cdf.lmscreg: Cumulative Distribution Function for LMS Quantile Regression

Description Usage Arguments Details Value Note Author(s) References See Also Examples

Description

Computes the cumulative distribution function (CDF) for observations, based on a LMS quantile regression.

Usage

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cdf.lmscreg(object, newdata = NULL, ...)

Arguments

object

A VGAM quantile regression model, i.e., an object produced by modelling functions such as vglm and vgam with a family function beginning with "lms.".

newdata

Data frame where the predictions are to be made. If missing, the original data is used.

...

Parameters which are passed into functions such as cdf.lms.yjn.

Details

The CDFs returned here are values lying in [0,1] giving the relative probabilities associated with the quantiles newdata. For example, a value near 0.75 means it is close to the upper quartile of the distribution.

Value

A vector of CDF values lying in [0,1].

Note

The data are treated like quantiles, and the percentiles are returned. The opposite is performed by qtplot.lmscreg.

The CDF values of the model have been placed in @post$cdf when the model was fitted.

Author(s)

Thomas W. Yee

References

Yee, T. W. (2004). Quantile regression via vector generalized additive models. Statistics in Medicine, 23, 2295–2315.

See Also

deplot.lmscreg, qtplot.lmscreg, lms.bcn, lms.bcg, lms.yjn.

Examples

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fit <- vgam(BMI ~ s(age, df=c(4, 2)), lms.bcn(zero = 1), data = bmi.nz)
head(fit@post$cdf)
head(cdf(fit))  # Same
head(depvar(fit))
head(fitted(fit))

cdf(fit, data.frame(age = c(31.5, 39), BMI = c(28.4, 24)))

Example output

Loading required package: stats4
Loading required package: splines
VGAM  s.vam  loop  1 :  loglikelihood = -6429.7568
VGAM  s.vam  loop  2 :  loglikelihood = -6327.3502
VGAM  s.vam  loop  3 :  loglikelihood = -6313.2224
VGAM  s.vam  loop  4 :  loglikelihood = -6312.8069
VGAM  s.vam  loop  5 :  loglikelihood = -6312.8166
VGAM  s.vam  loop  6 :  loglikelihood = -6312.8032
VGAM  s.vam  loop  7 :  loglikelihood = -6312.8088
VGAM  s.vam  loop  8 :  loglikelihood = -6312.8062
VGAM  s.vam  loop  9 :  loglikelihood = -6312.8074
VGAM  s.vam  loop  10 :  loglikelihood = -6312.8068
VGAM  s.vam  loop  11 :  loglikelihood = -6312.8071
VGAM  s.vam  loop  12 :  loglikelihood = -6312.807
        1         2         3         4         5         6 
0.2280309 0.6365499 0.6356761 0.4321450 0.4321311 0.9686738 
        1         2         3         4         5         6 
0.2280309 0.6365499 0.6356761 0.4321450 0.4321311 0.9686738 
      [,1]
1 22.77107
2 27.70033
3 28.18127
4 25.08380
5 26.46388
6 36.19648
       25%      50%      75%
1 23.00836 25.48922 28.44767
2 23.65211 26.19783 29.23269
3 24.07328 26.66334 29.75085
4 23.25503 25.75937 28.74518
5 24.53531 27.17650 30.32525
6 23.63164 26.17517 29.20742
        1         2 
0.7469759 0.2861046 

VGAM documentation built on Jan. 16, 2021, 5:21 p.m.