Q.reg | R Documentation |
Use this function to adequately confer the formula
in VGAM when fitting quantile regression models.
Q.reg(y, pvector = NULL, length.arg = NULL)
y |
Numeric, a vector or a matrix. It is the response or dependent
variable in the |
pvector |
A prototype vector. Entries are the conditional |
length.arg |
A length–1 positive integer. It is the number of |
Conditional quantile regression can be carried out using family
functions in VGAM and VGAMextra.
The formula
must be set up using this function, Q.reg
.
Here, the p
–quantiles of interest may be entered via
pvector
. Alternatively, use argument length.arg
by
establishing the length of pvector
.
Besides, the corresponding link must be entered.
For example, gamma1Qlink
is the proper link to fit models of conditional quantiles for
data distributed as Gamma via the family function
gamma1
.
See examples for further details.
A matrix, each column adequately arranged for regression on conditional quantiles, conforming with VGAM.
Indeed, this is equivalent to cbind(y, y, ...)
, where the
total number of columns is, either the length of pvector
, or
length.arg
.
Link functions for quantile regression in VGAM require the
vector of p
–quantiles of interest via the argument p
.
See normal1sdQlink
or
maxwellQlink
for instance.
Therefore, the integer entered at length.arg
in this function,
if utilized, must match the length of the vector p
. Else,
it will be recycled.
V. Miranda and T. W. Yee.
normal1sdQlink
,
maxwellQlink
,
gamma1Qlink
,
gamma1
,
vglm
,
vgam
### Quantile regression with data distributed as Maxwell(s) ###
set.seed(12073)
x2 <- seq(0, 100,length.out = 100) # independent variable
b0 <- 0.5 # true intercept
b1 <- 0.25 # true slope
b2 <- 0.02 # true second order coef.
alpha <- b0 + b1 * x2 + b2 * x2^2 # Quadratically modelling the parameters
nn <- 100 # Sample size
# The data as a data frame. #
mdata <- data.frame(y = rmaxwell(n = nn, rate = alpha), x2 = x2, x3 = x2^2)
# Quantile regression using our link function maxwellQlink(). #
# Quantiles 25%, 50%, 75% are of interest #
my.p <- c(0.25, 0.50, 0.75)
fit <- vglm(Q.reg(y, pvector = my.p) ~ x2 + x3,
# OPTIONALLY Q.reg(y, length = length(my.p)) ~ x2 + x3
maxwell(link = maxwellQlink(p = my.p)),
data = mdata, trace = TRUE, crit = "coef")
coef(fit, matrix = TRUE)
summary(fit)
head(predict(fit))
constraints(fit)
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