I am trying to incorporate CONTRAST and ESTIMATE statements to analyze the difference in growth rates of trees. I uncovered a code that was used in a previous study that did precisely what i want, however, the way the data was entered, and therefore the code was written, is different. The previous data set was imported from an excel sheet in which each growth parameter for each date was entered in separate columns, ie- GLD0306, GLD0706, GLD0707 etc. The current data sheet has the growth parameter as a single column with Date as a separate column. The other variable in question, either Fert or Nfix, will be Y or N. the code that was used before was: Data Loggrow; set yr6growth; lgld1=log10(gld0306);   lgld2=log10(gld0706);   lgld3=log10(gld0707);   lgld4=log10(gld0711); array lglda{4} lgld1 lgld2 lgld3 lgld4; do i = 1 to 4; if (i = 1) then MTH = 9; else if (i = 2) then MTH = 12;   else if (i = 3) then MTH = 24;   else if (i = 4) then MTH = 72; Time = MTH;   MTHC = MTH; log10gld = lglda{i}; output; end; drop i gld0306 gld0706 gld0707 gld0711 ht0306 ht0706 ht0707 ht0711 lgld1 lgld2 lgld3 lgld4; run; proc mixed data=loggrow order=data covtest nobound; where spc = "Parica"; class BLK fert nfix MTH ID; model log10gld = fert nfix mth fert*nfix fert*mth nfix*mth fert*nfix*mth; random BLK fert*BLK nfix*fert*BLK nfix*fert*BLK*ID; repeated /subject=nfix*fert*blk*ID type=sp(pow) (mth) r rcorr; LSMEANS FERT mth fert*mth/pdiff =all adjust = tukey; LSMEANS fert*mth/slice=mth; contrast 'diff y-n, mth 72-24' fert*mth 0 0 1 -1 0 0 -1 1; estimate 'diff y-n, mth 72-24' fert*mth 0 0 1 -1 0 0 -1 1; run;   When running this code withe new spreadsheet format, i get a "WARNING: The R matrix depends on observation order within subjects. Omitting observations from the analysis because of missing values can affect this matrix. Consider using a classification effect in the REPEATED statement to determine ordering in the R matrix."   The part that is confusing me is the in the Contrast and Estimate statements, since each of the months was assigned a different "i". However, in the new data set spread sheet, Date is its own column as is the growth variable (as opposed to having multiple growth variable columns, one for each date), and i do not understand how to order the subjects or how to write the classification effect to satisfy the code. My question is this: How to write the Contrast and Estimate statements to incorporate the new format?   These are the column headers and a row of data for the new data set: Year Measurement Date Month JulianDay Phase Block Plot Fert Nfix Row Tree Species GLD 2006 1 6/26/2005 9 ### 1 A 1 N N 1 8 Cedro 1.98 ... View more

I am trying to import data from an excel file into SAS University. I have 5 species and a dozen or so variables. When i run Proc IMPORT and Proc PRINT of the data it will read some of the Species and their rows of data, but not all. In fact, it will read 3 rows of data but not the other 275.  Why isn't SAS reading data that is clearly visible in the excel file itself? or How can i force SAS to read the data that it won't? this is the code i've written:   %web_drop_table(WORK.IMPORT); FILENAME FILEREF '/folders/myfolders/Trees.xlsx'; PROC IMPORT DATAFILE=FILEREF DBMS=XLSX OUT=WORK.IMPORT; GETNAMES=YES; RUN; data Trees; set import; if Measurement=2; if GLD>0; if DBH>0; if Height>0; run; proc print data=Trees; run;   Thanks in advance ... View more

I'm using SAS University and i copied and pasted a program into a new program window, gave it a name etc. It's ready to run. I run just the import statement, it imports it fine. I run the data statement, it runs just fine. However, when i try to run the Proc Univariate statement, i get: "Variable GLDcm in list does not match type prescribed for this list." It's just a column of numbers in an excel file. The same type of file, with the same type of columns of numbers, ran just fine in the other program, but now it won't run. However, if i convert GLDcm to logGLD using the logGLD=log(GLDcm); statement, then the Proc Univariate runs fine. I want to see if the untransformed data is normally distributed, so i don't want to do logGLD if it's not necessary. 1) Why won't SAS University recognize the column of data GLD_cm? if that can't be resolved,  2) how can i un-transform the logGLD back into regular numbers so that SAS will read the data?   Here's the code as i have it now: %web_drop_table(WORK.IMPORT); FILENAME FILEREF '/folders/myfolders/TreeGrowth.xlsx'; PROC IMPORT DATAFILE=FILEREF DBMS=XLSX OUT=WORK.IMPORT; GETNAMES=YES; RUN; data TreeGrowth; set import; if Hgt="Ht"; logGLD=log(GLDcm); logDBH=log(DBHcm); logHeight=log(Height); run; /*proc print data=TreeGrowth; run;*/ proc univariate Normal Plot data=TreeGrowth; var GLDcm logGLD; Histogram GLDcm logGLD/Normal; QQPLOT GLDcm logGLD; output out=AllEvents; run; ......................................... Thanks for the help ... View more

I have a data set that i want to analyze using proc GLM. The design is a split-plot factorial RCBD that has a repeated measure of  N2O (and CH4) over 9 measurements. The main-plot is Fert, the sub-plot is Nfix. There are 2 things that i would like assistance with:  1) the Type 3 tests of effects indicates that the Fert*Nfix*Date interaction is highly significant (p<0.0001), but the LSMeans slice for that interaction  doesn't seem to give relevant information, as it does for the other interactions. I have a suspicion that i need to code the Date2 input differently so that the code reflects that there are in fact multiple Dates for it to read. The file is an imported excel data set, not cards directly input into the program. How do i input the Date data so that Proc GLM analyses the two levels of treatment and the multiple Dates and their interactions for analysis? 2) How can i write a code to provide an output for the interactions in which each interaction is given a letter to indicate significant differences, like Tukey's HSD?   proc mixed data=AllEvents covtest; Class Block Fert Nfix PlotID Date2; Model logN2O= Fert Nfix Fert*Nfix Date2 Fert*Date2 Nfix*Date2 Fert*Nfix*Date2; Random Block Block*Fert Block*Fert*Date2; Repeated Date2 / subject=plotID Type=CS r; lsmeans Fert Nfix Fert*Nfix Fert*Date2 Nfix*Date2/pdiff adjust=tukey; lsmeans Fert*Date2 / slice=Fert; lsmeans Fert*Date2 / slice=Date2; lsmeans Nfix*Date2 / slice=Nfix; lsmeans Nfix*Date2 / slice=Date2; lsmeans Fert*Nfix*Date2 / slice=Fert*Nfix; run;   much appreciation ... View more