Nothing
inst/doc/procs-ttest.R
In procs: Recreates Some 'SAS®' Procedures in 'R'
## ----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 to pass CRAN checks
# options("procs.print" = FALSE)
#
# # Single sample test
# res1 <- proc_ttest(cls, var = Height, options = c(h0 = 65))
#
# # View results
# res1
# # $Statistics
# # VAR N MEAN STD STDERR MIN MAX
# # 1 Height 19 62.33684 5.127075 1.176232 51.3 72
# #
# # $ConfLimits
# # VAR MEAN LCLM UCLM STD LCLMSTD UCLMSTD
# # 1 Height 62.33684 59.86567 64.80801 5.127075 3.874083 7.582045
# #
# # $TTests
# # VAR DF T PROBT
# # 1 Height 18 -2.264144 0.03615222
#
## ----eval=FALSE, echo=TRUE----------------------------------------------------
# # Perform two-sample analysis
# res2 <- proc_ttest(cls, var = Height, class = Sex)
#
# # View results
# res2
# # $Statistics
# # VAR CLASS METHOD N MEAN STD STDERR MIN MAX
# # 1 Height F <NA> 9 60.588889 5.018328 1.672776 51.3 66.5
# # 2 Height M <NA> 10 63.910000 4.937937 1.561513 57.3 72.0
# # 3 Height Diff (1-2) Pooled NA -3.321111 NA 2.286282 NA NA
# # 4 Height Diff (1-2) Satterthwaite NA -3.321111 NA 2.288340 NA NA
# #
# # $ConfLimits
# # VAR CLASS METHOD MEAN LCLM UCLM STD LCLMSTD UCLMSTD
# # 1 Height F <NA> 60.588889 56.731461 64.446317 5.018328 3.389665 9.613966
# # 2 Height M <NA> 63.910000 60.377613 67.442387 4.937937 3.396487 9.014748
# # 3 Height Diff (1-2) Pooled -3.321111 -8.144744 1.502522 NA NA NA
# # 4 Height Diff (1-2) Satterthwaite -3.321111 -8.155098 1.512875 NA NA NA
# #
# # $TTests
# # VAR METHOD VARIANCES DF T PROBT
# # 1 Height Pooled Equal 17.00000 -1.452625 0.1645363
# # 2 Height Satterthwaite Unequal 16.72695 -1.451319 0.1651880
# #
# # $Equality
# # VAR METHOD NDF DDF FVAL PROBF
# # 1 Height Folded F 8 9 1.032825 0.9526904
#
## ----eval=FALSE, echo=TRUE----------------------------------------------------
# # Create sample data
# paird <- read.table(header = TRUE, text = '
# id before after
# 1 12 15
# 2 14 16
# 3 10 11
# 4 15 18
# 5 18 20
# 6 20 22
# 7 11 12
# 8 13 14
# 9 16 17
# 10 9 13')
#
## ----eval=FALSE, echo=TRUE----------------------------------------------------
# # Perform paired analysis
# res3 <- proc_ttest(paird, paired = "before * after")
#
# # View results
# res3
# # $Statistics
# # VAR1 VAR2 DIFF N MEAN STD STDERR MIN MAX
# # 1 before after before-after 10 -2 1.054093 0.3333333 -4 -1
# #
# # $ConfLimits
# # VAR1 VAR2 DIFF MEAN LCLM UCLM STD LCLMSTD UCLMSTD
# # 1 before after before-after -2 -2.754052 -1.245948 1.054093 0.725042 1.924362
# #
# # $TTests
# # VAR1 VAR2 DIFF DF T PROBT
# # 1 before after before-after 9 -6 0.0002024993
#
## ----eval=FALSE, echo=TRUE----------------------------------------------------
# # By grouping
# res5 <- proc_ttest(cls, var = "Height",
# by = "Sex", options = c(h0 = 65))
#
# # View Results
# res5
# # $Statistics
# # BY VAR N MEAN STD STDERR MIN MAX
# # 1 F Height 9 60.58889 5.018328 1.672776 51.3 66.5
# # 2 M Height 10 63.91000 4.937937 1.561513 57.3 72.0
# #
# # $ConfLimits
# # BY VAR MEAN LCLM UCLM STD LCLMSTD UCLMSTD
# # 1 F Height 60.58889 56.73146 64.44632 5.018328 3.389665 9.613966
# # 2 M Height 63.91000 60.37761 67.44239 4.937937 3.396487 9.014748
# #
# # $TTests
# # BY VAR DF T PROBT
# # 1 F Height 8 -2.637001 0.02985198
# # 2 M Height 9 -0.698041 0.50278618
#
## ----eval=FALSE, echo=TRUE----------------------------------------------------
# # Output dataset using "report" option
# res4 <- proc_ttest(paird,
# paired = "before * after",
# output = "report")
#
# # View results
# res4
# # $`diff1:Statistics`
# # N MEAN STD STDERR MIN MAX
# # 1 10 -2 1.054093 0.3333333 -4 -1
# #
# # $`diff1:ConfLimits`
# # MEAN LCLM UCLM STD LCLMSTD UCLMSTD
# # 1 -2 -2.754052 -1.245948 1.054093 0.725042 1.924362
# #
# # $`diff1:TTests`
# # DF T PROBT
# # 1 9 -6 0.0002024993
#
## ----eval=FALSE, echo=TRUE----------------------------------------------------
# # Output option "wide"
# res5 <- proc_ttest(cls, var = c("Height", "Weight"),
# options = c(h0 = 65),
# output = "wide")
#
# # View results
# res5
# # $Statistics
# # VAR N MEAN STD STDERR MIN MAX
# # 1 Height 19 62.33684 5.127075 1.176232 51.3 72
# # 2 Weight 19 100.02632 22.773933 5.224699 50.5 150
# #
# # $ConfLimits
# # VAR MEAN LCLM UCLM STD LCLMSTD UCLMSTD
# # 1 Height 62.33684 59.86567 64.80801 5.127075 3.874083 7.582045
# # 2 Weight 100.02632 89.04963 111.00300 22.773933 17.208272 33.678652
# #
# # $TTests
# # VAR DF T PROBT
# # 1 Height 18 -2.264144 3.615222e-02
# # 2 Weight 18 6.703988 2.754086e-06
#
# # Output option "long"
# res6 <- proc_ttest(cls, var = c("Height", "Weight"),
# options = c(h0 = 65),
# output = "long")
#
# # View results
# res6
# # $Statistics
# # STAT Height Weight
# # 1 N 19.000000 19.000000
# # 2 MEAN 62.336842 100.026316
# # 3 STD 5.127075 22.773933
# # 4 STDERR 1.176232 5.224699
# # 5 MIN 51.300000 50.500000
# # 6 MAX 72.000000 150.000000
# #
# # $ConfLimits
# # STAT Height Weight
# # 1 MEAN 62.336842 100.02632
# # 2 LCLM 59.865671 89.04963
# # 3 UCLM 64.808013 111.00300
# # 4 STD 5.127075 22.77393
# # 5 LCLMSTD 3.874083 17.20827
# # 6 UCLMSTD 7.582045 33.67865
# #
# # $TTests
# # STAT Height Weight
# # 1 DF 18.00000000 1.800000e+01
# # 2 T -2.26414390 6.703988e+00
# # 3 PROBT 0.03615222 2.754086e-06
#
# # Output option "stacked"
# res7 <- proc_ttest(cls, var = c("Height", "Weight"),
# options = c(h0 = 65),
# output = "stacked")
#
# # View results
# res7
# # $Statistics
# # VAR STAT VALUES
# # 1 Height N 19.000000
# # 2 Height MEAN 62.336842
# # 3 Height STD 5.127075
# # 4 Height STDERR 1.176232
# # 5 Height MIN 51.300000
# # 6 Height MAX 72.000000
# # 7 Weight N 19.000000
# # 8 Weight MEAN 100.026316
# # 9 Weight STD 22.773933
# # 10 Weight STDERR 5.224699
# # 11 Weight MIN 50.500000
# # 12 Weight MAX 150.000000
# #
# # $ConfLimits
# # VAR STAT VALUES
# # 1 Height MEAN 62.336842
# # 2 Height LCLM 59.865671
# # 3 Height UCLM 64.808013
# # 4 Height STD 5.127075
# # 5 Height LCLMSTD 3.874083
# # 6 Height UCLMSTD 7.582045
# # 7 Weight MEAN 100.026316
# # 8 Weight LCLM 89.049631
# # 9 Weight UCLM 111.003000
# # 10 Weight STD 22.773933
# # 11 Weight LCLMSTD 17.208272
# # 12 Weight UCLMSTD 33.678652
# #
# # $TTests
# # VAR STAT VALUES
# # 1 Height DF 1.800000e+01
# # 2 Height T -2.264144e+00
# # 3 Height PROBT 3.615222e-02
# # 4 Weight DF 1.800000e+01
# # 5 Weight T 6.703988e+00
# # 6 Weight PROBT 2.754086e-06
#
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procs documentation built on May 29, 2024, 2:12 a.m.
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## ----setup, include = FALSE---------------------------------------------------
knitr::opts_chunk$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 to pass CRAN checks
# options("procs.print" = FALSE)
#
# # Single sample test
# res1 <- proc_ttest(cls, var = Height, options = c(h0 = 65))
#
# # View results
# res1
# # $Statistics
# # VAR N MEAN STD STDERR MIN MAX
# # 1 Height 19 62.33684 5.127075 1.176232 51.3 72
# #
# # $ConfLimits
# # VAR MEAN LCLM UCLM STD LCLMSTD UCLMSTD
# # 1 Height 62.33684 59.86567 64.80801 5.127075 3.874083 7.582045
# #
# # $TTests
# # VAR DF T PROBT
# # 1 Height 18 -2.264144 0.03615222
#
## ----eval=FALSE, echo=TRUE----------------------------------------------------
# # Perform two-sample analysis
# res2 <- proc_ttest(cls, var = Height, class = Sex)
#
# # View results
# res2
# # $Statistics
# # VAR CLASS METHOD N MEAN STD STDERR MIN MAX
# # 1 Height F <NA> 9 60.588889 5.018328 1.672776 51.3 66.5
# # 2 Height M <NA> 10 63.910000 4.937937 1.561513 57.3 72.0
# # 3 Height Diff (1-2) Pooled NA -3.321111 NA 2.286282 NA NA
# # 4 Height Diff (1-2) Satterthwaite NA -3.321111 NA 2.288340 NA NA
# #
# # $ConfLimits
# # VAR CLASS METHOD MEAN LCLM UCLM STD LCLMSTD UCLMSTD
# # 1 Height F <NA> 60.588889 56.731461 64.446317 5.018328 3.389665 9.613966
# # 2 Height M <NA> 63.910000 60.377613 67.442387 4.937937 3.396487 9.014748
# # 3 Height Diff (1-2) Pooled -3.321111 -8.144744 1.502522 NA NA NA
# # 4 Height Diff (1-2) Satterthwaite -3.321111 -8.155098 1.512875 NA NA NA
# #
# # $TTests
# # VAR METHOD VARIANCES DF T PROBT
# # 1 Height Pooled Equal 17.00000 -1.452625 0.1645363
# # 2 Height Satterthwaite Unequal 16.72695 -1.451319 0.1651880
# #
# # $Equality
# # VAR METHOD NDF DDF FVAL PROBF
# # 1 Height Folded F 8 9 1.032825 0.9526904
#
## ----eval=FALSE, echo=TRUE----------------------------------------------------
# # Create sample data
# paird <- read.table(header = TRUE, text = '
# id before after
# 1 12 15
# 2 14 16
# 3 10 11
# 4 15 18
# 5 18 20
# 6 20 22
# 7 11 12
# 8 13 14
# 9 16 17
# 10 9 13')
#
## ----eval=FALSE, echo=TRUE----------------------------------------------------
# # Perform paired analysis
# res3 <- proc_ttest(paird, paired = "before * after")
#
# # View results
# res3
# # $Statistics
# # VAR1 VAR2 DIFF N MEAN STD STDERR MIN MAX
# # 1 before after before-after 10 -2 1.054093 0.3333333 -4 -1
# #
# # $ConfLimits
# # VAR1 VAR2 DIFF MEAN LCLM UCLM STD LCLMSTD UCLMSTD
# # 1 before after before-after -2 -2.754052 -1.245948 1.054093 0.725042 1.924362
# #
# # $TTests
# # VAR1 VAR2 DIFF DF T PROBT
# # 1 before after before-after 9 -6 0.0002024993
#
## ----eval=FALSE, echo=TRUE----------------------------------------------------
# # By grouping
# res5 <- proc_ttest(cls, var = "Height",
# by = "Sex", options = c(h0 = 65))
#
# # View Results
# res5
# # $Statistics
# # BY VAR N MEAN STD STDERR MIN MAX
# # 1 F Height 9 60.58889 5.018328 1.672776 51.3 66.5
# # 2 M Height 10 63.91000 4.937937 1.561513 57.3 72.0
# #
# # $ConfLimits
# # BY VAR MEAN LCLM UCLM STD LCLMSTD UCLMSTD
# # 1 F Height 60.58889 56.73146 64.44632 5.018328 3.389665 9.613966
# # 2 M Height 63.91000 60.37761 67.44239 4.937937 3.396487 9.014748
# #
# # $TTests
# # BY VAR DF T PROBT
# # 1 F Height 8 -2.637001 0.02985198
# # 2 M Height 9 -0.698041 0.50278618
#
## ----eval=FALSE, echo=TRUE----------------------------------------------------
# # Output dataset using "report" option
# res4 <- proc_ttest(paird,
# paired = "before * after",
# output = "report")
#
# # View results
# res4
# # $`diff1:Statistics`
# # N MEAN STD STDERR MIN MAX
# # 1 10 -2 1.054093 0.3333333 -4 -1
# #
# # $`diff1:ConfLimits`
# # MEAN LCLM UCLM STD LCLMSTD UCLMSTD
# # 1 -2 -2.754052 -1.245948 1.054093 0.725042 1.924362
# #
# # $`diff1:TTests`
# # DF T PROBT
# # 1 9 -6 0.0002024993
#
## ----eval=FALSE, echo=TRUE----------------------------------------------------
# # Output option "wide"
# res5 <- proc_ttest(cls, var = c("Height", "Weight"),
# options = c(h0 = 65),
# output = "wide")
#
# # View results
# res5
# # $Statistics
# # VAR N MEAN STD STDERR MIN MAX
# # 1 Height 19 62.33684 5.127075 1.176232 51.3 72
# # 2 Weight 19 100.02632 22.773933 5.224699 50.5 150
# #
# # $ConfLimits
# # VAR MEAN LCLM UCLM STD LCLMSTD UCLMSTD
# # 1 Height 62.33684 59.86567 64.80801 5.127075 3.874083 7.582045
# # 2 Weight 100.02632 89.04963 111.00300 22.773933 17.208272 33.678652
# #
# # $TTests
# # VAR DF T PROBT
# # 1 Height 18 -2.264144 3.615222e-02
# # 2 Weight 18 6.703988 2.754086e-06
#
# # Output option "long"
# res6 <- proc_ttest(cls, var = c("Height", "Weight"),
# options = c(h0 = 65),
# output = "long")
#
# # View results
# res6
# # $Statistics
# # STAT Height Weight
# # 1 N 19.000000 19.000000
# # 2 MEAN 62.336842 100.026316
# # 3 STD 5.127075 22.773933
# # 4 STDERR 1.176232 5.224699
# # 5 MIN 51.300000 50.500000
# # 6 MAX 72.000000 150.000000
# #
# # $ConfLimits
# # STAT Height Weight
# # 1 MEAN 62.336842 100.02632
# # 2 LCLM 59.865671 89.04963
# # 3 UCLM 64.808013 111.00300
# # 4 STD 5.127075 22.77393
# # 5 LCLMSTD 3.874083 17.20827
# # 6 UCLMSTD 7.582045 33.67865
# #
# # $TTests
# # STAT Height Weight
# # 1 DF 18.00000000 1.800000e+01
# # 2 T -2.26414390 6.703988e+00
# # 3 PROBT 0.03615222 2.754086e-06
#
# # Output option "stacked"
# res7 <- proc_ttest(cls, var = c("Height", "Weight"),
# options = c(h0 = 65),
# output = "stacked")
#
# # View results
# res7
# # $Statistics
# # VAR STAT VALUES
# # 1 Height N 19.000000
# # 2 Height MEAN 62.336842
# # 3 Height STD 5.127075
# # 4 Height STDERR 1.176232
# # 5 Height MIN 51.300000
# # 6 Height MAX 72.000000
# # 7 Weight N 19.000000
# # 8 Weight MEAN 100.026316
# # 9 Weight STD 22.773933
# # 10 Weight STDERR 5.224699
# # 11 Weight MIN 50.500000
# # 12 Weight MAX 150.000000
# #
# # $ConfLimits
# # VAR STAT VALUES
# # 1 Height MEAN 62.336842
# # 2 Height LCLM 59.865671
# # 3 Height UCLM 64.808013
# # 4 Height STD 5.127075
# # 5 Height LCLMSTD 3.874083
# # 6 Height UCLMSTD 7.582045
# # 7 Weight MEAN 100.026316
# # 8 Weight LCLM 89.049631
# # 9 Weight UCLM 111.003000
# # 10 Weight STD 22.773933
# # 11 Weight LCLMSTD 17.208272
# # 12 Weight UCLMSTD 33.678652
# #
# # $TTests
# # VAR STAT VALUES
# # 1 Height DF 1.800000e+01
# # 2 Height T -2.264144e+00
# # 3 Height PROBT 3.615222e-02
# # 4 Weight DF 1.800000e+01
# # 5 Weight T 6.703988e+00
# # 6 Weight PROBT 2.754086e-06
#
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