This function uses lm to fit a linear model to data and outputs the model object. 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.
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".
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.
This function returns an object of class "lm".
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
#>