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icon_array.js
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icon_array.js
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// Create an initial set of covariates
// where everything is set to 0 except
// for the intercept.
function init_covar(cats){
var covar = new Array(cats.length);
for(var i = 0; i < cats.length; i++){
if(cats[i] == "(Intercept)"){
covar[cats[i]] = 1;
} else {
covar[cats[i]] = 0;
}
}
return covar;
}
// This is an implementation of the estimation of
// response probabilities for a multinomial logit.
// See Agrest's Categorical Data Analysis 7.1.3
// (pg. 271 in the 2002 edition)
function update_covar(covar, coef, len, rows, cols){
var tmp_pct = new Array(len);
var pct_tot = 0;
var tot = 0;
var keys = Object.keys(covar);
// Get the total for the denominator
for(var i=0; i < rows; i++){
var subtot1 = 0;
var k = 0;
for(var j=0; j < cols; j++){
subtot1 = subtot1 + coef[j][i]*covar[keys[k]];
k++;
}
tot = tot + Math.exp(subtot1);
}
// Get each numerator
for(var i=0; i < rows; i++){
var subtot2 = 0;
var m = 0;
for(var j=0; j < cols; j++){
subtot2 = subtot2 + coef[j][i]*covar[keys[m]];
m++;
}
tmp_pct[i+1] = Math.exp(subtot2)/(1+tot); // Skip the baseline for now
pct_tot = pct_tot + tmp_pct[i+1];
}
tmp_pct[0] = 1 - pct_tot;
for(var q=0; q < tmp_pct.length; q++){
tmp_pct[q] = Math.round(tmp_pct[q]*100);
}
return tmp_pct;
}
function HealthvisIconArray() {
this.grid = null;
this.color_array = null;
this.init_color = null;
this.group_colors = null;
this.group_names = null;
this.formdata = [];
this.w = 700;
this.h = 500;
this.legw = this.w/3.5;
this.legh = this.h/2.5;
this.y = d3.scale.linear().domain([0,100]).range([this.h*0.98,this.h*0.02]);
// Need better way of setting plot dimensions
this.init = function(elementId, d3Params) {
var dimensions = healthvis.getDimensions(this.w, this.h);
this.w = dimensions.width;
this.h = dimensions.height;
this.legw = this.w/3.5;
this.legh = this.h/2.5;
this.grid = d3.select('.rChart svg')
.attr('width', this.w)
.attr('height', this.h)
.attr('class', 'chart');
this.flag = d3Params.obj_flag;
this.color_array = d3Params.color_array;
this.init_color = this.color_array.slice(0);
this.group_colors = d3Params.group_colors;
this.group_names = d3Params.group_names;
this.rows = d3Params.rows;
this.cols = d3Params.cols;
this.cats = d3Params.cats;
this.vtype = d3Params.vtype;
this.pcts = new Array(this.rows+1); // Add 1 for the baseline category (asumed to be this.pcts[0])
this.covar = init_covar(this.cats);
this.c_tmp = d3Params.coefs;
// Make a 2-D array so we can loop through and get probabilities for each category
var coefs = new Array(this.cols);
for(var i=0; i < this.cols; i++){
coefs[i] = new Array(this.rows);
for(var j=0; j < this.rows; j++){
coefs[i][j] = this.c_tmp[i*this.rows+j];
}
}
this.coefs = coefs;
this.data = [];
// These things should all depend on plot dimensions
var cellWidth = this.w/23;
var cellHeight = this.h/13;
var start = this.w/70;
var xpos = start+this.w/28;
var ypos = start;
var xBuffer = cellWidth+start;
var yBuffer = cellHeight+start;
var count = 0;
// Initialize an object giving position and color info
// for each rect.
for(var i=0; i < 10; i++){
this.data.push(new Array());
for(var j=0; j < 10; j++){
this.data[i].push({
width: cellWidth,
height: cellHeight,
x: xpos,
y: ypos,
color: this.color_array[count]
});
xpos += xBuffer;
count += 1;
}
xpos = start+this.w/28;
ypos += yBuffer;
}
};
this.visualize = function() {
this.row = this.grid.selectAll('.row')
.data(this.data)
.enter().append('svg:g')
.attr('class', 'row');
this.col = this.row.selectAll('.cell')
.data(function (d) { return d; })
.enter().append('svg:rect')
.attr('class', 'cell')
.attr('x', function(d) { return d.x; })
.attr('y', function(d) { return d.y; })
.attr('width', function(d) { return d.width; })
.attr('height', function(d) { return d.height; })
.style('fill', function(d,i) { return d.color; });
var yAxis = d3.svg.axis().scale(this.y).ticks(10).orient('left');
this.grid.append('svg:g')
.attr('class', 'y axis')
.attr('transform', 'translate('+this.w/24+',0)')
.call(yAxis);
// Add legend
var legend = this.grid.append('g')
.attr('class', 'legend')
.attr('x', this.w*(2/3))
.attr('y', this.h/4)
.attr('height', this.legh)
.attr('width', this.legw);
var lh = this.legh;
legend.selectAll('rect')
.data(this.group_colors).enter().append('rect')
.attr('x', this.w*(2/3))
.attr('y', function(d,i){return i*(lh/15)+lh/2.2;})
.attr('width', this.legw/20)
.attr('height', this.legh/20)
.style('fill', function(d) { return d; });
var group_names = this.group_names;
legend.selectAll('text')
.data(this.group_names).enter().append('text')
.attr('x', this.w*(2/3)+(1/35)*this.w)
.attr('y', function(d,i){return i*(lh/15)+lh/2;})
.text(function(d) { return d; });
};
this.update = function(formdata) {
var nums = null;
if(this.flag == 0){
for (var j=0; j<this.group_colors.length; j++) {
this.formdata[j] = parseFloat(formdata[j].value);
}
nums = this.formdata;
} else {
this.covar = init_covar(this.cats); // Reset everything
// Set the covariates correctly
for (var j=0; j<formdata.length; j++) {
if(this.vtype[j] == "factor"){
this.covar[(formdata[j].name+formdata[j].value)] = 1;
} else {
this.covar[formdata[j].name] = parseFloat(formdata[j].value);
}
}
this.pcts = update_covar(this.covar, this.coefs, this.pcts.length, this.rows, this.cols);
nums = this.pcts;
}
// Reset colors based on new numbers for each covariate
var sum=0;
var col_tmp = this.init_color.slice(0);
for(var k = 0; k < nums.length; k++){
for(var m = sum; m < (sum + nums[k]); m++){
col_tmp[m] = this.group_colors[k];
}
sum += nums[k];
}
if(sum > 100){
alert("Inputs total >100...figure will update, but it may not be how you want.");
}
// Reverse, since we need to go from bottom to top
this.color_array = col_tmp.reverse();
// Set new colors in data object
var count=0;
for(var i=0; i < 10; i++){
for(var j=0; j <10; j++){
this.data[i][j].color = this.color_array[count];
count += 1;
}
}
// Here is the transition: fill new colors
this.col.transition().style('fill', function(d) { return d.color; });
};
}
healthvis.register(new HealthvisIconArray());