Inconsistent results between glm() in R and manual implementation of logistic regression in Excel -


you'll find manual implementation of logistic regression in excel at: http://blog.excelmasterseries.com/2014/06/logistic-regression-performed-in-excel.html.

this implementation uses dataset below , reports following coefficients

b0 = 12.48285608

b1 = -0.117031374

b2 = -1.469140055

however, when analyze same dataset glm() in r, results not same, i.e.:

b0 = 1.687445

b1 = -0.012525

b2 = -0.116473

d <- structure(list(y = c(0l, 0l, 0l, 0l, 0l, 0l, 0l, 0l, 0l, 0l,  1l, 1l, 1l, 1l, 1l, 1l, 1l, 1l, 1l, 1l), x1 = c(78l, 73l, 73l,  71l, 68l, 59l, 57l, 49l, 35l, 27l, 59l, 57l, 44l, 38l, 36l, 36l,  22l, 22l, 15l, 10l), x2 = c(8l, 8l, 5l, 7l, 5l, 4l, 7l, 5l, 4l,  7l, 3l, 4l, 5l, 5l, 4l, 2l, 6l, 5l, 4l, 6l)), .names = c("y",  "x1", "x2"), class = "data.frame", row.names = c(na, -20l))    summary(glm(y ~ x1+x2, data=d), family=binomial(link='logit'))   # > summary(glm(y ~ x1+x2, data=d), family=binomial(link='logit')) #  # call: #   glm(formula = y ~ x1 + x2, data = d) #  # deviance residuals:  #   min        1q    median        3q       max   # -0.78318  -0.20641   0.07689   0.24375   0.49237   #  # coefficients: #   estimate std. error t value pr(>|t|)     # (intercept)  1.687445   0.319872   5.275 6.18e-05 *** #   x1          -0.012525   0.004376  -2.862   0.0108 *   #   x2          -0.116473   0.056959  -2.045   0.0567 .   # --- #   signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 #  # (dispersion parameter gaussian family taken 0.146843) #  # null deviance: 5.0000  on 19  degrees of freedom # residual deviance: 2.4963  on 17  degrees of freedom # aic: 23.139 #  # number of fisher scoring iterations: 2

why results differ?

you have family parameter in wrong place. should in glm() call, not summary() call.

summary(glm(y ~ x1+x2, data=d, family=binomial(link='logit')))

if don't include family in glm(), gaussian (linear) regression.


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