I transformed my stat data with logarithm, square root,... but my dependent variable doesn't achieve normality distribution yet.
Then, I know that the Box-Cox transformation permit us to find out the best transformation approach in order to achieve normality distribution and therefore apply parametric test such as ANOVA.
Can anybody help me in how I can perform this Box-Cox transformation in SPSS software? It is possible to apply through its syntax?
There is a Box Cox transformation syntax on Raynald's SPSS tools website. The data are just to give an example.
I added some simple syntax to easily see the results.
* Box-Cox transformation for all 31 values of lambda between -2 to 1
(increments of .1).
* Raynald Levesque 2003/11/08.
* http://www.spsstools.net/en/syntax/syntax-index/compute/box-cox-transformation/
GET FILE="C:\{SPSS user folder}\Employee data.sav".
COMPUTE var1=salary./* salary is a skewed test variable.
VECTOR lam(31) /xl(31).
LOOP idx=1 TO 31.
COMPUTE lam(idx)=-2.1 + idx * .1.
DO IF lam(idx)=0.
COMPUTE xl(idx)=LN(var1).
ELSE.
COMPUTE xl(idx)=(var1**lam(idx) - 1)/lam(idx).
END IF.
END LOOP.
* visual examination of results.
EXAMINE
VARIABLES= salary xl1 to xl31
/PLOT=NPPLOT
/stat descrip.
* numerical examination of results.
FREQUENCIES
/VARIABLES= salary, xl1 to xl31
/FORMAT= NOTABLE
/STATISTICS=SKEWNESS KURTOSIS.
The numerical examination works best after having copied the results in a spreadsheet.
This worked for me: "Go to Transform – Prepare Data for Modelling Automatic from the drop down list. In the Fields tab you can specify which variables to transform by moving them to the Inputs box. In the Settings tab click on Rescale Fields. Tick the box before ‘Rescale a continuous target with a Box-Cox transformation to reduce skew’. Click Run. This will create a new column with the transformed variable."
From: https://www.researchgate.net/post/How_can_I_do_Box-Cox_transformations_in_SPSS
You might want to double check with another source.
Related
How to display all the numbers from a given matrix in Euler Math Toolbox? It doesn't seem to show more than 8 columns at a time, which isn't enough as I frequently need to view more than that.
Output I'm getting as of now:
The documentation says:
Large matrices or vectors might not print in full size. To change this, toggle the behavior with:
largematrices on
largematrices off
or use the operator showlarge.
showlarge random(10,10)
See:
showlarge (Basic Utilities)
The link in the above documentation link directs here:
function prefix showlarge (x$)
Prints large matrices in full.
By default EMT eclipses lines and rows of large matrices. This can be used to see the full matrix. If the parameter is a variable the variable name will be printed.
The default can be changed with largematrices on/off.
x=random(20,5); showlarge(x)
showlarge(random(20,5))
See:
largematrices (Euler Core),
show (Maxima Documentation)
I am using Paraview 5.0.1. If any solution requires updating, I can try.
I want to programmatically obtain field plots (and corresponding PlotOverLine) of displacements and stresses in rotated coordinate systems.
What are appropriate/convenient/possible ways of doing this?
So far, I have created one Calculator filter for each component of displacements and stresses.
For instance, I used Calculators in 2D with results
(displacement.iHat)*cos(0.7853981625)+(displacement.jHat)*sin(0.7853981625)
(stress_3-stress_0)*sin(45.0*3.14159265/180)*cos(45.0*3.14159265/180)+stress_1*((cos(45.0*3.14159265/180))^2-(sin(45.0*3.14159265/180))^2)
It works fine, but it is quite cumbersome, in several aspects:
Creating them (one filter per component).
Plotting several of them in a single XY plot
Exporting them (one export per component).
Is there a simple way to do this?
PS: The Transform filter does not accomplish this. It rotates the view, not the fields.
Two solutions:
Ugly, inneficient solution
Use Transform and check "Transform All Input vectors"
Add a calculator and add a dummy array
Use transform the other way around, without checking "Transform All Input vectors"
Correct solution :
Compute the transformation yourself in a programmable filter
input = self.GetUnstructuredGridInput();
output = self.GetUnstructuredGridOutput();
output.ShallowCopy(input)
data = input.GetPointData().GetArray("YourArray")
vec = vtk.vtkDoubleArray();
vec.SetNumberOfComponents(3);
vec.SetName("TransformedVectors");
numPoints = input.GetNumberOfPoints()
for i in xrange(0, numPoints):
tuple = data.GetTuple(i)
transform(tuple) # implement the transform in python
vec.InsertNextTuple(tuple)
output.GetPointData().AddArray(vec)
I just started using SPSS, there is a option of Select cases that I was trying in SPSS, and later on finding frequency based on that filter.
For Eg:
Suppose Q1 has 12 parts, Q1_1 Q1_2 Q1_3 Q1_4 Q1_5 Q1_6 Q1_7 Q1_8 Q1_9 Q1_10 Q1_11 Q1_12
I want to see data in these variables based on a condition that I used in select cases. Now when I try to see frequencies of these variables based on the filter, only 4 out of 12 satisfy has data.
Now my question is can I hide rest 8 and show only 4 with data on my output window.
It's not entirely clear what you are trying to describe however reading between the lines, I'm guessing you are trying to delete tables generated from FREQUENCIES which may happen to be empty (likely due to a filter applied but perhaps not necessarily either)
You could do this with SPSS Scripting but avoiding that, you may want to explore using CTABLES, which though may not be in the exact same format as FREQUENCY table output it will still none the less retrieve the same information.
Solution below. Assumes Python Integration with SPSS SELECT VARIABLES installed and of course the CTABLE add-on module.
/****** Simulate example data ******/.
input program.
loop #j = 1 to 100.
compute ID=#j.
vector Q(12).
loop #i = 1 to 12.
do if #j<51 and #i<9.
compute Q(#i) = $sysmis.
else.
compute Q(#i) = trunc(rv.uniform(1,5)).
end if.
end loop.
end case.
end loop.
end file.
end input program.
execute.
/************************************/.
/* frequencies without filtering applied */.
freq q1 to q12.
/* frequencies WITH filtering applied */.
/* Empty table here shoult be removed */.
temp.
select if (ID<51).
freq q1 to q12.
spssinc select variables macroname="!Qp" /properties pattern = "^Q\d+$"/options separator="+" order=file.
spssinc select variables macroname="!Qs" /properties pattern = "^Q\d+$"/options separator=" " order=file.
temp.
select if (ID<51).
ctables /table (!Qp)[c][count colpct]
/categories variables=!Qs empty=exclude.
Note if you had assess empty variables at a total level then there is a function in spssaux2 (spssaux2.FindEmptyVars) which could help you find the empty variables and then you could build the syntax to exclude these and so contain the variables with only valid responses and then run FREQUENCIES. But I don't think spssaux2.FindEmptyVars will honor any filtering.
I'd like to calculate the standard deviation over two fields from the same dataset.
example:
MyFields1 = 10, 10
MyFields2 = 20
What I want now, is the standard deviation for (10,10,20), the expected result is 4.7
In SSRS I'd like to have something like this:
=StDevP(Fields!MyField1.Value + Fields!MyField2.Value)
Unfortunately this isn't possible, since (Fields!MyField1.Value + Fields!MyField2.Value) returns a single value and not a list of values. Is there no way to combine two fields from the same dataset into some kind of temporary dataset?
The only solutions I have are:
To create a new Dataset that contains all values from both fields. But this is very annoying because I need about twenty of those and I have six report parameters that need to filter every query. => It's probably getting very slow and annoying to maintain.
Write the formula by hand. But I don't really know how yet. StDevP is not that trivial to me. This is how I did it with Avg which is mathematically simpler:
=(SUM(Fields!MyField1.Value)+SUM(Fields!MyField2.Value))/2
found here: http://social.msdn.microsoft.com/Forums/is/sqlreportingservices/thread/7ff43716-2529-4240-a84d-42ada929020e
Btw. I know that it's odd to make such a calculation, but this is what my customer wants and I have to deliver somehow.
Thanks for any help.
CTDevP is standard deviation.
Such expression works fine for me
=StDevP(Fields!MyField1.Value + Fields!MyField2.Value) but it's deviation from one value (Fields!MyField1.Value + Fields!MyField2.Value) which is always 0.
you can look here for formula:
standard deviation (wiki)
I believe that you need to calculate this for some group (or full dataset), to do this you need set in the CTDevP your scope:
=StDevP(Fields!MyField1.Value + Fields!MyField2.Value, "MyDataSet1")
Which package is best for a heatmap/image with sorting on rows only, but don't show any dendrogram or other visual clutter (just a 2D colored grid with automatic named labels on both axes). I don't need fancy clustering beyond basic numeric sorting. The data is a 39x10 table of numerics in the range (0,0.21) which I want to visualize.
I searched SO (see this) and the R sites, and tried a few out. Check out R Graphical Manual to see an excellent searchable list of screenshots and corresponding packages.
The range of packages is confusing - which one is the preferred heatmap (like ggplot2 is for most other plotting)? Here is what I found out so far:
base::image - bad, no name labels on axes, no sorting/clustering
base::heatmap - options are far less intelligible than the following:
pheatmap::pheatmap - fantastic but can't seem to turn off the
dendrograms? (any hacks?)
ggplot2 people use geom_tile, as Andrie points out
gplots::heatmap.2 , ref - seems
to be favored by biotech people, but way overkill for my purposes. (no
relation to ggplot* or Prof Wickham)
plotrix::color2D.matplot also exists
base::heatmap is annoying, even with args heatmap(..., Colv=NA, keep.dendro=FALSE) it still plots the unwanted dendrogram on rows.
For now I'm going with pheatmap(..., cluster_cols=FALSE, cluster_rows=FALSE) and manually presorting my table, like this guy: Order of rows in heatmap?
Addendum: to display the value inside each cell, see: display a matrix, including the values, as a heatmap . I didn't need that but it's nice-to-have.
With pheatmap you can use options treeheight_row and treeheight_col and set these to 0.
just another option you have not mentioned...package bipartite as it is as simple as you say
library(bipartite)
mat<-matrix(c(1,2,3,1,2,3,1,2,3),byrow=TRUE,nrow=3)
rownames(mat)<-c("a","b","c")
colnames(mat)<-c("a","b","c")
visweb(mat,type="nested")