There are also functions that allow spline fits. In each of the following functions, x is a vector of regressor variables, y is the vector of response variables, and lambda is the smoothing argument. Larger values for lambda result in smoother splines.
where knots is the unique values in x, and the spline calculated using the coefficients in the other columns as described with the Spline Eval function.
evaluates the spline predictions using the coef matrix in the same form as returned by SplineCoef, in other words, knots||a||b||c||d. The x argument can be a scalar or a matrix of values to predict. The number of columns of coef can be any number greater than 1 and each is used for the next higher power. The powers of x are centered at the knot values. For example, the calculation for coef of knots||a||b||c||d is
j is such that knots[j] is the largest knot smaller than x
xx = x-knots[j] is the centered x value
An effect can be a column name, a crossing of several column names with asterisk (*) notation, or nested columns specified with subscript bracket ([ ]) notation. Additional effect options can appear after an ampersand (&) character. Some examples:
To show the launch window and fit the model at the same time, include Run Model in the script. To show the launch window but not immediately fit the model, use Add Script instead of Run Model.
Note: When you select Analyze > Fit Model in a data table that has a script named Model (or model), the launch window is filled in based on the script.
where each row of the matrix specifies coefficients for all the arguments in the model.
n+1 for the “Whole Model” test, where n is the number of effects not including the intercept
Each effect in each response function supports these, where each row of the matrix has coefficients for all the levels in the effect:
The Send command inside the Send command finds the named response and sends the list of commands to it. If you instead send the options directly to the fitObj with a single Send command, the options are sent to all responses.
A complete list of DOE commands is available in the JMP Scripting Index. Select Help > Scripting Index, select Objects from the menu, and then search for DOE.
Note: When scripting in the DOE platform, the recommended workflow is to use the Save Script to Script Window option. If you are creating your own script, results might not appear as expected due to the ordering of commands.
This vector is used to modify the diagonal elements of the X'X matrix used for finding the D-optimal design. The supplied vector is added to the current diagonal elements of the X'X matrix.
where n is the radius of the constraining sphere.
Use the Scene3DHardwareAcceleration command to set the Use Hardware Acceleration in Scatterplot 3D. For example,
There is no extra syntax needed to differentiate between the two table types (wide and tall) when they are read using JSL. This example is based on the CitySpecLimits.jmp sample data table. It places the spec limits data table inside an Import Spec Limits() expression.
"$SAMPLE_DATA/CitySpecLimits.jmp"
qc_col is the name of the column
qc_test is the test that failed
qc_sample is the sample number
qc_firstRow is the first row in the sample
qc_lastRow is the last row in the sample
One way to generate automatic alarms is to make a script and store it with the data table as a Data Table property named QC Alarm Script. To automatically write a message to the log whenever a test fails,
Control Chart(Alarm Script(Speak(match(
Example: X - and R-Charts
The following example uses the Coating.jmp sample data (taken from the ASTM Manual on Presentation of Data and Control Chart Analysis). The quality characteristic of interest is the Weight column. A subgroup sample of four is chosen.
Example: X - and S-charts with Varying Subgroup Sizes
This example uses Coating.jmp, but this time the quality characteristic of interest is the Weight 2 column.
Weight 2 has several missing values in the data, so you might notice the chart has uneven limits. Although, each sample has the same number of observations, samples 1, 3, 5, and 7 each have a missing value.
The Pickles.jmp data in the Quality Control sample data folder contains the acid content for vats of pickles. Because the pickles are sensitive to acidity and produced in large vats, high acidity ruins an entire pickle vat. The acidity in four vats is measured each day at 1, 2, and 3 PM. The data table records day, time, and acidity measurements.
Consider Clips1.jmp. The measure of interest is the gap between the ends of manufactured metal clips. To monitor the process for a change in average gap, subgroup samples of five clips are selected daily. A UWMA chart with a moving average span of three is examined.
The Washers.jmp data in the Quality Control sample data folder contains defect counts of 15 lots of 400 galvanized washers. The washers were inspected for finish defects such as rough galvanization and exposed steel. If a washer contained a finish defect, it was deemed nonconforming or defective. Thus, the defect count represents how many washers were defective for each lot of size 400.
Again, using the Washers.jmp data, we can specify a sample size variable, which would allow for varying sample sizes.
Note that although the points on the chart look the same as the NP-chart, the y axis, Avg and limits are all different since they are now based on proportions.
The Braces.jmp data in the Quality Control sample data folder records the defect count in boxes of automobile support braces. A box of braces is one inspection unit. The number of boxes inspected (per day) is the subgroup sample size, which can vary. The U-chart is monitoring the number of brace defects per subgroup sample size. The upper and lower bounds vary according to the number of units inspected.
C-charts are similar to U-charts in that they monitor the number of nonconformities in an entire subgroup, made up of one or more units. C-charts can also be used to monitor the average number of defects per inspection unit.
A phase is a group of consecutive observations in the data table. For example, phases might correspond to time periods during which a new process is brought into production and then put through successive changes. Phases generate, for each level of the specified Phase variable, a new sigma, set of limits, zones, and resulting tests.