print.htest.htestEnvStats.RdPrint objects of class "htest" or "htestEnvStats", respectively,
by simple print methods.
The functions print.htest and print.htestEnvStats are identical, and are a
modification of the R function print.htest.
The function print.htestEnvStats formats and prints the results of performing
a hypothesis test that was performed using one of the functions listed in the help file for
htestEnvStats.object. This method is automatically called by the
EnvStats generic function print when
given an object of class "htestEnvStats".
The function print.htest formats and prints the results of performing
a hypothesis test that was performed using a function that returns an object of
class "htest". Because of the design of R, the EnvStats method
print.htest is NOT automatically called when the user types a command at
the command prompt that returns an object of class "htest".
Instead, the EnvStats print function must be explicitly called to invoke
print.htest. See the EXAMPLES section below.
an object of class "htest" or "htestEnvStats".
See htest.object and htestEnvStats.object for details.
arguments that can be supplied to the format function.
The function print.htest is the "htest" method for the EnvStats generic
function print, and the function print.htestEnvStats is the
"htestEnvStats" method for the EnvStats generic function print.
These functions print null and alternative hypotheses, name of the test, estimated population
parameter(s) involved in the null hypothesis, estimation method (if present),
data name, sample size (if present), number of missing observations removed
prior to performing the test (if present), value of the test statistic,
parameters associated with the null distribution of the test statistic,
p-value associated with the test statistic, and confidence interval for the
population parameter (if present).
Invisibly returns the input x.
Chambers, J. M. and Hastie, T. J. (1992). Statistical Models in S. Wadsworth & Brooks/Cole.
# Create an object of class "htestEnvStats", then print it out.
#--------------------------------------------------------------
htestEnvStats.obj <- chenTTest(EPA.02d.Ex.9.mg.per.L.vec, mu = 30)
mode(htestEnvStats.obj)
#> [1] "list"
#[1] "list"
class(htestEnvStats.obj)
#> [1] "htestEnvStats"
#[1] "htestEnvStats"
names(htestEnvStats.obj)
#> [1] "statistic" "parameters" "p.value" "estimate" "null.value"
#> [6] "alternative" "method" "sample.size" "data.name" "bad.obs"
#> [11] "interval"
# [1] "statistic" "parameters" "p.value" "estimate"
# [5] "null.value" "alternative" "method" "sample.size"
# [9] "data.name" "bad.obs" "interval"
htestEnvStats.obj
#> $statistic
#> t
#> 1.574075
#>
#> $parameters
#> df
#> 59
#>
#> $p.value
#> z t Avg. of z and t
#> 0.05773508 0.06040889 0.05907199
#>
#> $estimate
#> mean sd skew
#> 34.566667 27.330598 2.365778
#>
#> $null.value
#> mean
#> 30
#>
#> $alternative
#> [1] "greater"
#>
#> $method
#> [1] "One-sample t-Test\n Modified for\n Positively-Skewed Distributions\n (Chen, 1995)"
#>
#> $sample.size
#> [1] 60
#>
#> $data.name
#> [1] "EPA.02d.Ex.9.mg.per.L.vec"
#>
#> $bad.obs
#> [1] 0
#>
#> $interval
#> $name
#> [1] "Confidence"
#>
#> $parameter
#> [1] "mean"
#>
#> $limits
#> LCL UCL
#> 29.82 Inf
#>
#> $type
#> [1] "Lower"
#>
#> $method
#> [1] "Based on z"
#>
#> $conf.level
#> [1] 0.95
#>
#> $sample.size
#> [1] 60
#>
#> $dof
#> [1] 59
#>
#> attr(,"class")
#> [1] "intervalEstimate"
#>
#> attr(,"class")
#> [1] "htestEnvStats"
#Results of Hypothesis Test
#--------------------------
#
#Null Hypothesis: mean = 30
#
#Alternative Hypothesis: True mean is greater than 30
#
#Test Name: One-sample t-Test
# Modified for
# Positively-Skewed Distributions
# (Chen, 1995)
#
#Estimated Parameter(s): mean = 34.566667
# sd = 27.330598
# skew = 2.365778
#
#Data: EPA.02d.Ex.9.mg.per.L.vec
#
#Sample Size: 60
#
#Test Statistic: t = 1.574075
#
#Test Statistic Parameter: df = 59
#
#P-values: z = 0.05773508
# t = 0.06040889
# Avg. of z and t = 0.05907199
#
#Confidence Interval for: mean
#
#Confidence Interval Method: Based on z
#
#Confidence Interval Type: Lower
#
#Confidence Level: 95%
#
#Confidence Interval: LCL = 29.82
# UCL = Inf
#============================================================================
# Now create an object of class "htest" and note the difference in how it is
# printed out depending on whether or not you explicitly use the print() command.
#--------------------------------------------------------------------------------
htest.obj <- t.test(EPA.02d.Ex.9.mg.per.L.vec, mu = 30, alternative = "greater")
class(htest.obj)
#> [1] "htest"
#[1] "htest"
names(htest.obj)
#> [1] "statistic" "parameter" "p.value" "conf.int" "estimate"
#> [6] "null.value" "stderr" "alternative" "method" "data.name"
# [1] "statistic" "parameter" "p.value" "conf.int" "estimate"
# [6] "null.value" "stderr" "alternative" "method" "data.name"
htest.obj
#>
#> One Sample t-test
#>
#> data: EPA.02d.Ex.9.mg.per.L.vec
#> t = 1.2943, df = 59, p-value = 0.1003
#> alternative hypothesis: true mean is greater than 30
#> 95 percent confidence interval:
#> 28.67044 Inf
#> sample estimates:
#> mean of x
#> 34.56667
#>
# One Sample t-test
#
#data: EPA.02d.Ex.9.mg.per.L.vec
#t = 1.2943, df = 59, p-value = 0.1003
#alternative hypothesis: true mean is greater than 30
#95 percent confidence interval:
# 28.67044 Inf
#sample estimates:
#mean of x
# 34.56667
print(htest.obj)
#>
#> Results of Hypothesis Test
#> --------------------------
#>
#> Null Hypothesis: mean = 30
#>
#> Alternative Hypothesis: True mean is greater than 30
#>
#> Test Name: One Sample t-test
#>
#> Estimated Parameter(s): mean of x = 34.56667
#>
#> Data: EPA.02d.Ex.9.mg.per.L.vec
#>
#> Test Statistic: t = 1.294273
#>
#> Test Statistic Parameter: df = 59
#>
#> P-value: 0.1003072
#>
#> 95% Confidence Interval: LCL = 28.67044
#> UCL = Inf
#>
#Results of Hypothesis Test
#--------------------------
#
#Null Hypothesis: mean = 30
#
#Alternative Hypothesis: True mean is greater than 30
#
#Test Name: One Sample t-test
#
#Estimated Parameter(s): mean of x = 34.56667
#
#Data: EPA.02d.Ex.9.mg.per.L.vec
#
#Test Statistic: t = 1.294273
#
#Test Statistic Parameter: df = 59
#
#P-value: 0.1003072
#
#95% Confidence Interval: LCL = 28.67044
# UCL = Inf
#==========
# Clean up
#---------
rm(htestEnvStats.obj, htest.obj)