# gumbel: Gumbel Regression Family Function In VGAM: Vector Generalized Linear and Additive Models

## Description

Maximum likelihood estimation of the 2-parameter Gumbel distribution.

## Usage

 1 2 3 4 5 6 gumbel(llocation = "identitylink", lscale = "loglink", iscale = NULL, R = NA, percentiles = c(95, 99), mpv = FALSE, zero = NULL) gumbelff(llocation = "identitylink", lscale = "loglink", iscale = NULL, R = NA, percentiles = c(95, 99), zero = "scale", mpv = FALSE) 

## Arguments

 llocation, lscale Parameter link functions for mu and sigma. See Links for more choices. iscale Numeric and positive. Optional initial value for sigma. Recycled to the appropriate length. In general, a larger value is better than a smaller value. A NULL means an initial value is computed internally. R Numeric. Maximum number of values possible. See Details for more details. percentiles Numeric vector of percentiles used for the fitted values. Values should be between 0 and 100. This argument uses the argument R if assigned. If percentiles = NULL then the mean will be returned as the fitted values. mpv Logical. If mpv = TRUE then the median predicted value (MPV) is computed and returned as the (last) column of the fitted values. This argument is ignored if percentiles = NULL. See Details for more details.
 zero A vector specifying which linear/additive predictors are modelled as intercepts only. The value (possibly values) can be from the set {1, 2} corresponding respectively to mu and sigma. By default all linear/additive predictors are modelled as a linear combination of the explanatory variables. See CommonVGAMffArguments for more details.

## Details

The Gumbel distribution is a generalized extreme value (GEV) distribution with shape parameter xi = 0. Consequently it is more easily estimated than the GEV. See gev for more details.

The quantity R is the maximum number of observations possible, for example, in the Venice data below, the top 10 daily values are recorded for each year, therefore R = 365 because there are about 365 days per year. The MPV is the value of the response such that the probability of obtaining a value greater than the MPV is 0.5 out of R observations. For the Venice data, the MPV is the sea level such that there is an even chance that the highest level for a particular year exceeds the MPV. If mpv = TRUE then the column labelled "MPV" contains the MPVs when fitted() is applied to the fitted object.

The formula for the mean of a response Y is μ+σ \times Euler where Euler is a constant that has value approximately equal to 0.5772. The formula for the percentiles are (if R is not given) location- scale*log[-log(P/100)] where P is the percentile argument value(s). If R is given then the percentiles are location- scale*log[-log(R*(1-P/100))].

## Value

An object of class "vglmff" (see vglmff-class). The object is used by modelling functions such as vglm, and vgam.

## Warning

When R is not given (the default) the fitted percentiles are that of the data, and not of the overall population. For example, in the example below, the 50 percentile is approximately the running median through the data, however, the data are the highest sea level measurements recorded each year (it therefore equates to the median predicted value or MPV).

## Note

Like many other usual VGAM family functions, gumbelff() handles (independent) multiple responses.

gumbel() can handle more of a multivariate response, i.e., a matrix with more than one column. Each row of the matrix is sorted into descending order. Missing values in the response are allowed but require na.action = na.pass. The response matrix needs to be padded with any missing values. With a multivariate response one has a matrix y, say, where y[, 2] contains the second order statistics, etc.

T. W. Yee

## References

Yee, T. W. and Stephenson, A. G. (2007). Vector generalized linear and additive extreme value models. Extremes, 10, 1–19.

Smith, R. L. (1986). Extreme value theory based on the r largest annual events. Journal of Hydrology, 86, 27–43.

Rosen, O. and Cohen, A. (1996). Extreme percentile regression. In: Haerdle, W. and Schimek, M. G. (eds.), Statistical Theory and Computational Aspects of Smoothing: Proceedings of the COMPSTAT '94 Satellite Meeting held in Semmering, Austria, 27–28 August 1994, pp.200–214, Heidelberg: Physica-Verlag.

Coles, S. (2001). An Introduction to Statistical Modeling of Extreme Values. London: Springer-Verlag.

rgumbel, dgumbelII, cens.gumbel, guplot, gev, gevff, venice.

## Examples

  1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 # Example 1: Simulated data gdata <- data.frame(y1 = rgumbel(n = 1000, loc = 100, scale = exp(1))) fit1 <- vglm(y1 ~ 1, gumbelff(perc = NULL), data = gdata, trace = TRUE) coef(fit1, matrix = TRUE) Coef(fit1) head(fitted(fit1)) with(gdata, mean(y1)) # Example 2: Venice data (fit2 <- vglm(cbind(r1, r2, r3, r4, r5) ~ year, data = venice, gumbel(R = 365, mpv = TRUE), trace = TRUE)) head(fitted(fit2)) coef(fit2, matrix = TRUE) sqrt(diag(vcov(summary(fit2)))) # Standard errors # Example 3: Try a nonparametric fit --------------------- # Use the entire data set, including missing values # Same as as.matrix(venice[, paste0("r", 1:10)]): Y <- Select(venice, "r", sort = FALSE) fit3 <- vgam(Y ~ s(year, df = 3), gumbel(R = 365, mpv = TRUE), data = venice, trace = TRUE, na.action = na.pass) depvar(fit3)[4:5, ] # NAs used to pad the matrix ## Not run: # Plot the component functions par(mfrow = c(2, 3), mar = c(6, 4, 1, 2) + 0.3, xpd = TRUE) plot(fit3, se = TRUE, lcol = "blue", scol = "limegreen", lty = 1, lwd = 2, slwd = 2, slty = "dashed") # Quantile plot --- plots all the fitted values qtplot(fit3, mpv = TRUE, lcol = c(1, 2, 5), tcol = c(1, 2, 5), lwd = 2, pcol = "blue", tadj = 0.1, ylab = "Sea level (cm)") # Plot the 99 percentile only year <- venice[["year"]] matplot(year, Y, ylab = "Sea level (cm)", type = "n") matpoints(year, Y, pch = "*", col = "blue") lines(year, fitted(fit3)[, "99%"], lwd = 2, col = "orange") # Check the 99 percentiles with a smoothing spline. # Nb. (1-0.99) * 365 = 3.65 is approx. 4, meaning the 4th order # statistic is approximately the 99 percentile. plot(year, Y[, 4], ylab = "Sea level (cm)", type = "n", main = "Orange is 99 percentile, Green is a smoothing spline") points(year, Y[, 4], pch = "4", col = "blue") lines(year, fitted(fit3)[, "99%"], lty = 1, col = "orange") lines(smooth.spline(year, Y[, 4], df = 4), col = "limegreen", lty = 2) ## End(Not run) 

### Example output Loading required package: stats4
VGLM    linear loop  1 :  loglikelihood = -2584.8736
VGLM    linear loop  2 :  loglikelihood = -2574.0893
VGLM    linear loop  3 :  loglikelihood = -2573.9813
VGLM    linear loop  4 :  loglikelihood = -2573.9813
VGLM    linear loop  5 :  loglikelihood = -2573.9813
location loge(scale)
(Intercept) 100.1486   0.9946078
location      scale
100.148615   2.703664
[,1]
[1,] 101.7092
[2,] 101.7092
[3,] 101.7092
[4,] 101.7092
[5,] 101.7092
[6,] 101.7092
 101.715
VGLM    linear loop  1 :  loglikelihood = -719.30727
VGLM    linear loop  2 :  loglikelihood = -706.68238
VGLM    linear loop  3 :  loglikelihood = -705.03014
VGLM    linear loop  4 :  loglikelihood = -705.02544
VGLM    linear loop  5 :  loglikelihood = -705.02542
VGLM    linear loop  6 :  loglikelihood = -705.02542

Call:
vglm(formula = cbind(r1, r2, r3, r4, r5) ~ year, family = gumbel(R = 365,
mpv = TRUE), data = venice, trace = TRUE)

Coefficients:
(Intercept):1 (Intercept):2        year:1        year:2
-8.447828e+02 -1.495767e-01  4.912844e-01  1.378431e-03

Degrees of Freedom: 102 Total; 98 Residual
Log-likelihood: -705.0254
95%      99%      MPV
1 68.07416 87.92128 108.4072
2 68.51604 88.39054 108.9047
3 68.95786 88.85977 109.4022
4 69.39961 89.32897 109.8998
5 69.84129 89.79814 110.3973
6 70.28290 90.26728 110.8949
location  loge(scale)
(Intercept) -844.7827502 -0.149576688
year           0.4912844  0.001378431
(Intercept):1 (Intercept):2        year:1        year:2
1.975734e+02  7.653386e+00  1.010370e-01  3.912663e-03
Warning message:
In npred.vlm(object, ...) :
contradiction in values after computing it two ways
VGAM  s.vam  loop  1 :  loglikelihood = -1137.5884
VGAM  s.vam  loop  2 :  loglikelihood = -1088.6181
VGAM  s.vam  loop  3 :  loglikelihood = -1079.7142
VGAM  s.vam  loop  4 :  loglikelihood = -1078.882
VGAM  s.vam  loop  5 :  loglikelihood = -1078.7252
VGAM  s.vam  loop  6 :  loglikelihood = -1078.713
VGAM  s.vam  loop  7 :  loglikelihood = -1078.7071
VGAM  s.vam  loop  8 :  loglikelihood = -1078.707
VGAM  s.vam  loop  9 :  loglikelihood = -1078.7066
VGAM  s.vam  loop  10 :  loglikelihood = -1078.7066
r1  r2  r3  r4 r5 r6 r7 r8 r9 r10
4 116 113  91  91 91 89 88 88 86  81
5 115 107 105 101 93 91 NA NA NA  NA


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