logistic regression - Error in glm() in R -

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i perform logistic regression errors - don't know mistake might be.

the structure of data:

'data.frame':   3911 obs. of  29 variables:  $ vn1              : factor w/ 2 levels "maennlich","weiblich": 1 1 2 1 1 2 1 1 1 1 ...  $ vn2c             : int  1976 1943 1927 1949 1965 1977 1986 1976 1944 1994 ...  $ vn35             : factor w/ 7 levels "keine angabe",..: 6 4 5 3 3 5 7 6 5 5 ...  $ v39              : factor w/ 8 levels "keine angabe",..: 8 4 5 8 7 7 5 6 6 6 ...  $ n39              : factor w/ 9 levels "keine angabe",..: 4 4 4 4 4 4 4 4 4 4 ...  $ v41              : factor w/ 7 levels "keine angabe",..: 6 5 5 2 7 7 5 5 6 6 ...  $ n41              : factor w/ 7 levels "keine angabe",..: 4 4 4 4 4 4 4 4 4 4 ...  $ vn42a            : factor w/ 8 levels "keine angabe",..: 8 4 8 8 5 5 6 6 6 4 ...  $ vn42b            : factor w/ 8 levels "keine angabe",..: 5 4 7 5 5 5 6 7 6 5 ...  $ vn43a            : factor w/ 8 levels "keine angabe",..: 7 5 8 6 2 6 6 2 7 7 ...  $ vn43b            : factor w/ 8 levels "keine angabe",..: 7 4 6 4 4 7 6 2 6 5 ...  $ vn62             : factor w/ 14 levels "keine angabe",..: 8 11 9 2 3 3 8 6 5 7 ...  $ vn119a           : factor w/ 15 levels "keine angabe",..: 6 3 8 14 10 8 14 8 6 6 ...  $ ostwest          : factor w/ 2 levels "ost","west": 2 2 2 2 2 2 2 2 2 2 ...  $ prefmerkel       : factor w/ 2 levels "steinbrueck",..: 1 2 2 na na na 2 2 1 1 ...  $ angst            : num  1 3 2 4 4 2 0 1 2 2 ...  $ crisismerkel     : num  0 4 3 0 1 1 3 2 2 2 ...  $ leadership42     : factor w/ 5 levels "trifft ueberhaupt nicht zu",..: 5 1 5 5 2 2 3 3 3 1 ...  $ leadership43     : factor w/ 5 levels "trifft ueberhaupt nicht zu",..: 4 2 5 3 na 3 3 na 4 4 ...  $ leadership       : num  1 -1 0 2 na -1 0 na -1 -3 ...  $ trustworthiness42: factor w/ 5 levels "trifft ueberhaupt nicht zu",..: 2 1 4 2 2 2 3 4 3 2 ...  $ trustworthiness43: factor w/ 5 levels "trifft ueberhaupt nicht zu",..: 4 1 3 1 1 4 3 na 3 2 ...  $ trustworthiness  : num  -2 0 1 1 1 -2 0 na 0 0 ...  $ ideology         : num  5 8 6 na na na 5 3 2 4 ...  $ pid              : factor w/ 10 levels "none","cdu/csu",..: 3 2 5 1 7 5 1 5 3 3 ...  $ age              : num  37 70 86 64 48 36 27 37 69 19 ...  $ agegroups        : factor w/ 7 levels "bis 25 jahre",..: 3 6 7 5 4 3 2 3 6 1 ...  $ gender           : factor w/ 2 levels "male","female": 1 1 2 1 1 2 1 1 1 1 ...  $ region           : factor w/ 2 levels "west","east": 1 1 1 1 1 1 1 1 1 1 ...

the regression command returns following error:

summary(glm(prefmerkel~angst+crisismerkel+leadership+trustworthiness+ideology+pid+agegroups+gender+region,data=gles))  error in glm.fit(x = c(1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,  :    na/nan/inf in 'y'  in addition: warning messages: 1: in ops.factor(y, mu) : ‘-’ nicht sinnvoll für faktoren 2: in ops.factor(eta, offset) : ‘-’ nicht sinnvoll für faktoren 3: in ops.factor(y, mu) : ‘-’ nicht sinnvoll für faktoren

you can't have factor/categorical response variables.

illustration:

> d=data.frame(f=factor(c(1,2,1,2,1,2)),x=runif(6)) > glm(f~x,data=d) error in glm.fit(x = c(1, 1, 1, 1, 1, 1, 0.351715633412823, 0.449422287056223,  :    na/nan/inf in 'y' in addition: warning messages: 1: in ops.factor(y, mu) : - not meaningful factors 2: in ops.factor(eta, offset) : - not meaningful factors 3: in ops.factor(y, mu) : - not meaningful factors

if want logistic regression should change them 0 , 1, or false , true, , use family=binomial:

# recode d$f==2 true, else false d$f=d$f=="2" # fit glm(f~x,data=d,family=binomial)  call:  glm(formula = f ~ x, family = binomial, data = d)  coefficients: (intercept)            x       -0.9066       1.8922    degrees of freedom: 5 total (i.e. null);  4 residual null deviance:      8.318  residual deviance: 8.092    aic: 12.09

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