## ----setup, include = FALSE--------------------------------------------------- knitr::opts_chunk$set( collapse = TRUE, comment = "#>" ) ## ----eval=FALSE, echo=TRUE---------------------------------------------------- # # Create sample data # cls <- read.table(header = TRUE, text = ' # Name Sex Age Height Weight # Alfred M 14 69.0 112.5 # Alice F 13 56.5 84.0 # Barbara F 13 65.3 98.0 # Carol F 14 62.8 102.5 # Henry M 14 63.5 102.5 # James M 12 57.3 83.0 # Jane F 12 59.8 84.5 # Janet F 15 62.5 112.5 # Jeffrey M 13 62.5 84.0 # John M 12 59.0 99.5 # Joyce F 11 51.3 50.5 # Judy F 14 64.3 90.0 # Louise F 12 56.3 77.0 # Mary F 15 66.5 112.0 # Philip M 16 72.0 150.0 # Robert M 12 64.8 128.0 # Ronald M 15 67.0 133.0 # Thomas M 11 57.5 85.0 # William M 15 66.5 112.0') # ## ----eval=FALSE, echo=TRUE---------------------------------------------------- # # Turn off printing for CRAN checks # options("procs.print" = FALSE) # # # Basic operation # proc_reg(cls, Weight ~ Height) # ## ----eval=FALSE, echo=TRUE---------------------------------------------------- # # Output dataset # res1 <- proc_reg(cls, Weight ~ Height) # # # View results # res1 # # MODEL TYPE DEPVAR RMSE Intercept Height Weight # # 1 MODEL1 PARMS Weight 11.22625 -143.0269 3.89903 -1 ## ----eval=FALSE, echo=TRUE---------------------------------------------------- # # Output dataset using "outseb" option # res2 <- proc_reg(cls, Weight ~ Height, options = outseb) # # # View results # res2 # # MODEL TYPE DEPVAR RMSE Intercept Height Weight # # 1 MODEL1 PARMS Weight 11.22625 -143.02692 3.8990303 -1 # # 2 MODEL1 SEB Weight 11.22625 32.27459 0.5160939 -1 ## ----eval=FALSE, echo=TRUE---------------------------------------------------- # # Output dataset using "press" and "edf" options # res3 <- proc_reg(cls, Weight ~ Height, options = v(press, edf)) # # # View results # res3 # # MODEL TYPE DEPVAR RMSE PRESS Intercept Height Weight IN P EDF RSQ # # 1 MODEL1 PARMS Weight 11.22625 2651.352 -143.0269 3.89903 -1 1 2 17 0.7705068 ## ----eval=FALSE, echo=TRUE---------------------------------------------------- # # Output dataset using "tableout" option # res3 <- proc_reg(cls, Weight ~ Height, options = tableout) # # # View results # res3 # # MODEL TYPE DEPVAR RMSE Intercept Height Weight # # 1 MODEL1 PARMS Weight 11.22625 -1.430269e+02 3.899030e+00 -1 # # 2 MODEL1 STDERR Weight 11.22625 3.227459e+01 5.160939e-01 NA # # 3 MODEL1 T Weight 11.22625 -4.431564e+00 7.554885e+00 NA # # 4 MODEL1 PVALUE Weight 11.22625 3.655789e-04 7.886816e-07 NA # # 5 MODEL1 L95B Weight 11.22625 -2.111204e+02 2.810167e+00 NA # # 6 MODEL1 U95B Weight 11.22625 -7.493348e+01 4.987893e+00 NA ## ----eval=FALSE, echo=TRUE---------------------------------------------------- # # Output dataset using "report" output # res4 <- proc_reg(cls, Weight ~ Height, output = "report") # # # View results # res4 # # $Observations # # stub NOBS # # 1 Number of Observations Read 19 # # 2 Number of Observations Used 19 # # # # $ANOVA # # stub DF SUMSQ MEANSQ FVAL PROBF # # 1 Model 1 7193.249 7193.2491 57.07628 7.886816e-07 # # 2 Error 17 2142.488 126.0287 NA NA # # 3 Corrected Total 18 9335.737 NA NA NA # # # # $Fitness # # RMSE DEPMEAN COEFVAR RSQ ADJRSQ # # 1 11.22625 100.0263 11.2233 0.7705068 0.7570072 # # # # $Coefficients # # stub DF EST STDERR T PROBT # # 1 Intercept 1 -143.02692 32.2745913 -4.431564 3.655789e-04 # # 2 Height 1 3.89903 0.5160939 7.554885 7.886816e-07 ## ----eval=FALSE, echo=TRUE---------------------------------------------------- # # View report using "clb" option # proc_reg(cls, Weight ~ Height, stats = clb) ## ----eval=FALSE, echo=TRUE---------------------------------------------------- # # View report using "hcc" option # proc_reg(cls, Weight ~ Height, stats = hcc) ## ----eval=FALSE, echo=TRUE---------------------------------------------------- # # View report using "p" option # proc_reg(cls, Weight ~ Height, stats = p) ## ----eval=FALSE, echo=TRUE---------------------------------------------------- # # By grouping # res5 <- proc_reg(cls, Weight ~ Height, by = Sex) # # # View results # res5 # # BY MODEL TYPE DEPVAR RMSE Intercept Height Weight # # 1 F MODEL1 PARMS Weight 9.586849 -117.3698 3.424405 -1 # # 2 M MODEL1 PARMS Weight 12.695426 -141.1010 3.912549 -1 ## ----eval=FALSE, echo=TRUE---------------------------------------------------- # # Multiple Models # res6 <- proc_reg(cls, list(Weight ~ Height, # Weight ~ Height + Age)) # # # View results # res6 # # MODEL TYPE DEPVAR RMSE Intercept Height Weight Age # # 1 MODEL1 PARMS Weight 11.22625 -143.0269 3.899030 -1 NA # # 2 MODEL2 PARMS Weight 11.51114 -141.2238 3.597027 -1 1.278393 ## ----eval=FALSE, echo=TRUE---------------------------------------------------- # # Shape wide # res7 <- proc_reg(cls, Weight ~ Height, # option = outseb, output = wide) # # # Wide results # res7 # # MODEL TYPE DEPVAR RMSE Intercept Height Weight # # 1 MODEL1 PARMS Weight 11.22625 -143.02692 3.8990303 -1 # # 2 MODEL1 SEB Weight 11.22625 32.27459 0.5160939 -1 # # # Shape long # res8 <- proc_reg(cls, Weight ~ Height, # option = outseb, output = long) # # # Long results # res8 # # MODEL DEPVAR STAT PARMS SEB # # 1 MODEL1 Weight RMSE 11.22625 11.2262500 # # 2 MODEL1 Weight Intercept -143.02692 32.2745913 # # 3 MODEL1 Weight Height 3.89903 0.5160939 # # 4 MODEL1 Weight Weight -1.00000 -1.0000000 # # # Shape stacked # res9 <- proc_reg(cls, Weight ~ Height, # options = outseb, output = stacked) # # # Stacked results # res9 # # MODEL DEPVAR TYPE STAT VALUES # # 1 MODEL1 Weight PARMS RMSE 11.2262500 # # 2 MODEL1 Weight PARMS Intercept -143.0269184 # # 3 MODEL1 Weight PARMS Height 3.8990303 # # 4 MODEL1 Weight PARMS Weight -1.0000000 # # 5 MODEL1 Weight SEB RMSE 11.2262500 # # 6 MODEL1 Weight SEB Intercept 32.2745913 # # 7 MODEL1 Weight SEB Height 0.5160939 # # 8 MODEL1 Weight SEB Weight -1.0000000 #