Missing Value Imputation
Pablo E. GutiƩrrez-Fonseca
26 February, 2023
Step 1
Load libraries.
libraries <- c('missForest')
lapply(libraries, require, character.only = TRUE)## [[1]]
## [1] TRUEStep 2
discharge.na = read.csv("D:/OneDrive - University of Vermont/Curriculum/19_ Personal webpage/TropicalFreshwaterEcology/_pages/Lectures/data/StreamEcology_MissingValue.csv")
head(discharge.na)## Day Rain Discharge
## 1 1 2.03 0.00910000
## 2 2 2.03 0.00904000
## 3 3 2.03 0.00903000
## 4 4 2.03 0.00903000
## 5 5 2.03 0.01058932
## 6 6 7.49 0.01070414summary(discharge.na)## Day Rain Discharge
## Min. : 1 Min. : 0.000 Min. :0.00841
## 1st Qu.: 92 1st Qu.: 0.340 1st Qu.:0.00962
## Median :183 Median : 2.790 Median :0.01233
## Mean :183 Mean : 5.578 Mean :0.01561
## 3rd Qu.:274 3rd Qu.: 8.740 3rd Qu.:0.01814
## Max. :365 Max. :47.500 Max. :0.11147
## NA's :110Step 3
Examine data.
summary(discharge.na)## Day Rain Discharge
## Min. : 1 Min. : 0.000 Min. :0.00841
## 1st Qu.: 92 1st Qu.: 0.340 1st Qu.:0.00962
## Median :183 Median : 2.790 Median :0.01233
## Mean :183 Mean : 5.578 Mean :0.01561
## 3rd Qu.:274 3rd Qu.: 8.740 3rd Qu.:0.01814
## Max. :365 Max. :47.500 Max. :0.11147
## NA's :110Step 4
Plot data.
plot(discharge.na$Discharge,type="l", lwd=2.0)
Step 5
Imputate missing data.
# ntree number of trees to grow in each forest.
# maxiter maximum number of iterations to be performed given the stopping criterion is
# not met beforehand.
discharge.imp <- missForest(discharge.na, maxiter = 4, ntree = 100,
variablewise = FALSE, decreasing = FALSE, verbose = F, replace = TRUE,
classwt = NULL, cutoff = NULL, strata = NULL,
sampsize = NULL, nodesize = NULL, maxnodes = NULL,
xtrue = NA, parallelize = "no")
discharge.missf <- discharge.imp$ximpStep 6
Examine data imputation.
summary(discharge.missf)## Day Rain Discharge
## Min. : 1 Min. : 0.000 Min. :0.00841
## 1st Qu.: 92 1st Qu.: 0.340 1st Qu.:0.00994
## Median :183 Median : 2.790 Median :0.01163
## Mean :183 Mean : 5.578 Mean :0.01454
## 3rd Qu.:274 3rd Qu.: 8.740 3rd Qu.:0.01622
## Max. :365 Max. :47.500 Max. :0.11147Step 7
Plot data imputation.
plot(discharge.missf$Discharge,type="l", lwd=2.0)
Step 8
Comparison of imputed data.
par(mfrow=c(1,3))
plot(discharge.na$Discharge,type="l",main="Original Discharge", lwd=2.0)
abline(h=0.0154, lty=2, lwd=4, col='green')
plot(discharge.missf$Discharge,type="l",main="Imputated Discharge", lwd=2.0)
abline(h=0.0154, lty=2, lwd=4, col='red')
plot(discharge.na$Rain,type="l",main="Rain", lwd=2.0)
abline(h=10.1, lty=2, lwd=4, col='blue')
Step 9
How big are the errors of the imputation?
# Out Of Bag (OOBerror)= imputation error estimate (normalized root mean squared error)
# The OOB observations can be used for example for estimating the prediction error of RF.
# The performance is assessed using the normalized root mean squared error (NRMSE)
# Good performance leads to a value close to 0 and bad performance to a value around 1.
NRMSE <- discharge.imp$OOBerror
NRMSE## NRMSE
## 6.541362e-05# How big are the errors of the imputation?
# Not too bad, around >1%. NRMSE is The normalized root mean squared error, it is defined as:
# The Normalized Root Mean Square Error (NRMSE) the RMSE facilitates the comparison between models with different scales.
Hmisc
Harrell Miscellaneous
Step 1
# rm(list=ls(all=TRUE)) #give R a blank slateStep 2
library("Hmisc")Step 3
discharge.na = read.csv("D:/OneDrive - University of Vermont/Curriculum/19_ Personal webpage/TropicalFreshwaterEcology/_pages/Lectures/data/StreamEcology_MissingValue.csv")
head(discharge.na)## Day Rain Discharge
## 1 1 2.03 0.00910000
## 2 2 2.03 0.00904000
## 3 3 2.03 0.00903000
## 4 4 2.03 0.00903000
## 5 5 2.03 0.01058932
## 6 6 7.49 0.01070414Step 4
summary(discharge.na)## Day Rain Discharge
## Min. : 1 Min. : 0.000 Min. :0.00841
## 1st Qu.: 92 1st Qu.: 0.340 1st Qu.:0.00962
## Median :183 Median : 2.790 Median :0.01233
## Mean :183 Mean : 5.578 Mean :0.01561
## 3rd Qu.:274 3rd Qu.: 8.740 3rd Qu.:0.01814
## Max. :365 Max. :47.500 Max. :0.11147
## NA's :110plot(discharge.na$Discharge,type="l")
Step 5
paste('How many missing values?', ' ', length(which(is.na(discharge.na))))## [1] "How many missing values? 110"Step 6
# formula: The ~ Sepal.Length + . argument indicates what formula to use. In this case, we want all variables' missing data to be imputed, so we add each one.
# data: the data frame with missing data.
# n.impute: number of multiple imputations. 5 is frequently used, but 10 or more doesn't hurt
set.seed(1)
impute_arg <- aregImpute(Discharge ~ Rain , data = discharge.na, n.impute=100)## Iteration 1
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Iteration 100 # print(impute_arg, digits=3)
# impute_arg$imputed$DischargeStep 7
# The R square values indicate the liklihood that the predicted missing values in the 'irt'
# dataframe match what we would actually observe if participants had answered all questions.
# Higher values are better.
# To use one of the iterations in our original data set, we can use the transcend function:
completetable <- impute.transcan(impute_arg, imputation=4, data=discharge.na,
list.out=TRUE,pr=FALSE, check=FALSE)
# head(completetable)Step 8
plot(completetable$Discharge,type="l")
Compare methods
Step 1
# Compare methods
windows()
par(mfrow=c(1,2))
plot(completetable$Discharge,type="l",main="HMisc")
abline(h=0.0154, lty=2)
plot(discharge.missf$Discharge,type="l",main="Missforest")
abline(h=0.0154, lty=2)