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]] > > ______________________________________________ > R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see > https://stat.ethz.ch/mailman/listinfo/r-help > PLEASE do read the posting guide http://www.R-project.org/posting-guide.html > and provide commented, minimal, self-contained, reproducible code. ______________________________________________ R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
