Prostate: Prostate Cancer Data

In Brq: Bayesian Analysis of Quantile Regression Models

Description

The data is taken from a prostate cancer study (Stamey et al., 1989). It has 97 rows and 9 columns. The response variable is the logarithm of prostate-specific antigen. The covariates are eight clinical measures: the logarithm of cancer volume (lcavol), the logarithm of prostate weight (lweight), age, the logarithm of the amount of benign prostatic hyperplasia (lbph), seminal vesicle invasion (svi), the logarithm of capsular penetration (lcp), the Gleason score (gleason) and the percentage Gleason score 4 or 5 (pgg45).

Usage

data("Prostate")

Format

The data frame has the following components:

• lcavol : The logarithm of cancer volume.

• lweight : The logarithm of prostate weight.

• age : Age

• lbph : The logarithm of the amount of benign prostatic hyperplasia.

• svi : Seminal vesicle invasion.

• lcp : The logarithm of capsular penetration.

• gleason : Gleason score.

• pgg45 : Percentage Gleason scores 4 or 5.

• lpsa : The logarithm of prostate-specific antigen.

Source

Stamey, T. A., Kabalin, J. N., McNeal, J. E., Johnstone, I. M., Freiha, F., Redwine, E. A., and Yang, N. (1989). Prostate specific antigen in the diagnosis and treatment of adenocarcinoma of the prostate. II. Radical prostatectomy treated patients. The Journal of urology, 141(5), 1076-1083.

References

Stamey, T. A., Kabalin, J. N., McNeal, J. E., Johnstone, I. M., Freiha, F., Redwine, E. A., & Yang, N. (1989). Prostate specific antigen in the diagnosis and treatment of adenocarcinoma of the prostate. II. Radical prostatectomy treated patients. The Journal of urology, 141(5), 1076-1083.

Examples

data(Prostate)
y=Prostate[,9]
x=as.matrix(Prostate[, -9])
y=y-mean(y)
x=scale(x)
fit=Brq(y~0+x, method="BLqr")
plot(fit, plottype="hist",D=1:8)
plot(fit, plottype="hist",D=1:8, col2=4,lwd=2)
plot(fit, plottype="trace",D=1:8, col4=4)

Example output

Brq documentation built on July 1, 2020, 7:07 p.m.