T-test with Improved Layout

Performs a one- or two-sample t-test using data. In the two-sample case, the user can specify whether or not observations are matched, and whether or not equal variances should be presumed.

Usage

ttest(
  var1,
  var2 = NA,
  by = NA,
  geom = FALSE,
  null.hypoth = 0,
  alternative = "two.sided",
  var.eq = FALSE,
  conf.level = 0.95,
  matched = FALSE,
  more.digits = 0
)

Arguments

var1

a (non-empty) numeric vector of data values.

var2

an optional (non-empty) numeric vector of data.

by

a variable of equal length to that of var1 with two outcomes. This will be used to define strata for a t-test on var1.

geom

a logical indicating whether the geometric mean should be calculated and displayed.

null.hypoth

a number specifying the null hypothesis for the mean (or difference in means if performing a two-sample test). Defaults to zero.

alternative

a string: one of "less", "two.sided", or "greater" specifying the form of the test. Defaults to a two-sided test.

var.eq

a logical value, either TRUE or FALSE (default), specifying whether or not equal variances should be presumed in a two-sample t-test. Also controls robust standard errors.

conf.level

confidence level of the test. Defaults to 0.95.

matched

a logical value, either TRUE or FALSE, indicating whether or not the variables of a two-sample t-test are matched. Variables must be of equal length.

more.digits

a numeric value specifying whether or not to display more or fewer digits in the output. Non-integers are automatically rounded down.

Value

a list of class ttest. The print method lays out the information in an easy-to-read format.

tab

A formatted table of descriptive and inferential statistics (total number of observations, number of missing observations, mean, standard error of the mean estimate, standard deviation), along with a confidence interval for the mean.

df

Degrees of freedom for the t-test.

p

P-value for the t-test.

tstat

Test statistic for the t-test.

var1

The user-supplied first data vector.

var2

The user-supplied second data vector.

by

The user-supplied stratification variable.

par

A vector of information about the type of test (null hypothesis, alternative hypothesis, etc.)

geo

A formatted table of descriptive and inferential statistics for the geometric mean.

call

The call made to the ttest function.

Details

Missing values must be given by NA to be recognized as missing values.

See also

t.test

Examples

# Read in data set
data(psa)
attach(psa)
#> The following objects are masked from psa (pos = 3):
#> 
#>     age, bss, grade, inrem, nadirpsa, obstime, pretxpsa, ps, ptid

# Perform t-test
ttest(pretxpsa, null.hypoth = 100, alternative = "greater", more.digits = 1)
#> 
#> Call:
#> ttest(var1 = pretxpsa, null.hypoth = 100, alternative = "greater", 
#>     more.digits = 1)
#> 
#> One-sample t-test :
#>  
#> Summary:
#>  Variable Obs Missing  Mean Std. Err. Std. Dev.        95% CI
#>  pretxpsa  50       7 670.8     196.4      1288 [274.5, 1067]
#> 
#>  Ho:  mean <= 100 ; 
#>  Ha:  mean > 100 
#>  t = 2.9066 , df = 42 
#>  Pr(T > t) =  0.002905293 

# Define new binary variable as indicator
# of whether or not bss was worst possible
bssworst <- bss
bssworst[bss == 1] <- 0
bssworst[bss == 2] <- 0
bssworst[bss == 3] <- 1

# Perform t-test allowing for unequal
# variances between strata -#
ttest(pretxpsa, by = bssworst)
#> 
#> Call:
#> ttest(var1 = pretxpsa, by = bssworst)
#> 
#> Two-sample t-test allowing for unequal variances :
#>  
#> Summary:
#>           Group Obs Missing Mean Std. Err. Std. Dev.          95% CI
#>    bssworst = 0  18       2  119      36.2       145     [41.5, 196]
#>    bssworst = 1  30       4 1016     307.2      1566   [383.5, 1649]
#>      Difference  48       6 -897     309.3      <NA> [-1533.4, -261]
#> 
#>  Ho: difference in  means = 0 ; 
#>  Ha: difference in  means != 0 
#>  t = -2.901 , df = 25.7 
#>  Pr(|T| > t) =  0.00752391 

# Perform matched t-test
ttest(pretxpsa, nadirpsa, matched = TRUE, conf.level = 99/100, more.digits = 1)
#> 
#> Call:
#> ttest(var1 = pretxpsa, var2 = nadirpsa, conf.level = 99/100, 
#>     matched = TRUE, more.digits = 1)
#> 
#> Two-sample (matched) t-test  :
#>  
#> Summary:
#>         Group Obs Missing   Mean Std. Err. Std. Dev.             99% CI
#>      pretxpsa  50       7 670.75    196.36   1287.64 [140.951, 1200.55]
#>      nadirpsa  50       0  16.36      5.55     39.25     [1.486, 31.23]
#>    Difference  50       7 655.88    193.57      1254 [133.622, 1178.14]
#> 
#>  Ho: difference in  means = 0 ; 
#>  Ha: difference in  means != 0 
#>  t = 3.3884 , df = 42 
#>  Pr(|T| > t) =  0.00153846