Description Usage Arguments Details Value References Examples
This function detects outliers using the maximum normed residual method described in CMH171G. This method identifies a value as an outlier if the absolute difference between the value and the sample mean divided by the sample standard deviation exceeds a critical value.
1  maximum_normed_residual(data = NULL, x, alpha = 0.05)

data 
a data.frame 
x 
the variable in the data.frame for which to find the MNR
or a vector if 
alpha 
the significance level for the test. Defaults to 0.05 
data
is an optional argument. If data
is given, it
should be a
data.frame
(or similar object). When data
is specified, the
value of x
is expected to be a variable within data
. If
data
is not specified, x
must be a vector.
The maximum normed residual test is a test for outliers. The test statistic is given in CMH171G. Outliers are identified in the returned object.
The maximum normed residual test statistic is defined as:
MNR = max  x_i x_bar  / s
When the value of the MNR test statistic exceeds the critical value defined in Section 8.3.3.1 of CMH171G, the corresponding value is identified as an outlier. It is then removed from the sample, and the test statistic is computed again and compared with the critical value corresponding with the new sample. This process is repeated until no values are identified as outliers.
an object of class mnr
This object has the following fields:
call
the expression used to call this function
data
the original data used to compute the MNR
alpha
the value of alpha given by the user
mnr
the computed MNR test statistic
crit
the critical value given the sample size and the
significance level
outliers
a data.frame containing the index
and
value
of each of the identified outliers
n_outliers
the number of outliers found
“Composite Materials Handbook, Volume 1. Polymer Matrix Composites Guideline for Characterization of Structural Materials,” SAE International, CMH171G, Mar. 2012.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25  library(dplyr)
carbon.fabric.2 %>%
filter(test=="FC" & condition=="ETW2" & batch=="A") %>%
maximum_normed_residual(strength)
## Call:
## maximum_normed_residual(data = ., x = strength)
##
## MNR = 1.958797 ( critical value = 1.887145 )
##
## Outliers ( alpha = 0.05 ):
## Index Value
## 6 44.26
carbon.fabric.2 %>%
filter(test=="FC" & condition=="ETW2" & batch=="B") %>%
maximum_normed_residual(strength)
## Call:
## maximum_normed_residual(data = ., x = strength)
##
## MNR = 1.469517 ( critical value = 1.887145 )
##
## No outliers detected ( alpha = 0.05 )

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