[R] Regression model fitting
Allaisone1 at hotmail.com
Fri May 4 17:20:37 CEST 2018
Hi all ,
I have a dataframe (Hypertension) with following headers :-
ID Hypertension(before drug A) Hypertension(On drug A) On drug B? Healthy diet?
1 160 90 True True
2 190 140 False False
3 170 110 True False
I wanted to study whether patients on drug A + on drug B + on healthy diet would have better
blood pressure control (reduction) compared to patients with drug A but not on drug B or not on healthy diet or not on both.
I considered my outcome(y) variable to be hypertension measurements for all patients
on drug A (column 2). Columns 1,3 and 4 are my explanatory(x) variables variables(column 1 is just the baseline measurements to adjust for the effect of drug A compared to the baseline) . So my regression formula using lm() function in R is as follow :-
Regression <- lm (formula= Hypertension(On drug A)~Hypertension(before drug A) +
(On drug B?*Healthy diet?)) , data = Hypertension)
I expect that the result of "(On drug B?*Healthy diet?)" coefficient in the model would give the correct answer to my question.
Is this the best formula to answer my question or there would be better methods ?.
[[alternative HTML version deleted]]
More information about the R-help