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, ...)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.
This function returns an object of class "lm".
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)
#>
#> Call:
#> lm(formula = Doubling_time ~ Student, data = data_doubling_time)
#>
#> Residuals:
#> Min 1Q Median 3Q Max
#> -3.8699 -0.8091 -0.0815 0.8474 2.9019
#>
#> Coefficients:
#> Estimate Std. Error t value Pr(>|t|)
#> (Intercept) 19.9619 1.1484 17.383 1.54e-13 ***
#> StudentB -0.3713 1.6241 -0.229 0.821
#> StudentC -0.5929 1.6241 -0.365 0.719
#> StudentD -1.1728 1.6241 -0.722 0.479
#> StudentE -0.6286 1.6241 -0.387 0.703
#> StudentF 0.7416 1.6241 0.457 0.653
#> StudentG 0.4885 1.6241 0.301 0.767
#> StudentH -0.8083 1.6241 -0.498 0.624
#> StudentI 0.6775 1.6241 0.417 0.681
#> StudentJ 1.2115 1.6241 0.746 0.464
#> ---
#> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#>
#> Residual standard error: 1.989 on 20 degrees of freedom
#> Multiple R-squared: 0.1753, Adjusted R-squared: -0.1958
#> F-statistic: 0.4725 on 9 and 20 DF, p-value: 0.8761
#>