diff --git a/inst/doc/v07_excesso-zeros.R b/inst/doc/v07_excesso-zeros.R index 6fd1d860fd3a9646d1cd075ef624f0d983b33b6b..4960463149e40824826ed80d92a02526c0388cd4 100644 --- a/inst/doc/v07_excesso-zeros.R +++ b/inst/doc/v07_excesso-zeros.R @@ -283,3 +283,187 @@ cbind("vglm_posnegbin" = exp(coef(mcount)[2]), "zeroinfl" = m3HBN$theta) +## ------------------------------------------------------------------------ + +sinistros <- read.table("base.txt", header = TRUE) +str(sinistros) +## help(sinistros) + + +## ------------------------------------------------------------------------ + +summary(sinistros) + + +## ------------------------------------------------------------------------ + +library(pscl) + +## Preditores +f0 <- nsinist ~ 1 + offset(log(expos)) +f1 <- nsinist ~ sexo + valor + offset(log(expos)) +f2 <- nsinist ~ sexo * valor + offset(log(expos)) + +## Poisson +m0P <- glm(f0, data = sinistros, family = poisson) +m1P <- glm(f1, data = sinistros, family = poisson) +m2P <- glm(f2, data = sinistros, family = poisson) + +## Hurdle Poisson +m0HP <- hurdle(f0, data = sinistros, dist = "poisson") +m1HP <- hurdle(f1, data = sinistros, dist = "poisson") +m2HP <- hurdle(f2, data = sinistros, dist = "poisson") + +## Zero Inflated Poisson +m0ZP <- zeroinfl(f0, data = sinistros, dist = "poisson") +m1ZP <- zeroinfl(f1, data = sinistros, dist = "poisson") +m2ZP <- zeroinfl(f2, data = sinistros, dist = "poisson") + +## Binomial Negativa +library(MASS) +m0BN <- glm.nb(f0, data = sinistros) +m1BN <- glm.nb(f1, data = sinistros) +m2BN <- glm.nb(f2, data = sinistros) + +## Hurdle Binomial Negativa +m0HBN <- hurdle(f0, data = sinistros, dist = "negbin") +m1HBN <- hurdle(f1, data = sinistros, dist = "negbin") +m2HBN <- hurdle(f2, data = sinistros, dist = "negbin") + +## Zero Inflated Poisson +m0ZBN <- zeroinfl(f0, data = sinistros, dist = "negbin") +m1ZBN <- zeroinfl(f1, data = sinistros, dist = "negbin") +m2ZBN <- zeroinfl(f2, data = sinistros, dist = "negbin") + + +## ------------------------------------------------------------------------ + +## Via log-verossimilhança +cbind("Poisson" = sapply(list(m0P, m1P, m2P), logLik), + "HUPoisson" = sapply(list(m0HP, m1HP, m2HP), logLik), + "ZIPoisson" = sapply(list(m0ZP, m1ZP, m2ZP), logLik), + "BinNeg" = sapply(list(m0BN, m1BN, m2BN), logLik), + "HUBinNeg" = sapply(list(m0HBN, m1HBN, m2HBN), logLik), + "ZIBinNeg" = sapply(list(m0ZBN, m1ZBN, m2ZBN), logLik) + ) + + +## ------------------------------------------------------------------------ + +## Testes de razão de verossimilhanças +lmtest::lrtest(m0ZP, m1ZP, m2ZP) + +lmtest::lrtest(m0ZBN, m1ZBN, m2ZBN) + + +## ------------------------------------------------------------------------ + +## Para a componente de contagens não negativas é necessário um modelo +## Binomial Negativa, considerando superdispersão dos dados? +vuong(m1ZP, m1ZBN) + + +## ------------------------------------------------------------------------ + +## Estimativas do modelo proposto +summary(m1ZP) + +## Retira efeito de sexo na componente das contagens nulas +m3ZP <- zeroinfl(nsinist ~ sexo + valor + offset(log(expos)) | + offset(log(expos)) + valor, + data = sinistros) + +lmtest::lrtest(m1ZP, m3ZP) + + +## ------------------------------------------------------------------------ + +## Avaliação de diferentes especificações para a componente de contagens +## nulas +m3ZP.probi <- zeroinfl(nsinist ~ sexo + valor + offset(log(expos)) | + offset(log(expos)) + valor, link = "probit", + data = sinistros) + +m3ZP.clogl <- zeroinfl(nsinist ~ sexo + valor + offset(log(expos)) | + offset(log(expos)) + valor, link = "cloglog", + data = sinistros) + +## Comparação das funções de ligação +sapply(list("logit" = m3ZP, "probit" = m3ZP.probi, + "cloglog" = m3ZP.clogl), logLik) + + +## ----diag2, cache = TRUE------------------------------------------------- + +##====================================================================== +## Uma pequena avaliação dos resíduos +p1 <- xyplot(residuals(m3ZP) ~ fitted(m3ZP), + ## aspect = "xy", + type = c("p", "g", "spline")) + +p2 <- qqmath(~residuals(m3ZP, type = "pearson"), + ## aspect = "xy", + type = c("p", "g"), + panel = function(x, ...) { + panel.qqmathline(x, ...) + panel.qqmath(x, ...) + }) + +qqsimul <- hnp::hnp(m3ZP, plot = FALSE, sim = 50) + +p3 <- with(qqsimul, + xyplot(residuals ~ x, pch = 20) + + as.layer( + xyplot(median + lower + upper ~ x, + type = "l", col = 1, lty = c(2, 1, 1))) + ) + +print(p1, split = c(1, 1, 3, 1), more = TRUE) +print(p2, split = c(2, 1, 3, 1), more = TRUE) +print(p3, split = c(3, 1, 3, 1), more = FALSE) + + +## ------------------------------------------------------------------------ + +## Região para predição +pred <- expand.grid(sexo = c("M", "F"), + valor = 1:200, expos = 1) +##------------------------------------------- +## Estimando as médias +aux <- predict(m3ZP, newdata = pred, type = "response") +predmu <- cbind(pred, fit = aux) + +xyplot(fit ~ valor, + groups = sexo, + data = predmu, + type = c("l", "g"), + auto.key = list( + columns = 2, cex.title = 1, + lines = TRUE, points = FALSE, + title = "Número de crianças")) + + +## ------------------------------------------------------------------------ + +##------------------------------------------- +## Estimando probabilidades +pred <- expand.grid(sexo = c("M", "F"), + valor = c(1, 50), expos = 1) + +py <- c(t(predict(m3ZP, type = "prob", at = 0:5, newdata = pred))) +carac <- with(pred, paste("Sexo:", sexo, "Valor:", valor)) +da <- data.frame(y = 0:5, py = py, + sexo = rep(c("M", "F"), each = 6), + valor = rep(c(1, 50), each = 12)) + +useOuterStrips( + barchart(py ~ y | sexo + factor(valor), data = da, + stack = FALSE, horizontal = FALSE, + as.table = TRUE, between = list(y = 0.5), + scales = list( y = "free", x = list(labels = 0:5)) + ), + strip = strip.custom(strip.names = TRUE, var.name = "Sexo"), + strip.left = strip.custom(strip.names = TRUE, var.name = "Valor") +) + + diff --git a/inst/doc/v07_excesso-zeros.html b/inst/doc/v07_excesso-zeros.html index 8e3309acbb318c61fe0048c18d1a3eaa0760c075..1a458986e89a67b531a482846821c0b44069bf45 100644 --- a/inst/doc/v07_excesso-zeros.html +++ b/inst/doc/v07_excesso-zeros.html @@ -113,6 +113,13 @@ table.header { <li><a href="#predicao">Predição</a></li> <li><a href="#ajuste-em-separado-das-componentes-do-modelo-hurdle">Ajuste em separado das componentes do modelo Hurdle</a></li> </ul></li> +<li><a href="#numero-de-sinistros-em-uma-seguradora-de-veiculos">Número de sinistros em uma seguradora de veículos</a><ul> +<li><a href="#analise-exploratoria-1">Análise exploratória</a></li> +<li><a href="#ajuste-de-modelos-1">Ajuste de modelos</a></li> +<li><a href="#comparacao-dos-ajustes-1">Comparação dos ajustes</a></li> +<li><a href="#avaliacao-do-modelo-proposto-1">Avaliação do modelo proposto</a></li> +<li><a href="#predicao-1">Predição</a></li> +</ul></li> </ul> </div> @@ -231,7 +238,7 @@ xyplot(lnpeixes ~ ncriancas + npessoas, data = peixe, jitter.x = TRUE, type = c("p", "g", "spline"))</code></pre> -<p><img 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" 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" title alt style="display: block; margin: auto;" /></p> </div> <div id="ajuste-de-modelos" class="section level2"> <h2>Ajuste de modelos</h2> @@ -612,6 +619,264 @@ cbind("vglm_posnegbin" = coef(mcount)[-2], ## (Intercept):2 0.3232437 0.3232322</code></pre> </div> </div> +<div id="numero-de-sinistros-em-uma-seguradora-de-veiculos" class="section level1"> +<h1>Número de sinistros em uma seguradora de veículos</h1> +<div id="analise-exploratoria-1" class="section level2"> +<h2>Análise exploratória</h2> +<pre class="r"><code>sinistros <- read.table("base.txt", header = TRUE) +str(sinistros)</code></pre> +<pre><code>## 'data.frame': 16483 obs. of 7 variables: +## $ tipo : Factor w/ 6 levels "hatch","mono",..: 1 2 5 4 2 1 1 4 5 5 ... +## $ idade : int 59 45 42 63 36 33 35 63 54 32 ... +## $ sexo : Factor w/ 2 levels "F","M": 1 2 2 1 1 2 2 2 2 2 ... +## $ civil : Factor w/ 4 levels "Casado","Divorciado",..: 1 3 1 1 1 1 1 1 1 1 ... +## $ valor : num 24.6 23.4 86.6 77.5 25.9 ... +## $ expos : num 0.5 0.7 0.79 0.01 0.51 0.79 0.81 0.01 0.76 0.79 ... +## $ nsinist: int 1 0 0 0 0 0 0 0 0 0 ...</code></pre> +<pre class="r"><code>## help(sinistros)</code></pre> +<p>Dados referentes ao acompanhamento de clientes de uma seguradora de automóveis ao longo de um ano. Foram registrados as variáveis descritas abaixo para 16483 clientes.</p> +<ul> +<li><code>tipo</code>: Tipo de veículo segurado. Fator com seis níveis (<em>hatch</em>, <em>mono</em>, <em>picape</em>, <em>sedan</em>, <em>wagon</em> e <em>suv</em>).</li> +<li><code>sexo</code>: Sexo do cliente. Fator com dois níveis, <em>M</em> para clientes do sexo masculino e <em>F</em> para feminino.</li> +<li><code>civil</code>: Estado civil do cliente. Fator com quatro níveis (<em>Casado</em>, <em>Divorciado</em>, <em>Solteiro</em> e <em>Viuvo</em>).</li> +<li><code>valor</code>: Valor do veículo segurado, em 1000 reais.</li> +<li><code>expos</code>: Período de cobertura do cliente durante o ano sob análise.</li> +<li><code>nsinist</code>: Número de sinistros registrados no período.</li> +</ul> +<pre class="r"><code>summary(sinistros)</code></pre> +<pre><code>## tipo idade sexo civil +## hatch :6080 Min. :18.00 F:6694 Casado :13327 +## mono :1546 1st Qu.:41.00 M:9789 Divorciado: 729 +## picape:1197 Median :52.00 Solteiro : 1896 +## sedan :4696 Mean :52.13 Viuvo : 531 +## suv :2499 3rd Qu.:62.00 +## wagon : 465 Max. :96.00 +## valor expos nsinist +## Min. : 1.60 Min. :0.0100 Min. :0.0000 +## 1st Qu.: 22.07 1st Qu.:0.5900 1st Qu.:0.0000 +## Median : 31.92 Median :0.7800 Median :0.0000 +## Mean : 36.03 Mean :0.6483 Mean :0.0617 +## 3rd Qu.: 45.14 3rd Qu.:0.8100 3rd Qu.:0.0000 +## Max. :196.65 Max. :1.0000 Max. :5.0000</code></pre> +</div> +<div id="ajuste-de-modelos-1" class="section level2"> +<h2>Ajuste de modelos</h2> +<pre class="r"><code>library(pscl) + +## Preditores +f0 <- nsinist ~ 1 + offset(log(expos)) +f1 <- nsinist ~ sexo + valor + offset(log(expos)) +f2 <- nsinist ~ sexo * valor + offset(log(expos)) + +## Poisson +m0P <- glm(f0, data = sinistros, family = poisson) +m1P <- glm(f1, data = sinistros, family = poisson) +m2P <- glm(f2, data = sinistros, family = poisson) + +## Hurdle Poisson +m0HP <- hurdle(f0, data = sinistros, dist = "poisson") +m1HP <- hurdle(f1, data = sinistros, dist = "poisson") +m2HP <- hurdle(f2, data = sinistros, dist = "poisson") + +## Zero Inflated Poisson +m0ZP <- zeroinfl(f0, data = sinistros, dist = "poisson") +m1ZP <- zeroinfl(f1, data = sinistros, dist = "poisson") +m2ZP <- zeroinfl(f2, data = sinistros, dist = "poisson") + +## Binomial Negativa +library(MASS) +m0BN <- glm.nb(f0, data = sinistros) +m1BN <- glm.nb(f1, data = sinistros) +m2BN <- glm.nb(f2, data = sinistros) + +## Hurdle Binomial Negativa +m0HBN <- hurdle(f0, data = sinistros, dist = "negbin") +m1HBN <- hurdle(f1, data = sinistros, dist = "negbin") +m2HBN <- hurdle(f2, data = sinistros, dist = "negbin") + +## Zero Inflated Poisson +m0ZBN <- zeroinfl(f0, data = sinistros, dist = "negbin") +m1ZBN <- zeroinfl(f1, data = sinistros, dist = "negbin") +m2ZBN <- zeroinfl(f2, data = sinistros, dist = "negbin")</code></pre> +</div> +<div id="comparacao-dos-ajustes-1" class="section level2"> +<h2>Comparação dos ajustes</h2> +<pre class="r"><code>## Via log-verossimilhança +cbind("Poisson" = sapply(list(m0P, m1P, m2P), logLik), + "HUPoisson" = sapply(list(m0HP, m1HP, m2HP), logLik), + "ZIPoisson" = sapply(list(m0ZP, m1ZP, m2ZP), logLik), + "BinNeg" = sapply(list(m0BN, m1BN, m2BN), logLik), + "HUBinNeg" = sapply(list(m0HBN, m1HBN, m2HBN), logLik), + "ZIBinNeg" = sapply(list(m0ZBN, m1ZBN, m2ZBN), logLik) + )</code></pre> +<pre><code>## Poisson HUPoisson ZIPoisson BinNeg HUBinNeg ZIBinNeg +## [1,] -4123.756 -3822.441 -3723.772 -3808.049 -3822.030 -3723.759 +## [2,] -4113.112 -3813.065 -3714.354 -3802.173 -3812.650 -3714.290 +## [3,] -4113.102 -3811.952 -3713.520 -3802.157 -3811.594 -3713.485</code></pre> +<pre class="r"><code>## Testes de razão de verossimilhanças +lmtest::lrtest(m0ZP, m1ZP, m2ZP)</code></pre> +<pre><code>## Likelihood ratio test +## +## Model 1: nsinist ~ 1 + offset(log(expos)) +## Model 2: nsinist ~ sexo + valor + offset(log(expos)) +## Model 3: nsinist ~ sexo * valor + offset(log(expos)) +## #Df LogLik Df Chisq Pr(>Chisq) +## 1 2 -3723.8 +## 2 6 -3714.4 4 18.8370 0.0008461 *** +## 3 8 -3713.5 2 1.6681 0.4342916 +## --- +## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1</code></pre> +<pre class="r"><code>lmtest::lrtest(m0ZBN, m1ZBN, m2ZBN)</code></pre> +<pre><code>## Likelihood ratio test +## +## Model 1: nsinist ~ 1 + offset(log(expos)) +## Model 2: nsinist ~ sexo + valor + offset(log(expos)) +## Model 3: nsinist ~ sexo * valor + offset(log(expos)) +## #Df LogLik Df Chisq Pr(>Chisq) +## 1 3 -3723.8 +## 2 7 -3714.3 4 18.9367 0.0008088 *** +## 3 9 -3713.5 2 1.6097 0.4471484 +## --- +## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1</code></pre> +<pre class="r"><code>## Para a componente de contagens não negativas é necessário um modelo +## Binomial Negativa, considerando superdispersão dos dados? +vuong(m1ZP, m1ZBN)</code></pre> +<pre><code>## Vuong Non-Nested Hypothesis Test-Statistic: +## (test-statistic is asymptotically distributed N(0,1) under the +## null that the models are indistinguishible) +## ------------------------------------------------------------- +## Vuong z-statistic H_A p-value +## Raw -0.1757064 model2 > model1 0.43026 +## AIC-corrected -0.1757064 model2 > model1 0.43026 +## BIC-corrected -0.1757064 model2 > model1 0.43026</code></pre> +</div> +<div id="avaliacao-do-modelo-proposto-1" class="section level2"> +<h2>Avaliação do modelo proposto</h2> +<pre class="r"><code>## Estimativas do modelo proposto +summary(m1ZP)</code></pre> +<pre><code>## +## Call: +## zeroinfl(formula = f1, data = sinistros, dist = "poisson") +## +## Pearson residuals: +## Min 1Q Median 3Q Max +## -0.3036 -0.2174 -0.2045 -0.1932 17.6045 +## +## Count model coefficients (poisson with log link): +## Estimate Std. Error z value Pr(>|z|) +## (Intercept) 0.082267 0.146473 0.562 0.574 +## sexoM -0.144597 0.120124 -1.204 0.229 +## valor -0.006457 0.003420 -1.888 0.059 . +## +## Zero-inflation model coefficients (binomial with logit link): +## Estimate Std. Error z value Pr(>|z|) +## (Intercept) 2.764149 0.163096 16.948 < 2e-16 *** +## sexoM 0.038435 0.132175 0.291 0.77121 +## valor -0.012444 0.003989 -3.120 0.00181 ** +## --- +## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 +## +## Number of iterations in BFGS optimization: 22 +## Log-likelihood: -3714 on 6 Df</code></pre> +<pre class="r"><code>## Retira efeito de sexo na componente das contagens nulas +m3ZP <- zeroinfl(nsinist ~ sexo + valor + offset(log(expos)) | + offset(log(expos)) + valor, + data = sinistros) + +lmtest::lrtest(m1ZP, m3ZP)</code></pre> +<pre><code>## Likelihood ratio test +## +## Model 1: nsinist ~ sexo + valor + offset(log(expos)) +## Model 2: nsinist ~ sexo + valor + offset(log(expos)) | offset(log(expos)) + +## valor +## #Df LogLik Df Chisq Pr(>Chisq) +## 1 6 -3714.4 +## 2 5 -3714.4 -1 0.0846 0.7712</code></pre> +<pre class="r"><code>## Avaliação de diferentes especificações para a componente de contagens +## nulas +m3ZP.probi <- zeroinfl(nsinist ~ sexo + valor + offset(log(expos)) | + offset(log(expos)) + valor, link = "probit", + data = sinistros) + +m3ZP.clogl <- zeroinfl(nsinist ~ sexo + valor + offset(log(expos)) | + offset(log(expos)) + valor, link = "cloglog", + data = sinistros) + +## Comparação das funções de ligação +sapply(list("logit" = m3ZP, "probit" = m3ZP.probi, + "cloglog" = m3ZP.clogl), logLik)</code></pre> +<pre><code>## logit probit cloglog +## -3714.396 -3716.296 -3717.328</code></pre> +<pre class="r"><code>##====================================================================== +## Uma pequena avaliação dos resíduos +p1 <- xyplot(residuals(m3ZP) ~ fitted(m3ZP), + ## aspect = "xy", + type = c("p", "g", "spline")) + +p2 <- qqmath(~residuals(m3ZP, type = "pearson"), + ## aspect = "xy", + type = c("p", "g"), + panel = function(x, ...) { + panel.qqmathline(x, ...) + panel.qqmath(x, ...) + }) + +qqsimul <- hnp::hnp(m3ZP, plot = FALSE, sim = 50)</code></pre> +<pre><code>## Zero-inflated Poisson model</code></pre> +<pre class="r"><code>p3 <- with(qqsimul, + xyplot(residuals ~ x, pch = 20) + + as.layer( + xyplot(median + lower + upper ~ x, + type = "l", col = 1, lty = c(2, 1, 1))) + ) + +print(p1, split = c(1, 1, 3, 1), more = TRUE) +print(p2, split = c(2, 1, 3, 1), more = TRUE) +print(p3, split = c(3, 1, 3, 1), more = FALSE)</code></pre> +<p><img 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" title alt style="display: block; margin: auto;" /></p> +</div> +<div id="predicao-1" class="section level2"> +<h2>Predição</h2> +<pre class="r"><code>## Região para predição +pred <- expand.grid(sexo = c("M", "F"), + valor = 1:200, expos = 1) +##------------------------------------------- +## Estimando as médias +aux <- predict(m3ZP, newdata = pred, type = "response") +predmu <- cbind(pred, fit = aux) + +xyplot(fit ~ valor, + groups = sexo, + data = predmu, + type = c("l", "g"), + auto.key = list( + columns = 2, cex.title = 1, + lines = TRUE, points = FALSE, + title = "Número de crianças"))</code></pre> +<p><img 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" title alt style="display: block; margin: auto;" /></p> +<pre class="r"><code>##------------------------------------------- +## Estimando probabilidades +pred <- expand.grid(sexo = c("M", "F"), + valor = c(1, 50), expos = 1) + +py <- c(t(predict(m3ZP, type = "prob", at = 0:5, newdata = pred))) +carac <- with(pred, paste("Sexo:", sexo, "Valor:", valor)) +da <- data.frame(y = 0:5, py = py, + sexo = rep(c("M", "F"), each = 6), + valor = rep(c(1, 50), each = 12)) + +useOuterStrips( + barchart(py ~ y | sexo + factor(valor), data = da, + stack = FALSE, horizontal = FALSE, + as.table = TRUE, between = list(y = 0.5), + scales = list( y = "free", x = list(labels = 0:5)) + ), + strip = strip.custom(strip.names = TRUE, var.name = "Sexo"), + strip.left = strip.custom(strip.names = TRUE, var.name = "Valor") +)</code></pre> +<p><img src="data:image/png;base64,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" title alt style="display: block; margin: auto;" /></p> +</div> +</div> <style type="text/css"> hr.footer { diff --git a/vignettes/v07_excesso-zeros.Rmd b/vignettes/v07_excesso-zeros.Rmd index d01eb397cfba05622228adb7270def2bc108dd91..e8d956b93ba32e61d13e30a6f71e3757183b0773 100644 --- a/vignettes/v07_excesso-zeros.Rmd +++ b/vignettes/v07_excesso-zeros.Rmd @@ -338,4 +338,223 @@ cbind("vglm_posnegbin" = exp(coef(mcount)[2]), ``` +# Número de sinistros em uma seguradora de veículos # +## Análise exploratória ## + +```{r} + +sinistros <- read.table("base.txt", header = TRUE) +str(sinistros) +## help(sinistros) + +``` + +Dados referentes ao acompanhamento de clientes de uma seguradora de +automóveis ao longo de um ano. Foram registrados as variáveis descritas +abaixo para 16483 clientes. + +* `tipo`: Tipo de veículo segurado. Fator com seis níveis (_hatch_, + _mono_, _picape_, _sedan_, _wagon_ e _suv_). +* `sexo`: Sexo do cliente. Fator com dois níveis, _M_ para clientes do + sexo masculino e _F_ para feminino. +* `civil`: Estado civil do cliente. Fator com quatro níveis (_Casado_, + _Divorciado_, _Solteiro_ e _Viuvo_). +* `valor`: Valor do veículo segurado, em 1000 reais. +* `expos`: Período de cobertura do cliente durante o ano sob análise. +* `nsinist`: Número de sinistros registrados no período. + +```{r} + +summary(sinistros) + +``` + +## Ajuste de modelos ## + +```{r} + +library(pscl) + +## Preditores +f0 <- nsinist ~ 1 + offset(log(expos)) +f1 <- nsinist ~ sexo + valor + offset(log(expos)) +f2 <- nsinist ~ sexo * valor + offset(log(expos)) + +## Poisson +m0P <- glm(f0, data = sinistros, family = poisson) +m1P <- glm(f1, data = sinistros, family = poisson) +m2P <- glm(f2, data = sinistros, family = poisson) + +## Hurdle Poisson +m0HP <- hurdle(f0, data = sinistros, dist = "poisson") +m1HP <- hurdle(f1, data = sinistros, dist = "poisson") +m2HP <- hurdle(f2, data = sinistros, dist = "poisson") + +## Zero Inflated Poisson +m0ZP <- zeroinfl(f0, data = sinistros, dist = "poisson") +m1ZP <- zeroinfl(f1, data = sinistros, dist = "poisson") +m2ZP <- zeroinfl(f2, data = sinistros, dist = "poisson") + +## Binomial Negativa +library(MASS) +m0BN <- glm.nb(f0, data = sinistros) +m1BN <- glm.nb(f1, data = sinistros) +m2BN <- glm.nb(f2, data = sinistros) + +## Hurdle Binomial Negativa +m0HBN <- hurdle(f0, data = sinistros, dist = "negbin") +m1HBN <- hurdle(f1, data = sinistros, dist = "negbin") +m2HBN <- hurdle(f2, data = sinistros, dist = "negbin") + +## Zero Inflated Poisson +m0ZBN <- zeroinfl(f0, data = sinistros, dist = "negbin") +m1ZBN <- zeroinfl(f1, data = sinistros, dist = "negbin") +m2ZBN <- zeroinfl(f2, data = sinistros, dist = "negbin") + +``` + +## Comparação dos ajustes ## + +```{r} + +## Via log-verossimilhança +cbind("Poisson" = sapply(list(m0P, m1P, m2P), logLik), + "HUPoisson" = sapply(list(m0HP, m1HP, m2HP), logLik), + "ZIPoisson" = sapply(list(m0ZP, m1ZP, m2ZP), logLik), + "BinNeg" = sapply(list(m0BN, m1BN, m2BN), logLik), + "HUBinNeg" = sapply(list(m0HBN, m1HBN, m2HBN), logLik), + "ZIBinNeg" = sapply(list(m0ZBN, m1ZBN, m2ZBN), logLik) + ) + +``` + +```{r} + +## Testes de razão de verossimilhanças +lmtest::lrtest(m0ZP, m1ZP, m2ZP) + +lmtest::lrtest(m0ZBN, m1ZBN, m2ZBN) + +``` + +```{r} + +## Para a componente de contagens não negativas é necessário um modelo +## Binomial Negativa, considerando superdispersão dos dados? +vuong(m1ZP, m1ZBN) + +``` + +## Avaliação do modelo proposto ## + +```{r} + +## Estimativas do modelo proposto +summary(m1ZP) + +## Retira efeito de sexo na componente das contagens nulas +m3ZP <- zeroinfl(nsinist ~ sexo + valor + offset(log(expos)) | + offset(log(expos)) + valor, + data = sinistros) + +lmtest::lrtest(m1ZP, m3ZP) + +``` + +```{r} + +## Avaliação de diferentes especificações para a componente de contagens +## nulas +m3ZP.probi <- zeroinfl(nsinist ~ sexo + valor + offset(log(expos)) | + offset(log(expos)) + valor, link = "probit", + data = sinistros) + +m3ZP.clogl <- zeroinfl(nsinist ~ sexo + valor + offset(log(expos)) | + offset(log(expos)) + valor, link = "cloglog", + data = sinistros) + +## Comparação das funções de ligação +sapply(list("logit" = m3ZP, "probit" = m3ZP.probi, + "cloglog" = m3ZP.clogl), logLik) + +``` + +```{r diag2, cache = TRUE} + +##====================================================================== +## Uma pequena avaliação dos resíduos +p1 <- xyplot(residuals(m3ZP) ~ fitted(m3ZP), + ## aspect = "xy", + type = c("p", "g", "spline")) + +p2 <- qqmath(~residuals(m3ZP, type = "pearson"), + ## aspect = "xy", + type = c("p", "g"), + panel = function(x, ...) { + panel.qqmathline(x, ...) + panel.qqmath(x, ...) + }) + +qqsimul <- hnp::hnp(m3ZP, plot = FALSE, sim = 50) + +p3 <- with(qqsimul, + xyplot(residuals ~ x, pch = 20) + + as.layer( + xyplot(median + lower + upper ~ x, + type = "l", col = 1, lty = c(2, 1, 1))) + ) + +print(p1, split = c(1, 1, 3, 1), more = TRUE) +print(p2, split = c(2, 1, 3, 1), more = TRUE) +print(p3, split = c(3, 1, 3, 1), more = FALSE) + +``` + +## Predição ## + +```{r} + +## Região para predição +pred <- expand.grid(sexo = c("M", "F"), + valor = 1:200, expos = 1) +##------------------------------------------- +## Estimando as médias +aux <- predict(m3ZP, newdata = pred, type = "response") +predmu <- cbind(pred, fit = aux) + +xyplot(fit ~ valor, + groups = sexo, + data = predmu, + type = c("l", "g"), + auto.key = list( + columns = 2, cex.title = 1, + lines = TRUE, points = FALSE, + title = "Número de crianças")) + +``` + +```{r} + +##------------------------------------------- +## Estimando probabilidades +pred <- expand.grid(sexo = c("M", "F"), + valor = c(1, 50), expos = 1) + +py <- c(t(predict(m3ZP, type = "prob", at = 0:5, newdata = pred))) +carac <- with(pred, paste("Sexo:", sexo, "Valor:", valor)) +da <- data.frame(y = 0:5, py = py, + sexo = rep(c("M", "F"), each = 6), + valor = rep(c(1, 50), each = 12)) + +useOuterStrips( + barchart(py ~ y | sexo + factor(valor), data = da, + stack = FALSE, horizontal = FALSE, + as.table = TRUE, between = list(y = 0.5), + scales = list( y = "free", x = list(labels = 0:5)) + ), + strip = strip.custom(strip.names = TRUE, var.name = "Sexo"), + strip.left = strip.custom(strip.names = TRUE, var.name = "Valor") +) + +```