小编TiF*_*TiF的帖子

我需要得到特定行(这里:年)的一列(这里是:得分)的平均值.具体来说,我想知道三个时期的平均分数:

• 期间1:年<= 1983
• 期间2:年> = 1984年,年<= 1990年
• 第3期:年> = 1991年

这是我数据的结构:

  country year     score        
 Algeria 1980     -1.1201501 
 Algeria 1981     -1.0526943 
 Algeria 1982     -1.0561565 
 Algeria 1983     -1.1274560 
 Algeria 1984     -1.1353926 
 Algeria 1985     -1.1734330 
 Algeria 1986     -1.1327666 
 Algeria 1987     -1.1263586 
 Algeria 1988     -0.8529455 
 Algeria 1989     -0.2930265 
 Algeria 1990     -0.1564207 
 Algeria 1991     -0.1526328 
 Algeria 1992     -0.9757842 
 Algeria 1993     -0.9714060 
 Algeria 1994     -1.1422258 
 Algeria 1995     -0.3675797 
 ...

计算出的平均值应添加到附加列中的df("平均值"),即第1期的相同平均值,第2期等.

它应该是这样的:

country year     score         mean   
 Algeria 1980     -1.1201501     -1.089
 Algeria 1981     -1.0526943     -1.089
 Algeria 1982     -1.0561565 …

我试图在多重估算数据上运行一个多级模型(用Amelia创建); 样本基于群集样本,群组= 24,N = 150.

library("ZeligMultilevel")
ML.model.0 <- zelig(dv~1 + tag(1|group), model="ls.mixed",
data=a.out$imputations)
summary(ML.model.0)

此代码生成以下错误代码:

Error in object[[1]]$result$call : 
$ operator not defined for this S4 class

如果我运行OLS回归,它可以工作:

model.0 <- zelig(dv~1, model="ls", data=a.out$imputations)
m.0 <- coef(summary(model.0)) 
print(m.0, digits = 2)

      Value Std. Error t-stat  p-value
[1,]    45       0.34    130 2.6e-285

我很高兴提供一个有效的例子.

require(Zelig)
require(Amelia)
require(ZeligMultilevel)

data(freetrade)
length(freetrade$country) #grouping variable

#Imputation of missing data

a.out <- amelia(freetrade, m=5, ts="year", cs="country")

# Models: (1) OLS; (2) multi-level 

model.0 <- zelig(polity~1, model="ls", …

为了在匹配过程之前和之后直观地显示协变量平衡,我编写了以下代码:

library(lattice)
library(gridExtra)

StandBias.df= data.frame(
  Category= rep(c("A", "B", "C", "D", "E", "F"), 2),
  Groups = factor(c(rep("0", 6), rep("1", 6))),
  Values = c(0.26, 0.4, 0.3, 0.21, 0.6, 0.14, 0.12, -0.04, -0.23, 0.08, 0.14, -0.27))

d1<-dotplot(Category ~Values, data = StandBias.df, groups = Groups,
            main = "Standardized Mean Differences", col = c("black", "grey50"), pch=c(22,15), xlab=NULL,
            key=list(text=list(c("Pre-Matching", "Post-Matching")),
                     points=list(col = c("black", "grey50"), pch=c(22,15)), 
                     space="bottom", border=T))

Ttest.df = data.frame(
  Category= rep(c("A", "B", "C", "D", "E", "F"), 2),
  Groups = factor(c(rep("0", 6), rep("1", 6))),
  Values = …

Imagine you have yearly data for some sort of expenses. You are interested in the percent difference between the first value (t0) and each subsequent value (t1, ... -> tx) BUT only for a specific group of observations, i.e. with the next group, a new series of subsequent years starts.

Example:

    value <- c(10225,10287,10225,10087,10344,10387,10387,14567,13992,15432)
    case <- c(A,A,A,B,B,B,B,B,C,C)

    year    value   case   change
    1989    10225   A      0.00
    1990    10287   A      0.61 # ((100/10225)*10287)-100
    1991    10262   A      0.36
    1995    10087   B      0.00
    1996 …