Fit several models with different response variables
fit_models( df = NULL, yname = NULL, xname = NULL, modeltype = NULL, drop_non_numeric = FALSE, ... )
| df | A data.frame object |
|---|---|
| yname | The outcome variable |
| xname | The predictor variable(s) |
| modeltype | A character specifying the model type e.g lm for linear model |
| drop_non_numeric | Should non numeric columns be dropped? Defaults to FALSE |
| ... | Other arguments to specific model types. |
A list of model objects that can be used later.
data("yields", package="manymodelr") fit_models(df=yields,yname=c("height","yield"),xname="weight",modeltype="lm") #> [[1]] #> [[1]][[1]] #> #> Call: #> lm(formula = height ~ weight, data = use_df) #> #> Coefficients: #> (Intercept) weight #> 0.5661 -0.2174 #> #> #> [[1]][[2]] #> #> Call: #> lm(formula = yield ~ weight, data = use_df) #> #> Coefficients: #> (Intercept) weight #> 519.142 1.793 #> #> #> #many model types fit_models(df=yields,yname=c("height","yield"),xname="weight", modeltype=c("lm", "glm")) #> [[1]] #> [[1]][[1]] #> #> Call: #> lm(formula = height ~ weight, data = use_df) #> #> Coefficients: #> (Intercept) weight #> 0.5661 -0.2174 #> #> #> [[1]][[2]] #> #> Call: #> lm(formula = yield ~ weight, data = use_df) #> #> Coefficients: #> (Intercept) weight #> 519.142 1.793 #> #> #> #> [[2]] #> [[2]][[1]] #> #> Call: glm(formula = height ~ weight, data = use_df) #> #> Coefficients: #> (Intercept) weight #> 0.5661 -0.2174 #> #> Degrees of Freedom: 999 Total (i.e. Null); 998 Residual #> Null Deviance: 45.82 #> Residual Deviance: 44.37 AIC: -271.4 #> #> [[2]][[2]] #> #> Call: glm(formula = yield ~ weight, data = use_df) #> #> Coefficients: #> (Intercept) weight #> 519.142 1.793 #> #> Degrees of Freedom: 999 Total (i.e. Null); 998 Residual #> Null Deviance: 91170 #> Residual Deviance: 91080 AIC: 7356 #> #>