Question

我正在处理调查数据。我需要对数据进行一些表格和回归分析。附加数据后，这是我用于四个变量表的代码：

ftable（var1，var2，var3，var4）

这是我用于数据的回归代码：

logit.1＆lt; - glm（var4~var3 + var2 + var1，family = binomial（link =“logit”））摘要（logit.1）

到目前为止，未加权的分析非常有用。但是如何对加权数据进行相同的分析呢？这是一些额外的信息：数据集中有四个反映采样结构的变量。这些是

strat：stratum（城市或（分县）农村）。

clust：一系列采访是同一随机游走的一部分

vill_neigh_code：村庄或社区代码

sweight：weight

Answer 1

library(survey)

data(api)

# example data set
head( apiclus2 )

# instead of var1 - var4, use these four variables:
ftable( apiclus2[ , c( 'sch.wide' , 'comp.imp' , 'both' , 'awards' ) ] )

# move it over to x for faster typing
x <- apiclus2


# also give x a column of all ones
x$one <- 1

# run the glm() function specified.
logit.1 <-
    glm( 
        comp.imp ~ target + cnum + growth , 
        data = x ,
        family = binomial( link = 'logit' )
    )

summary( logit.1 )

# now create the survey object you've described
dclus <-
    svydesign(
        id = ~dnum + snum , # cluster variable(s)
        strata = ~stype ,   # stratum variable
        weights = ~pw ,     # weight variable
        data = x ,
        nest = TRUE
    )

# weighted counts
svyby( 
    ~one , 
    ~ sch.wide + comp.imp + both + awards , 
    dclus , 
    svytotal 
)


# weighted counts formatted differently
ftable(
    svyby( 
        ~one , 
        ~ sch.wide + comp.imp + both + awards , 
        dclus , 
        svytotal ,
        keep.var = FALSE
    )
)


# run the svyglm() function specified.
logit.2 <-
    svyglm( 
        comp.imp ~ target + cnum + growth , 
        design = dclus ,
        family = binomial( link = 'logit' )
    )

summary( logit.2 )

调查数据的交叉表（加权和未加权）

1 个答案: