Quantile Plot for LMS Quantile Regression
Plots quantiles associated with a LMS quantile regression.
qtplot.lmscreg(object, newdata = NULL,
percentiles = object@misc$percentiles,
show.plot = TRUE, ...)object |
A VGAM quantile regression model, i.e.,
an object produced by modelling functions such as |
newdata |
Optional data frame for computing the quantiles. If missing, the original data is used. |
percentiles |
Numerical vector with values between 0 and 100 that specify the percentiles (quantiles). The default are the percentiles used when the model was fitted. |
show.plot |
Logical. Plot it? If |
... |
Graphical parameter that are passed into
|
The ‘primary’ variable is defined as the main covariate upon which the regression or smoothing is performed. For example, in medical studies, it is often the age. In VGAM, it is possible to handle more than one covariate, however, the primary variable must be the first term after the intercept.
A list with the following components.
fitted.values |
A vector of fitted percentile values. |
percentiles |
The percentiles used. |
plotqtplot.lmscreg does the actual plotting.
Thomas W. Yee
Yee, T. W. (2004). Quantile regression via vector generalized additive models. Statistics in Medicine, 23, 2295–2315.
## Not run: fit <- vgam(BMI ~ s(age, df = c(4, 2)), lms.bcn(zero=1), data = bmi.nz) qtplot(fit) qtplot(fit, perc = c(25, 50, 75, 95), lcol = "blue", tcol = "blue", llwd = 2) ## End(Not run)
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