LorMe (version 1.1.0)

auto_signif_test: Automatic significance test

Description

Automatically conduct significance testing

Usage

auto_signif_test(
  data,
  treatment_col,
  value_col,
  paired,
  subject_col,
  prior = FALSE,
  comparison_method = NULL,
  equally_rep = TRUE,
  output = "console",
  output_dir = "./",
  filename = "auto_signif_test",
  report = TRUE
)

Value

auto_signif_test returns results of significant test and print report in console or file. See details in example.

See results return in t_test_report, wilcox_test_report, anova_report, kruskal_report.

Arguments

data

Data frame containing the treatment, value and other information.

treatment_col

Numeric indicating where treatment locates (column number) in data.

value_col

Numeric indicating where treatment value (column number) in data.

paired

Logical indicating whether you want a paired t-test.

subject_col

Only meaningful when Pair is ture. Numeric indicating where subject of treatment (column number) in data.

prior

logical. Whether conducted prior comparisons.

comparison_method

Character string. Only use for more than 2 treatment. Default would automatically choose method. Method of multiple comparison,must be one of "SNK", "Tukey", "bonferroni","LSD" or "Scheffe".

equally_rep

Logical indicating Whether all treatments have same number of replication.

output

A character string indicating output style. Default: "console", which print the report in console. And "file" is available to output report into text-file.

output_dir

Default:"./". Available only when output="file". The direction of output file.

filename

A character string indicating file name of output file. Only work when output set as 'file'.

report

Logical. If print report to console. Default:TRUE

Examples

### Here shows different types of experimental design ###
data("cotton", package = "agricolae")

### Two randomly designed groups ###
sig_results <- auto_signif_test(
  data = cotton,
  treatment_col = 1,
  value_col = 5
)

### Two paired design groups ###
sig_results <- auto_signif_test(
  data = cotton,
  treatment_col = 1,
  value_col = 5,
  paired = TRUE,
  subject_col = 2
)

### More than two randomly designed groups ###
sig_results <- auto_signif_test(
  data = cotton,
  treatment_col = 2,
  value_col = 5
)
head(sig_results)  # Check outputs

### Conduct prior comparisons ###
sig_results <- auto_signif_test(
  data = cotton,
  treatment_col = 2,
  value_col = 5,
  prior = TRUE
)
head(sig_results)  # Check outputs
print(sig_results$basicdata)  # Check statistical summary
print(sig_results$anova_model)  # Extract ANOVA model
print(sig_results$anova_summary)  # Check ANOVA summary
print(sig_results$multiple_comparison_model)  # Extract multiple comparison model
print(sig_results$comparison_results)  # Check between-group comparison
print(sig_results$comparison_letters)  # Check letters (can be used in visualization)

## Change multiple comparison method (maybe not illegal!!)
sig_results <- auto_signif_test(
  data = cotton,
  treatment_col = 2,
  value_col = 5,
  prior = TRUE,
  comparison_method = "LSD"
)
head(sig_results)  # Check outputs
print(sig_results$comparison_letters)  # Note that letters become different