visualization - Visualising logistic regression using the effects package in R -

i using effects package in r plot effects of categorical , numerical predictors in binomial logistic regression estimated using lme4 package. dependent variable presence or absence of virus in individual animal , predictive factors various individual traits (eg. sex, age, month/year captured, presence of parasites, scaled mass index (smi), site random variable).

when use alleffects function on regression, plots below. when compared model summary output below, can see slope of each line appears zero, regardless of estimated coefficients, , there strange going on scale of y-axes ticks , tick labels appear overwritten on same point.

here code model , summary output:

library(lme4) library(effects) virus1.mod<-glmer(virus1~ age + sex + month.yr + parasites + smi + (1|site) , data=virus1data, family=binomial) virus1.effects<-alleffects(virus1.mod) plot(virus1.effects, ylab="probability(infected)", rug=false)    > summary(virus1.mod)     generalized linear mixed model fit maximum likelihood ['glmermod']      family: binomial ( logit )     formula: virus1 ~ age + sex + month.yr + parasite + smi + (1 | site)        data: virus1data           aic      bic   loglik deviance     189.5721 248.1130 -76.7860 153.5721      random effects:      groups name        variance  std.dev.      site   (intercept) 4.729e-10 2.175e-05     number of obs: 191, groups: site, 6      fixed effects:                    estimate std. error z value pr(>|z|)       (intercept)   5.340e+00  2.572e+00   2.076  0.03789 *     agej          1.126e+00  8.316e-01   1.354  0.17583       sexm         -3.943e-02  4.562e-01  -0.086  0.93113       month.yrfeb-08  -2.259e+01  6.405e+04   0.000  0.99972       month.yrfeb-09  -2.201e+01  2.741e+04  -0.001  0.99936       month.yrjan-08.516e+00  8.175e-01  -3.078  0.00208 **     month.yrjan-09 -2.607e+00  8.066e-01  -3.232  0.00123 **     month.yrjul-08 -1.428e+00  8.571e-01  -1.666  0.09563 .     month.yrjul-09 -2.795e+00  1.170e+00  -2.389  0.01691 *     month.yrjun-08 -2.259e+01  3.300e+04  -0.001  0.99945       month.yrmar-09 -5.451e-01  6.705e-01  -0.813  0.41622       month.yrmar-08 -1.863e+00  7.921e-01  -2.352  0.01869 *     month.yrmay-09 -6.319e-01  8.956e-01  -0.706  0.48047       month.yrmay-08  3.818e-01  1.015e+00   0.376  0.70691       month.yrsep-08 2.563e+01  5.806e+05   0.000  0.99996       parasitetrue -6.329e-03  4.834e-01  -0.013  0.98955       smi          -3.438e-01  1.616e-01  -2.127  0.03342 * 

and str of data frame:

> str(virus1data) 'data.frame':     191 obs. of  8 variables:  $ virus1   : factor w/ 2 levels "0","1": 1 1 1 1 1 2 1 2 1 1 ...  $ age     : factor w/ 2 levels "a","j": 1 1 1 1 1 1 1 1 1 1 ...  $ sex     : factor w/ 2 levels "f","m": 2 2 2 2 1 1 2 1 2 2 ...  $ site    : factor w/ 6 levels “site1”,"site2”,"site3",..: 1 1 1 1 2 2 2 3 2 3 ...  $ rep : factor w/ 7 levels "nrf","l","nr",..: 3 7 3 7 1 1 3 1 7 7 ...  $ month.yr  : factor w/ 17 levels "feb-08","feb-09",..: 4 5 5 5 13 7 14 9 9 9 ...  $ parasite : factor w/ 2 levels "false","true": 1 1 2 1 1 2 2 1 2 1 ...  $ smi     : num  14.1 14.8 14.5 13.1 15.3 ...  - attr(*, "na.action")=class 'omit'  named int [1:73] 6 12 13 21 22 23 24 25 26 27 ...   .. ..- attr(*, "names")= chr [1:73] "1048" "1657" "1866" "2961" ... 

without making actual data available, have idea of might causing this? have used function different dataset (same independent variables different virus response variable, , different records) without problems.

this first time have posted on cv, hope question appropriate , have provided enough (and right) information.