Research Statistics Central

Objective and Background :  You have text data that is UTF encoded and need SAS/R to read and write datasets with that encoding.  If you have ever printed or viewed text information, and seen something like Giuffr?Ÿ’e?ƒe?Ÿƒ?ÿ?›ƒ?ªƒ?›?Ÿ’e›ƒ?ª­?Ÿƒeee, then you are running into this encoding issue.  Computers store text using numbers, with each number assigned to a particular character.  See  https://en.wikipedia.org/wiki/ASCII  to find that the character & is stored as 38 when using the ASCII encoding.  Unicode is popular internationally because it encodes special characters such as accented letters, and UTF-8 is a widely used version ( https://en.wikipedia.org/wiki/UTF-8 ).  In UTF-8 the & character is stored as 26, and you can imagine how the jumbled example above arises from the confusion of what letters are being stored. Solution 1 :  Use options to request that individual datasets be read and written in a particular encodin...

Objective :  Transformed data, for example log(y), is analyzed to correct normality or equal variance requirements.  But we want to report means and standard errors in the original units. SAS example : data one;  do treat=1 to 3;  do rep=1 to 5;    y=10 + treat+ exp(rannor(111));    logy=log(y);    output;  end;end; run; proc mixed plots=all;   class treat;   model y=treat;   lsmeans treat/pdiff; run; proc mixed plots=all;   class treat;   model logy=treat;   lsmeans treat/pdiff; run; The original data, variable y, might have units of pounds.  If a transformation is needed, we simply calculate a new variable by applying a mathematical function known to improve normality or equal variance, and run the same analysis on the new variable.  Commonly used choices are listed in the second table below. However, looking at the results for both analyses we see treat Mean Y S...

Objective : I am running a statistical analysis in SAS, and the default ODS graphics look good, but I need them to be publication quality. SAS can automatically create some nice graphs, and has greatly increased the availability of graphs within procedures.  If you like what you see, you might copy graphs directly from the SAS output window, or possibly you save graphs and output to a pdf or other external file format.  But this output will be low quality, generally 75 dpi.  Instead, add the following statements to write graphics directly to files, allowing control of format and quality. ods graphics on /       width=7in       imagefmt=tiff       imagemap=off       imagename="MyPlot"       border=off; ods listing file="Body.rtf" style=journal gpath="."  dpi=600; Once these statements have been submitted, all graphs created by subsequent procedures will be written  to files na...

Objective :  You are running ANOVAs or regressions in SAS, and wish there was a way to avoid writing the dozens of commands needed to conduct the analysis and generate recommended diagnostics and summary of results, not to mention the hundreds of possible options that might be needed to access recommended methods.  A possible solution is to download a copy of danda.sas below, and use this macro collection to run the dozens of commands with one statement.  We will also have future posts covering various uses of danda.sas, giving examples as always. danda.sas is under continued development, check this page for updates. Date                       Version               Link 2021/03/15             2.12.030          danda.sas 2021/03/15       ...

Objective :  You are running a linear model, for example ANOVA or regression, and are using the "ANOVA table" to decide which terms in the model are influencing the dependent variable.  You check the numerator and denominator degrees of freedom, as recommended , to guard against modeling errors and use of wrong error terms.  Reported values disagree with what you expected, so now what? Make sure your expected numbers are calculated correctly : An example model is (last term is the residual error) [Model 1]       y = u + block + treat + block*treat + rep(block*treat) If numbers of levels are b=2 for blocks, t=2 for treats, and r=5 for reps, then we expect degrees of freedom (DF) to be DF[block] = b-1 = 1 DF[treat] = t-1 = 1 DF[block*treat] = (b-1)*(t-1) = 1*1 = 1 DF[rep(block*treat)] = (r-1)*(b)*(t) = 4*2*2 = 16 Number of observations is b*t*r = 20, and DF add to 19, which is 20 minus the one DF for the intercept, as expected. DF rules are: ...

Objective :  Individuals are measured for several characteristics, and we want to group the individuals based on similarities across all characteristics. Statistical options are cluster analysis, principle components (PCA), and biplots.  PCA has the advantage of combining correlated variables together, reducing the complexity of explaining why individuals cluster together.  And biplots adds some nice features to PCA.  This post compares these choices. Example :  10 farms measured for nutrients in grass, and the primary question is to see if/which farms have similar nutrient profiles. Create a random dataset with 4 nutrients measured on 4 pastures in each of 10 farms. Run the SAS code for producing biplots.  Here we restrict the number of PC to 2 (n=2), in general you would use PCA to decide how many components are needed.  Prinqual requires all variables to be processed by a Transform statement, here we use the identity transformation so the variab...

Objective : We are comparing means using ANOVA, and our treatment levels are amounts of something.  Thus regression hypotheses may shed light on how the treatments differ, for example is there an overall linear trend for the response variable to increase or decrease with treatment level.  This is addressed by adding orthogonal polynomial contrasts to our ANOVA, which may require that we add contrast coefficients. Example :  Treatments are amounts of corn in the diet, specifically 62%, 65%, 68%, 71% and 74%. SAS :  IML product has an orthogonal polynomial calculator.  Additional code here attempts to make the coefficients whole numbers by dividing by the smallest non-zero number.  Note IML may not be available, depending on your license. proc iml; trtlevels={0.62, 0.65,0.68,0.71,0.74}; **this is only user input; ntrt=nrow(trtlevels); coeff=orpol(trtlevels); coeff = coeff[,2:ntrt]; div=abs(coeff); zerloc=loc(div<1e-14); if n...