Cfrgtkky

I am working with the Survey of Consumer Finances dataset and am looking to do analysis across years. My initial thought is to combine them into the same svyimputationList, but my attempts don't seem to actually combine the data. they remain as different lists within a larger svyimputationList. There could be other ways to do analysis across years (I mainly looking to be able to run some regressions) so those answers are also welcome.

The class of the scf_design sets are "svyimuptationList".

Below is the code I have used in order to gather the datasets and my attempt to combine.

library(lodown)

library(survey)

library(mitools)

library(plyr)



        scf_cat <-

  get_catalog( "scf" ,

               output_dir = file.path( path.expand( "~" ) , "SCF" ) )

 # 2016 only

scf_cat_16 <- subset( scf_cat , year == 2016 )

#2013 only

scf_cat_13 <- subset( scf_cat , year == 2013 )

 # download the microdata to your local computer

scf_cat_16 <- lodown( "scf" , scf_cat_16 )

scf_cat_13 <- lodown( "scf" , scf_cat_13 )

 #setup the data to control for the survey style and MI

scf_imp_16 <- readRDS( file.path( path.expand( "~" ) , "SCF" , "scf 2016.rds" ) )

 scf_rw_16 <- readRDS( file.path( path.expand( "~" ) , "SCF" , "scf 2016 rw.rds" ) )

 scf_imp_13 <- readRDS( file.path( path.expand( "~" ) , "SCF" , "scf 2013.rds" ) )

 scf_rw_13 <- readRDS( file.path( path.expand( "~" ) , "SCF" , "scf 2013 rw.rds" ) )

 #APPLYING THE WEIGHTS TO THE DATA#####

scf_design_16 <- 

  svrepdesign( 

    weights = ~wgt , 

    repweights = scf_rw_16[ , -1 ] , 

    data = imputationList( scf_imp_16 ) , 

    scale = 1 ,

    rscales = rep( 1 / 998 , 999 ) ,

    mse = FALSE ,

    type = "other" ,

    combined.weights = TRUE

  )

 scf_design_13 <- 

  svrepdesign( 

    weights = ~wgt , 

    repweights = scf_rw_13[ , -1 ] , 

    data = imputationList( scf_imp_13 ) , 

    scale = 1 ,

    rscales = rep( 1 / 998 , 999 ) ,

    mse = FALSE ,

    type = "other" ,

    combined.weights = TRUE

  )





scf_design <- rbind(scf_design_16,scf_design_13)

This is the answer that I have come up with after looking into what Anthony Damico commented:

It follows the form of stacking the data frames within the list of imputations and the replicate weight files to ultimately combine them to the svyimputationList.

scf_cat <-

  get_catalog( "scf" ,

               output_dir = file.path( path.expand( "~" ) , "SCF" ) )



yearslist <- list(10,13,16)

y1<- NULL

y2<- NULL

y3<- NULL

y4<- NULL

y5<- NULL

imp <- NULL

rw <- NULL

#loop to load in the desired years and assign the weights

for (i in 1:length(yearslist)){



  year <- yearslist[i]

  fullyear <- as.numeric(paste0("20",year))

  df1 = scf_cat[FALSE,]





  df1 <- subset( scf_cat , year == fullyear )

  df2 <- lodown( "scf" , df1)



  scf_imp <- readRDS( file.path( path.expand( "~" ) , "SCF" , paste0("scf ",fullyear,".rds" ) ))



  for( i in 1:length(scf_imp)){



    scf_imp[[i]][["year"]] <- fullyear



    scf_imp[[i]] <- select(scf_imp[[i]],c("yy1","one","year","wgt","edcl","hhsex","age","married","kids","lf","lifecl",

                                          "networth","racecl4","racecl","occat1","income","norminc","wsaved",

                                          "spendmor","spendless","late","late60","hpayday","bshopnone",

                                          "bshopgrdl","bshopmodr","checking","hcheck",

                                          "saving","hsaving","mmda","cds","irakh","anypen","vehic","hvehic",

                                          "own","nown","newcar2","newcar1","veh_inst","hveh_inst","x7543","x7540",

                                          "x2206","x2209","x2210","x2211","x2212","x2213","x2219"))



  }



  scf_rw <- readRDS( file.path( path.expand( "~" ) , "SCF" , paste0("scf ",fullyear," rw.rds" ) ))



  #piecing together the dataframes

  #figure out a loop for this

  y1<- rbind(scf_imp[[1]],y1)

  y2<- rbind(scf_imp[[2]],y2)

  y3<- rbind(scf_imp[[3]],y3)  

  y4<- rbind(scf_imp[[4]],y4)

  y5<- rbind(scf_imp[[5]],y5)



  rw <- rbind(scf_rw,rw)





  #include or exclude incrimental saving

  assign(paste0("scf_imp_",year),scf_imp)

  assign(paste0("scf_rw_",year),scf_rw)





  assign(paste0("scf_design_",year),

         svrepdesign(

           weights = ~wgt ,

           repweights = scf_rw[ , -1 ] ,

           data = imputationList( scf_imp ) ,

           scale = 1 ,

           rscales = rep( 1 / 998 , 999 ) ,

           mse = FALSE ,

           type = "other" ,

           combined.weights = TRUE

         ))



  rm(df1)

  rm(df2)

  rm(scf_imp)

  rm(scf_rw)

}



#piecing together the imputations

imp <- list(y1,y2,y3,y4,y5)



#creating the design using the set of 3 years

design_full <- svrepdesign(

  weights = ~wgt ,

  repweights = rw[ , -1 ] ,

  data = imputationList( imp ) ,

  scale = 1 ,

  rscales = rep( 1 / 998 , 999 ) ,

  mse = FALSE ,

  type = "other" ,

  combined.weights = TRUE

)