[Asjad Naqvi]

1. Just a brief response to the request for a joyplots command by Igor Paploski. I agree with what Maarten Buis and Nick Cox said. Joyplots are very context-specific and work under certain circumstances. The number of parameters required to provide full functionality is quite a lot. It is definitely quicker to create them manually. As Nick said, they are basically overlapping area graphs and the guide provides the code. If you have the code block in place, you can easily copy and modify it to fit different data sets. There is not so much programming involved here. If the creation of extra junk variables is the issue, then just replace them with tempvars. I intentionally don't go so far in the guides to accommodate more basic users, but there is definitely a lot of scope for code optimization.

2. My wish list for Stata:

a) dofile editor:
i) It will be great if syntax highlighting is different colors for different commenting-out options (* vs // vs /* */). This will help discern actual comments inside the code versus marked-out code.

b) Graph options
i) ability to scale marker weights based on a starting and ending value
ii) ability to scale colors, and line widths based on a starting and ending value
iii) Marker labels can be read from variables. Please also allow this for angles, colors, marker sizes. This will make grouping so much easier on graphs

c) A long-term wishlist
i) Ability to read pixels (to process images, geotiffs, edge detection, native machine learning routines, etc)
ii) color scaling within graph elements defined by starting and ending color. For example, higher values on the y-axis are darker than lower values for the same line.

[John Mullahy]

Might there be a way to add an option to the –centile– command that allows it to compute percentiles in the precisely same way as do –summarize, d– and –estat bootstrap, percentile– (and probably other commands)?

. sysuse auto
(1978 automobile data)

. tempfile tboot

. qui bootstrap, reps(500) saving(`tboot'): reg price mpg, level(50)

. estat bootstrap, percentile

Linear regression                               Number of obs     =         74
                                                Replications      =        500

------------------------------------------------------------------------------
             |    Observed               Bootstrap
       price | coefficient       Bias    std. err.  [50% conf. interval]
-------------+----------------------------------------------------------------
         mpg |  -238.89435  -6.540619   56.911373   -283.3891  -206.8067   (P)
       _cons |   11253.061   133.0593   1356.8499    10512.26   12328.61   (P)
------------------------------------------------------------------------------
Key: P: Percentile

. use `tboot'
(bootstrap: regress)

. sum, d

                           _b[mpg]
-------------------------------------------------------------
      Percentiles      Smallest
 1%    -380.3653      -436.6451
 5%    -344.9039      -410.6723
10%    -324.1469      -385.3353       Obs                 500
25%    -283.3891      -385.0602       Sum of wgt.         500

50%    -237.3672                      Mean           -245.435
                        Largest       Std. dev.      56.91137
75%    -206.8067      -121.5362
90%    -175.8918      -111.9515       Variance       3238.904
95%    -159.8261      -107.5473       Skewness      -.2622398
99%     -130.299      -100.4849       Kurtosis       2.846957

                          _b[_cons]
-------------------------------------------------------------
      Percentiles      Smallest
 1%     8331.229       7704.635
 5%     9282.614        7916.47
10%     9701.941        8226.62       Obs                 500
25%     10512.26       8312.646       Sum of wgt.         500

50%     11250.26                      Mean           11386.12
                        Largest       Std. dev.       1356.85
75%     12328.61       14624.41
90%     13168.51       14663.63       Variance        1841042
95%     13638.37          14861       Skewness       .1261091
99%     14560.25       15460.15       Kurtosis       2.709669

. centile *, c(25 75)

                                                          Binom. interp.   
    Variable |       Obs  Percentile    Centile        [95% conf. interval]
-------------+-------------------------------------------------------------
      _b_mpg |       500         25   -283.3945       -288.5923   -277.2365
             |                   75   -206.7666       -212.8225   -200.2001
     _b_cons |       500         25    10512.16        10293.56    10656.54
             |                   75    12332.11        12182.53    12567.85

[Rich Goldstein]

re: #209 - to follow up on Leonardo Guizzetti 's comment, please see

help datetime##s4

and, if this is not what you mean, please clarify

[Leonardo Guizzetti]

It would be great if there could be an option with the month() function to automatically create month labels instead of always having to create a value label ourselves (i.e. local mon 1 "Jan" 2 "Feb" 3 "Mar" ...)

Have you tried to use date time formats? That's their purpose.

[Adam Streff]

It would be great if there could be an option with the month() function to automatically create month labels instead of always having to create a value label ourselves (i.e. local mon 1 "Jan" 2 "Feb" 3 "Mar" ...)