Function working as testbench for comparison of imputing models

Description

Function working as testbench for comparison of imputing models

Usage

impute_errors(dataIn, smps = "mcar", methods = c("na.approx",
  "na.interp", "na_interpolation", "na.locf", "na_mean"),
  methodPath = NULL, errorParameter = "rmse", errorPath = NULL,
  blck = 50, blckper = TRUE, missPercentFrom = 10,
  missPercentTo = 90, interval = 10, repetition = 10,
  addl_arg = NULL)

Arguments

Details

The default methods for impute_errors are na.approx, na.interp, na_interpolation, na.locf, and na_mean. See the help file for each for additional documentation. Additional arguments for the imputation functions are passed as a list of lists to the addl_arg argument, where the list contains one to many elements that are named by the methods. The elements of the master list are lists with arguments for the relevant methods. See the examples.

A user-supplied function can also be passed to methods as an additional imputation method. A character string indicating the path of the function must also be supplied to methodPath. The path must point to a function where the first argument is the time series to impute.

An alternative error function can also be passed to errorParameter if errorPath is not NULL. The function specified in errorPath must have two arguments where the first is a vector for the observed time series and the second is a vector for the predicted time series.

The smps argument indicates the type of sampling for generating missing data. Options are smps = 'mcar' for missing completely at random and smps = 'mar' for missing at random. Additional information about the sampling method is described in sample_dat. The relevant arguments for smps = 'mar' are blck and blckper which greatly affect the sampling method.

Infinite comparisons are removed with a warning if errorParameter = 'mape'. This occurs if any of the observed values in the original time series are zero. Error estimates for such datasets are evaluated only for non-zero observations.

Value

Returns an error comparison for imputation methods as an errprof object. This object is structured as a list where the first two elements are named Parameter and MissingPercent that describe the error metric used to assess the imputation methods and the intervals of missing observations as percentages, respectively. The remaining elements are named as the chr strings in methods of the original function call. Each remaining element contains a numeric vector of the average error at each missing percent of observations. The errprof object also includes an attribute named errall as an additional list that contains all of the error estimates for every imputation method and repetition.

See Also

sample_dat

Examples

## Not run: 
# default options
aa <- impute_errors(dataIn = nottem)
aa
plot_errors(aa)

# change the simulation for missing obs
aa <- impute_errors(dataIn = nottem, smps = 'mar')
aa
plot_errors(aa)

# use one interpolation method, increase repetitions
aa <- impute_errors(dataIn = nottem, methods = 'na.interp', repetition = 100)
aa
plot_errors(aa)

# change the error metric
aa <- impute_errors(dataIn = nottem, errorParameter = 'mae')
aa
plot_errors(aa)

# passing additional arguments to imputation methods
impute_errors(dataIn = nottem, addl_arg = list(na_mean = list(option = 'mode')))

## End(Not run)

Mean Absolute Error Calculation

Description

takes difference between Original data and Predicted data as input

Usage

mae(obs, pred)

Arguments

Value

maeVal as Mean Absolute Error

Examples

## Generate 100 random numbers within some limits
x <- sample(1:7, 100, replace = TRUE)
y <- sample(1:4, 100, replace = TRUE)
z <- mae(x, y)
z

Mean Absolute Percent Error Calculation

Description

takes difference between Original data and Predicted data as input

Usage

mape(obs, pred)

Arguments

Value

mapeVal as Mean Absolute Error

Examples

## Generate 100 random numbers within some limits
x <- sample(1:7, 100, replace = TRUE)
y <- sample(1:4, 100, replace = TRUE)
z <- mape(x, y)
z

Function to plot the Error Comparison

Description

Function to plot the Error Comparison

Usage

plot_errors(dataIn, plotType = c("boxplot"))

## S3 method for class 'errprof'
plot_errors(dataIn, plotType = c("boxplot"))

Arguments

Value

A ggplot object that can be further modified. The entire range of errors are shown if plotType = "boxplot", otherwise the averages are shown if plotType = "bar" or "line".

Examples

aa <- impute_errors(dataIn = nottem)

# default plot
plot_errors(aa)
## Not run: 
# bar plot of averages at each repetition
plot_errors(aa, plotType = 'bar')

# line plot of averages at each repetition
plot_errors(aa, plotType = 'line')

# change the plot aesthetics

library(ggplot2)
p <- plot_errors(aa)
p + scale_fill_brewer(palette = 'Paired', guide_legend(title = 'Default'))
p + theme(legend.position = 'top')
p + theme_minimal()
p + ggtitle('Distribution of error for imputed values')
p + scale_y_continuous('RMSE')

## End(Not run)

Plot imputations

Description

Plot imputations for data from multiple methods

Usage

plot_impute(dataIn, smps = "mcar", methods = c("na.approx",
  "na.interp", "na_interpolation", "na.locf", "na_mean"),
  methodPath = NULL, blck = 50, blckper = TRUE, missPercent = 50,
  showmiss = FALSE, addl_arg = NULL)

Arguments

Details

See the documentation for impute_errors for an explanation of the arguments.

Value

A ggplot object showing the imputed data for each method. Red points are labelled as 'imputed' and blue points are labelled as 'retained' from the original data set. Missing data that were removed can be added to the plot as open circles if showmiss = TRUE. See the examples for modifying the plot.

Examples

# default
plot_impute(dataIn = nottem)

# change missing percent total
plot_impute(dataIn = nottem, missPercent = 10)

# show missing values
plot_impute(dataIn = nottem, showmiss = TRUE)

# use mar sampling
plot_impute(dataIn = nottem, smps = 'mar')

# change the plot aesthetics
## Not run: 
library(ggplot2)
p <- plot_impute(dataIn = nottem, smps = 'mar')
p + scale_colour_manual(values = c('black', 'grey'))
p + theme_minimal()
p + ggtitle('Imputation examples with different methods')
p + scale_y_continuous('Temp at Nottingham Castle (F)')

## End(Not run)

Print method for errprof

Description

Print method for errprof class

Usage

## S3 method for class 'errprof'
print(x, ...)

Arguments

Value

list output for the errprof object

Root Mean Square Error Calculation

Description

takes difference between Original data and Predicted data as input

Usage

rmse(obs, pred)

Arguments

Value

rmseVal as Root Mean Square Error

Examples

## Generate 100 random numbers within some limits
x <- sample(1:7, 100, replace = TRUE)
y <- sample(1:4, 100, replace = TRUE)
z <- rmse(x, y)
z

Sample time series data

Description

Sample time series using completely at random (MCAR) or at random (MAR)

Usage

sample_dat(datin, smps = "mcar", repetition = 10, b = 10,
  blck = 50, blckper = TRUE, plot = FALSE)

Arguments

Value

Input data with NA values for the sampled observations if plot = FALSE, otherwise a plot showing the missing observations over the complete dataset.

The missing data if smps = 'mar' are based on random sampling by blocks. The start location of each block is random and overlapping blocks are not counted uniquely for the required sample size given by b. Final blocks are truncated to ensure the correct value of b is returned. Blocks are fixed at 1 if the proportion is too small, in which case "mcar" should be used. Block sizes are also truncated to the required sample size if the input value is too large if blckper = FALSE. For the latter case, this is the same as setting blck = 1 and blckper = TRUE.

For all cases, the first and last observation will never be removed to allow comparability of interpolation schemes. This is especially relevant for cases when b is large and smps = 'mar' is used. For example, method = na.approx will have rmse = 0 for a dataset where the removed block includes the last n observations. This result could provide misleading information in comparing methods.

Examples

a <- rnorm(1000)

# default sampling
sample_dat(a)

# use mar sampling
sample_dat(a, smps = 'mar')

# show a plot of one repetition
sample_dat(a, plot = TRUE)

# show a plot of one repetition, mar sampling
sample_dat(a, smps = 'mar', plot = TRUE)

# change plot aesthetics
library(ggplot2)
p <- sample_dat(a, plot = TRUE)
p + scale_colour_manual(values = c('black', 'grey'))
p + theme_minimal()
p + ggtitle('Example of simulating missing data')