About PaigeMiller

PaigeMiller · ‎03-19-2018

so the first value would be 10+20+30=60, then midway is 30 which is 10+20 so 2 years Still not following this at all. the second would be 20+30+40=90 / 2 = 45 , 45-20 = 25 then 25/30 = 5/6th So in the first example, there is no subtraction happening, but in this example there is a subtraction in the math?

PaigeMiller · ‎03-19-2018

It ignores the multi-variate nature of your predictor variables ... it ignores the correlations between the predictors, it ignores interactions between predictors. If you are going to fit a model with multiple predictors, I am skeptical of the approach of looking at predictor variables one at a time.

PaigeMiller · ‎03-19-2018

This is all too brief a description for us to comment (what are the 4 columns for salary???), plus furthermore I think that this is an issue you need to discuss with your clients in more detail to find out what they want; and if possible, discuss with a statistician at your company.

PaigeMiller · ‎03-19-2018

What is wrong with this approach? Go to your favorite search engine and type in "problems with stepwise regression". A better approach, in my opinion, is to use a logistic version of Partial Least Squares Regression. Again, your favorite search engine will find examples. PROC PLS in SAS does the calculations.

PaigeMiller · ‎03-19-2018

@89974114 wrote: I'm looking for a way to find the middle point of the three years, the average sale point the updated dataset would read data test; input one two three average; datalines; 10 20 30 2 20 30 40 1.5 10 30 50 2.1 10 10 10 1.5 ; run; I have to admit I am not following this, I don't see how you have computed the average value.

PaigeMiller · ‎03-19-2018

PROC TRANSPOSE

PaigeMiller · ‎03-19-2018

Let's suppose you can identify some threshold values for the reds, yellows and greens. For example, 0-0.5 is green, 0.5 to 1 is yellow and above 1 is red. Then, for example: proc format; value ryg = 0-<0.5=lightmoderategreen 0.5-<1=yellow 1-high=lightmoderatered; run; proc report data=have; columns a b c d e; define b/style={background=ryg.}; /* Repeat as needed for other columns */ run; If you really want, you can have SAS compute percentiles for your variables and then use the percentiles as cutoffs; this is a little more involved and requires either macros or CALL EXECUTE.

PaigeMiller · ‎03-19-2018

I won't look at the attached .xlsx file, because downloading and opening .xlsx files are security risk. However, the question I have for you is: did you actually look at the .xlsx file that is the problem? What is in column A? Is it the expected data, or is it the unexpected extra column? Can you show us a screen capture of what the first few rows and columns of the .xlsx file which is causing the problem? Long-term, this is a problem using .xlsx files to transmit the data, that the format of the file can change and then your code won't work.

PaigeMiller · ‎03-19-2018

To get parenthesis around your standard errors, and asterisks next to the p-values, you need to create a PICTURE format, and assign that format to the proper variable in PROC REPORT. http://documentation.sas.com/?cdcId=pgmmvacdc&cdcVersion=9.4&docsetId=proc&docsetTarget=p0n990vq8gxca6n1vnsracr6jp2c.htm&locale=en

PaigeMiller · ‎03-19-2018

@UGAstudent wrote: Thank you! Im controlling for ART duration, so when interpreting for lets say level 1, im not also controlling for levels 2 and 3 since they are the same variable correct? I would say this is not correct. There is no such concept of "controlling for" for dummy variables representing levels of a categorical variable.

PaigeMiller · ‎03-19-2018

@UGAstudent wrote: Thank you, Ive added in the lsmeans command. The lsmeans estimate represents severity of disease. So is the interpretation of the LsMeans simply that the higher the mean, the more severity occurs in that particular CD4 level? Yes, this is the estimated mean value of Factor1 based upon the level of CD4_LEVEL. You can also turn on other options in the LSMEANS statement to test whether or not the cd4_level values has statistically different means.

PaigeMiller · ‎03-19-2018

@Reeza wrote: How do I get my level 3 data to show up or interpret them I was told that this was the correct output for what Im trying to do and that I only need 2 estimates to calculate the 3rd but Im unsure of how to do that. To determine the effect for Level 1, everything else held constant: Level1: Intercept + Level1 Level2: Intercept + Level2 Level3: Intercept I would quibble with this -- I would say the following: Level1: Intercept + Level1 Level2: Intercept + Level2 Level3: Intercept + Level3

PaigeMiller · ‎03-18-2018

@Astounding wrote: At any rate, you would be well advised to treat SEX as a CLASS variable within PROC REG. Most regression procedures will automatically create the proper dummy variables when you use a CLASS statement. PROC GLM, not PROC REG To @kthartma: there is no need to create your own dummy variables. PROC GLM will create them for you, and also avoid the programming error you are having.

PaigeMiller · ‎03-18-2018

Yes, of course it would be possible to write this as a macro. I don't know if it would be faster on millions of observations because I never tried. As @Reeza has stated, PROC EXPAND might also be a good solution. Then, there is this solution, which works for regression coefficients with PROC REG, but something similar using PROC SUMMARY ought to work for standard deviations https://communities.sas.com/t5/SAS-Procedures/How-to-capture-the-regression-coeffecient-for-certain-time/m-p/445977#M69436 (and this could be customized to handle the different number of days in each month)

PaigeMiller · ‎03-18-2018

This is the default way that SAS (all procedures) handles categorical variables. There are different ways to parameterize the model, each with different coefficients, but each parameterization produces the same model and predictions. Under your parameterization, the value of the last level alphabetically (in your case, level 3) is set to zero. The true effect for level 1 is really intercept + level 1 coefficient = 7.76 + 0.3812, and so on. Instead of trying to interpret the model coefficients of class variables, you probably want to use the LSMEANS command and interpret the LSMEANS values. A better idea, in my opinion, is to treat CD4 as a continuous variable rather than as a class variable CD4_LEVELS.

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