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leg
MRDCr
Commits
637fd5c9
Commit
637fd5c9
authored
May 04, 2016
by
Walmes Marques Zeviani
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Remove a chamada de pacotes no meio.
parent
fc435800
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vignettes/v04_poisson_generelizada.Rmd
vignettes/v04_poisson_generelizada.Rmd
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vignettes/v04_poisson_generelizada.Rmd
View file @
637fd5c9
...
...
@@ -81,14 +81,13 @@ gamma <- 0
fy <- dpg0(y = y, theta = theta, gamma = gamma)
plot(fy ~ y, type = "h")
lines(y + 0.3, dpois(y, lambda = theta), type = "h", col = 2)
lines(y + 0.3, dpois(y, lambda = theta), type = "h", col = 2,
xlab = "y", ylab = "f(y)")
```
## Recursos interativos com o `rpanel` ##
```{r, eval=FALSE}
library(rpanel)
react <- function(panel){
with(panel,
{
...
...
@@ -213,7 +212,7 @@ rp.checkbox(panel = panel,
rp.do(panel = panel, action = react)
```
## O
espaço p
aramétrico ##
## O
Espaço P
aramétrico ##
```{r}
#-----------------------------------------------------------------------
...
...
@@ -224,16 +223,18 @@ rp.do(panel = panel, action = react)
# dpg1(y = 0:10, lambda = 1, alpha = -0.1)
# undebug(dpg1)
library(latticeExtra)
y <- 0:200
fun <- Vectorize(vectorize.args = c("theta", "gamma"),
FUN = function(theta, gamma) {
sum(dpg0(y = y, theta = theta, gamma = gamma))
})
grid <- list(theta = seq(0.5, 50, by = 0.5),
gamma = seq(-0.98, 0.98, by = 0.02))
grid <- list(theta = seq(1, 50, by = 1),
gamma = seq(-0.5, 1, by = 0.05))
str(grid)
y <- 0:500
my <- max(y)
grid$sum <- with(grid, outer(theta, gamma, fun))
grid <- with(grid,
cbind(expand.grid(theta = theta, gamma = gamma),
...
...
@@ -242,16 +243,16 @@ grid <- with(grid,
levelplot(sum ~ theta + gamma,
data = subset(grid, round(sum, 3) == 1),
col.regions = gray.colors) +
layer(panel.abline(a = 0, b = -1/200))
layer(panel.abline(a = 0, b = -1/my)) +
layer(panel.abline(h = 0, lty = 2))
#-----------------------------------------------------------------------
fun <- Vectorize(vectorize.args = c("lambda", "alpha"),
FUN = function(lambda, alpha) {
sum(dpg1(y = y, lambda = lambda, alpha = alpha))
})
# dpg1(y = 0:10, lambda = 5, alpha = -0)
# dpois(0:10, lambda = 5)
grid <- list(lambda = seq(0.2, 50, by = 0.2),
alpha = seq(-0.98, 0.98, by = 0.02))
grid$sum <- with(grid, outer(lambda, alpha, fun))
...
...
@@ -262,15 +263,16 @@ grid <- with(grid,
levelplot(sum ~ lambda + alpha,
data = subset(grid, round(sum, 3) == 1),
col.regions = gray.colors)
col.regions = gray.colors) +
layer(panel.abline(h = 0, lty = 2)) +
layer(panel.curve(-1/x))
# Já que lambda * alpha > -1, então alpha = -1/lambda dá a fronteira.
```
## Verossimilhança e Estimação ##
```{r}
library(bbmle)
library(corrplot)
# Função de log-Verossimilhança da Poisson Generalizada na
# parametrização de modelo de regressão.
llpg <- function(theta, y, X, offset = NULL) {
...
...
@@ -402,8 +404,6 @@ V <- cov2cor(vcov(m3))
corrplot.mixed(V, upper = "ellipse", col = "gray50")
dev.off()
library(plyr)
# Tamanho das covariâncias com \alpha.
each(sum, mean, max)(abs(V[1, -1]))
...
...
@@ -421,7 +421,6 @@ crossprod(L %*% coef(m3),
solve(L %*% vcov(m3) %*% t(L),
L %*% coef(m3)))
library(car)
# Teste de Wald para interação (poderia ser LRT, claro).
# É necessário um objeto glm.
linearHypothesis(model = m0,
...
...
@@ -432,9 +431,6 @@ linearHypothesis(model = m0,
#-----------------------------------------------------------------------
# Predição com bandas de confiança.
library(doBy)
library(multcomp)
X <- LSmatrix(m0, effect = c("umid", "K"))
pred <- attr(X, "grid")
...
...
@@ -469,11 +465,9 @@ pred$pgen <- cbind(pred$pgen,
FUN = function(x) {
exp(pred$pgen$eta + x)
}))
str(pred$pgen)
pred <- ldply(pred, .id = "modelo")
pred <- arrange(pred, umid, K, modelo)
str(pred)
key <- list(type = "o", divide = 1,
lines = list(pch = 1:nlevels(pred$modelo),
...
...
@@ -597,14 +591,12 @@ V <- V[-1, -1]
U <- chol(V)
aux <- sqrt(apply(X %*% t(U), MARGIN = 1,
FUN = function(x) { sum(x^2) }))
pred$pgen$eta <- c(X %*% coef(m3)[-1])
pred$pgen <- cbind(pred$pgen,
apply(outer(aux, qn, FUN = "*"), MARGIN = 2,
FUN = function(x) {
exp(pred$pgen$eta + x)
}))
str(pred$pgen)
pred <- ldply(pred, .id = "modelo")
pred <- arrange(pred, umid, K, modelo)
...
...
@@ -818,11 +810,9 @@ str(pred$pois)
# Matrix de covariância completa e sem o alpha.
V <- vcov(m3)
V <- V[-1,-1]
U <- chol(V)
aux <- sqrt(apply(X %*% t(U), MARGIN = 1,
FUN = function(x) { sum(x^2) }))
pred$pgen$eta <- c(X %*% coef(m3)[-1])
pred$pgen <- cbind(pred$pgen,
apply(outer(aux, qn, FUN = "*"), MARGIN = 2,
...
...
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