## ----setup, include = FALSE--------------------------------------------------- knitr::opts_chunk$set( collapse = TRUE, comment = "#>" ) ## ----eval=FALSE, echo=TRUE---------------------------------------------------- # # Create sample data # plants <- read.table(header = TRUE, text = ' # Plot Fertilizer Water Yield Field # 1 A 12 24 North # 2 A 14 28 North # 3 A 16 29 North # 4 A 13 25 North # 5 A 15 27 South # 6 A 17 31 South # 7 A 14 27 South # 8 A 16 30 South # 9 A 18 32 South # 10 B 10 18 North # 11 B 12 21 North # 12 B 14 23 North # 13 B 11 18 North # 14 B 13 21 South # 15 B 15 25 South # 16 B 12 20 South # 17 B 14 24 South # 18 B 16 25 South') # ## ----eval=FALSE, echo=TRUE---------------------------------------------------- # # Turn off printing for CRAN checks # options("procs.print" = FALSE) # # # Basic operation # proc_glm(plants, Yield ~ Fertilizer + Water, class = Fertilizer) # ## ----eval=FALSE, echo=TRUE---------------------------------------------------- # # Output dataset # res1 <- proc_glm(plants, Yield ~ Fertilizer + Water, class = Fertilizer) # # # View results # res1 # # MODEL DEPVAR SOURCE TYPE DF SS MEANSQ FVAL PROBF # # 1 MODEL1 Yield Fertilizer SS1 1 186.88889 186.88889 371.8497 5.349832e-12 # # 2 MODEL1 Yield Water SS1 1 109.35000 109.35000 217.5718 2.459793e-10 # # 3 MODEL1 Yield Fertilizer SS3 1 48.53376 48.53376 96.5668 6.284856e-08 # # 4 MODEL1 Yield Water SS3 1 109.35000 109.35000 217.5718 2.459793e-10 ## ----eval=FALSE, echo=TRUE---------------------------------------------------- # # Output dataset using "ss3" keyword # res2 <- proc_glm(plants, Yield ~ Fertilizer + Water, class = Fertilizer, # stats = ss3) # # # View results # res2 # # MODEL DEPVAR SOURCE TYPE DF SS MEANSQ FVAL PROBF # # 1 MODEL1 Yield Fertilizer SS3 1 48.53376 48.53376 96.5668 6.284856e-08 # # 2 MODEL1 Yield Water SS3 1 109.35000 109.35000 217.5718 2.459793e-10 ## ----eval=FALSE, echo=TRUE---------------------------------------------------- # # Output using "report" output # res3 <- proc_glm(plants, Yield ~ Fertilizer + Water, class = Fertilizer, # output = "report") # # # View results # res3 # # $ClassLevels # # CLASS LEVELS VALUES # # 1 Fertilizer 2 A B # # # # $NObs # # LABEL NOBS # # 1 Number of Observations Read 18 # # 2 Number of Observations Used 18 # # # # $ANOVA # # SOURCE DF SUMSQ MEANSQ FVAL PROBF # # 1 Model 2 296.238889 148.1194444 294.7108 9.132695e-13 # # 2 Error 15 7.538889 0.5025926 NA NA # # 3 Corrected Total 17 303.777778 NA NA NA # # # # $FitStatistics # # RSQ COEFVAR RMSE DEPMEAN # # 1 0.9751829 2.84841 0.7089377 24.88889 # # # # $TypeI # # SOURCE DF SUMSQ MEANSQ FVAL PROBF # # 1 Fertilizer 1 186.8889 186.8889 371.8497 5.349832e-12 # # 2 Water 1 109.3500 109.3500 217.5718 2.459793e-10 # # # # $TypeIII # # SOURCE DF SUMSQ MEANSQ FVAL PROBF # # 1 Fertilizer 1 48.53376 48.53376 96.5668 6.284856e-08 # # 2 Water 1 109.35000 109.35000 217.5718 2.459793e-10 ## ----eval=FALSE, echo=TRUE---------------------------------------------------- # # View report using "solution" keyword # proc_glm(plants, Yield ~ Fertilizer + Water, class = Fertilizer, # stats = solution) ## ----eval=FALSE, echo=TRUE---------------------------------------------------- # # View report using "p" keyword # proc_glm(plants, Yield ~ Fertilizer + Water, class = Fertilizer, # stats = p) ## ----eval=FALSE, echo=TRUE---------------------------------------------------- # # View report using "lsmeans" parameter # proc_glm(plants, Yield ~ Fertilizer + Water, class = Fertilizer, # lsmeans = Fertilizer) ## ----eval=FALSE, echo=TRUE---------------------------------------------------- # # By grouping # res5 <- proc_glm(plants, Yield ~ Fertilizer + Water, class = Fertilizer, # by = Field) # # # View results # res5 # # BY MODEL DEPVAR SOURCE TYPE DF SS MEANSQ FVAL PROBF # # 1 North MODEL1 Yield Fertilizer SS1 1 84.50000 84.50000 122.71784 1.044349e-04 # # 2 North MODEL1 Yield Water SS1 1 31.55714 31.55714 45.82988 1.068828e-03 # # 3 North MODEL1 Yield Fertilizer SS3 1 19.96891 19.96891 29.00049 2.977727e-03 # # 4 North MODEL1 Yield Water SS3 1 31.55714 31.55714 45.82988 1.068828e-03 # # 5 South MODEL1 Yield Fertilizer SS1 1 102.40000 102.40000 179.20000 3.042972e-06 # # 6 South MODEL1 Yield Water SS1 1 39.20000 39.20000 68.60000 7.298423e-05 # # 7 South MODEL1 Yield Fertilizer SS3 1 21.60000 21.60000 37.80000 4.683778e-04 # # 8 South MODEL1 Yield Water SS3 1 39.20000 39.20000 68.60000 7.298423e-05 ## ----eval=FALSE, echo=TRUE---------------------------------------------------- # # Multiple Models # res6 <- proc_glm(plants, list(Yield ~ Fertilizer, # Yield ~ Fertilizer + Water), # class = Fertilizer, stats = ss3) # # # View results # res6 # # MODEL DEPVAR SOURCE TYPE DF SS MEANSQ FVAL PROBF # # 1 MODEL1 Yield Fertilizer SS3 1 186.88889 186.88889 25.58175 1.164801e-04 # # 2 MODEL2 Yield Fertilizer SS3 1 48.53376 48.53376 96.56680 6.284856e-08 # # 3 MODEL2 Yield Water SS3 1 109.35000 109.35000 217.57185 2.459793e-10 ## ----eval=FALSE, echo=TRUE---------------------------------------------------- # # Shape wide # res7 <- proc_glm(plants, Yield ~ Fertilizer + Water, class = Fertilizer, # stats = ss3, output = wide) # # # Wide results # res7 # # MODEL DEPVAR SOURCE TYPE DF SS MEANSQ FVAL PROBF # # 1 MODEL1 Yield Fertilizer SS3 1 48.53376 48.53376 96.5668 6.284856e-08 # # 2 MODEL1 Yield Water SS3 1 109.35000 109.35000 217.5718 2.459793e-10 # # # Shape long # res8 <- proc_glm(plants, Yield ~ Fertilizer + Water, class = Fertilizer, # stats = ss3, output = long) # # # Long results # res8 # # MODEL DEPVAR SOURCE TYPE STAT COL1 # # 1 MODEL1 Yield Fertilizer SS3 DF 1.000000e+00 # # 2 MODEL1 Yield Fertilizer SS3 SS 4.853376e+01 # # 3 MODEL1 Yield Fertilizer SS3 MEANSQ 4.853376e+01 # # 4 MODEL1 Yield Fertilizer SS3 FVAL 9.656680e+01 # # 5 MODEL1 Yield Fertilizer SS3 PROBF 6.284856e-08 # # 6 MODEL1 Yield Water SS3 DF 1.000000e+00 # # 7 MODEL1 Yield Water SS3 SS 1.093500e+02 # # 8 MODEL1 Yield Water SS3 MEANSQ 1.093500e+02 # # 9 MODEL1 Yield Water SS3 FVAL 2.175718e+02 # # 10 MODEL1 Yield Water SS3 PROBF 2.459793e-10 # # # Shape stacked # res9 <- proc_glm(plants, Yield ~ Fertilizer + Water, class = Fertilizer, # stats = ss3, output = stacked) # # # Stacked results # res9 # # MODEL DEPVAR SOURCE TYPE STAT VALUES # # 1 MODEL1 Yield Fertilizer SS3 DF 1.000000e+00 # # 2 MODEL1 Yield Fertilizer SS3 SS 4.853376e+01 # # 3 MODEL1 Yield Fertilizer SS3 MEANSQ 4.853376e+01 # # 4 MODEL1 Yield Fertilizer SS3 FVAL 9.656680e+01 # # 5 MODEL1 Yield Fertilizer SS3 PROBF 6.284856e-08 # # 6 MODEL1 Yield Water SS3 DF 1.000000e+00 # # 7 MODEL1 Yield Water SS3 SS 1.093500e+02 # # 8 MODEL1 Yield Water SS3 MEANSQ 1.093500e+02 # # 9 MODEL1 Yield Water SS3 FVAL 2.175718e+02 # # 10 MODEL1 Yield Water SS3 PROBF 2.459793e-10 #