Contradiction between zero-inflated poisson model coefficients and graph of the model?

Question

EDIT: Added an reproducible example

For one of my models, it seems the coefficients and the graphed out model do not agree.

I'm working with adverse effects data, in which intense reactions are rare and the number of zeros is huge - thus using ZIP model. This is a reproducible code for the problem:

LogFile <- file("ITreatLog.txt", open="wt")
sink(LogFile, append=FALSE, split=TRUE, type = "output")

#Loading libraries
if (!require("pscl")) {
  install.packages("pscl")
  library("pscl")
}
if (!require("lme4")) {
  install.packages("lme4")
  library("lme4")
}
if (!require("ggplot2")) {
  install.packages("ggplot2")
  library("ggplot2")
}
rm(list=ls())

zinb <- read.csv("Reproducible.csv", fileEncoding="UTF-8-BOM", sep = ";")
zinb <- within(zinb, {
  Group.1 <- factor(Group.1)
})

#Replace negative values (Missing) with sysmis
zinb[zinb==-1]<-NA
zinb[zinb==-2]<-NA

var <- "CA_T"
covar <- "DaySleep.1"

f <- formula("CA_T.1 ~ Group.1 + Age + DaySleep.1")
m1 <- zeroinfl(f, data = zinb)

#Code for printing out a graph of the predictions        
scatterwidth=0.3
GraphData <- expand.grid(seq(4,12,1), factor(0:1), 27)
xname = "Hours of Sleep"
colnames(GraphData) <- c(covar, "Group.1", "Age")                         
GraphData$phat <- predict(m1, GraphData)
GraphData <- subset(GraphData, subset=(phat<=100)) #Intensities over 100 make no logical sense
plotline = ggplot(GraphData, aes(group=Group.1, x = !!ensym(covar), y = phat, colour = factor(Group.1)))+
          geom_point() + geom_line() + scale_linetype_discrete() + labs(x=xname, y="Intensity", color="Group") +
          scale_color_hue(labels=c("Sham", "Active")) + 
          theme(legend.position = "bottom",legend.background=element_rect(fill="transparent"), legend.margin = margin(t=-0.2,r=0,b=0,l=0, unit="cm"))
plotline <- plotline + scale_x_continuous(breaks=seq(4,12,1))

ggsave("Reproducible.png",plot=plotline, width=14, height = 14, unit="cm")

sink()

And the model summary for m1:

Count model coefficients (poisson with log link):
            Estimate Std. Error z value Pr(>|z|)    
(Intercept) 2.386609   0.282484   8.449  < 2e-16 ***
Group.11    0.210817   0.061288   3.440 0.000582 ***
Age         0.015874   0.004769   3.328 0.000873 ***
DaySleep.1  0.105312   0.029839   3.529 0.000417 ***

Zero-inflation model coefficients (binomial with logit link):
            Estimate Std. Error z value Pr(>|z|)  
(Intercept) -4.74756    2.61227  -1.817   0.0692 .
Group.11     0.06700    0.56561   0.118   0.9057  
Age          0.05143    0.04667   1.102   0.2704  
DaySleep.1   0.42271    0.26037   1.624   0.1045 

My interpretation of this is that longer sleep (DaySleep.1) is associated with more intense adverse effect (the estimate beign positive). However, then I graph the model out, and output by above code is this:

I'm stumped. For other models, the graphs look like I'd expect them to from the coefficients. With this one, the coefficients suggest that longer sleep increases the adverse effects, while the graph seems to suggest the opposite association.

What am I doing wrong?

Finally, a copy of the dataset the code uses:

CA_T.1;DaySleep.1;Age;Group.1
0;8;21;0
0;8.33;24;0
30;9;22;1
0;7;27;1
10;8.5;21;0
20;6.5;35;0
35;8;21;1
30;6;25;0
0;9.5;23;1
20;8.5;23;1
30;6.5;23;0
50;7.86;24;1
0;7.5;45;0
-1;8;22;1
80;8;26;1
55;8.5;22;1
-1;8.5;22;0
70;8.3;34;0
70;8.33;29;0
0;4.3;28;1
30;8;22;1
0;7.75;25;1
0;7;27;1
0;8.66;22;0
40;9;24;0
40;5;24;1
-2;7;23;0
0;8;25;0
0;9;29;0
0;9;26;1
5;7.25;25;0
70;7.83;20;1
0;10;26;0
50;7.5;43;1
10;6.45;20;0
50;8.25;30;1
40;6;43;0
0;8.3;31;1
0;8.5;31;0
80;6;27;1
10;6;36;0
0;8;34;1
0;8.5;18;0
0;6;21;1
85;7;22;0
0;8;25;1
0;7.5;40;0
20;7.5;28;0
0;7.5;40;1
40;7;22;1
5;6;25;1
0;7;28;1
0;9.33;28;0
0;6.16;25;0
0;6.25;29;0
-1;6.87;22;0
30;7.16;22;1
-1;7.3;25;1
0;8;27;1
70;7;29;0

Answer 1

I had more or less the same doubt regarding the coefficient signs in the "inflated" part using stata (the ZI coefficients in R)... The second part give the effect on the likelihood to get a 0.

In the following example (using Stata), the number of fish caught depends positively of the number of fishmen but the displayed coefficient is negative. The given interpretation is the following: "The inflate coefficient for persons suggests that for each unit increase in person the log odds of an inflated zero decrease by .564."