Converting SAS Code
Hello, in my experience the most direct path of converting SAS code to R is by using dplyr. dplyr provides the filter function, the first part of your code could look like this, assuming your datasets are stored as data.frames: library(dplyr) yield <- filter(stress, field != "YV", field != "HV", barcode != "16187DD4015", barcode != "16187DD6002") (and so on for the other barcodes.) For mixed effects look into the lme4 package, lmer should use the reml criterion per default, the model specifications work very different in R. Look into the vingette [1] of the lme4 package chapter 2.1. gives an explanation of the used model formulas. You should get the coeficients of the fitted glmer model with the coef function. The Plots and univariate statistics work very different in R, have a look at the functions group_by and summarise provided by the dplyr package for calculating univariate statistics by groups, and the ggplot 2 package for plotting. Tobi [1] https://cran.r-project.org/web/packages/lme4/vignettes/lmer.pdf
On Fri, 2017-09-29 at 07:47 -0500, Andrew Harmon wrote:
Hello all, My statistical analysis training up until this point has been entirely done in SAS. The code I frequently used was: *Yield Champagin; data yield; set stress; if field='YV' then delete; if field='HB' then delete; if barcode='16187DD4015' then delete; if barcode='16187DD6002' then delete; if barcode='16187DD2007' then delete; if barcode='16187DD5016' then delete; if barcode='16187DD8007' then delete; if barcode='16187DD7010' then delete; if barcode='16187DD7007' then delete; if barcode='16187DD8005' then delete; if barcode='16187DD6004' then delete; if barcode='16187DD5008' then delete; if barcode='16187DD7012' then delete; if barcode='16187DD6010' then delete; run; quit; Title'2016 Asilomar Stress Relief champagin yield'; proc mixed method=reml data=yield; class rep Management Foliar_Fungicide Chemical_Treatment; model Grain_Yield__Mg_h_ =Management|Foliar_Fungicide|Chemical_Treatment Final_Stand__Plants_A_ / outpred=resids residual ddfm=kr; random rep rep*Management rep*Management*Foliar_Fungicide; lsmeans Management|Foliar_Fungicide|Chemical_Treatment / pdiff; ods output diffs=ppp lsmeans=means; ods listing exclude diffs lsmeans; run; quit; %include'C:\Users\harmon12\Desktop\pdmix800.sas'; %pdmix800(ppp,means,alpha=0.10,sort=yes); ods graphics off; run; quit; proc univariate data=resids normal plot; id Barcode Grain_Yield__Mg_h_ pearsonresid; var resid; proc print data=resids (obs=3);run; Can someone please help me convert my code to R? Any help would be much appreciated. Thanks, Andrew Harmon [[alternative HTML version deleted]]
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