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_model(data, Y_value, Fixed_Factor, ...)This function returns an object of class "lm".
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 arguments to pass on to lm if required.
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.
The model output can be used to extract coefficients and other information, including post-hoc comparisons. If your experiment design has random factors, use the related function mixed_model.
This function is related to link{simple_anova}.
Output of this function can be used with posthoc_Pairwise, posthoc_Levelwise and posthoc_vsRef, or with emmeans.
#fixed factors provided as a vector
Doubmodel <- simple_model(data = data_doubling_time,
Y_value = "Doubling_time",
Fixed_Factor = "Student")
#get summary
summary(Doubmodel)
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