<commit_before>% Response to reviewers
% Bivariate spatial point patterns . . . by <NAME>

Dear Christophe,

We thank you and the reviewers for the positive comments on the 
manuscript.


Please see below for my response to each reviewer.  Comments from
each reviewer are numbered and indented below.  Changes made to the
manuscript are highlighted in blue and annotated in the manuscript
with the corresponding number.  e.g. R2.5 refers to point 5 of referee
2.


## Reviewer 1

> 1.1) eq. 2: correct equation is $\sqrt{K_{12}/\pi}$

> 1.2) Fig. 3: I wonder why there is no confidence interval. It will gives
> an estimate of d_\mbox{min}.

> 1.3) Choice of d_\mbox{min} is questionnable. You mainly described an
> hard-core process (or Strauss process). You may find the set of
> d, the exclusion distance compatible with your data. Compatible
> means that the process does not go out the confidence interval
> (at 5% level for example).


> 1.4) Fig 4 since we mostly print inblack and white, I personnaly
> prefer different symbols than different colors.

> 1.5) Why do you present the results for functional independence ?

> 1.6) According to Fig. 6 you reject random for distances between 100
> and 150. Please discuss.
<commit_msg>Note colour for cell types.<commit_after>% Response to reviewers
% Bivariate spatial point patterns . . . by <NAME>

Dear Christophe,

We thank you and the reviewers for the positive comments on the 
manuscript.


Please see below for my response to each reviewer.  Comments from
each reviewer are numbered and indented below.  Changes made to the
manuscript are highlighted in blue and annotated in the manuscript
with the corresponding number.  e.g. R2.5 refers to point 5 of referee
2.

Yours faithfully,

Stephen


## Reviewer 1

> 1.1) eq. 2: correct equation is $\sqrt{K_{12}/\pi}$

> 1.2) Fig. 3: I wonder why there is no confidence interval. It will gives
> an estimate of d_\mbox{min}.

> 1.3) Choice of d_\mbox{min} is questionnable. You mainly described an
> hard-core process (or Strauss process). You may find the set of
> d, the exclusion distance compatible with your data. Compatible
> means that the process does not go out the confidence interval
> (at 5% level for example).


> 1.4) Fig 4 since we mostly print inblack and white, I personnaly
> prefer different symbols than different colors.

I have used colour consistently throughout the paper to refer to
different cell types: the colours in figure 1 are used in figure 2, 4
and 7.  Also, the colours in figure 8 naturally reflect the colour
sensitivity of the cell types.  So, if possible, I'd like to keep the
colours.


> 1.5) Why do you present the results for functional independence ?

> 1.6) According to Fig. 6 you reject random for distances between 100
> and 150. Please discuss.

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