Macroplot plotting is controlled by the macros in the text area provided.
Each macro must occupy its own line. If the first character of a macro is not A-Z, the line will be considered a comment and ignored
The first macro, which is obligatory, initializes the plot. The macro is
Bitmap Initialize width(in pixels), height(in pixels), red(0-255) blue(0-255), green(0-255) transparency(0-255)
Example : Bitmap Initialize 700 500 255 255 255 255 which provides a landscape area 700 pixels wide, 500 pixel high, with white background
The following are default settings when the bitmap is initiated.
Lines are black (0 0 0 255) and 3 pixels in width
Fill color for bars and dots are black (0 0 0 255), and the fill type is set to fill only (1) (see Fill Type)
Dots (circl and square) are set to 5 pixels radius (diameter=11 pixels)
Fonts are set as follows
Font face is set to sans-serif. Serif, sans-serif, and monospace are available to all browsers, user can use any font available to his/her browser
Font size is set to 16 pixels high
Font color, both line and fill are set to black (0 0 0 255), and fill type to 1 (fill only) (see Font Type)
Macros for plotting on the bitmap begin with the keyword Bitmap, and the coordinates are x=number of pixels from the left border and y=number of pixels from the top border
A central plotting area is also defined
By default, at initialization, as 15% from the left and bottom, 5% from right and top
defined by user as Plot Pixels left top right bottom, these being number of pixels from the left and top border
e.g. Plot Pixels 105 25 665 425 would be the same as the default setting for a bitmap of 700 pixels wide and 500 pixels high
The values of the data used in plotting in this central area can be defined as follows
Plot Values left top right bottom, these being the extreme values used in the data
e.g.Plot Values 0 100 10 50 represents x values of 0 on the left to 10 to the right, and y values of 50 at the bottom to 100 to the top
After the values are declared, all plotting in the central area uses macros beginning with the keyword Plot, and the coordinates are the values in the data
To Harvest the Bitmap
Right click on the bitmap
From the pop up, select Take Screen Shot. The bitmap will be marked up
Click on the marked up area. The bitmap will be focussed and a copy button appear on right bottom of bitmap
Click the Copy buttion to copy bitmap to the clipboard
Paste the bitmap from the clipboard to other applications (ctrl+v for Windows or command+v for Mac)
Macros
This panel lists and describes all macros used in this version of MacroPlot by Javascript. They are divided into the following sub-panels
Initialization and settings
Plotting areas, coordinates used, and drawing of x and y axis
Drawing lines, bars, dots, text, and other shapes
Initialization
This sub-panel lists those macros that initialized the bitmap, and set the parametrs for drawing
Initialize Plotting
Bitmap Initialize w h r g b t is the first and obligatory macro, which Initializes the bitmap
w and h are width and height of the bitmap in number of pixels. The most common dimensions are
w=700 and h= 500 for landscape orientation
w=500 and h=700 for portrait orientation
Both 500 for square bitmap
r g b t represents red, green, blue and transparency values for the background, each value is 0 for non-existence to 255 for maximum intensity. The most commonly used background is white (255 255 255 255)
For most plotting programs on StatsToDo the macro used is Bitmap Initialize 700 500 255 255 255 255, a landscape orientation with white background
Settings for lines
The settings provide parameters for all subsequent plotting until the parameter is reset
Line Color r g b t sets the line color of red, green, blue and transparency values, each value is 0 for non-existence to 255 for maximum intensity. On initialization of the bitmap, line color is lines is set by default to black (0 0 0 255)
Line Thick p sets the thickness of lines to p pixels. On initialiszation, the default setting is 3 pixels for line thickness
Settings for fills
When bars, dots, arcs and wedges are plotted, the interior of these symbols are called fills, and they are set as follows
Fill Color r g b t sets the filling color of red, green, blue and transparency values, each value is 0 for non-existence to 255 for maximum intensity. On initialization of the bitmap, fill color is lines is set by default to black (0 0 0 255).
Fill Type t sets how the fills are to be used, t can be one of the following
t=0: only the outline, defined by the line parameters, are plotted. Fill is ignored
t=1: only fill is carried out, outline is ignored
t=2: both outline and fill are plotted
When the plot is initialized, the default setting for fill type is t=1
Settings for fonts
These set the font characteristics for text output. Please note: settings for lines and fills for fonts are separate and independent to those for general line and shape plottings
Font Face name sets the font face. The program will accept all fonts supported by the user's border. The 3 fonts accepted by all browsers are serif, sans-serif, and monospace. On initialization, sans-serif is set by default
Font Style s where s can be either normal or bold. On initialization the default setting is bold
Font Size h where h is the height of the text in pixels. On initialization, the default font size is set to 16
Font Thick p where p is the thickness of the outline of the font. On initialization, this is set to p=1
Font LColor r g b t sets the color of the outline of the font. On initialization this is set to black (0 0 0 255)
Font FColor r g b t sets the fill color of the of the font. On initialization this is set to black (0 0 0 255)
Font Color r g b t sets both LColor and FColor to the same color. On initialization this is set to black (0 0 0 255)
Font Type t where t determines which part of the font is drawn, and can be one of the following
t=0: only the outline of the font, defined by the thick and LColor parameter is drawn
t=1: only the fill of the font is drawn
t=2: both outline and fill are drawn
When the plot is initialized, the default setting for Font type is t=1
Please Note: When the bitmap is initialized, the default settings, which are suitable for most situations, are automatically set, so users need not worry about these settings unless he/she has a different preference.
Axis & Coordinates
This sub-panel presents macros that define the plotting areas, and creating the x and y axis for plotting
Drawing on the bitmap
When plotting on the initialized bitmap
the horizontal coordinate x is the number of pixels from the left border
the vertical coordinate y is the number of pixels from the top border
The macro used begins with the keyword Bitmap
Drawing on the plotting area
In most cases, there is a need to draw and label the x and y axis, and drawing coordinates used are the actual values of the data. The macros used for these all begins with the keyword Plot, and are purposes are as follows
Plot Pixels lp tp rp bp defines an area for plotting
lp defines the left border of the plotting area, in the number of pixels from the left border of the bitmap. In most cases this is 15% of the bitmap's width
tp defines the top of the plotting area, in the number of pixels from the top border of the bitmap. In most cases this is 5% of the height
rp defines the right border of the plotting area, in the number of pixels from the left border of the bitmap. In most cases this is 95% of the width (or 5% from the right border of the bitmap)
bp defines the bottom border of the plotting area, in the number of pixels from the top border of the bitmap. In most cases this is 85% of the height (or 15% from the bottom)
An example is that is that, in a landscape orientated bitmap of 700 pixels width and 500 pixel height, Plot Pixels 105 25 665 425 sets the central area for plotting that is 15% from the left and bottom, and 5% from the top and right.
This macro is usually not necessary if the 5%/15% setting suits the user, as this is the default setting when the bitmap is initialized
Plot Values lv tv rv bv defines the data values to be used in plotting
lv is the extreme data value for the horizontal variable x on the left
tv is the extreme data value for the vertical variable y at the top
rv is the extreme data value for horizontal variable x on the right
bv is the extreme data value for the vertical variable y at the bottom
Plot Logx 1 sets the horizontal x axis to the log scale. Normal scale is set on initialization, or reset by Plot Logx 0
Plot Logy 1 sets the vertical y axis to the log scale. Normal scale is set on initialization, or reset by Plot Logy 0
Plot XLabel label distance places the label for the horizontal x axis, below the bottom of the plotting area
lable is a single word text string, using the underscore _ to represent spaces if necessary
space is the number of pixels between the bottom of the plot area and the label text string
Plot YLabel label distance places the label for the vertical y axis, on the left of plotting area
lable is a single word text string, using the underscore _ to represent spaces if necessary
space is the number of pixels between the left of the plot area and the label text string
The quickest and easiest way to draw axis
The following 4 macros are sufficient to draw the x and y axis under most circumstances
Plot XAxis y nsIntv nbIntv len gap line will mark out and numerate the horizontal x axis
y is the y value on which the x axis lie
nsIntv is the number of small intervals between the vertical line marks, 10 to 20 are recommended
nbIntv is the number of big intervals between the numerical scales, 5 to 10 are recommended
len is the length of the mark in pixels, +ve value downwards and negative value upwards. -10 is recommended
gap is the number of pixels between the numerical scaling text and the y value of the axis, +ve values for text below axis and negative value for text above axis. 3 is recommended
Line determines the axis line is drawn, 0 for no line, 1 for line
Plot YAxis x nsIntv nbIntv len gap line will mark out and numerate the vertical y axis
x is the x value on which the y axis lie
nsIntv is the number of small intervals between the horizontal line marks, 10 to 20 are recommended
nbIntv is the number of big intervals between the numerical scales, 5 to 10 are recommended
len is the length of the mark in pixels, +ve value to the right and negative value to the left. 10 is recommended
gap is the number of pixels between the numerical scaling text and the y value of the axis, +ve values for text to the right of axis and negative value for text to the left of axis. -3 is recommended
Line determines the axis line is drawn, 0 for no line, 1 for line
Plot AutoXLogScale y len gap line will mark and numerate the x axis if it is in log scale
The x axis must be set to the log scale by Plot Logx 1. If axis not set to log this macro will abort
y is the y value on which the x axis lie
len is the length of the mark in pixels, +ve value downwards and negative value upwards. -10 is recommended
gap is the number of pixels between the numerical scaling text and the y value of the axis, +ve values for text below axis and negative value for text above axis. 3 is recommended
Line determines the axis line is drawn, 0 for no line, 1 for line
Plot AutoYLogScale x len gap line will mark and numerate the y axis if it is in log scale
The y axis must be set to the log scale by Plot Logy 1. If axis not set to log this macro will abort
x is the x value on which the x axis lie
len is the length of the mark in pixels, +ve value downwards and negative value upwards. -10 is recommended
gap is the number of pixels between the numerical scaling text and the y value of the axis, +ve values for text below axis and negative value for text above axis. 3 is recommended
Line determines the axis line is drawn, 0 for no line, 1 for line
Other methods of drawing axis
Users may wish to draw individual part of the axis, and the following macros can be used
Plot XLine y Draws the horizontal x axis line at the y value y
Plot YLine x Draws the vertical y axis line at the x value y
Plot XMark y begin interval len marks the horizontal x axis with a series of vertical marks
y is the y value where the axis is to be marked
begin is the value for the first mark
interval is the interval between marks
len is the length of the mark line in pixels, +ve downwards, -ve upwards
Plot YMark x start interval len marks the vertical y axis with a series of horizontal marks
x is the x value where the axis is to be marked
start is the value for the first mark
interval is the interval between marks
len is the length of the mark line in pixels, +ve to the right, -ve to the left
Plot XScale y start interval gap writes the numerical scales for the horizontal x axis
y is the y value for the axis
start is the first value to be written
interval is the interval between numerical scales
gap is the space in pixels between the scale text and the axis, +ve for text below axis, -ve for text above axis
The number of decimal points in the scale is the same as that of the interval value
Plot YScale x start interval gap writes the numerical scales for the vertical y axis
x is the x value for the axis
start is the first value to be written
interval is the interval between numerical scales
gap is the space in pixels between the scale text and the axis, +ve for text to the right of axis, -ve for text to the left of axis
The number of decimal points in the scale is the same as that of the interval value
Plot XMarkIntv y interval len marks the horizontal x axis with a series of vertical marks
y is the y value of the axis
interval is the interval between the marks, beginning at 0 and while in range
len is the length of the mark line in pixels, +ve downwards, -ve upwards
Plot YMarkIntv x interval len marks the vertical y axis with a series of horizontal marks
x is the x value of the axis
interval is the interval between the marks, beginning at 0 and while in range
len is the length of the mark line in pixels, +ve to the right, -ve to the left
Plot XScaleIntv y interval gap writes the numerical scales for the horizontal x axis
y is the y value of the axis
interval is the interval between the numerical scales, beginning at 0 and while in range
gap is the space in pixels between the scale text and the axis, +ve for text below axis, -ve for text above axis
The number of decimal points in the scale is the same as that of the interval value
Plot YScaleIntv x interval gap writes the numerical scales for the vertical y axis
x is the x value of the axis
interval is the interval between the numerical scales, beginning at 0 and while in range
gap is the space in pixels between the scale text and the axis, +ve for text to the right of axis, -ve for text to the left of axis
The number of decimal points in the scale is the same as that of the interval value
Drawings
This sub-panel describes those macros that draws the plotting objects. Drawing are performed in two environments
Macros that begins with the keyword Bitmap uses pixel values as coordinates, where x is the number of pixels from the left border, and y the number of pixels from the top border
Macros that begins with the keyword Plot uses actual data values (as defined in the Plot Values lv tv rv bv macro, as coordinates
Drawing lines
The thickness and color of any line drawn is set by the Line macros (see setting sub-panel). The default setting is black line 3 pixels in width
Bitmap Line x1 y1 x2 y2 draws the line from x1y1 to x2y2
x1 and x2 are number of pixels from the left border
y1 and y2 are number of pixels from the top border
Plot Line x1 y1 x2 y2 draws the line from x1y1 to x2y2
x1 and x2 are data values for the horizontal variable x
y1 and y2 are data variables for the vertical variable y
Plot PixLine x y hpix vpix draws a line
x and y are data values for the horizonal x value and verticsl y value. This defines the coordinate at the origin of the line
hpix is the number of pixels horizontally from the origin, +ve value to the right, -ve value to the left
vpix is the number of pixels vertically from the origin, +ve value downwards, -ve value upwards
The line is then drawn between the origin and that defined by hpix and vpix
Drawing bars
The color and thickness of the outline are defined in the Line macro. The color of the fill is defined in the fill color and Fill Type macro. The default setting is black (0 0 0 255) for both line and fill color, and the Fill type is set to 1, only the fill and no outlines. These settings are suitable for most circumstances, but user can change them is so required.
Bitmap Bar x1 y1 x2 y2 draws a bar the corner of which are x1y1 and x2y2. X and y are number of pixels from the left and top border of the bitmap
Plot Bar x1 y1 x2 y2 draws a bar the corner of which are x1y1 and x2y2. X and y are data values as defined in Plot Values lv tv rv bv
Bar Wide w sets the width / height of bars for Plot VBar and Plot HBar
w is the half width of the bar, so a VBar is 2w+1 pixels in width, and HBar is 2w+1 pixels in height
The default value for w is 7 pixels (making width/height of 15 pixels), unless the user changes it
Plot VBar x y1 y2 hshift draws a vertical bar
x is the data value for the horizontal x variable. The is the center of the vertical bar
y1 and y2 are values for the vertical y variable. They define the vertical ends of the bar
hshift is the number of pixels the whole bar is shefted horizontally, +ve value to the left and +ve value to the right. In most cases this is 0 (no shift). However, if there are more than 1 bar in the same position, shifting some of them will avoid the bars overlapping and obscuring each other
The width of the vertical bar is set by default at 7, (width of bar=15 pixels)
Plot HBar x1 x2 y vshift draws a horizontal bar
x1 and x2 are data values for the horizontal x variable. They define the horizontal ends of the bar
y is the value for the vertical y variable, and defines and center of the horizontal bar
vshift is the number of pixels the whole bar is shefted vertically, -ve value upwards and +ve value downwards. In most cases this is 0 (no shift). However, if there are more than 1 bar in the same position, shifting some of them will avoid the bars overlapping and obscuring each other
Theheight of the horizontal bar is set by default at 7, (height of bar=15 pixels)
Drawing dots
There are only 2 dot types, circle and square. If more than 2 tyoes of dats are required, they can be distinguished by the colours of the outline and fill, and by their sizes. Settingsd for dot parameters are in the settings sub-panel
Bitmap Circle x y radius and Bitmap Square x y radius draws a circle or a square dot
x and y are the number of pixels from the left and top border
Radius is in number of pixels. The diameter of the dot is 2Radius+1 pixels
Plot Circle x y radius hshift vshift and Plot Square x y radius hshift vshift draws a circle or a square dot
x and y are the data values of the horizontal x variable and vertical y variable, as defined by Plot Values lv tv rv bv
Radius is in number of pixels. The diameter of the dot is 2Radius+1 pixels
hshift is the number of pixels the dot is shifted horizontally, -ve value to the left, +ve value to the right
vshift is the number of pixels the dot is shifted vertically, -ve value upwards, +ve value downwards
In most cases there is no shift (0 0), but id there are more than 1 dot in the same position, shifting avoids the dots superimposing over and obscuring each other
Dot Radius r sets the radius of the dot in pixels. The diameter of the dot is 2radius+1 pixels. The default radius is 5
Dot Type t where t is either circle or square. The default setting is circle
Plot Dot x y hshift vshift draws the dot, with its parameters (shape size color outline fill) already pre-set
x and y are the data values of the horizontal x variable and vertical y variable, as defined by Plot Values lv tv rv bv
hshift is the number of pixels the dot is shifted horizontally, -ve value to the left, +ve value to the right
vshift is the number of pixels the dot is shifted vertically, -ve value upwards, +ve value downwards
In most cases there is no shift (0 0), but if there are more than 1 dot in the same position, shifting avoids the dots superimposing over and obscuring each other
Drawing text
The color, outline, fill, font, and weight of text are preset (see settings). The default settinfs are sans-sherif, black fill only, and 16pxs high
Bitmap HText x y ha va txt draws text horizontally on the bitmap
x and y are number of pixels fom the left and top borders, and together being the reference coordinate of the text
ha is horizontal adjust
ha=0: the left end of the text is at the x coordinate
ha=1: the center of the text is at the x coordinate
ha=2: the right end of the text is at the x coordinate
va is vertical adjust
va=0: the top of the text is at the y coordinate
va=1: the center of the text is at the x coordinate
va=2: the bottom end of the text is at the x coordinate
txt is the text to be drawn. It must be a single word with no gaps. Spaces can be represented by the underscore _
Plot HText x y ha va txt hshift vshift draws text horizontally on the bitmap
x and y are data values as defined by Plot Values lv tv rv bv, and together being the reference coordinate of the text
ha is horizontal adjust
ha=0: the left end of the text is at the x coordinate
ha=1: the center of the text is at the x coordinate
ha=2: the right end of the text is at the x coordinate
va is vertical adjust
va=0: the top of the text is at the y coordinate
va=1: the center of the text is at the x coordinate
va=2: the bottom end of the text is at the x coordinate
txt is the text to be drawn. It must be a single word with no gaps. Spaces can be represented by the underscore _
hshift is the number of pixels the text is shifted horizontally, -ve value to the left, +ve value to the right
vshift is the number of pixels the text is shifted vertically, -ve value upwards, +ve value downwards
In most cases there is no shift (0 0), but if there are other structures in the same position, shifting avoids the text and structures obscuring each other
Bitmap VText x y ha va txt draws text vertically (90 degrees anticlockwise from horizontal) on the bitmap
x and y are number of pixels fom the left and top borders, and together being the reference coordinate of the text
ha is horizontal adjust
ha=0: the left end of the text is at the x coordinate
ha=1: the center of the text is at the x coordinate
ha=2: the right end of the text is at the x coordinate
va is vertical adjust
va=0: the top of the text is at the y coordinate
va=1: the center of the text is at the x coordinate
va=2: the bottom end of the text is at the x coordinate
txt is the text to be drawn. It must be a single word with no gaps. Spaces can be represented by the underscore _
Plot VText x y ha va txt hshift vshift draws text vertically (90 degrees anticlockwise from horizontal) on the bitmap
x and y are data values as defined by Plot Values lv tv rv bv, and together being the reference coordinate of the text
ha is horizontal adjust
ha=0: the left end of the text is at the x coordinate
ha=1: the center of the text is at the x coordinate
ha=2: the right end of the text is at the x coordinate
va is vertical adjust
va=0: the top of the text is at the y coordinate
va=1: the center of the text is at the x coordinate
va=2: the bottom end of the text is at the x coordinate
txt is the text to be drawn. It must be a single word with no gaps. Spaces can be represented by the underscore _
hshift is the number of pixels the text is shifted horizontally, -ve value to the left, +ve value to the right
vshift is the number of pixels the text is shifted vertically, -ve value upwards, +ve value downwards
In most cases there is no shift (0 0), but if there are other structures in the same position, shifting avoids the text and structures obscuring each other
Other miscellaneous drawings
Bitmap Arc x y radius startDeg endDeg rotate draws an arc.
x and y are number of pixels from the left and top border, and together form the center of the arc
radius is the radius of the arc, in number of pixels
startDeg and endDeg are the degrees (360 degrees in full circle) of the arc
rotate defines the direction of the arc, 0 for clockwise, 1 for anti-clockwise
Bitmap Wedge x y radius startDeg endDeg shift rotate draws a wedge, essentially an arc with lines to the center
x and y are number of pixels from the left and top border, and together form the center of the wedge
radius is the radius of the edge, in number of pixels
startDeg and endDeg are the degrees (360 degrees in full circle) of the wedge
shift is the number of pixels that the wedge is moved centrifugally (away from the center). This is used in pie charts to separate the wedges of the pie
rotate defines the direction of the wedge, 0 for clockwise, 1 for anti-clockwise
Plot Curve a b1 b2 b3 b4 b5 x1 x2 draws a polynomial curve
The curve is y=a + b1x + b2x^{2} + b3x^{3} + b4x^{4} + b5x^{5}. Where higher power is not needed, 0 is used to represent the the coefficient b
The curve is drawn from data value x from x1 to x2
Plot Normal mean sd height draws a normal distribution curve
mean and sd (Standard Deviation) are as in the data horizontal variable variable x
height is the maximum height (where x=mean) of the curve as in the vertical variable y
Color Palettes
Plain Colors
0 0 0 #000000
0 0 63 #00003f
0 0 127 #00007f
0 0 191 #0000bf
0 0 255 #0000ff
0 63 0 #003f00
0 63 63 #003f3f
0 63 127 #003f7f
0 63 191 #003fbf
0 63 255 #003fff
0 127 0 #007f00
0 127 63 #007f3f
0 127 127 #007f7f
0 127 191 #007fbf
0 127 255 #007fff
0 191 0 #00bf00
0 191 63 #00bf3f
0 191 127 #00bf7f
0 191 191 #00bfbf
0 191 255 #00bfff
0 255 0 #00ff00
0 255 63 #00ff3f
0 255 127 #00ff7f
0 255 191 #00ffbf
0 255 255 #00ffff
63 0 0 #3f0000
63 0 63 #3f003f
63 0 127 #3f007f
63 0 191 #3f00bf
63 0 255 #3f00ff
63 63 0 #3f3f00
63 63 63 #3f3f3f
63 63 127 #3f3f7f
63 63 191 #3f3fbf
63 63 255 #3f3fff
63 127 0 #3f7f00
63 127 63 #3f7f3f
63 127 127 #3f7f7f
63 127 191 #3f7fbf
63 127 255 #3f7fff
63 191 0 #3fbf00
63 191 63 #3fbf3f
63 191 127 #3fbf7f
63 191 191 #3fbfbf
63 191 255 #3fbfff
63 255 0 #3fff00
63 255 63 #3fff3f
63 255 127 #3fff7f
63 255 191 #3fffbf
63 255 255 #3fffff
127 0 0 #7f0000
127 0 63 #7f003f
127 0 127 #7f007f
127 0 191 #7f00bf
127 0 255 #7f00ff
127 63 0 #7f3f00
127 63 63 #7f3f3f
127 63 127 #7f3f7f
127 63 191 #7f3fbf
127 63 255 #7f3fff
127 127 0 #7f7f00
127 127 63 #7f7f3f
127 127 127 #7f7f7f
127 127 191 #7f7fbf
127 127 255 #7f7fff
127 191 0 #7fbf00
127 191 63 #7fbf3f
127 191 127 #7fbf7f
127 191 191 #7fbfbf
127 191 255 #7fbfff
127 255 0 #7fff00
127 255 63 #7fff3f
127 255 127 #7fff7f
127 255 191 #7fffbf
127 255 255 #7fffff
191 0 0 #bf0000
191 0 63 #bf003f
191 0 127 #bf007f
191 0 191 #bf00bf
191 0 255 #bf00ff
191 63 0 #bf3f00
191 63 63 #bf3f3f
191 63 127 #bf3f7f
191 63 191 #bf3fbf
191 63 255 #bf3fff
191 127 0 #bf7f00
191 127 63 #bf7f3f
191 127 127 #bf7f7f
191 127 191 #bf7fbf
191 127 255 #bf7fff
191 191 0 #bfbf00
191 191 63 #bfbf3f
191 191 127 #bfbf7f
191 191 191 #bfbfbf
191 191 255 #bfbfff
191 255 0 #bfff00
191 255 63 #bfff3f
191 255 127 #bfff7f
191 255 191 #bfffbf
191 255 255 #bfffff
255 0 0 #ff0000
255 0 63 #ff003f
255 0 127 #ff007f
255 0 191 #ff00bf
255 0 255 #ff00ff
255 63 0 #ff3f00
255 63 63 #ff3f3f
255 63 127 #ff3f7f
255 63 191 #ff3fbf
255 63 255 #ff3fff
255 127 0 #ff7f00
255 127 63 #ff7f3f
255 127 127 #ff7f7f
255 127 191 #ff7fbf
255 127 255 #ff7fff
255 191 0 #ffbf00
255 191 63 #ffbf3f
255 191 127 #ffbf7f
255 191 191 #ffbfbf
255 191 255 #ffbfff
255 255 0 #ffff00
255 255 63 #ffff3f
255 255 127 #ffff7f
255 255 191 #ffffbf
255 255 255 #ffffff
Color Palletes
Table of colors used on this web site
255 255 255 #ffffff
224 224 224 #e0e0e0
128 128 128 #808080
128 0 0 #800000
255 0 0 #ff0000
96 48 96 #603060
48 16 64 #301040
96 96 160 #6060a0
160 160 96 #a0a060
160 160 0 #a0a000
153 191 164 #99bfa4
160 160 96 #a0a060
97 24 0 #611800
204 63 200 #cc3fc8
224 224 224 #e0e0e0
Patterns of complementary colors
A
105 93 70 #695d46
255 113 44 #ff712c
207 194 145 #cfc291
161 232 217 #a1e8d9
255 246 197 #fff6c5
B
115 0 70 #730046
201 60 0 #c93c00
232 136 1 #e88801
255 194 0 #ffc200
191 187 17 #bfbb11
C
97 24 0 #611800
140 115 39 #8c7327
71 164 41 #47a429
153 191 164 #99bfa4
242 239 189 #f2efbd
D
20 87 110 #14576e
140 33 90 #8c215a
230 133 38 #e68526
195 102 163 #c366a3
242 207 242 #f2cff2
E
64 1 1 #400101
48 115 103 #307367
96 166 133 #60a685
242 236 145 #f2ec91
229 249 186 #e5f9ba
F
55 89 21 #375915
166 60 60 #a63c3c
115 108 73 #736c49
166 157 129 #a69d81
242 224 201 #f2e0c9
G
115 36 94 #73245e
166 69 33 #a64521
217 182 78 #d9b64e
242 218 145 #f2da91
242 242 242 #f2f2f2
H
255 77 0 #ff4d00
102 87 71 #665747
125 179 0 #7db300
153 138 122 #998a7a
217 195 98 #d9c362
I
128 0 38 #800026
128 128 83 #808053
92 153 122 #5c997a
163 204 143 #a3cc8f
255 230 153 #ffe699
Explanations
Introductiona1
Systematic Review is a method of searching, selecting, cataloguing, and summarising research results to produce Evidence to guide
clinical decision making. Meta-analysis is the mathematical component of Systematic Review, providing algorithms to make
sense and decisions from the vast amount of information obtained.
Meta-analysis itself is a very large and developing subject, and this page covers only meta-analysis of Effects Size (ES) and its Standard Error (SE), mostly applying to data and comparison between two groups. Meta-analysis for prediction studies are separately presented in the MetaPrediction.php page
The page will discuss meta-analysis as it is commonly used, analysing and combining sets of data of similar studies
from multiple sources. The algorithms used and example data are as presented in the Javascript program panel.
Data input
The data input consists of a 2 column table, each row from a separate study, column 1 being the effect size, and column 2
its Standard Error. The assumption is that the Effect Size is a population based normally distributed measurement.
Any groups of measurements (presumably normally distributed, with a mean and the standard Error of the mean, can be used for meta-analysis.
Type of Data
Effect Size and its Standard Error
Single group Data
Mean
Mean
Proportion
Arcsine Transformation of Proportion
Correlation Coefficient
Fisher's Z Transformation
Difference Between Two Means
Means
Difference between Two Means
Means
Standard Difference between Two Means
Means
Ratio between Two Means
Difference Between Two Proportion
Means
Risk Difference
Means
Risk Ratios
Means
Odds Ratios
The table to the right shows the different types of data that can be analysed and combined using meta-analysis.
Example The example will use the default data from the Javascript program panel. Please note that
the data is computer generated to demonstrate the procedures and to assist explanation, and is not representing any real
clinical information.
A common obstetric problem is that of miscarriages, and there was a proposition that miscarriage was caused by hormonal dysfunction in early pregnancy, and providing a supplement of hormones in early pregnancy my reduce miscarriage rate. We wish to study this proposition by conducting a meta-analysis on the available data
Aborted(Pos)
Live Birth (Neg)
Effect Size
Tmt (Pos1)
Control (Pos2)
Tmt (Neg1)
Control (Neg2)
Risk Diff
SE
4
6
14
10
-0.1528
0.1557
3
10
47
50
-0.1067
0.0587
2
10
28
30
-0.1833
0.0822
10
10
70
50
-0.0417
0.0607
12
20
138
160
-0.0311
0.0322
35
34
183
206
0.0189
0.0335
78
83
422
467
0.0051
0.0223
65
68
355
382
0.0037
0.0244
Eight Controlled trials were reviewed, and the results of the trials listed in the table to the right. From each trial,
Group 1 received hormonal supplement (treatment group), and Group 2 did not (Control Group). The number of pregnancies that
aborted (Outcome Positive) and went on to live birth (Outcome Negative) were observed, and the Risk Difference and its
Standard Error, as calculated in differences between two proportions page.
Heterogeneity
Heterogeneity describes whether the studies included in the meta-analysis can be concluded as representing results of similar
studies, and can be combined to produce a summary conclusion. When no significant heterogeneity exists, data may be combined.
When minor heterogeneity exists, some investigations to exclude causes and statistical adjustments to conclusions may be necessary.
Where major heterogeneity exists, combining the data should not proceed without detailed investigation, as the heterogeneity
itself may be an interesting lead to aspects of the subject that has hitherto not been considered.
The following programs for heterogeneity are offered in the Javascript program panel
The Q Test is the oldest and most commonly used test of homogeneity, and nearly universally used. It is a Chi Square
Test of goodness of fit, whether the studies included in the meta-analysis can be considered to be from the same population.
A significant Chi Square, (P<0.05), indicates that significant heterogeneity exists.
In the example provided, The Q Test shows Chi Sq=10.21, p=0.18, so a conclusion that no significant heterogeneity exists can be
made.
The I^{2} Test is derived from the Q Test, and partitions the total variance in the data into that between studies and within studies, I^{2} being the percent of total variance attributable to between studies.
Altman (see references) argued that the I^{2} allows a more nuanced interpretation of heterogeneity over a simple
significant or not significant statement as offered by the Q Test. This allows a researcher to make decisions regarding whether
further investigation or partition of the data are necessary, according to the aims of the meta-analysis. Altman suggested
that an I^{2}<30% can be considered minor and probably acceptable. I^{2}>70%
should be considered major and must be resolved in some way before meta-analysis can proceed. In between some judgement should be
exercised as to how to proceed, and usually this means a statistical adjustment, using the Random Effect Model (see combining
data)
In the example provided, I^{2}=31.4%, marginal between minor and moderate. This indicates that some in depth analysis
would be useful if the data available contain sufficient information for this purpose, and that, when combining the data,
the Random Effect Model should be used ( see panel on combining data).
The z Test calculates the mean and Standard Deviation of all the data in the meta-analysis, then recalibrates each
effect size in terms of the number of Standard Errors (z) away from the mean. Cut off values of 1.96 (95% confidence
interval) or 2 (rounded value for extreme) are used, so that an effect further than these from the overall mean can be
considered heterogeneous.
In the example provided, study 3, z is 2.1 Standard Deviations from the mean, indicating that this groups is possibly heterogeneous
from all others.
The Radial Plot is a regression analysis between an expression of variance
(1/Standard Error) against the z statistics (ES / SE) of each study. The regression line is drawn, with ± 2 Standard
Deviations, and the z statistics are plotted calculated in relationship to these regression lines. Studies that are
more than 2z away from the mean regression line are then considered heterogeneous.
In the example provided, study 2, 3, and 5 deviates significantly from the regression line, and can be considered
heterogeneous to the remaining studies.
Overview
Most published meta-analysis use the Q Test as a check on heterogeneity, then move on to combine the data, so the Q Test can
be considered the standard and the first test to use. Altman encourages the use of the I^{2} Test, which allows the
researcher to make more nuanced decisions in the face of marginal levels of heterogeneity.
The z Test and the Radial Plot allow detailed examination of every study in the dataset, so that they are useful for decisions
on how to exclude or partition the studies when dealing with severely heterogeneous datasets. They are usually not presented
in meta-analysis reports as general conclusions about heterogeneity.
Publication Bias
Publication Bias is the concept that, either out of ignorance or being entrepreneureal, many researchers conduct substandard
research, mostly in terms of insufficient sample size. The results are then offered for publication if a significant result
is obtained, but shelved or rejected by editors when significant results are absent. As a result of this tendency, the
scientific literature contains a bias of excessive significant results.
Although the concept is generally accepted, the problem is that the extent of the bias is not knowable, only estimated from
assumptions and using what information that is available in the meta-analysis dataset. Although a number of tests are available,
they are all based on the idea that, if a publication bias exists, the excessive significant results have a tendency
to contain smaller sample size, and therefore have wider Standard Errors or variances.
In the example provided, the constant a = -2.0045, SEa=0.9945, z=-2.0156, p=0.0219. A significant level of publication bias
is therefore indicated.
The Radial Plot is used both to identify heterogeneity and publication bias. The constant in the regression formula
represents the presence of bias.
The Rank Correlation between Standardized Effect Size and its variance is based on the argument that, if publication bias
exists, then the smaller studies with larger variances are more likely to have a larger effect size, so that a correlation
would exist between these two parameters. The values are weight adjusted and ranked before correlation is calculated, and
a significant correlation signifies the presence of publication bias.
In the example provided, the z value for Rank Correlation is -1.7321 p = 0.0416. A significant level of publication bias
is therefore indicated.
The Rosenthal File drawer is based on a calculation of the fail-safe N, the number of unpublished null results necessary to render the presented dataset non-significant, and compare this with the tolerance level. If the fail-safe N is less than the tolerance level, then publication bias is considered likely.
There are confusion in the literature whether the one or two tail calculation for the fail-safe N should be used. Rosenthal's original paper (see references) used the one tail test, but the manual by Sutton et.al (see references) suggested the two tail test more appropriate. StatsToDo provides calculation for both, but interprets according to the one tail N, as suggested by Rosenthal.
In the example provided, the tolerable number is 50 and fail-safe N (one tail) is 5, suggesting that publication bias should be suspected
Funnel Plot and the Trim and Fill Procedure is a complex and controversial topic, and will be discussed in its own panel in this page.
Overview
All of the procedures provide an estimate of possible publication bias, but none are entirely satisfactory. The Rank Correlation is the easiest to understand, but it lacks power, and the other two procedures are more powerful, but intuitively difficult to understand.
Summary Effect Size
If, after testing the data for heterogeneity and publication bias, a conclusion that combining the data is valid, a number of procedures can be used to combine the data and produce a summary effect size and its Standard Error. Amongst these, the two most common
procedures are :
The Fixed Effect Model , which assumes that all the studies are subsets of a single overarching study, that the
population and environmental characteristics are similar. The model does not take into account variations between studies when
drawing its conclusions.
The Random Effect Model , which assumes that all the studies are individual studies, similar only in that they
study the same issue and uses the same statistical methodology. Variations between studies are therefore taken into account
in the conclusions.
The recommendation is that the Fixed Effect Model should be used when there is no heterogeneity as it is more powerful. The alternative suggestion is that the Random Effect Model should be used in all cases, because the results are the same as the
Fixed Effect Model if no heterogeneity exists, and the model is more valid if heterogeneity does exist.
In the example provided, using the Fixed Effect Model, the Summary Effect=-0.0115, and Standard Error=0.0126. Using the
Random Effect Model, Summary Effect=-0.0193, and Standard Error=0.017. These are nearly the same because there is little
heterogeneity in the data
Displaying Results. A Forest plot is the most common method of displaying the results of meta-analysis, as shown in
the plot on the right. The central tendency (round dot) its 95% confidence interval from each study are displayed, and the
combined summary effect (square dot) and its 95% confidence interval placed at the bottom
Trim and Fill
Please Note : Funnel Plot with Trim and Fill produces two plots, the Funnel Plot, and the Forest Plot with adjusted Summary
Effect. The last two buttons in the Javascript program panel therefore activate the same procedures and
produce the numerical results. They differ only that one preduces the Funnel Plot and the other the Forest Plot. The reason is
to make graphical editing less confusing.
The Funnel Plot is a visual method to evaluate the existence of publication bias. The plot has the effect size as its x
axis, and the inverse of Standard Error (1/SE) as the y axis.
If there is no bias, one would expect that the larger studies (ones with
smaller Standard Error therefore near the top of the plot) to cluster near the mean effect size value, while the smaller
studies (ones with larger Standard Errors therefore cluster near the bottom of the plot), to cluster towards the extreme ends
of the effect size values. The plot should therefore resemble that of an inverted funnel.
If publication bias exists, then the smaller studies without significant findings would be missing, and the plot would become
asymmetrical. Such a plot would be intuitively easy to understand and interpret, but the down side is that is depends on
subjective interpretations
The funnel plot using the example provided is shown above and to the left, and it is obvious that the right side of the funnel
is missing, and therefore considerable publication bias exists.
Trim and Fill is a procedure to replace what might have been the studies that had been left out to cause the
publication bias.
The idea is to take the study with the most extreme effect size on the Funnel, and create a data point
equidistance from the mean on the other side of the Funnel. This is performed one data point a time, and the mean
value recalculated, until the funnel is no longer lop sided.
The results are as shown in the plot to the right. The two most extreme data points on the left of the plot are replicated
on the right side (unfilled dots).
Adjusted Summary Effect After Trim and Fill, the new Summary Effect, using the Random Effect Model and including the
replicated "fill" data points, can be calculated. The result Effect Size=-0.0134, and Standard Error=0.0183. The final
Forest Plot is shown to the left. The filled dots represent the original data, unfilled dots are those added from Trim and Fill, and the square
the adjusted Effect Size.
Overview Funnel Plot plus Trim and Fill is a powerful tool to enable completion of meta-analysis in the face of probable
publication bias. However the method is only valid if the underlying assumptions are valid, that the imbalance in extreme
effect size studies with wide Standard Error are in fact due to an underlying publication bias, that by replicating these values
to the other side of the null position can, at least approximately, correct this bias. These assumptions cannot be easily made.
For references to original descriptions of the algorithm
Hunter, J. E., & Schmidt, F. L. (2004). Methods of meta-analysis: Correcting error and bias in research findings (Second edition). Newbury Park, CA: Sage
Hedges, L. V., & Olkin, I. (1985). Statistical methods for meta-analysis. Orlando, FL: Academic Press
Publication Bias
Rosenthal R (1979). The "File Drawer Problem" and Tolerance for Null Results. Psychological Bulletin Vol.86, No.3, p638-641 [On Line pdf Link]
Sutton AJ, Abrams KR, Jones DR, Sheldon TA, and Song F (2000)
Methods for Meta-Analysis in Medical Research. John Wiley & Sons Ltd.,
Chichester UK. ISBN 0-471-49066-0.p 109-122
For calculations of Combined Summary Effect Size using Fixed and Random Effect Models :
Sutton AJ, Abrams KR, Jones DR, Sheldon TA, and Song F (2000)
Methods for Meta-Analysis in Medical Research. John Wiley & Sons Ltd.,
Chichester UK. ISBN 0-471-49066-0.p 58-63, p 80-82
For meta-regression
Hedges LV and Olkin I (1985) Statistical Methods for meta-Analysis.
Academic Press Ltd., Oval Rd., London, UK. ISBN 0-12-336380-2 p.169-173
Thompson SG and Sharp SJ (1999) Explaining heterogeneity in Meta-Analysis
Statistics in Medicine 18 : p. 2693-2708
Sutton AJ, Abrams KR, Jones DR, Sheldon TA, and Song F (2000) Methods for
Meta-Analysis in Medical Research. John Wiley & Sons Ltd., Chichester UK.
ISBN 0-471-49066-0. p.95-97