The goal of igate is to provide you with a quick and powerful, yet easy to understand toolbox that lets you extract relevant process parameters from manufacturing data, validate these parameters and find their optimal ranges and automatically create concise reports of the conducted analysis.
The igate package implements the initial Guided Analytics for parameter Testing and controland Extraction (iGATE) framework for manufacturing data.
Having identified a manufacturing ‘problem’ to be investigated, a
data set is assembled for a ‘typical’ period of operation,
i.e. excluding known disturbances such as maintenance or equipment
failures. This data set includes the so called target variable,
a direct indication or proxy for the problem under consideration and the
variale whose variation we want to explain. It also includes a number of
covariate parameters representing suspected sources of
variation (SSVs), i.e. variables that we consider potentially
influetial for the value of the target. Parameters with
known and explainable relationships with the target variable should be
excluded from the analysis, although this can be addressed in an
iterative manner though subsequent exclusion and repeating of the
process. The iGATE procedure consists of the following seven steps
(detailed explanations follow below):
versus argument of igate/
categorical.igate.Steps 1-4 are performed using the igate function for
continuous target variables or the categorical.igate
function for categorical target variables. Especially for categorical
targets with few categories robust.categorical.igate is a
robustified version of categorical.igate and should be
considered.
When running igate/ categorical.igate with
default settings, any outliers for the target variable are excluded and
the observations corresponding to the best 8 (B) and worst 8 (W)
instances of the target variable are identified. For each of these 16
observations, each SSV is inspected in turn. The distribution of the
values of the SSV of the 8 BOB and 8 WOW are analyzed by applying the Tukey-Duckworth
test (see reference in link for original paper). If the critical
value returned by the test is larger than 6 (this corresponds to a
p-value of less than 0.05), the SSV is retained as being potentially
significant. This test was chosen for its simplicity and ease of
interpretation and visualization. SSVs failing the test are highly
unlikely to be influential whilst SSVs passing the test may be
influential. The Wilcoxon-Rank test performed in step three of iGATE
serves as a possibly more widely known alternative, that might, however,
be harder to explain to non-statisticians. The main function of these
steps is to facilitate dimensionality reduction in the data set to
generate a manageable population for expert consideration.
Step 5 is performed by calling igate.regressions, resp.
categorical.freqplot. These functions produce a regression
(for continuous target) resp. frequency (for categorical targets) plot
and save it to the current working directory. A domain expert familiar
with the manufacturing process should review these plots and decide
which parameters to keep for further analysis based on goodness of fit
of the data to the plot.
For the validation step, the production period from which the
validation data is selected is dependent on the business situation, but
should be from a period of operation consistent with that from which the
initial population was drawn, i.e. similar product types, similar level
of equipment status etc. The validation step then considers all the
retained SSV as a collective in terms of good and bad bands, and
extracts from the validation sample all the records which satisfy the
condition that all retained SSVs lie within these bands. The expectation
is that where all the SSVs lie within the good band, then the target
should also correspond to the best performance, and vice versa where the
retained SSVs all lie in the bad bands we expect to see bad performance.
The application gives feedback on the extent to which this criterion is
satisfied in order to help the user conclude the exploration and make
recommendations for subsequent improvements. Validation is performed via
the validate function.
We consider the last step, the reporting of the results in a
standardized manner, an integral part of iGATE that ensures that
knowledge about past analyses is retained within a company. This is
achieved by calling the report function.
You can install the development version from GitHub with:
# install.packages("devtools")
devtools::install_github("stefan-stein/igate")This is a basic example which shows you how to conduct an iGATE
analysis for a continuous target variable. We are using the built in
iris data set and consider "Sepal.Length" as
out target.
library(igate)
set.seed(123)
n <- nrow(iris)*2/3
rows <- sample(1:nrow(iris), n)
df <- iris[rows, ]
results <- igate(df, target = "Sepal.Length", good_end = "high", savePlots = TRUE)
resultsThe significant variables are shown alongside their count summary
statistic from the Tukey-Duckworth Test as well as the p-value from the
Wilcoxon-Rank test. Also, we see the good and bad control bands as well
as several summary statistics to ascertain the randomness in the results
(see documentation of igate for details). Remember to use
the option savePlots = TRUE if you want to save the boxplot
of the target variable as a png. This png will be needed for producing
the final report of the analysis.
For details on how to conduct the other steps in the iGATE framework, please refer to the package vignette, by running
browseVignettes("igate")