Nothing
## ----setup, include=FALSE-----------------------------------------------------
knitr::opts_chunk$set(echo = FALSE)
## ---- echo=TRUE,message=FALSE,warning=FALSE-----------------------------------
pkgs <- c("miceFast", "mice", "ggplot2", "dplyr", "data.table")
inst <- lapply(pkgs, library, character.only = TRUE)
## ----echo=TRUE----------------------------------------------------------------
set.seed(123456)
## ----eval=FALSE,echo=TRUE-----------------------------------------------------
# system.file("extdata", "performance_validity.R", package = "miceFast")
## ----eval=FALSE,echo=TRUE-----------------------------------------------------
# system.file("extdata", "images", package = "miceFast")
## ----echo=TRUE----------------------------------------------------------------
# airquality dataset with additional variables
data(air_miss)
## ---- echo=TRUE---------------------------------------------------------------
upset_NA(air_miss, 6)
## ----echo=TRUE----------------------------------------------------------------
# VIF - values bigger than 10 (around) suggest that there might be a collinearity problem.
# VIF is high for Solar.R and x_character which is obvious - x_character is a factor version of numeric Solar.R
air_miss %>%
do(vifs = VIF(.,
posit_y = "Ozone",
posit_x = c(
"Solar.R",
"Wind",
"Temp",
"x_character",
"Day",
"weights",
"groups"
)
)) %>%
unlist()
# IMPUTATIONS
air_miss <- air_miss %>%
# Imputations with a grouping option (models are separately assessed for each group)
# taking into account provided weights
group_by(groups) %>%
do(mutate(., Solar_R_imp = fill_NA(
x = .,
model = "lm_pred",
posit_y = "Solar.R",
posit_x = c("Wind", "Temp", "Intercept"),
w = .[["weights"]]
))) %>%
ungroup() %>%
# Imputations - discrete variable
mutate(x_character_imp = fill_NA(
x = .,
model = "lda",
posit_y = "x_character",
posit_x = c("Wind", "Temp")
)) %>%
# logreg was used because almost log-normal distribution of Ozone
# imputations around mean
mutate(Ozone_imp1 = fill_NA(
x = .,
model = "lm_bayes",
posit_y = "Ozone",
posit_x = c("Intercept"),
logreg = TRUE
)) %>%
# imputations using positions - Intercept, Temp
mutate(Ozone_imp2 = fill_NA(
x = .,
model = "lm_bayes",
posit_y = 1,
posit_x = c(4, 6),
logreg = TRUE
)) %>%
# multiple imputations (average of x30 imputations)
# with a factor independent variable, weights and logreg options
mutate(Ozone_imp3 = fill_NA_N(
x = .,
model = "lm_noise",
posit_y = "Ozone",
posit_x = c("Intercept", "x_character_imp", "Wind", "Temp"),
w = .[["weights"]],
logreg = TRUE,
k = 30
)) %>%
mutate(Ozone_imp4 = fill_NA_N(
x = .,
model = "lm_bayes",
posit_y = "Ozone",
posit_x = c("Intercept", "x_character_imp", "Wind", "Temp"),
w = .[["weights"]],
logreg = TRUE,
k = 30
)) %>%
group_by(groups) %>%
do(mutate(., Ozone_imp5 = fill_NA(
x = .,
model = "lm_pred",
posit_y = "Ozone",
posit_x = c("Intercept", "x_character_imp", "Wind", "Temp"),
w = .[["weights"]],
logreg = TRUE
))) %>%
do(mutate(., Ozone_imp6 = fill_NA_N(
x = .,
model = "pmm",
posit_y = "Ozone",
posit_x = c("Intercept", "x_character_imp", "Wind", "Temp"),
w = .[["weights"]],
logreg = TRUE,
k = 20
))) %>%
ungroup() %>%
# Average of a few methods
mutate(Ozone_imp_mix = rowMeans(select(., starts_with("Ozone_imp")))) %>%
# Protecting against collinearity or low number of observations - across small groups
# Be carful when using a data.table grouping option
# because of lack of protection against collinearity or low number of observations.
# There could be used a tryCatch(fill_NA(...),error=function(e) return(...))
group_by(groups) %>%
do(mutate(., Ozone_chac_imp = tryCatch(
fill_NA(
x = .,
model = "lda",
posit_y = "Ozone_chac",
posit_x = c("Intercept", "Month", "Day", "Temp", "x_character_imp"),
w = .[["weights"]]
),
error = function(e) .[["Ozone_chac"]]
))) %>%
ungroup()
## ----echo=TRUE----------------------------------------------------------------
# Distribution of imputations vs Distribution of initial data
compare_imp(air_miss, origin = "Ozone", target = "Ozone_imp_mix")
# or
compare_imp(air_miss, origin = "Ozone", target = c("Ozone_imp2", "Ozone_imp_mix"))
## ----echo=TRUE----------------------------------------------------------------
data(air_miss)
setDT(air_miss)
# VIF - values bigger than 10 (around) suggest that there might be a collinearity problem.
# VIF is high for Solar.R and x_character which is obvious - x_character is a factor version of numeric Solar.R
air_miss[, .(VIF(.SD,
posit_y = "Ozone",
posit_x = c(
"Solar.R",
"Wind",
"Temp",
"x_character",
"Day",
"weights",
"groups"
)
))]
# IMPUTATIONS
# Imputations with a grouping option (models are separately assessed for each group)
# taking into account provided weights
air_miss[, Solar_R_imp := fill_NA_N(
x = .SD,
model = "lm_bayes",
posit_y = "Solar.R",
posit_x = c("Wind", "Temp", "Intercept"),
w = .SD[["weights"]],
k = 100
), by = .(groups)] %>%
# Imputations - discrete variable
.[, x_character_imp := fill_NA(
x = .SD,
model = "lda",
posit_y = "x_character",
posit_x = c("Wind", "Temp", "groups")
)] %>%
# logreg was used because almost log-normal distribution of Ozone
# imputations around mean
.[, Ozone_imp1 := fill_NA(
x = .SD,
model = "lm_bayes",
posit_y = "Ozone",
posit_x = c("Intercept"),
logreg = TRUE
)] %>%
# imputations using positions - Intercept, Temp
.[, Ozone_imp2 := fill_NA(
x = .SD,
model = "lm_bayes",
posit_y = 1,
posit_x = c(4, 6),
logreg = TRUE
)] %>%
# model with a factor independent variable
# multiple imputations (average of x30 imputations)
# with a factor independent variable, weights and logreg options
.[, Ozone_imp3 := fill_NA_N(
x = .SD,
model = "lm_noise",
posit_y = "Ozone",
posit_x = c("Intercept", "x_character_imp", "Wind", "Temp"),
w = .SD[["weights"]],
logreg = TRUE,
k = 30
)] %>%
.[, Ozone_imp4 := fill_NA_N(
x = .SD,
model = "lm_bayes",
posit_y = "Ozone",
posit_x = c("Intercept", "x_character_imp", "Wind", "Temp"),
w = .SD[["weights"]],
logreg = TRUE,
k = 30
)] %>%
.[, Ozone_imp5 := fill_NA(
x = .SD,
model = "lm_pred",
posit_y = "Ozone",
posit_x = c("Intercept", "x_character_imp", "Wind", "Temp"),
w = .SD[["weights"]],
logreg = TRUE
), .(groups)] %>%
.[, Ozone_imp6 := fill_NA_N(
x = .SD,
model = "pmm",
posit_y = "Ozone",
posit_x = c("Intercept", "x_character_imp", "Wind", "Temp"),
w = .SD[["weights"]],
logreg = TRUE,
k = 10
), .(groups)] %>%
# Average of a few methods
.[, Ozone_imp_mix := apply(.SD, 1, mean), .SDcols = Ozone_imp1:Ozone_imp6] %>%
# Protecting against collinearity or low number of observations - across small groups
# Be careful when using a data.table grouping option
# because of lack of protection against collinearity or low number of observations.
# There could be used a tryCatch(fill_NA(...),error=function(e) return(...))
.[, Ozone_chac_imp := tryCatch(
fill_NA(
x = .SD,
model = "lda",
posit_y = "Ozone_chac",
posit_x = c("Intercept", "Month", "Day", "Temp", "x_character_imp"),
w = .SD[["weights"]]
),
error = function(e) .SD[["Ozone_chac"]]
), .(groups)]
## ----echo=TRUE----------------------------------------------------------------
# install.packages("mice")
data <- cbind(as.matrix(mice::nhanes), intercept = 1, index = 1:nrow(mice::nhanes))
model <- new(miceFast)
model$set_data(data) # providing data by a reference
model$get_ridge()
model$update_var(2, model$impute("lm_pred", 2, 5)$imputations)
# OR not recommended
# data[,2] = model$impute("lm_pred",2,5)$imputations
# model$set_data(data) #Updating the object
model$update_var(3, model$impute("lda", 3, c(1, 2))$imputations)
# Old slow syntax model$update_var(4,rowMeans(sapply(1:10,function(x) model$impute("lm_bayes",4,c(1,2,3))$imputations)))
# New syntax - impute_N
model$update_var(4, model$impute_N("lm_bayes", 4, c(1, 2, 3), 10)$imputations)
# When working with 'Big Data'
# it is recommended to occasionally manually invoke a garbage collector `gc()`
# Be careful with `update_var` because of the permanent update at the object and data
# That is why `update_var` could be used only ones for a certain column
# check which variables was updated - inside the object
model$which_updated()
head(model$get_data(), 3)
head(data, 3)
head(mice::nhanes, 3)
rm(model)
## ----echo=TRUE----------------------------------------------------------------
data <- cbind(as.matrix(airquality[, -5]), intercept = 1, index = 1:nrow(airquality))
weights <- rgamma(nrow(data), 3, 3) # a numeric vector - positive values
groups <- as.numeric(airquality[, 5]) # a numeric vector not integers - positive values - sorted increasingly
model <- new(miceFast)
model$set_data(data) # providing data by a reference
model$set_w(weights) # providing by a reference
model$set_g(groups) # providing by a reference
# impute adapt to provided parameters like w or g
# Simple mean - permanent imputation at the object and data
model$update_var(1, model$impute("lm_pred", 1, c(6))$imputations)
model$update_var(2, model$impute_N("lm_bayes", 2, c(1, 3, 4, 5, 6), 10)$imputations)
# Printing data and retrieving an old order
head(cbind(model$get_data(), model$get_g(), model$get_w())[order(model$get_index()), ], 4)
head(airquality, 3)
head(cbind(model$get_data(), model$get_g(), model$get_w()), 3)
head(cbind(data, groups, weights), 3)
rm(model)
## ----echo=TRUE----------------------------------------------------------------
data <- cbind(as.matrix(airquality[, -5]), intercept = 1, index = 1:nrow(airquality))
weights <- rgamma(nrow(data), 3, 3) # a numeric vector - positive values
# groups = as.numeric(airquality[,5]) # a numeric vector not integers - positive values
groups <- as.numeric(sample(1:8, nrow(data), replace = T)) # a numeric vector not integers - positive values
model <- new(miceFast)
model$set_data(data) # providing by a reference
model$set_w(weights) # providing by a reference
model$set_g(groups) # providing by a reference
# impute adapt to provided parmaters like w or g
# Warning - if data is not sorted increasingly by the g then it would be done automatically
# during a first imputation
# Simple mean - permanent imputation at the object and data
model$update_var(1, model$impute("lm_pred", 1, 6)$imputations)
model$update_var(2, model$impute_N("lm_bayes", 2, c(1, 3, 4, 5, 6), 10)$imputations)
# Printing data and retrieving an old order
head(cbind(model$get_data(), model$get_g(), model$get_w())[order(model$get_index()), ], 4)
head(airquality, 4)
head(cbind(model$get_data(), model$get_g(), model$get_w()), 4) # is ordered by g
head(cbind(data, groups, weights), 4) # is sorted by g cause we provide data by a reference
rm(model)
## ----echo=TRUE----------------------------------------------------------------
# str(mtcars)
mtcars$cyl <- factor(mtcars$cyl)
mtcars$gear <- factor(mtcars$gear)
mtcars_mat <- model.matrix.lm(~., mtcars, na.action = "na.pass")
# str(mtcars_mat)
## ----echo=TRUE----------------------------------------------------------------
airquality2 <- airquality
airquality2$Temp2 <- airquality2$Temp**2
airquality2$Month <- factor(airquality2$Month)
# car::vif(lm(Ozone ~ ., data = airquality2))
## ----echo=TRUE----------------------------------------------------------------
data_DT <- data.table(airquality2)
data_DT[, .(vifs = VIF(
x = .SD,
posit_y = "Ozone",
posit_x = c("Solar.R", "Wind", "Temp", "Month", "Day", "Temp2"), correct = FALSE
))]
data_DT[, .(vifs = VIF(
x = .SD,
posit_y = 1,
posit_x = c(2, 3, 4, 5, 6, 7), correct = TRUE
))]
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