r - what are the steps to perform logistic regression? - Cross Validated
str(newshad2) 'data.frame': 462 obs. of 10 variables: $ sbp : int 160 144 118 170 134 132 142 114 114 132 ... $ tobacco : num 12 0.01 0.08 7.5 13.6 6.2 4.05 4.08 0 0 ... $ ldl : num 5.73 4.41 3.48 6.41 3.5 6.47 3.38 4.59 3.83 5.8 ... $ adiposity: num 23.1 28.6 32.3 38 27.8 ... $ typea : int 49 55 52 51 60 62 59 62 49 69 ... $ obesity : num 25.3 28.9 29.1 32 26 ... $ alcohol : num 97.2 2.06 3.81 24.26 57.34 ... $ age : int 52 63 46 58 49 45 38 58 29 53 ... $ chd : int 1 1 0 1 1 0 0 1 0 1 ... $ famhist : num 2 1 2 2 2 2 1 2 2 2 ...
i want predict heart disease chd giving input of other variables.
myfit <- glm(as.factor(chd) ~ ., data = newshad2, family = binomial (link='logit')) summary(myfit) call: glm(formula = as.factor(chd) ~ ., family = binomial(link = "logit"), data = newshad2) deviance residuals: min 1q median 3q max -1.778 -0.821 -0.439 0.889 2.543 coefficients: estimate std. error z value pr(>|z|) (intercept) -7.076091 1.340486 -5.28 0.00000013 *** sbp 0.006504 0.005730 1.14 0.25637 tobacco 0.079376 0.026603 2.98 0.00285 ** ldl 0.173924 0.059662 2.92 0.00355 ** adiposity 0.018587 0.029289 0.63 0.52570 typea 0.039595 0.012320 3.21 0.00131 ** obesity -0.062910 0.044248 -1.42 0.15509 alcohol 0.000122 0.004483 0.03 0.97835 age 0.045225 0.012130 3.73 0.00019 *** famhist 0.925370 0.227894 4.06 0.00004896 *** --- signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 (dispersion parameter binomial family taken 1) null deviance: 596.11 on 461 degrees of freedom residual deviance: 472.14 on 452 degrees of freedom aic: 492.1 number of fisher scoring iterations: 5
how interpret these results , should next? should remove, 1 variable @ time statistically insignificant , run glm model again?
if main objective "predict" heart disease, split data training , development/test set , check overall accuracy. 1 way predictions model (fit) below.
model = glm(y ~ x1 + x2 + ... ) predict(model, new_data, type = "response")
additionally, calculate other metrics such precision, recall, , average precision further test generalizability of model. more information regarding such metrics, refer here.
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