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leg
MRDCr
Commits
f9725bc0
Commit
f9725bc0
authored
May 03, 2016
by
Walmes Marques Zeviani
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Adiciona análise do número de grãos.
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229a5010
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vignettes/v04_poisson_generelizada.Rmd
vignettes/v04_poisson_generelizada.Rmd
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vignettes/v04_poisson_generelizada.Rmd
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f9725bc0
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@@ -245,8 +245,8 @@ fun <- Vectorize(vectorize.args = c("lambda", "alpha"),
sum(dpg1(y = y, lambda = lambda, alpha = alpha))
})
dpg1(y = 0:10, lambda = 5, alpha = -0)
dpois(0:10, lambda = 5)
#
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))
...
...
@@ -486,6 +486,139 @@ xyplot(fit ~ K | umid, data = pred,
panel = panel.cbH)
```
## Número de Grãos Produzidas em Soja ##
Análise do número de grãos por pacela do experimento com soja.
```{r}
#-----------------------------------------------------------------------
xyplot(ngra ~ K | umid, data = soja, layout = c(NA, 1),
type = c("p", "smooth"),
strip = strip.custom(strip.names = TRUE, var.name = "Umidade"))
#-----------------------------------------------------------------------
# Modelo Poisson.
m0 <- glm(ngra ~ bloc + umid * K, data = soja, family = poisson)
# Diagnóstico.
par(mfrow = c(2, 2))
plot(m0); layout(1)
# Medidas de decisão.
anova(m0, test = "Chisq")
summary(m0)
#-----------------------------------------------------------------------
# Modelo Poisson Generalizado.
L <- with(soja, list(y = ngra, offset = 1, X = model.matrix(m0)))
# Usa as estimativas do Poisson como valore iniciais.
start <- c(alpha = 0, coef(m0))
parnames(llpg) <- names(start)
# Com alpha fixo em 0 corresponde à Poisson.
m2 <- mle2(llpg, start = start, data = L,
fixed = list(alpha = 0), vecpar = TRUE)
# Mesma medida de ajuste e estimativas.
c(logLik(m2), logLik(m0))
# Poisson Generalizada.
m3 <- mle2(llpg, start = start, data = L, vecpar = TRUE)
# Teste para nulinidade do parâmetro de dispersão (H_0: alpha == 0).
anova(m3, m2)
cbind("PoissonGLM" = c(NA, coef(m0)),
"PoissonML" = coef(m2),
"PGeneraliz" = coef(m3))
# Perfil para o parâmetro de dispersão.
plot(profile(m3, which = "alpha"))
abline(v = 0, lty = 2)
V <- cov2cor(vcov(m3))
corrplot.mixed(V, upper = "ellipse", col = "gray50")
dev.off()
# Tamanho das covariâncias com \alpha.
each(sum, mean, max)(abs(V[1, -1]))
# Teste de Wald para a interação.
a <- c(0, attr(model.matrix(m0), "assign"))
ai <- a == max(a)
L <- t(replicate(sum(ai), rbind(coef(m3) * 0), simplify = "matrix"))
L[, ai] <- diag(sum(ai))
# Cáclculo da estatística Chi-quadrado.
crossprod(L %*% coef(m3),
solve(L %*% vcov(m3) %*% t(L),
L %*% coef(m3)))
linearHypothesis(model = m0,
hypothesis.matrix = L,
vcov. = vcov(m3),
coef. = coef(m3))
#-----------------------------------------------------------------------
# Predição com bandas de confiança.
X <- LSmatrix(m0, effect = c("umid", "K"))
pred <- attr(X, "grid")
pred <- transform(pred,
K = as.integer(K),
umid = factor(umid))
pred <- list(pois = pred, pgen = pred)
# Quantil normal.
qn <- qnorm(0.975) * c(lwr = -1, fit = 0, upr = 1)
# Preditos pela Poisson.
aux <- confint(glht(m0, linfct = X),
calpha = univariate_calpha())$confint
colnames(aux)[1] <- "fit"
pred$pois <- cbind(pred$pois, exp(aux))
str(pred$pois)
# Matrix de covariância completa e sem o alpha (marginal).
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,
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),
lty = 1, col = 1),
text = list(c("Poisson", "Poisson Generelizada")))
xyplot(fit ~ K | umid, data = pred,
layout = c(NA, 1), as.table = TRUE,
xlim = extendrange(range(pred$K), f = 0.1),
key = key, pch = pred$modelo,
ly = pred$lwr, uy = pred$upr, cty = "bars", length = 0,
prepanel = prepanel.cbH,
desloc = 8 * scale(as.integer(pred$modelo), scale = FALSE),
panel = panel.cbH)
```
## Número de Capulhos Produzidos em Algodão ##
```{r}
...
...
@@ -559,9 +692,6 @@ L <- t(replicate(sum(ai), rbind(coef(m3) * 0), simplify = "matrix"))
L[, ai] <- diag(sum(ai))
# Teste de Wald explicito.
# t(L %*% coef(m3)) %*%
# solve(L %*% vcov(m3) %*% t(L)) %*%
# (L %*% coef(m3))
crossprod(L %*% coef(m3),
solve(L %*% vcov(m3) %*% t(L),
L %*% coef(m3)))
...
...
@@ -635,7 +765,7 @@ update(p1, type = "p", layout = c(NA, 1),
as.layer(p2, under = TRUE)
```
## Mosca Branca
## Mosca Branca
##
```{r}
data(ninfas, package = "MRDCr")
...
...
@@ -654,8 +784,6 @@ xyplot(ntot ~ dias | cult, data = ninfas,
grid = TRUE, as.table = TRUE, layout = c(NA, 2),
xlab = "Dias", ylab = "Número total de ninfas")
ninfas$dias <- scale(ninfas$dias)
# IMPORTANT: Foi usado um offset de 100 por problemas de
# over/underflow. O problema não é com a Poisson mas com a Poisson
# Generalizada. Todas as análises consideram um offset de 100, portanto,
...
...
@@ -729,9 +857,6 @@ L <- t(replicate(sum(ai), rbind(coef(m3) * 0), simplify = "matrix"))
L[, ai] <- diag(sum(ai))
# Teste de Wald explicito.
# t(L %*% coef(m3)) %*%
# solve(L %*% vcov(m3) %*% t(L)) %*%
# (L %*% coef(m3))
crossprod(L %*% coef(m3),
solve(L %*% vcov(m3) %*% t(L),
L %*% coef(m3)))
...
...
@@ -777,7 +902,6 @@ V <- V[-1,-1]
U <- chol(V)
aux <- sqrt(apply(X %*% t(U), MARGIN = 1,
FUN = function(x) { sum(x^2) }))
pred$pgen$eta <- c(X %*% coef(m3)[-1])
pred$pgen <- cbind(pred$pgen,
apply(outer(aux, qn, FUN = "*"), MARGIN = 2,
...
...
@@ -809,6 +933,34 @@ xyplot(ntot ~ aval | cult, data = ninfas,
prepanel = prepanel.cbH,
panel.groups = panel.cbH,
panel = panel.superpose), under = TRUE)
#-----------------------------------------------------------------------
fun <- Vectorize(vectorize.args = c("lambda", "alpha"),
FUN = function(lambda, alpha) {
sum(dpg1(y = y, lambda = lambda, alpha = alpha))
})
# dpg1(y = 0:10, lambda = 5, alpha = -0)
# dpois(0:10, lambda = 5)
head(sort(subset(pred, modelo = "pois")$fit, decreasing = TRUE))
coef(m3)["alpha"]
y <- 0:400
grid <- list(lambda = seq(10, 200, by = 2),
alpha = seq(-0.05, 0.1, by = 0.001))
grid$sum <- with(grid, outer(lambda, alpha, fun))
grid <- with(grid,
cbind(expand.grid(lambda = lambda, alpha = alpha),
data.frame(sum = c(sum))))
levelplot(sum ~ lambda + alpha,
data = subset(grid, round(sum, 3) == 1),
col.regions = gray.colors) +
layer(panel.abline(h = 0.05))
```
## Pacote VGAM ##
...
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