interaction_twofacaov | R Documentation |
The function performs a pairwise two-way ANOVA of interaction effects of Factor2 control to Factor2 treatment for every Factor1. The p-values are adjusted using Benjamini-Hochberg correction procedure. #' Caution: In the actual version it is necessary to provide a Label column(grouping variable 1 and 2 separated by label delimiter (label_delim)) in addition to grouping variable 1 and 2 necessary.
interaction_twofacaov(
root_norm,
col_grouping1 = "Factor1",
col_grouping2 = "Factor2",
col_value = "LengthMM",
control = "20",
label_delim = ";",
draw_out = F,
file_base = "2fac_ANOVA_BH_corrected",
axis_label_size = 0.7,
p_value_size = 0.8
)
root_norm |
data.frame; normalized Rootdetection data set |
col_grouping1 |
string; column name of the first grouping variable (Factor1) |
col_grouping2 |
string; column name of the second grouping variable (Factor2) |
col_value |
string; name of the column containing values (dependent variable) (LengthMM) |
control |
string; name of the grouping variable 2 (Factor2) control condition |
label_delim |
character; defines how Factor1 and Factor2 are separated in Label |
draw_out |
logical; If TRUE Matrix containing p-values is plotted in pdf file |
file_base |
string; file name of the pdf output (is needed if draw_out = T) |
axis_label_size |
numeric; font size of axis labels |
p_value_size |
numeric; font size of the p-values printed in pdf file |
list; matrices containing p-values for pairwise two way ANOVA (treatment effect)
# get data.frame containing p-values for pairwise two-way ANOVA of interaction effects
root_norm <- norm_10mm_standard(root_output)
interaction_twofacaov(root_norm, control = "20", label_delim = ";", draw_out = FALSE)
# get data.frame and plot as pdf output
root_norm <- norm_10mm_standard(root_output)
interaction_twofacaov(root_norm,
control = "20", label_delim = ";",
draw_out = TRUE, file_base = "2fac_ANOVA_BH_corrected"
)
# function creates a pdf file 2fac_ANOVA_BH_corrected_28.pdf
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