One of two functions for simple ANOVA tables and linear models without random effects, which use lm to fit a linear models.
link{simple_anova}
link{simple_model}
simple_anova(data, Y_value, Fixed_Factor, ...)a data table object, e.g. data.frame or tibble.
name of column containing quantitative (dependent) variable, provided within "quotes". The following transformations are permitted: "log(Y_value)", "log(Y_value + c)" where c a positive number, "logit(Y_value)" or "logit(Y_value/100)" which may be useful when Y_value are percentages (note quotes outside the log or logit calls); "sqrt(Y_value)" or "(Y_value)^2" should also work. During posthoc-comparisons, log and logit transformations will be back-transformed to the original scale. Other transformations, e.g., "sqrt(Y_value)" will not be back-transformed. Check out the regrid and ref_grid for details if you need back-transformation to the response scale.
name(s) of categorical fixed factors (independent variables) provided within quotes (e.g., "A") or as a vector if more than one (e.g., c("A", "B"). If a numeric variable(s) is used, transformations similar to Y_value are permitted.
any additional argument to pass on to lm if required.
ANOVA table of class "anova" and "data.frame".
Update in v0.2.1: This function uses lm to fit a linear model to data, passes it on to Anova, and outputs the ANOVA table with type II sum of squares with F statistics and P values.
(Previous versions produced type I sum of squares using anova call.)
It requires a data table, one quantitative dependent variable and one or more independent variables. If your experiment design has random factors, use the related function mixed_anova.
This function is related to link{simple_model}.
#Basic usage
simple_anova(data = data_doubling_time,
Y_value = "Doubling_time",
Fixed_Factor = "Student")
#> Anova Table (Type II tests)
#>
#> Response: Doubling_time
#> Sum Sq Mean sq Df F value Pr(>F)
#> Student 16.824 1.8694 9 0.4725 0.8761
#> Residuals 79.126 3.9563 20