我需要为某些医疗数据提供ANCOVA模型。我习惯使用SAS proc GLM，我想知道在获得SAS时，是否有可能获得置信区间的置信区间。
我正在使用LSmatrix包中的doBy来获取群组的意思。
model <- aov(yield ~ block + N * P + K, npk)
library(doBy)
k1 <- LSmatrix(model, effect="block")
linest(model,K=k1)
estimate se df t.stat p.value block
1 54.025 1.977116 14 27.32515 1.510775e-13 1
2 57.450 1.977116 14 29.05747 6.479499e-14 2
3 60.775 1.977116 14 30.73922 2.981292e-14 3
4 50.125 1.977116 14 25.35258 4.229573e-13 4
5 50.525 1.977116 14 25.55490 3.792520e-13 5
6 56.350 1.977116 14 28.50111 8.457748e-14 6
我想得到区块1的区别 - 区块2和置信区间（或标准错误），但我对如何从这里继续进行了迷失。
结果可能是这样的：
estimate se upper.CI lower.CI
1-2 -3.425 ?????? ????? ?????
1-3 -6.750 ?????? ????? ?????
...
如果有人能告诉我如何计算第一个，我可以管理其余部分：）
提前致谢。
答案 0 :(得分：2)
> pacman::p_load(lsmeans, multcompView)
>
> eindzl <- lm(yield ~ block + N * P + K, npk)
> anova(eindzl)
Analysis of Variance Table
Response: yield
Df Sum Sq Mean Sq F value Pr(>F)
block 5 343.29 68.659 4.3911 0.012954 *
N 1 189.28 189.282 12.1055 0.003684 **
P 1 8.40 8.402 0.5373 0.475637
K 1 95.20 95.202 6.0886 0.027114 *
N:P 1 21.28 21.282 1.3611 0.262841
Residuals 14 218.90 15.636
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
> eindzl.rg <- ref.grid(eindzl)
> eindzl.lsm <- lsmeans(eindzl.rg, "block")
> cld(eindzl.lsm, alpha = .05)
block lsmean SE df lower.CL upper.CL .group
4 50.125 1.977116 14 45.88451 54.36549 1
5 50.525 1.977116 14 46.28451 54.76549 1
1 54.025 1.977116 14 49.78451 58.26549 12
6 56.350 1.977116 14 52.10951 60.59049 12
2 57.450 1.977116 14 53.20951 61.69049 12
3 60.775 1.977116 14 56.53451 65.01549 2
Results are averaged over the levels of: N, P, K
Confidence level used: 0.95
P value adjustment: tukey method for comparing a family of 6 estimates
significance level used: alpha = 0.05
pairs(eindzl.lsm)
contrast estimate SE df t.ratio p.value
1 - 2 -3.425 2.796064 14 -1.225 0.8180
1 - 3 -6.750 2.796064 14 -2.414 0.2160
1 - 4 3.900 2.796064 14 1.395 0.7295
1 - 5 3.500 2.796064 14 1.252 0.8049
1 - 6 -2.325 2.796064 14 -0.832 0.9564
2 - 3 -3.325 2.796064 14 -1.189 0.8348
2 - 4 7.325 2.796064 14 2.620 0.1560
2 - 5 6.925 2.796064 14 2.477 0.1960
2 - 6 1.100 2.796064 14 0.393 0.9985
3 - 4 10.650 2.796064 14 3.809 0.0190
3 - 5 10.250 2.796064 14 3.666 0.0248
3 - 6 4.425 2.796064 14 1.583 0.6217
4 - 5 -0.400 2.796064 14 -0.143 1.0000
4 - 6 -6.225 2.796064 14 -2.226 0.2856
5 - 6 -5.825 2.796064 14 -2.083 0.3485
Results are averaged over the levels of: N, P, K
P value adjustment: tukey method for comparing a family of 6 estimates
>
II。解决方案2
如果您需要帮助确定公式，请随意使用您自己的公式来计算上限和下限，或访问CrossValidated。此示例基于标准错误并显示您需要的代码。 CrossValidated用于统计。
pacman::p_load(data.table,doBy)
model <- aov(yield ~ block + N * P + K, npk)
k1 <- LSmatrix(model, effect="block")
linest(model,K=k1)
tmp <- linest(model,K=k1)
tmp <- cbind(as.data.frame(tmp$coef), as.data.frame(tmp$grid))
tmp2 <- as.data.frame(matrix(ncol=4,nrow=9))
setnames(tmp2, c("group","estimate","lower bound of CI", "upper bound of CI"))
for(i in 1:5){
n <- i + 1
tmp2$estimate[i] <- tmp$estimate[tmp$block == i] - tmp$estimate[tmp$block == n]
tmp2$group[i] <- paste(i,n,sep="-")
tmp2[i,3] <- (tmp$estimate[tmp$block == i] - tmp$se[tmp$block == i]) - (tmp$estimate[tmp$block == n] + tmp$se[tmp$block == n])
tmp2[i,4] <- (tmp$estimate[tmp$block == i] + tmp$se[tmp$block == i]) - (tmp$estimate[tmp$block == n] - tmp$se[tmp$block == n])
}
for(i in 1:4){
n <- i + 2
nn <- 5+i
tmp2$estimate[nn] <- tmp$estimate[tmp$block == i] - tmp$estimate[tmp$block == n]
tmp2$group[nn] <- paste(i,n,sep="-")
tmp2[nn,3] <- (tmp$estimate[tmp$block == i] - tmp$se[tmp$block == i]) - (tmp$estimate[tmp$block == n] + tmp$se[tmp$block == n])
tmp2[nn,4] <- (tmp$estimate[tmp$block == i] + tmp$se[tmp$block == i]) - (tmp$estimate[tmp$block == n] - tmp$se[tmp$block == n])
}
tmp2
group estimate lower bound of CI upper bound of CI
1 1-2 -3.425 -7.379232 0.5292322
2 2-3 -3.325 -7.279232 0.6292322
3 3-4 10.650 6.695768 14.6042322
4 4-5 -0.400 -4.354232 3.5542322
5 5-6 -5.825 -9.779232 -1.8707678
6 1-3 -6.750 -10.704232 -2.7957678
7 2-4 7.325 3.370768 11.2792322
8 3-5 10.250 6.295768 14.2042322
9 4-6 -6.225 -10.179232 -2.2707678
请注意，如果您没有选择此特定软件包和功能，那么您可以在没有太多自定义操作的情况下获得这样的结果。通常你在R中键入的方式比在SAS中输入的方式少。