R devel - Oct 2018 - Feature request: Have t.test return (group-wise) SD and N

Dear readers,

Lm returns all information necessary to reconstruct summary statistics by group.
However, t.test only returns the group means, and not the group SDs, or even the
group Ns. These cannot be reconstructed from the test statistic and df, because
the df are already pooled, except under a very strict assumption of equality of
groups and variances.

I need these summary statistics for a package that performs Bayesian inference
for frequentist analyses, through normal approximation of the posterior. To make
the package as user friendly as possible, I would like it to have S3 methods for
commonly used frequentist analyses in R, such as: lm and t.test.

As per the R-project feature request guidelines, I would like to gauge how
people feel about adding functionality to t.test, so that it will return either
the model data, as lm() does, or full summary statistics (per group)? I can mask
the t.test function with an enhanced version, but I feel like there is value in
making this functionality available to all.

Thank you sincerely for your input. Below is a reproducible example,
illustrating the problem.

Best,
Caspar

d <- structure(list(Petal.Length = c(1.4, 1.4, 1.3, 1.5, 1.4, 1.7,
                                     1.4, 1.5, 1.4, 1.5, 1.5, 1.6, 1.4, 1.1,
1.2, 1.5, 1.3, 1.4, 1.7,
                                     1.5, 1.7, 1.5, 1, 1.7, 1.9, 1.6, 1.6, 1.5,
1.4, 1.6, 1.6, 1.5,
                                     1.5, 1.4, 1.5, 1.2, 1.3, 1.4, 1.3, 1.5,
1.3, 1.3, 1.3, 1.6, 1.9,
                                     1.4, 1.6, 1.4, 1.5, 1.4, 4.7, 4.5, 4.9, 4,
4.6, 4.5, 4.7, 3.3,
                                     4.6, 3.9, 3.5, 4.2, 4, 4.7, 3.6, 4.4, 4.5,
4.1, 4.5, 3.9, 4.8,
                                     4, 4.9, 4.7, 4.3, 4.4, 4.8, 5, 4.5, 3.5,
3.8, 3.7, 3.9, 5.1,
                                     4.5, 4.5, 4.7, 4.4, 4.1, 4, 4.4, 4.6, 4,
3.3, 4.2, 4.2, 4.2,
                                     4.3, 3, 4.1),
                    Species = structure(rep(c(1L, 2L), each = 50), .Label =
c("setosa", "versicolor"), class = "factor")),
                    row.names = c(NA, 100L), class = "data.frame")
# lm model
m_lm <- lm(Petal.Length ~ Species, d)
# Extract group means:
aggregate(m_lm$model$Petal.Length, list(m_lm$model$Species), mean)
# Extract group SDs:
aggregate(m_lm$model$Petal.Length, list(m_lm$model$Species), sd)
# Extract group Ns:
table(m_lm$model$Species)

# t.test model
m_t <- t.test(d$Petal.Length[1:50], d$Petal.Length[51:100], var.equal = TRUE)
# Extract group means:
m_t$estimate
# Extract group SDs:
# Not available
# Extract group Ns:
# Not available

Dr. Caspar J. van Lissa
Assistant professor of developmental data science
Utrecht University, dept. Methodology & Statistics
Sjoerd Groenmangebouw C1.01, 3584CH Utrecht, the Netherlands. Secretariat: +31
30 253 4438


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