This function detects outliers using the maximum normed residual method described in CMH-17-1G. 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.
the variable in the data.frame for which to find the MNR
or a vector if
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
x is expected to be a variable within
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 CMH-17-1G. 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 220.127.116.11 of CMH-17-1G, 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
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
outliers a data.frame containing the
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, CMH-17-1G, Mar. 2012.
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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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