Nothing
## ---- include = FALSE---------------------------------------------------------
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>"
)
## -----------------------------------------------------------------------------
library(COINr)
ASEM <- build_example_coin(up_to = "new_coin", quietly = TRUE)
## -----------------------------------------------------------------------------
l_avail <- get_data_avail(ASEM, dset = "Raw", out2 = "list")
## -----------------------------------------------------------------------------
head(l_avail$Summary)
## -----------------------------------------------------------------------------
min(l_avail$Summary$Dat_Avail)
## -----------------------------------------------------------------------------
df_avail <- get_stats(ASEM, dset = "Raw", out2 = "df")
head(df_avail[c("iCode", "N.Avail", "Frc.Avail")], 10)
## -----------------------------------------------------------------------------
min(df_avail$Frc.Avail)
## -----------------------------------------------------------------------------
# some data to use as an example
# this is a selected portion of the data with some missing values
df1 <- ASEM_iData[37:46, 36:39]
print(df1, row.names = FALSE)
## -----------------------------------------------------------------------------
Impute(df1, f_i = "i_mean")
## -----------------------------------------------------------------------------
# demo of i_mean() function, which is built in to COINr
x <- c(1,2,3,4, NA)
i_mean(x)
## -----------------------------------------------------------------------------
# row grouping
groups <- c(rep("a", 5), rep("b", 5))
# impute
dfi2 <- Impute(df1, f_i = "i_median_grp", f_i_para = list(f = groups))
# display
print(dfi2, row.names = FALSE)
## -----------------------------------------------------------------------------
Impute(df1, f_i = "i_mean", impute_by = "row", normalise_first = FALSE)
## -----------------------------------------------------------------------------
Impute(df1, f_i = "i_mean", impute_by = "row", normalise_first = TRUE, directions = rep(1,4))
## -----------------------------------------------------------------------------
ASEM <- Impute(ASEM, dset = "Raw", f_i = "i_mean")
ASEM
## -----------------------------------------------------------------------------
ASEM <- Impute(ASEM, dset = "Raw", f_i = "i_mean_grp", use_group = "GDP_group", )
## -----------------------------------------------------------------------------
ASEM <- Impute(ASEM, dset = "Raw", f_i = "i_mean", impute_by = "row",
group_level = 2, normalise_first = TRUE)
## ---- eval=FALSE--------------------------------------------------------------
# # this function takes a data frame input and returns an imputed data frame using amelia
# i_EM <- function(x){
# # impute
# amOut <- Amelia::amelia(x, m = 1, p2s = 0, boot.type = "none")
# # return imputed data
# amOut$imputations[[1]]
# }
## ---- eval=FALSE--------------------------------------------------------------
# # impute raw data set
# coin <- Impute(coin, dset = "Raw", f_i = i_EM, impute_by = "df", group_level = 2)
## -----------------------------------------------------------------------------
# copy
dfp <- ASEM_iData_p
# create NA for GB in 2022
dfp$LPI[dfp$uCode == "GB" & dfp$Time == 2022] <- NA
## -----------------------------------------------------------------------------
dfp$LPI[dfp$uCode == "GB" & dfp$Time == 2021]
## -----------------------------------------------------------------------------
# build purse
ASEMp <- new_coin(dfp, ASEM_iMeta, split_to = "all", quietly = TRUE)
# impute raw data using latest available value
ASEMp <- Impute(ASEMp, dset = "Raw", f_i = "impute_panel")
## -----------------------------------------------------------------------------
get_data(ASEMp, dset = "Imputed", iCodes = "LPI", uCodes = "GBR", Time = 2021)
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