logistic regression - glm function in R uses incorrect coefficient? -

i have test scores of 2 groups, , b.

    test.a=c(1.12, 1, 2, 1.4, 2)     test.b=c(2, 1, 1.5, 1.7, 1) 

if person scores on 1.1, want label him/her positive.

    test.a=ifelse(test.a>1.1,'positive','negative')     test.b=ifelse(test.b>1.1,'positive', 'negative')     test.ab=c(test.a, test.b) 

the status binary response variable indicates whether person has disease or not (0 = no diseae, 1=disease)

   status=c(rep(0,2), rep(1,3))    status=as.factor(status)    test.ab=as.factor(test.ab)    test.data=data.frame(status, test.ab)    test.fit=glm(status~test.ab, data=test.data, family="binomial")    summary(test.fit) 

the summary function returns

   call:    glm(formula = status ~ test.ab, family = "binomial", data = test.data)     deviance residuals:     min      1q  median      3q     max      -1.58   -0.90    0.82    0.82    1.48       coefficients:                     estimate std. error z value pr(>|z|)    (intercept)       -0.693      1.225   -0.57     0.57    test.abpositive    1.609      1.483    1.09     0.28 

i don't understand why positive appended test.ab? shouldn't coefficient test.ab have specified in data.frame , in glm() command?

try

test.a=c(1.12, 1, 2, 1.4, 2) test.b=c(2, 1, 1.5, 1.7, 1) test.a=ifelse(test.a>1.1,1,0) test.b=ifelse(test.b>1.1,1,0)   test.ab=c(test.a, test.b)   status=c(rep(0,2), rep(1,3)) status=as.factor(status) test.data=data.frame(status, test.ab) test.fit=glm(status~test.ab, data=test.data, family="binomial") summary(test.fit)