# R/mean_comparison_HSD.R In violinplotter: Plotting and Comparing Means with Violin Plots

#### Defines functions mean_comparison_HSD

```# Tukey's mean comparison and grouping using fixed effect linear modeling with optional plotting of grouping letters
#
# @usage mean_comparison_HSD(formula, data=NULL, explanatory_variable_name, alpha=0.05,
#                    LOG=FALSE, BASE=10, PLOT=FALSE)
#
# @param formula R's compact symbolic form to represent linear models with fixed additive and interaction effects (See ?formula for more information) [mandatory]
# @param data data.frame containing the response and explanatory variables which forms the formula above [default=NULL]
# @param explanatory_variable_name string referring to the variable name of the explanatory variable whose class means will be compared [mandatory]
# @param alpha numeric significance level for Tukey's mean comparison [default=0.05]
# @param LOG logical referring to whether to transform the explanatory variable into the logarithm scale [default=FALSE]
# @param BASE numeric referring to the logarithm base to transform the explanatory variable with [default=1]
# @param PLOT logical referring to whether plot the mean comparison grouping letters into an existing plot [default=FALSE]
#
# @return Tukey's honest significant difference grouping table with response variable categorical means, grouping, level names and corresponding numeric counterparts
# @return Appends honest significant difference grouping letters into an existing plot
#
# @examples
# x1 = rep(rep(rep(c(1:5), each=5), times=5), times=5)
# x2 = rep(rep(letters[6:10], each=5*5), times=5)
# x3 = rep(letters[11:15], each=5*5*5)
# y = rep(1:5, each=5*5*5) + rnorm(rep(1:5, each=5), length(x1))
# data = data.frame(x1, x2, x3, y)
# formula = y ~ x1 + x2 + x3 + (x2:x3)
# DF = parse_formula(formula=formula, data=data)
# plot_violin_1x(dat=DF, response_variable_name="y", explanatory_variable_name="x3")
# HSD = mean_comparison_HSD(formula, data=data, explanatory_variable_name="x3", PLOT=TRUE)
#
#' @importFrom stats aov anova sd
#' @importFrom graphics text
#
mean_comparison_HSD = function(formula, data=NULL, explanatory_variable_name, alpha=0.05, LOG=FALSE, BASE=10, PLOT=FALSE) {
### parse the formula and generate the dataframe with explicit interaction terms if expressed in the formula
df = parse_formula(formula=formula, data=data, IMPUTE=FALSE, IMPUTE_METHOD=mean)
response_var = df[,1]; response_var_name = colnames(df)[1]
# explanatory_var = df[,2:ncol(df)]; explanatory_var_name = colnames(df)[2:ncol(df)]
### linear modelling
mod = aov(formula, data=df)
anova_table = as.data.frame(anova(mod))
if (anova_table\$Pr[rownames(anova_table) == explanatory_variable_name] < alpha){
message(paste0(explanatory_variable_name, " has a significant effect on the response variable!"))
} else {
message(paste0(explanatory_variable_name, " has a no significant effect on the response variable!"))
}
### computate the means per explanatory variable level
means = eval(parse(text=paste0("aggregate(",  response_var_name, "~ `", explanatory_variable_name, "`, data=df, FUN=mean)")))
colnames(means) = c("LEVELS", "MEANS")
means = means[order(means\$MEANS, decreasing=TRUE), ]
### compute the HSD pairwise comparison
hsd = eval(parse(text=paste0("as.data.frame(TukeyHSD(mod, conf.level=", 1.00-alpha, ")\$`", explanatory_variable_name, "`)")))
### add "LEVEL_" string to allow for explanatory variable that are originally numeric to be easily set as list names
factor_labels = matrix(paste0("LEVEL_", unlist(strsplit(rownames(hsd), "-"))), ncol=2, byrow=TRUE)
hsd\$factor1 = factor_labels[,1]
hsd\$factor2 = factor_labels[,2]
factors_all = paste0("LEVEL_", as.character(means\$LEVELS))
### initialize the list of HSD grouping of each response variable level
GROUPING_LIST = eval(parse(text=paste0("list('LEVEL_", paste(as.character(means\$LEVELS), collapse="'=c(), 'LEVEL_"), "'=c())")))
### generate the vector of letters and numbers for grouping
letters_vector = c(letters, LETTERS, 1:(nrow(hsd)^2))
### iterate across response variable level
letter_counter = 1
for (f in factors_all){
# f = factors_all[1]
### subset the current factor level
subhsd = hsd[(hsd\$factor1==f) | (hsd\$factor2==f), ]
### identify the factor levels that are not significantly from the current factor level: f
nonsigfactors = unique(c(subhsd\$factor1[subhsd\$p > 0.05], subhsd\$factor2[subhsd\$p > 0.05]))
nonsigfactors = nonsigfactors[!(nonsigfactors %in% f)]
### define the current letter grouping
new_letter_bool = 0 ### for testing if we need a new letter
### iterate across non-significantly different factor levels to the current factor
for (g in nonsigfactors){
# g = nonsigfactors[1]
f_letters = eval(parse(text=paste0("GROUPING_LIST\$`", f, "`"))) ### currect factor grouping
g_letters = eval(parse(text=paste0("GROUPING_LIST\$`", g, "`"))) ### grouping of the non-siginificantly different factor level
### if we have all significantly different means at the start
if (is.na(g)){
eval(parse(text=paste0("GROUPING_LIST\$`", f, "` = c(", "GROUPING_LIST\$`", g, "`, '", letter_add, "')")))
new_letter_bool = new_letter_bool + 1
} else if ( !((sum(f_letters %in% g_letters)>0) | (sum(g_letters %in% f_letters)>0)) | is.null(f_letters) ) {
### test if the current factor level is the same as the non-siginificantly different factor level or if we are at the start
eval(parse(text=paste0("GROUPING_LIST\$`", g, "` = c(", "GROUPING_LIST\$`", g, "`, '", letter_add, "')")))
new_letter_bool = new_letter_bool + 1
}
}
### add the current letter grouping
if ((new_letter_bool>0) | (length(nonsigfactors)==0)){
eval(parse(text=paste0("GROUPING_LIST\$`", f, "` = c(", "GROUPING_LIST\$`", f, "`, '", letter_add, "')")))
letter_counter = letter_counter + 1
}
}
### prepare the grouping list
GROUPING_LIST = as.matrix(lapply(GROUPING_LIST, FUN=paste, collapse=""))
GROUPING_LIST = data.frame(LEVELS=gsub("LEVEL_", "", as.character(rownames(GROUPING_LIST))), GROUPING=as.character(GROUPING_LIST[,1]))
### prepare the explanatory variable names and corresponding numbers
x_levels = eval(parse(text=paste0("levels(as.factor(df\$`", explanatory_variable_name, "`))")))
x_numbers = tryCatch(as.numeric(gsub("_", "-", as.character(x_levels))),
warning=function(e){as.numeric(as.factor(x_levels))})
if (LOG==TRUE){
### transform the level names into the corresponding level names we used previously (x_levels and x_numbers) because we will be merging dataframes below
if(sum(is.na(suppressWarnings(log(x_numbers, base=BASE)))) == 0){
x_numbers = log(x_numbers, base=BASE)
} else {
x_numbers = log(x_numbers + (abs(min(x_numbers)) + 1), base=BASE)
}
}
X_LEVELS_AND_NUMBERS = data.frame(LEVELS=x_levels, NUMBERS=x_numbers)
### merge and append the grouping letters together with the means
MERGE_GROUPING_DF = merge(merge(GROUPING_LIST, X_LEVELS_AND_NUMBERS, by="LEVELS"), means, by="LEVELS")
if(PLOT){
text(x=MERGE_GROUPING_DF\$NUMBERS, y=max(response_var)+sd(response_var), lab=as.character(MERGE_GROUPING_DF\$GROUPING))
}
return(MERGE_GROUPING_DF)
}
```

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violinplotter documentation built on March 25, 2020, 5:10 p.m.