How are multiple comparisons specified in glht model?
a specification of the linear hypotheses to be tested. Linear functions can be specified by either the matrix of coefficients or by symbolic descriptions of one or more linear hypotheses. Multiple comparisons in AN (C)OVA models are specified by objects returned from function mcp.
How to get the p value of a regression?
Model p-value: If you want to obtain the p-value of the overall regression model, this blog post outlines a function to return the p-value: In the case of a simple regression with one predictor, the model p-value and the p-value for the coefficient will be the same.
How to get the final p-value from the summary?
For the final p-value displayed at the end of summary (), the function uses pf () to calculate from the summary (fit)$fstatistic values.
How to pull out the p-value and R-squared?
How do you pull out the p-value (for the significance of the coefficient of the single explanatory variable being non-zero) and R-squared value from a simple linear regression model? For example… I know that summary (fit) displays the p-value and R-squared value, but I want to be able to stick these into other variables.
When to use least square means for multiple comparisons?
Least Square Means for Multiple Comparisons. Comparisons of values across groups in linear models, cumulative link models, and other models can be conducted easily with the lsmeans package. Importantly, it can make comparisons among interactions of factors. L.S. means stands for least square means.
What does LS mean mean in treatment levels?
L.S. means stands for least square means . Least square means are means for treatment levels that are adjusted for means of other factors in the model. For a more complete explanation, see the What are least square means? chapter. Adjustment of p -values for multiple comparisons is indicated with the adjust= option in the lsmeans function.
How is the model specified in the lsmeans function?
In the lsmeans function, model specifies the model object that was previously fitted. Note the specialized formula where pairwise indicates that all pairwise comparisons should be conducted, and Speaker indicates the variable whose levels will be compared.