,newdf,interval="confidence") par(op)