Calculate two sample Student’s t-test with summary statistics

These are simple scripts to facilitate the computation of the two independent
samples Student’s t test when only the summary statistics of the data are
available, but not the raw data. To compute the t statistic, you will need
the following:

Group means
Group standard deviations
Group sizes

Instructions

R

Installation

The function is housed in an R package. To use the function, first you must
install the package. To do so, execute the following in your R console:

if (!require(devtools)) install.packages("devtools")
devtools::install_github("SpectrumHealthResearch/t-test/R")

Example

# Load the package into your session
library(stt)
# Using chickwts data from datasets
summary_chicks <- tapply(
  chickwts$weight,
  chickwts$feed,
  function(x) c(mean = mean(x), sd = sd(x), n = length(x))
)
# We now have a named list of vectors containing summary stats
summary_chicks
# Execute the t test using summary stats
with(summary_chicks,
     summary_t_test(
       linseed["mean"],
       soybean["mean"],
       linseed["sd"],
       soybean["sd"],
       linseed["n"],
       soybean["n"],
       alternative = "less",
       method = "sat"
       )
     )
##     Two Sample t-test (satterthwaite)
##
## data:  summary statistics
## t = -1.3246, df = 23.63, p-value = 0.09899
## alternative hypothesis: true difference in means is less than 0
## 95 percent confidence interval:
##     -Inf 8.09536
## sample estimates:
## difference in means
##           -27.67857
# Compare with stats::t.test using raw data
t.test(
  weight ~ feed,
  chickwts,
  feed %in% c("linseed", "soybean"),
  alternative = "less"
)
##     Welch Two Sample t-test
##
## data:  weight by feed
## t = -1.3246, df = 23.63, p-value = 0.09899
## alternative hypothesis: true difference in means is less than 0
## 95 percent confidence interval:
##     -Inf 8.09536
## sample estimates:
## mean in group linseed mean in group soybean
##              218.7500              246.4286

SAS

There are many ways to make use of this program. One suggested route:

Clone this repository or download the SAS code in the SAS folder.
Run the macro definition in the source editor or using %include.
Run the macro call.