st: st:LaTeX code for statistical reference

In SachaEpskamp/staTeX: Print statistical results in Sweave

Description

This function attempts to extract information on statistic, degrees of freedom and p-value from a given object, and returns a LaTeX code.

This function attemps to extract information needed to make a LaTeX code of the statistic in the following format (for an F value):

(F(df1,df2)=..., p=...)

Some arguments can be passed to staTeX to modify the way the statistic is printed. For example, 'digits' an be used to change the amount of digits printed and 'sig' can be used to change the way to show only significance cutoff scores. For example, with p=0.02 this would return: (F(df1,df2)=..., p<0.05)

This is aimed to facilitate the use of Sweave especially for users new to R. This is currently in development and is so far only a few commonly used statistics are implemented, but this should change in future versions.

If you have any ideas on other ways to facilitate using Sweave (except of course existing methods such as xtable) please contact me. Also, if you know of an object that is not implemented please contact me as well.

Usage

  st(x, ...)

  ## S3 method for class 'htest'
 st(x, ...)

  ## S3 method for class 'aov'
 st(x, ...)

  ## S3 method for class 'lm'
 st(x, ...)

  ## S3 method for class 'anova'
 st(x, ...)

  ## S3 method for class 'Anova.mlm'
 st(x, ...)

  ## Default S3 method:
 st(x, ...)

Arguments

x	An input object. Currently supported are a numerical vector and objects of classes "htest" (depending on statistic), "lm", "aov" and "anova".
...	Arguments sent to staTeX

Value

A strings containing LaTeX code, or a named vector of strings containing LaTeX code

Author(s)

Sacha Epskamp ([e-mail])

See Also

staTeX stNP

Examples

### Using examples from documentation of the used functions
# t-test:
require(graphics)
tTest <- t.test(1:10,y=c(7:20))

st(tTest)

# Significance:
st(tTest,sig=TRUE)

tTest <- t.test(1:10,y=1:10)
st(tTest,sig=TRUE)

# Correlation test:
## Hollander & Wolfe (1973), p. 187f.
## Assessment of tuna quality.  We compare the Hunter L measure of
##  lightness to the averages of consumer panel scores (recoded as
##  integer values from 1 to 6 and averaged over 80 such values) in
##  9 lots of canned tuna.

x <- c(44.4, 45.9, 41.9, 53.3, 44.7, 44.1, 50.7, 45.2, 60.1)
y <- c( 2.6,  3.1,  2.5,  5.0,  3.6,  4.0,  5.2,  2.8,  3.8)

##  The alternative hypothesis of interest is that the
##  Hunter L value is positively associated with the panel score.
corTest <- cor.test(x, y, method = "kendall", alternative = "greater")

st(corTest)

# Chi-square test:
M <- as.table(rbind(c(762, 327, 468), c(484,239,477)))
dimnames(M) <- list(gender=c("M","F"),
                    party=c("Democrat","Independent", "Republican"))
chisqTest <- chisq.test(M)

st(chisqTest)

# Linear model:
## Annette Dobson (1990) "An Introduction to Generalized Linear Models".
## Page 9: Plant Weight Data.
ctl <- c(4.17,5.58,5.18,6.11,4.50,4.61,5.17,4.53,5.33,5.14)
trt <- c(4.81,4.17,4.41,3.59,5.87,3.83,6.03,4.89,4.32,4.69)
group <- gl(2,10,20, labels=c("Ctl","Trt"))
weight <- c(ctl, trt)
lm.D9 <- lm(weight ~ group)
lm.D90 <- lm(weight ~ group - 1) # omitting intercept

st(lm.D9)
st(lm.D90)

## Not run: 
# ANOVA:
## From Venables and Ripley (2002) p.165.
utils::data(npk, package="MASS")

## Set orthogonal contrasts.
op <- options(contrasts=c("contr.helmert", "contr.poly"))
npk.aov <- aov(yield ~ block + N*P*K, npk)

st(npk.aov)

## End(Not run)

SachaEpskamp/staTeX documentation built on May 9, 2019, 12:10 p.m.