One of two functions for simple ANOVA tables and linear models without random effects, which use lm to fit a linear models.

1. link{simple_anova}

2. link{simple_model}

simple_model(data, Y_value, Fixed_Factor, ...)

## Arguments

data

a data table object, e.g. data.frame or tibble.

Y_value

name of column containing quantitative (dependent) variable, provided within "quotes".

Fixed_Factor

name(s) of categorical fixed factors (independent variables) provided as a vector if more than one or within "quotes".

...

any additional arguments to pass on to lm if required.

## Value

This function returns an object of class "lm".

## Details

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.

## Examples

#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
#>