23 Set
2015
23 Set
'15
13:50
Considerando a funcao que calcula a estatistica, e' mais apropriado usar
stype='w' na funcao boot()
O CI fica menos amplo que o obtido por GLM:
plot(boo <- boot(a,SMR2,999,stype='w'))
boot.ci(boo)
m <- glm(Hosp_Death ~ offset(log(SAPS3Pro2)), poisson, data=a)
(coef.stats <- coef(summary(m)))
hist(boo$t)
abline(v=exp(coef.stats[1,1] +
qnorm(c(0.025,0.5,0.975))*coef.stats[1,2]), col=2)
abline(v=quantile(boo$t, c(0.025,0.975)), col=4)
Elias
On 23/09/15 15:40, Pedro Emmanuel Alvarenga Americano do Brasil wrote:
> Amigos de R,
>
> Desculpa pela demora, so consegui voltar nessa análise hoje. Bom
> tentei fazer o script recomendado pelo Elias. O pedaço do plot do
> bootstrap funciona, mas o pedaço para gerar o intervalo de confiança
> retorna um erro.
>
> >library(boot) > SMR2 <-
> function(a,w=1){length(a$Hosp_Death[which(a$Hosp_Death==1)])*w/(length(a$SAPS3Pro2)
> * mean(a$SAPS3Pro2))*w} > SMR2(a) [1] 1.000319
> >plot(boo <- boot(a,SMR2,999,stype='i')) > boot.ci
> <http://boot.ci>(boo) Error in bca.ci <http://bca.ci>(boot.out, conf,
> index[1L], L = L, t = t.o, t0 = t0.o, : estimated adjustment 'w' is
> infinite
>
> Achei muito estranho a função plot do bot funcionar e o boot.ci
> <http://boot.ci> não funcionar. Possivelmente ha necessidade de
> resolver alguma configuração para esse pedação do script.
>
> Pedro Brasil
>
> 2015-09-23 0:19 GMT-03:00 ASANTOS <alexandresantosbr@yahoo.com.br
> <mailto:alexandresantosbr@yahoo.com.br>>:
>
> Mauro, segue o script,
>
>
> dput(a[sample(1:nrow(a),100),c("Hosp_Death","SAPS3Pro2")])
> structure(list(Hosp_Death = c(0L, 1L, 0L, 1L, 0L, 0L, 0L, 1L,
> 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
> 1L, 1L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L,
> 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L,
> 1L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L,
> 1L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 1L, 1L, 0L, 0L, 0L, 0L,
> 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L), SAPS3Pro2 = c(0.1205,
> 0.3768, 0.315, 0.3768, 0.0311, 0.0719, 0.1735, 0.75, 0.01, 0.1454,
> 0.1093, 0.0511, 0.0891, 0.3353, 0.0073, 0.0401, 0.0044, 0.0239,
> 0.5246, 0.1591, 0.0453, 0.0511, 0.8715, 0.1735, 0.6037, 0.4405,
> 0.2047, 0.0208, 0.1591, 0.2047, 0.1205, 0.2759, 0.0273, 0.0044,
> 0.2759, 0.0157, 0.0643, 0.2215, 0.2047, 0.1735, 0.1591, 0.0273,
> 0.0062, 0.0311, 0.0157, 0.4192, 0.0643, 0.1735, 0.239, 0.4618,
> 0.0401, 0.0802, 0.0273, 0.0239, 0.0025, 0.0157, 0.0311, 0.0157,
> 0.0719, 0.239, 0.0574, 0.0157, 0.0085, 0.0085, 0.1093, 0.2215,
> 0.1591, 0.0719, 0.0044, 0.8563, 0.0453, 0.2047, 0.1454, 0.0273,
> 0.0891, 0.0802, 0.0239, 0.0135, 0.0802, 0.2571, 0.1735, 0.0157,
> 0.3768, 0.2759, 0.0891, 0.2047, 0.1735, 0.1454, 0.0643, 0.0311,
> 0.0453, 0.2759, 0.0239, 0.0181, 0.4405, 0.0085, 0.0311, 0.1205,
> 0.6579, 0.1093)), .Names = c("Hosp_Death", "SAPS3Pro2"), row.names
> = c(21875L,
> 6572L, 48643L, 11869L, 35683L, 48246L, 23919L, 29666L, 3676L,
> 45549L, 36453L, 14510L, 5082L, 1898L, 41549L, 25481L, 28590L,
> 38198L, 12822L, 12834L, 33267L, 34088L, 47720L, 30063L, 18326L,
> 11582L, 11454L, 34960L, 18785L, 11385L, 20605L, 28105L, 25496L,
> 1607L, 48766L, 36534L, 41868L, 45312L, 37206L, 26927L, 38944L,
> 21087L, 22343L, 1332L, 11496L, 29485L, 38316L, 4486L, 19757L,
> 45768L, 33028L, 12205L, 13150L, 41270L, 2780L, 44400L, 19696L,
> 26015L, 14651L, 39093L, 24905L, 17870L, 35016L, 42851L, 20464L,
> 33155L, 24924L, 33220L, 15379L, 28989L, 33286L, 34782L, 48534L,
> 9045L, 20403L, 40222L, 8821L, 31240L, 12465L, 11024L, 24407L,
> 45729L, 7412L, 22344L, 17737L, 3514L, 2335L, 22491L, 22493L,
> 14077L, 20346L, 12114L, 36868L, 16431L, 25112L, 31272L, 10877L,
> 14391L, 8422L, 45130L), class = "data.frame")
>
>
> SMR <- function(obs.var,pred.var){
> if(length(obs.var) != length(pred.var)){
> stop("Length of pred.var and obs.var differ.")
> }
> if(any(min(pred.var) <0 | max(pred.var) > 1)){
> stop("The individual predicted death must range from 0 to 1.")
> }
> if(any(levels(as.factor(obs.var)) != c(0,1))){
> stop("Observed death variable must be coded as 0 and 1.")
> }
> O <- length(obs.var[which(obs.var==1)])
> E <- length(pred.var) * mean(pred.var)
> SMR <- O / E
> lowerCL <- O / E * (1 - 1 / (9 * O) - 1.96 / (3 * sqrt(O)))^3 * 100
> upperCL <- (O + 1) / E * (1 - (1 / (9 * (O + 1))) + 1.96 / (3 *
> sqrt(O + 1)))^3 * 100
> output <- c(SMR=SMR,lower.Cl=lowerCL,upper.Cl=upperCL)
> output
> }
>
>
> SMR2 <-
> function(a,w=1){length(a$Hosp_Death[which(a$Hosp_Death==1)])*w/(length(a$SAPS3Pro2)
> * mean(a$SAPS3Pro2))*w}
>
>
> boot.ci <http://boot.ci>(boot(a, SMR2, R = 10, stype =
> "w",parallel = "multicore"))
>
> x <- boot.ci <http://boot.ci>(boot(a, SMR2, R = 10, stype =
> "w",parallel = "snow"))
> SMR2 <- function(a,w=1)
> sum(w*a$Hosp_Death)/sum(w*a$SAPS3Pro2)
>
> SMR2(a) ### SMR observada nos dados
>
> require(boot)
>
> plot(boo <- boot(a,SMR2,999,stype='i'))
> boot.ci <http://boot.ci>(boo)
>
> Em 22/09/2015 23:14, Mauro Sznelwar escreveu:
>
> Alguém poderia passar o código anterior, pois eu apaguei o que
> chegou antes.
>
>
>
> vc pode fazer assim:
>
> SMR2 <- function(a,w=1)
> sum(w*a$Hosp_Death)/sum(w*a$SAPS3Pro2)
>
> SMR2(a) ### SMR observada nos dados
>
> require(boot)
>
> plot(boo <- boot(a,SMR2,999,stype='i'))
> boot.ci <http://boot.ci>(boo)
>
> .
>
>
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> ======================================================================
> Alexandre dos Santos
> Proteção Florestal
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