est_param: Estimation of mean and/or variance for models fitted with...

In fhernanb/model: This Package Contains Useful Functions for Modeling Regresion

Estimation of mean and/or variance for models fitted with gamlss.

Description

This function estimates parameters like mean, standard deviation for a gamlss object. This function is very useful when the mean or standard deviation do not match with mu, sigma or some function of mu and/or sigma.

Usage

est_param(mod, fun = "mean", m = 100, data, newdata = NULL)

Arguments

mod	a gamlss object.
fun	the estimated parameter, by default is mean but can be change for sd or var.
m	a number to indicate the number of observations to simulate, by default its values is 100.
data	the original data frame used to fit mod.
newdata	a data frame containing new values for the explanatory variables.

Details

The function obtains the fitted values for mu, sigma, nu and tau. The functions simulates m observations and then it obtains the mean/fun defined by the user.

Value

est_param function returns a vector.

Examples

# Example for NOrmal regression ------------------------
# Here E(y) = mu and Var(y)= sigma
n <- 7
x <- runif(n=n)
y <- rnorm(n=n, mean=-5 + 10 * x, sd=exp(1 + 2 * x))
data1 <- data.frame(y, x)
data1

library(gamlss)
mod1 <- gamlss(y ~ x, sigma.fo=~x, family=NO, data=data1)

# Obtaining the means
m1 <- fitted(mod1, what="mu")
m2 <- est_param(mod1, m=10000, fun="mean")
cbind(m1, m2) # Comparing the estimated means
cor(m1, m2)

# Obtaining the standard deviations
s1 <- fitted(mod1, what="sigma")
s2 <- est_param(mod1, m=10000, fun="sd")
cbind(s1, s2) # Comparing the estimated standard deviations
cor(s1, s2)

# Example for GAmma regression --------------------------
# Here E(y) = mu and Var(y)= sigma^2 mu^2
n <- 7
x <- runif(n=n)
y <- rGA(n=n, mu=exp(-1 + 5 * x), sigma=exp(-1 + 2 * x))
data2 <- data.frame(y, x)
data2

mod2 <- gamlss(y ~ x, sigma.fo=~x, family=GA, data=data2)

summary(mod2)

# Obtaining the means
m1 <- fitted(mod2, what="mu")
m2 <- est_param(mod2, m=10000, fun="mean")
cbind(m1, m2) # Comparing the estimated means
cor(m1, m2)

# Obtaining the standard deviations
s1 <- fitted(mod2, what="sigma") * fitted(mod2, what="mu")
s2 <- est_param(mod2, m=10000, fun="sd")
cbind(s1, s2) # Comparing the estimated standard deviations
cor(s1, s2)

# To predict mean and standard deviation for new observations
new_data <- data.frame(x=c(0.26, 0.52, 0.77))

est_param(mod=mod2, fun="mean", m=10000, data=data2, newdata=new_data)
est_param(mod=mod2, fun="sd"  , m=10000, data=data2, newdata=new_data)

fhernanb/model documentation built on March 3, 2024, 12:15 p.m.