heatmap help - how to display grouped data across 3 levels (LO, MED, HI) ?

조회 수: 11 (최근 30일)
How can I show 3 levels in a heatmap (LO MED, HI) and not just two (FALSE, TRUE for example) across n=4 GROUPS for a single outcome variable (continuous variable)? And is it possbile to (also) show % of total for the OUTCOME variable by GROUP? and not just mean or median.
Here is some working code and a dataset, but displays only two levels (LO, HI) for each GROUP:
load CHRX.mat % loads as CH = Chelsea; RX = Roxbury
P = prctile(CH.VLFN, 75);
isVHI = CH.VLFN>=P==1;
VHI = isVHI; % this is the HIGH level
P1 = prctile(CH.VLFN, 25);
isVLO = CH.VLFN<=P1==1;
VLO = isVLO; % thius is the LOW level
isIQR = ~VHI&~VLO==1;
IQR = isIQR; % this is the MEDIUM level
CH1 = addvars(CH, VLO); CH2 = addvars(CH1 ,IQR); CH3 = addvars(CH2, VHI)
CH3 = 40914×26 table
DateTime LAeq VLFN LFN MFN HFN Day Season DayNumber Wkend RushHR Site Temp Hum WD_Deg ImpactWind WS Rain mbBar WS_HI RF UFPConc lnUFP VLO IQR VHI ____________________ _____ ______ ______ ______ ______ _____ ______ _________ _____ ______ _____ ____ _____ ______ __________ ______ ____ ______ _____ _____ _______ ______ _____ _____ _____ 15-Apr-2016 12:34:00 54.15 82.3 68.426 57.616 53.474 true false 6 false false false 8.9 34.06 80 railway 16.668 0 1031.3 false false 22575 10.025 false false true 15-Apr-2016 12:36:00 53.25 79.223 66.039 55.502 53.072 true false 6 false false false 8.9 34.06 80 railway 16.668 0 1031.3 false false 14450 9.5784 false false true 15-Apr-2016 12:39:00 53.7 77.488 67.369 56.525 53.579 true false 6 false false false 8.9 34.06 80 railway 16.668 0 1031.3 false false 12500 9.4335 false false true 15-Apr-2016 12:41:00 54.8 70.488 69.756 56.901 54.207 true false 6 false false false 8.9 34.06 80 railway 16.668 0 1031.3 false false 18820 9.8427 false true false 15-Apr-2016 12:43:00 63.55 72.117 70.894 58.103 63.814 true false 6 false false false 8.9 34.06 80 railway 16.668 0 1031.3 false false 19750 9.8909 false false true 15-Apr-2016 12:44:00 53.75 73.194 70.446 56.569 52.921 true false 6 false false false 8.9 34.06 80 railway 16.668 0 1031.3 false false 22250 10.01 false false true 15-Apr-2016 12:45:00 52.65 65.916 68.539 54.082 52.55 true false 6 false false false 8.9 34.06 80 railway 16.668 0 1031.3 false false 27800 10.233 false true false 15-Apr-2016 12:46:00 53.3 71.771 69.697 54.478 52.908 true false 6 false false false 8.9 34.06 80 railway 16.668 0 1031.3 false false 20400 9.9233 false false true 15-Apr-2016 12:47:00 54.95 69.95 69.938 56.176 54.107 true false 6 false false false 8.9 34.06 80 railway 16.668 0 1031.3 false false 11700 9.3673 false true false 15-Apr-2016 12:49:00 53.9 65.075 69.134 54.696 52.966 true false 6 false false false 8.9 34.06 80 railway 16.668 0 1031.3 false false 16700 9.7232 false true false 15-Apr-2016 12:50:00 53.8 72.045 69.219 55.651 53.241 true false 6 false false false 8.9 34.06 80 railway 16.668 0 1031.3 false false 13150 9.4842 false false true 15-Apr-2016 12:51:00 59.85 72.058 70.259 59.153 59.137 true false 6 false false false 8.9 34.06 80 railway 16.668 0 1031.3 false false 14380 9.5736 false false true 15-Apr-2016 12:52:00 56.3 70.284 70.176 57.921 55.563 true false 6 false false false 8.9 34.06 80 railway 16.668 0 1031.3 false false 14100 9.5539 false true false 15-Apr-2016 12:54:00 55.4 65.599 71.151 57.015 54.276 true false 6 false false false 8.9 34.06 80 railway 16.668 0 1031.3 false false 17300 9.7585 false true false 15-Apr-2016 12:55:00 54.7 63.082 68.384 55.673 54.231 true false 6 false false false 8.9 34.06 80 railway 16.668 0 1031.3 false false 21600 9.9804 false true false 15-Apr-2016 12:57:00 53.25 74.909 68.499 54.707 53.208 true false 6 false false false 8.9 34.06 80 railway 16.668 0 1031.3 false false 12350 9.4214 false false true
%% HEATMAPS
% note: ImpactWind is the GROUP categorical variable (n=4)
% note: UFPConc is the OUTCOME, continuous variable or ColorVariable
format shortG
data = CH3; % Chelsea
figure()
h = heatmap(data,'ImpactWind','VHI','ColorVariable','UFPConc')
h =
HeatmapChart (Mean of UFPConc) with properties: SourceTable: [40914×26 table] XVariable: 'ImpactWind' YVariable: 'VHI' ColorVariable: 'UFPConc' ColorMethod: 'mean' Use GET to show all properties
h.YDisplayLabels = {'LO','HI'}
h =
HeatmapChart (Mean of UFPConc) with properties: SourceTable: [40914×26 table] XVariable: 'ImpactWind' YVariable: 'VHI' ColorVariable: 'UFPConc' ColorMethod: 'mean' Use GET to show all properties

채택된 답변

Voss
Voss 2024년 1월 10일
You can make a variable that has three distinct values, say 0 for LO, 1 for MEDIUM, and 2 for HI, which can be constructed as follows:
level = 2*VHI + IQR;
and then add that variable to your table and create the heatmap based on that.
load CHRX.mat % loads as CH = Chelsea; RX = Roxbury
VHI = CH.VLFN >= prctile(CH.VLFN, 75); % this is the HIGH level
VLO = CH.VLFN <= prctile(CH.VLFN, 25); % this is the LOW level
IQR = ~VHI & ~VLO; % this is the MEDIUM level
level = VHI*2+IQR;
CH = addvars(CH,level);
%% HEATMAPS
% note: ImpactWind is the GROUP categorical variable (n=4)
% note: UFPConc is the OUTCOME, continuous variable or ColorVariable
format shortG
data = CH; % Chelsea
figure()
h = heatmap(data,'ImpactWind','level','ColorVariable','UFPConc');
h.YDisplayLabels = {'LO','MED','HI'};

추가 답변 (1개)

William Rose
William Rose 2024년 1월 10일
Make a single variable Vstat with three categories: LOW, MED, HIGH, rather than three separate variables.
load CHRX.mat % loads as CH = Chelsea; RX = Roxbury
Phi = prctile(CH.VLFN, 75);
Plo = prctile(CH.VLFN, 25);
[Vstat{1:height(CH),1}]=deal('MED'); % initialize Vstat
for i=1:height(CH)
if CH.VLFN(i)<Plo, Vstat{i}='LOW';
elseif CH.VLFN(i)>Phi, Vstat{i}='HIGH';
end
end
data=addvars(CH,Vstat); % Chelsea
%% HEATMAPS
% note: ImpactWind is the GROUP categorical variable (n=4)
% note: UFPConc is the OUTCOME, continuous variable or ColorVariable
format shortG
figure()
h = heatmap(data,'ImpactWind','Vstat','ColorVariable','UFPConc');
h.YDisplayLabels = {'HIGH','MED','LOW'};
There's probably a more elegant way to do it than the for loop I use below, but my way seems to work. Good luck.

카테고리

Help CenterFile Exchange에서 Data Distribution Plots에 대해 자세히 알아보기

태그

제품


릴리스

R2023b

Community Treasure Hunt

Find the treasures in MATLAB Central and discover how the community can help you!

Start Hunting!

Translated by