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, ...)

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

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
#>