Package 'prmisc'

Description

Printing a specified number of decimals and ignore leading zeros

Usage

decimals_only(x, decimals = 2, decimals1 = FALSE)

Arguments

Value

Character vector of length length(x). The number(s) in the required format.

Author(s)

Martin Papenberg [email protected]

Examples

decimals_only(c(0.23456, 0.873, 0.3456), decimals = 3)

Description

Force printing a specified number of decimals for a number

Usage

force_decimals(x, decimals = 2, round_zero = TRUE)

Arguments

Details

By default, force_decimals() will round numbers that are small enough down to zero, e.g., 0.0004 will be rounded to 0.00. If round_zero = FALSE, force_decimals(0.0004) will return "< 0.01" instead. See examples.

Value

Character vector of length length(x). The number(s) in the required format.

Author(s)

Martin Papenberg [email protected]

Examples

force_decimals(c(1.23456, 0.873, 2.3456))
force_decimals(c(1.23456, 0.873, 2.3456), 3)

force_decimals(c(0.004, 0.001, 0.0005, 0.02))
force_decimals(c(0.004, 0.001, 0.0005, 0.02), round_zero = FALSE)
force_decimals(c(0.004, 0.001, 0.0005, 0.02), 3, round_zero = FALSE)

Description

Print a number having a specified number of digits or as integer

Usage

force_or_cut(x, decimals = 2)

Arguments

Details

If x integer, only the integer is printed, if x is a decimal number, the decimals are printed

Value

Character vector of length length(x). The number(s) in the required format.

Author(s)

Martin Papenberg [email protected]

Examples

force_or_cut(c(1:3, 1.23456, 0.873, 2.3456))

Description

Format a p-value according to APA standards

Usage

format_p(pvalues, decimals = 3, numbers_only = FALSE, latex = TRUE)

Arguments

Value

Character vector of length length(pvalues). A string representation of the p value(s) to be used in Rmarkdown documents.

Examples

# Format a p-value, default is 3 decimals
format_p(0.03123)
format_p(0.000001231)
format_p(0.000001231, decimals = 2)
format_p(0.3123, decimals = 2)
format_p(0.3123, latex = FALSE)
# Format several p-values with one function call
format_p(c(0.3123, 0.001, 0.00001, 0.19))
format_p(c(.999, .9999, 1))
format_p(c(0.3123, 0.001, 0.00001, 0.19, .99999), numbers_only = TRUE)

Description

Print the results of an afex ANOVA

Usage

print_anova(afex_object, italic_eta = TRUE, decimals = 2, decimals_p = 3)

Arguments

Details

To use this function, you have to install the package afex to compute an ANOVA object, see aov_car. Pass this object as the first argument.

According to APA style, the Greek eta symbol - indicating the effect size in the ANOVA - should be printed in non-italic font. However, the standard \eta symbol is written in italic. To print a non-italic eta, use the argument italic_eta = FALSE. However, this option requires that you load the Latex package upgreek in the YAML header of your R markdown document. To this end, use the following option in your YAML header:

header-includes:
  -\usepackage{upgreek}

This option only works for Latex/PDF output.

Value

A list whose elements are strings describing the effects of the ANOVA (main effects and interactions); to be included in an R markdown document.

Author(s)

Martin Papenberg [email protected]

References

Singmann, H., Bolker, B., Westfall, J., & Aust, F. (2019). afex: Analysis of Factorial Experiments. https://CRAN.R-project.org/package=afex

Examples

library("afex")
# see ?aov_ez
data(md_12.1)
aov_results <- aov_ez("id", "rt", md_12.1, within = c("angle", "noise"))
print_anova(aov_results)

# Print nonitalic eta, which is required according to APA guidelines
print_anova(aov_results, italic_eta = FALSE)

# Example using other (or no) effect size index
pes <- aov_ez("id", "rt", md_12.1, within = c("angle", "noise"),
              anova_table = list(es = "pes"))
print_anova(pes)
print_anova(pes, italic_eta = FALSE)
noes <- aov_ez("id", "rt", md_12.1, within = c("angle", "noise"),
               anova_table = list(es = "none"))
print_anova(noes)

## Access individual elements of the ANOVA print list:
aovpr <- print_anova(aov_results, italic_eta = FALSE)
# By index: 
aovpr[[1]]
# By name (main effect): 
aovpr$angle
aovpr[["angle"]]
# By name (interaction effect, here, the $-notation does not work 
# due to non-standard `:` in name): 
aovpr[["angle:noise"]]

Description

Print the results of a chi-square test

Usage

print_chi2(
  x,
  es = TRUE,
  correct = FALSE,
  decimals = 2,
  decimals_p = 3,
  italic_greek = TRUE
)

Arguments

Details

The argument es only has an effect if x is passed as a 2x2 contingency table. In this case, the phi coefficient is computed as a measure of effect size (see Cohen, 1988, page 223).

According to APA style, the Greek chi symbol (and the phi coefficient) should be printed in non-italic font. However, the standard symbols \chi and \phi are written in italic. To print non-italic symbols, use the argument italic_greek = FALSE. However, this option requires that you load the Latex package upgreek in the YAML header of your R markdown document. To this end, use the following option in your YAML header:

header-includes:
  -\usepackage{upgreek}

This option only works for Latex/PDF output.

Value

A string describing the results of the chi-square test to be printed in Rmarkdown documents.

Author(s)

Martin Papenberg [email protected]

References

Cohen, J. (1988). Statistical power analysis for the behavioral sciences (2nd ed.). Hillsale, NJ: Lawrence Erlbaum.

Examples

# Pass a matrix
x <- matrix(c(12, 5, 7, 7), ncol = 2)
print_chi2(x) # does not use continuity correction by default
print_chi2(x, correct = TRUE) # uses continuity correction

# Pass a table
tab <- table(rbinom(150, 1, 0.5), rbinom(150, 1, 0.1))
print_chi2(tab, correct = FALSE)

# Pass a chi-squared test object
print_chi2(chisq.test(tab, correct = FALSE))

# Use non italic chi symbol
print_chi2(tab, italic_greek = FALSE)

Description

Printing the results of a significance test for a correlation coefficient

Usage

print_cortest(cor_object, decimals = 2, decimals_p = 3)

Arguments

Value

A string describing the significance test; to be included in an R markdown document.

Author(s)

Martin Papenberg [email protected]

Examples

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)
cor_results <- cor.test(x, y)
print_cortest(cor_results)

Description

Print mean and standard deviation

Usage

print_mean_sd(
  x,
  decimals_M = 2,
  decimals_SD = 2,
  parentheses = TRUE,
  short = FALSE,
  na.rm = FALSE
)

Arguments

Details

The following formatting options are available:

• When parentheses is TRUE: $(M = XX, SD = XX)$

• When parentheses is FALSE: $M = XX, SD = XX$

• When short is TRUE: $XX (XX)$

Value

A string with information on mean and standard deviation in x.

Author(s)

Juliane Nagel [email protected]

Examples

print_mean_sd(rnorm(100, 0, 1))

print_mean_sd(1:20, decimals_M = 0, decimals_SD = 3)

print_mean_sd(c(2, 10, 12.5, 3), parentheses = FALSE)

Description

Print the results of a t-test

Usage

print_ttest(
  t_object,
  d_object = NULL,
  decimals = 2,
  decimals_p = 3,
  confidence = FALSE
)

Arguments

Details

To use this function, you need to install the R package effectsize to compute Cohen's d; pass this object as the second argument.

Value

A string describing the t-test; to be included in an R markdown document.

Author(s)

Martin Papenberg [email protected]

References

Cohen, J. (1988). Statistical power analysis for the behavioral sciences (2nd ed.). Hillsale, NJ: Lawrence Erlbaum.

Examples

ttest <- t.test(1:10, y = c(7:20), var.equal = TRUE)
library("effectsize") # for Cohen's d
cohend <- cohens_d(1:10, c(7:20))
print_ttest(ttest, cohend) # include this call in Rmd inline code

# An example for paired data:
data(sleep) # ?sleep
tt <- t.test(sleep$extra[sleep$group == 1], 
             sleep$extra[sleep$group == 2], paired = TRUE)
cd <- cohens_d(sleep$extra[sleep$group == 1], 
               sleep$extra[sleep$group == 2], paired = TRUE)
print_ttest(tt, cd)
# Print the confidence interval
print_ttest(tt, cd, confidence = TRUE)
# The information about the CI is taken from the effectsize object:
cd <- cohens_d(sleep$extra[sleep$group == 1], 
               sleep$extra[sleep$group == 2], paired = TRUE, ci = .8)
print_ttest(tt, cd, confidence = TRUE)
# effect size object can be left out:
print_ttest(tt)

Description

Print the results of a Wilcoxon rank sum test (Mann-Whitney-U test)

Usage

print_wilcoxon_rs(
  wc_object,
  decimals_p = 3,
  consistent = NULL,
  group1 = NULL,
  group2 = NULL,
  groupvar = NULL,
  effsize = NULL,
  neg = FALSE,
  N = NULL,
  decimals_eff = NULL
)

Arguments

Details

In order to calculate a Wilcoxon rank sum test, the argument paired in wilcox.test needs to be FALSE. Otherwise, a Wilcoxon signed rank test will be computed instead and the statistics printed by print_wilcoxon_rs will be misleading.

Note that the test statistic W calculated in wilcox.test that is printed as test statistic U in print_wilcoxon_rs will correspond to the U of the first of the two groups compared in wilcox.test. In the default method of wilcox.test, this is the vector assigned to x. In the formula method, this is the first group as identified by the grouping variable. Some software, like SPSS, consistently reports the smaller or larger U. If you wish to mimic this, you can specify the desired behaviour by providing the consistent argument. Setting consistent to 'min' will print the smaller of the two U, setting it to 'max' will print the larger U. In order to do so, you need to provide the n for both groups.

You can either do that by passing the data of both groups to the arguments group1 and group2, respectively. From those, print_wilcoxon_rs will calculate the group sizes. Any vector with the length of the corresponding group size will produce the desired result. This is the recommended option if your wc_object has been created using the default version of wilcox.test, i.e. if groups were passed as x and y. The order in which you pass group1 and group2 to print_wilcoxon_rs does not have to correspond to the order in which x and y were passed to wilcox.test.

Alternatively, you can pass a grouping variable to the argument groupvar. From this, print_wilcoxon_rs will calculate group sizes. This is the recommended option if your wc_object has been created using the formula version of wilcox.test, i.e. if a grouping variable was passed after the ~. To prevent mistakes, you can either use group1 and group2 or groupvar.

By default, when consistent is not provided, print_wilcoxon_rs will print U using W as taken from wilcox.test.

There are three options available for calculating an effect size via the argument effsize:

• the point biserial correlation r (effsize = 'r'), which is calculated by dividing Z-score by the square root of N. According to Cohen (1988) a small, medium and large effect correspond to r = .1, .3 and .5, respectively. Currently, print_wilcoxon_rs infers the Z-score from the p-value.

• r squared (identical to eta squared; effsize = 'rsqu'), which is the ratio of variability associated with an effect compared to the ratio of overall variance

• d (effsize = 'd'), which is calculated from r as follows:

2*r/(sqrt(1-r^2))

According to Cohen (1988), a small, medium and large effect correspond to r = .2, .5 and .8, respectively.

Information on the direction of the effect (as indicated in the sign of the Z-score) or in which order the groups have been compared is not included in the output of wilcox.test. Hence, only absolute effect sizes can be calculated. It is advised to report absolute effect sizes unless there is a meaningful for the two groups tested (e.g. Fritz et al, 2012). In some cases, it might be desired to specify the direction of an effect by including the sign of the effect. To that end, it is possible to print the negative effect size with neg = TRUE, but caution is advised: You should always check if the sign of the effect size you report is the correct one, especially in the case of one-sided testing.

Value

A string describing the results of the Wilcoxon test; to be included in an R markdown document.

Author(s)

Juliane Nagel [email protected]

References

Cohen, J. (1988). Statistical power analysis for the behavioral sciences (2nd ed.). Hillsdale, NJ: Erlbaum.

Fritz, C. O., Morris, P. E., & Richler, J. J. (2012). Effect size estimates: Current use, calculations, and interpretation. Journal of Experimental Psychology: General, 141(1), 2-18. http://dx.doi.org/10.1037/a0024338

Examples

data(iris)
dat <- subset(iris, Species %in% c("setosa", "versicolor"))
wc_iris <- wilcox.test(dat$Sepal.Length ~ dat$Species, correct = FALSE)
# include this call in Rmd inline code
print_wilcoxon_rs(wc_iris)

wc_iris2 <- wilcox.test(dat$Sepal.Width ~ dat$Species, correct = FALSE)
                        
print_wilcoxon_rs(wc_iris2, consistent = "min", 
                  group1 = dat$Sepal.Width[dat$Species == "setosa"], 
                  group2 = dat$Sepal.Width[dat$Species == "versicolor"])

print_wilcoxon_rs(wc_iris2, consistent = "max", 
                  groupvar = dat$Species)

print_wilcoxon_rs(wc_iris2, consistent = "max", 
                  groupvar = dat$Species, effsize = "rsqu")