error: X and GROUP must have the same length.but the lengths are the same. This must be a malfunctin how can i fix please help

Question

Laura 2023-5-2

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此问题的直接链接

https://ww2.mathworks.cn/matlabcentral/answers/1956664-error-x-and-group-must-have-the-same-length-but-the-lengths-are-the-same-this-must-be-a-malfunctin

评论： Laura 2023-5-10

i have 6 feature mats files, 3 structs per mat file giving 18 struct total I want perform anova test per 6 struct so i ahve grouped but error:X and GROUP must have the same length.

Error in anova_cntrldiet (line 38)

[p_profileCounts, tbl_profileCounts, stats_profileCounts] = anova1([profileCounts1, profileCounts2, profileCounts3], {'Mouse1', 'Mouse2', 'Mouse3'});

please assist in fixing error so i can perform anova test thank you.

clear all;
% Load the features:
feature1 = load('feature1.mat');
feature2 = load('feature2.mat');
feature3 = load('feature3.mat');
feature4 = load('feature4.mat');
feature5 = load('feature5.mat');
feature6 = load('feature6.mat');
% Combine all of the structs into a single feature for each mouse
mouse1 = struct('lobule1', feature1.features, 'lobule2', feature2.features);
mouse2 = struct('lobule1', feature3.features, 'lobule2', feature4.features);
mouse3 = struct('lobule1', feature5.features, 'lobule2', feature6.features);
% Extract the desired fields from each mouse
profileCounts1 = [mouse1.lobule1.profileCounts; mouse1.lobule2.profileCounts];
totalArea1 = [mouse1.lobule1.totalArea; mouse1.lobule2.totalArea];
avgSize1 = [mouse1.lobule1.avgSize; mouse1.lobule2.avgSize];
AvgCircularityy1 = [mouse1.lobule1.AvgCircularityy; mouse1.lobule2.AvgCircularityy];
AvgFeret1 = [mouse1.lobule1.AvgFeret; mouse1.lobule2.AvgFeret];
AvgMinFeret1 = [mouse1.lobule1.AvgMinFeret; mouse1.lobule2.AvgMinFeret];
profileCounts2 = [mouse2.lobule1.profileCounts; mouse2.lobule2.profileCounts];
totalArea2 = [mouse2.lobule1.totalArea; mouse2.lobule2.totalArea];
avgSize2 = [mouse2.lobule1.avgSize; mouse2.lobule2.avgSize];
AvgCircularityy2 = [mouse2.lobule1.AvgCircularityy; mouse2.lobule2.AvgCircularityy];
AvgFeret2 = [mouse2.lobule1.AvgFeret; mouse2.lobule2.AvgFeret];
AvgMinFeret2 = [mouse2.lobule1.AvgMinFeret; mouse2.lobule2.AvgMinFeret];
profileCounts3 = [mouse3.lobule1.profileCounts; mouse3.lobule2.profileCounts];
totalArea3 = [mouse3.lobule1.totalArea; mouse3.lobule2.totalArea];
avgSize3 = [mouse3.lobule1.avgSize; mouse3.lobule2.avgSize];
AvgCircularityy3 = [mouse3.lobule1.AvgCircularityy; mouse3.lobule2.AvgCircularityy];
AvgFeret3 = [mouse3.lobule1.AvgFeret; mouse3.lobule2.AvgFeret];
AvgMinFeret3 = [mouse3.lobule1.AvgMinFeret; mouse3.lobule2.AvgMinFeret];
[p_profileCounts, tbl_profileCounts, stats_profileCounts] = anova1([profileCounts1, profileCounts2, profileCounts3], {'Mouse1', 'Mouse2', 'Mouse3'});
Error using anova1
X and GROUP must have the same length.
[p_totalArea, tbl_totalArea, stats_totalArea] = anova1([totalArea1, totalArea2, totalArea3], {'Mouse1', 'Mouse2', 'Mouse3'});
[p_avgSize, tbl_avgSize, stats_avgSize] = anova1([avgSize1, avgSize2, avgSize3], {'Mouse1', 'Mouse2', 'Mouse3'});
[p_AvgCircularityy, tbl_AvgCircularityy, stats_AvgCircularityy] = anova1([AvgCircularityy1, AvgCircularityy2, AvgCircularityy3], {'Mouse1', 'Mouse2', 'Mouse3'});
[p_AvgFeret, tbl_AvgFeret, stats_AvgFeret] = anova1([AvgFeret1, AvgFeret2, AvgFeret3], {'Mouse1', 'Mouse2', 'Mouse3'});
[p_AvgMinFeret, tbl_AvgMinFeret, stats_AvgMinFeret] = anova1([AvgMinFeret1, AvgMinFeret2, AvgMinFeret3], {'Mouse1', 'Mouse2', 'Mouse3'});
% Perform pairwise comparisons between each pair of mice for each feature
[pairwise_profileCounts, tbl_pairwise_profileCounts, stats_pairwise_profileCounts] = ...
    anova2([profileCounts1', profileCounts2', profileCounts3'], 1, 'off');
[pairwise_totalArea, tbl_pairwise_totalArea, stats_pairwise_totalArea] = ...
    anova2([totalArea1', totalArea2', totalArea3'], 1, 'off');
[pairwise_avgSize, tbl_pairwise_avgSize, stats_pairwise_avgSize] = ...
    anova2([avgSize1', avgSize2', avgSize3'], 1, 'off');
[pairwise_AvgCircularityy, tbl_pairwise_AvgCircularityy, stats_pairwise_AvgCircularityy] = ...
    anova2([AvgCircularityy1', AvgCircularityy2', AvgCircularityy3'], 1, 'off');
[pairwise_AvgFeret, tbl_pairwise_AvgFeret, stats_pairwise_AvgFeret] = ...
    anova2([AvgFeret1', AvgFeret2', AvgFeret3'], 1, 'off');
[pairwise_AvgMinFeret, tbl_pairwise_AvgMinFeret, stats_pairwise_AvgMinFeret] = ...
    anova2([AvgMinFeret1', AvgMinFeret2', AvgMinFeret3'], 1, 'off');
% Display the results
disp('Profile Counts:');
disp(tbl_profileCounts);
disp(pairwise_profileCounts);
disp(' ');
disp('Total Area:');
disp(tbl_totalArea);
disp(pairwise_totalArea);
disp(' ');
disp('Average Size:');
disp(tbl_avgSize);
disp(pairwise_avgSize);
disp(' ');
disp('Average Circularity:');
disp(tbl_AvgCircularityy);
disp(pairwise_AvgCircularityy);
disp(' ');
disp('Average Feret:');
disp(tbl_AvgFeret);
disp(pairwise_AvgFeret);
disp(' ');
disp('Average Minimum Feret:');
disp(tbl_AvgMinFeret);
disp(pairwise_AvgMinFeret);
disp(' ');

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Cris LaPierre 2023-5-2

Please share your mat files. You can attach them to your post using the paperclip icon

Laura 2023-5-2

@Cris LaPierre Sorry, please see attached. Each has 3 structs

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Answer 1

Cris LaPierre 2023-5-2

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此回答的直接链接

https://ww2.mathworks.cn/matlabcentral/answers/1956664-error-x-and-group-must-have-the-same-length-but-the-lengths-are-the-same-this-must-be-a-malfunctin#answer_1227934

The issue is that X is a 2x108 but Group is 1x3. They must be the same length.

See this example: https://www.mathworks.com/help/stats/anova1.html#buos6wf-1

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Laura 2023-5-4

在 MATLAB Online 中打开

@Walter Roberson @Cris LaPierre

clc; % Clear the command window.

close all; % Close all figures (except those of imtool.)

clear; % Erase all existing variables. Or clearvars if you want.

workspace; % Make sure the workspace panel is showing.

% load the feature matrices

feature1 = load('feature1.mat');

feature2 = load('feature2.mat');

feature3 = load('feature3.mat');

feature4 = load('feature4.mat');

feature5 = load('feature5.mat');

feature6 = load('feature6.mat');

% Combine the first two structs into a single struct

mouse1 = [feature1.features, feature2.features];

% Combine the next two structs into a single struct

mouse2 = [feature3.features, feature4.features];

% Combine the last two structs into a single struct

mouse3 = [feature5.features, feature6.features];

% Extract the desired fields from each mouse

profileCounts = [mouse1.profileCounts; mouse2.profileCounts; mouse3.profileCounts];

totalArea = [mouse1.totalArea; mouse2.totalArea; mouse3.totalArea];

avgSize = [mouse1.avgSize; mouse2.avgSize; mouse3.avgSize];

AvgCircularityy = [mouse1.AvgCircularityy; mouse2.AvgCircularityy; mouse3.AvgCircularityy];

AvgFeret = [mouse1.AvgFeret; mouse2.AvgFeret; mouse3.AvgFeret];

AvgMinFeret = [mouse1.AvgMinFeret; mouse2.AvgMinFeret; mouse3.AvgMinFeret];

% Perform ANOVA on each field to compare the means of the three mice

[~, p_profileCounts] = anova1(profileCounts);

[~, p_totalArea] = anova1(totalArea);

[~, p_avgSize] = anova1(avgSize);

[~, p_AvgCircularityy] = anova1(AvgCircularityy);

[~, p_AvgFeret] = anova1(AvgFeret);

[~, p_AvgMinFeret] = anova1(AvgMinFeret);

% Display the p-values for each field

disp(['Profile Counts p-value: ', num2str(p_profileCounts)])

Error using num2str
Input to num2str must be numeric.

disp(['Total Area p-value: ', num2str(p_totalArea)])

disp(['Average Size p-value: ', num2str(p_avgSize)])

disp(['Average Circularity p-value: ', num2str(p_AvgCircularityy)])

disp(['Average Feret p-value: ', num2str(p_AvgFeret)])

disp(['Average Minimum Feret p-value: ', num2str(p_AvgMinFeret)])

Laura 2023-5-4

编辑：Laura 2023-5-4

在 MATLAB Online 中打开

@Walter Roberson

clc; % Clear the command window.
close all; % Close all figures (except those of imtool.)
clear; % Erase all existing variables. Or clearvars if you want.
workspace; % Make sure the workspace panel is showing.
% load the feature matrices
feature1 = load('feature1.mat');
feature2 = load('feature2.mat');
feature3 = load('feature3.mat');
feature4 = load('feature4.mat');
feature5 = load('feature5.mat');
feature6 = load('feature6.mat');
% Combine the first two structs into a single struct
mouse1 = [feature1.features, feature2.features];
% Combine the next two structs into a single struct
mouse2 = [feature3.features, feature4.features];
% Combine the last two structs into a single struct
mouse3 = [feature5.features, feature6.features];
%extract fields
profileCounts = [mouse1(1).profileCounts; mouse1(2).profileCounts; mouse1(3).profileCounts; mouse1(4).profileCounts; mouse1(5).profileCounts; mouse1(6).profileCounts; mouse2(1).profileCounts; mouse2(2).profileCounts; mouse2(3).profileCounts; mouse2(4).profileCounts; mouse2(5).profileCounts; mouse2(6).profileCounts; mouse3(1).profileCounts; mouse3(2).profileCounts; mouse3(3).profileCounts; mouse3(4).profileCounts; mouse3(5).profileCounts; mouse3(6).profileCounts];
totalArea = [mouse1(1).totalArea; mouse1(2).totalArea; mouse1(3).totalArea; mouse1(4).totalArea; mouse1(5).totalArea; mouse1(6).totalArea; mouse2(1).totalArea; mouse2(2).totalArea; mouse2(3).totalArea; mouse2(4).totalArea; mouse2(5).totalArea; mouse2(6).totalArea; mouse3(1).totalArea; mouse3(2).totalArea; mouse3(3).totalArea; mouse3(4).totalArea; mouse3(5).totalArea; mouse3(6).totalArea];
avgSize = [mouse1(1).avgSize; mouse1(2).avgSize; mouse1(3).avgSize; mouse1(4).avgSize; mouse1(5).avgSize; mouse1(6).avgSize; mouse2(1).avgSize; mouse2(2).avgSize; mouse2(3).avgSize; mouse2(4).avgSize; mouse2(5).avgSize; mouse2(6).avgSize; mouse3(1).avgSize; mouse3(2).avgSize; mouse3(3).avgSize; mouse3(4).avgSize; mouse3(5).avgSize; mouse3(6).avgSize];
AvgCircularityy = [mouse1(1).AvgCircularityy; mouse1(2).AvgCircularityy; mouse1(3).AvgCircularityy; mouse1(4).AvgCircularityy; mouse1(5).AvgCircularityy; mouse1(6).AvgCircularityy; mouse2(1).AvgCircularityy; mouse2(2).AvgCircularityy; mouse2(3).AvgCircularityy; mouse2(4).AvgCircularityy; mouse2(5).AvgCircularityy; mouse2(6).AvgCircularityy; mouse3(1).AvgCircularityy; mouse3(2).AvgCircularityy; mouse3(3).AvgCircularityy; mouse3(4).AvgCircularityy; mouse3(5).AvgCircularityy; mouse3(6).AvgCircularityy];
AvgFeret = [mouse1(1).AvgFeret; mouse1(2).AvgFeret; mouse1(3).AvgFeret; mouse1(4).AvgFeret; mouse1(5).AvgFeret; mouse1(6).AvgFeret; mouse2(1).AvgFeret; mouse2(2).AvgFeret; mouse2(3).AvgFeret; mouse2(4).AvgFeret; mouse2(5).AvgFeret; mouse2(6).AvgFeret; mouse3(1).AvgFeret; mouse3(2).AvgFeret; mouse3(3).AvgFeret; mouse3(4).AvgFeret; mouse3(5).AvgFeret; mouse3(6).AvgFeret];
AvgMinFeret = [mouse1(1).AvgMinFeret; mouse1(2).AvgMinFeret; mouse1(3).AvgMinFeret; mouse1(4).AvgMinFeret; mouse1(5).AvgMinFeret; mouse1(6).AvgMinFeret; mouse2(1).AvgMinFeret; mouse2(2).AvgMinFeret; mouse2(3).AvgMinFeret; mouse2(4).AvgMinFeret; mouse2(5).AvgMinFeret; mouse2(6).AvgMinFeret; mouse3(1).AvgMinFeret; mouse3(2).AvgMinFeret; mouse3(3).AvgMinFeret; mouse3(4).AvgMinFeret; mouse3(5).AvgMinFeret; mouse3(6).AvgMinFeret];
zoneArea = [mouse1(1).zoneArea; mouse1(2).zoneArea; mouse1(3).zoneArea; mouse1(4).zoneArea; mouse1(5).zoneArea; mouse1(6).zoneArea; mouse2(1).zoneArea; mouse2(2).zoneArea; mouse2(3).zoneArea; mouse2(4).zoneArea; mouse2(5).zoneArea; mouse2(6).zoneArea; mouse3(1).zoneArea; mouse3(2).zoneArea; mouse3(3).zoneArea; mouse3(4).zoneArea; mouse3(5).zoneArea; mouse3(6).zoneArea];
% Perform one-way ANOVA on profileCounts
[p, tbl, stats] = anova1(profileCounts);
% Display the ANOVA table
disp(tbl);
% If p < 0.05, reject null hypothesis
if p < 0.05
    disp('There is a significant difference between the means');
else
    disp('There is no significant difference between the means');
end

Cris LaPierre 2023-5-9

编辑：Cris LaPierre 2023-5-9

在 MATLAB Online 中打开

To compare the mean between 2 groups, it seems you should be using the student t-test.

h = ttest2(x,y) returns a test decision for the null hypothesis that the data in vectors x and y comes from independent random samples from normal distributions with equal means and equal but unknown variances, using the two-sample t-test. The alternative hypothesis is that the data in x and y comes from populations with unequal means. The result h is 1 if the test rejects the null hypothesis at the 5% significance level, and 0 otherwise.

mouse1 = load('feature1.mat');

mouse2 = load('feature2.mat');

mouse3 = load('feature3.mat');

mouse4 = load('feature4.mat');

mouse5 = load('feature5.mat');

mouse6 = load('feature6.mat');

M1 = ftTbl(mouse1)

M1 = 36×8 table

filename totalArea avgSize zoneArea AvgCircularityy AvgFeret AvgMinFeret profileCounts ___________________________________________________________________________ _________ _______ __________ _______________ ________ ___________ _____________ "1_M1_EF_EF_12pm_dendra2_actin647_lipitox594.lif - TileScan 1_Merged-2.tif" 72670 78.903 1.4486e+06 0.40546 19.831 9.609 921 "1_M1_EF_EF_12pm_dendra2_actin647_lipitox594.lif - TileScan 1_Merged-2.tif" 54125 68.426 6.4922e+05 0.39959 18.937 8.9743 791 "1_M1_EF_EF_12pm_dendra2_actin647_lipitox594.lif - TileScan 1_Merged-2.tif" 48869 70.416 5.6909e+05 0.40305 18.917 8.7841 694 "1_M1_EF_EF_12pm_dendra2_actin647_lipitox594.lif - TileScan 1_Merged-2.tif" 60194 66.439 5.5381e+05 0.45254 16.708 8.2881 906 "1_M1_EF_EF_12pm_dendra2_actin647_lipitox594.lif - TileScan 1_Merged-2.tif" 50301 60.823 5.4692e+05 0.47879 16.119 7.7157 827 "1_M1_EF_EF_12pm_dendra2_actin647_lipitox594.lif - TileScan 1_Merged-2.tif" 66712 67.935 5.4315e+05 0.44417 16.924 8.2069 982 "1_M1_EF_EF_12pm_dendra2_actin647_lipitox594.lif - TileScan 1_Merged-2.tif" 62755 58.925 5.4083e+05 0.47177 15.928 7.6416 1065 "1_M1_EF_EF_12pm_dendra2_actin647_lipitox594.lif - TileScan 1_Merged-2.tif" 62999 61.764 5.3932e+05 0.48272 15.312 7.5959 1020 "1_M1_EF_EF_12pm_dendra2_actin647_lipitox594.lif - TileScan 1_Merged-2.tif" 63311 64.21 5.383e+05 0.49491 15.033 7.6053 986 "1_M1_EF_EF_12pm_dendra2_actin647_lipitox594.lif - TileScan 1_Merged-2.tif" 69779 60.051 5.3753e+05 0.51435 14.579 7.4954 1162 "1_M1_EF_EF_12pm_dendra2_actin647_lipitox594.lif - TileScan 1_Merged-2.tif" 74854 63.008 5.3697e+05 0.53082 14.065 7.1372 1188 "1_M1_EF_EF_12pm_dendra2_actin647_lipitox594.lif - TileScan 1_Merged-2.tif" 84682 68.072 5.3654e+05 0.55422 14.099 7.1703 1244 "2_M1_EF_EF_12pm_dendra2_actin647_lipitox594.lif - TileScan 1_Merged-3.tif" 52206 74.794 1.3145e+06 0.42258 18.799 9.2848 698 "2_M1_EF_EF_12pm_dendra2_actin647_lipitox594.lif - TileScan 1_Merged-3.tif" 56015 64.017 5.838e+05 0.43294 17.094 8.3941 875 "2_M1_EF_EF_12pm_dendra2_actin647_lipitox594.lif - TileScan 1_Merged-3.tif" 52195 60.975 5.6908e+05 0.41228 18.225 8.4264 856 "2_M1_EF_EF_12pm_dendra2_actin647_lipitox594.lif - TileScan 1_Merged-3.tif" 60359 62.034 5.625e+05 0.44288 16.976 8.1117 973

M2 = ftTbl(mouse2);

M3 = ftTbl(mouse3);

M4 = ftTbl(mouse4);

M5 = ftTbl(mouse5);

M6 = ftTbl(mouse6);

x = [M1;M2];

y = [M3;M4];

[h,p] = ttest2(x.totalArea,y.totalArea)

h = 1

p = 0.0043

% If p < 0.05, reject null hypothesis

if h == 1

disp('There is a significant difference between the means');

else

disp('There is no significant difference between the means');

end

There is a significant difference between the means

boxchart([x.totalArea,y.totalArea])

function Tout = ftTbl(Tin)

T = struct2table(Tin.features);

T2 = varfun(@transpose,T(:,2:8));

T2 = addvars(T2,repmat(string(T.filename.'),12,1),'newVariableNames','filename','Before',1);

T3 = varfun(@(x)reshape(x,[],1),T2);

T3.Properties.VariableNames = T.Properties.VariableNames;

Tout = T3;

end

Cris LaPierre 2023-5-9

在 MATLAB Online 中打开

In my code, x is what you are calling group 1, and is made up of the 'totalArea' features from features1 and features2, and y is what you refer to as group 2, made from the same data buf for features3 & 4.

The ttest2 function assumes a normal distribution, and compares the two groups, x and y, and tests if they come from the same population.

The anova1 test is for comparing 3+ groups, based on what I've (quickly) read. If you want to compare all 3 groups at once, then this could work. This compares 3 groups. Your previous group was comparing 72, which had me confused of exactly what you were trying to test for.

F1 = load('feature1.mat');

F2 = load('feature2.mat');

F3 = load('feature3.mat');

F4 = load('feature4.mat');

F5 = load('feature5.mat');

F6 = load('feature6.mat');

T1 = ftTbl(F1);

T2 = ftTbl(F2);

T3 = ftTbl(F3);

T4 = ftTbl(F4);

T5 = ftTbl(F5);

T6 = ftTbl(F6);

m1 = [T1;T2];

m2 = [T3;T4];

m3 = [T5;T6];

% Perform one-way ANOVA on profileCounts

[p, tbl, stats] = anova1([m1.totalArea, m2.totalArea, m3.totalArea]);

% If p < 0.05, reject null hypothesis

if p < 0.05

disp('There is a significant difference between the means');

else

disp('There is no significant difference between the means');

end

There is a significant difference between the means

function Tout = ftTbl(Tin)

T = struct2table(Tin.features);

T2 = varfun(@transpose,T(:,2:8));

T2 = addvars(T2,repmat(string(T.filename.'),12,1),'newVariableNames','filename','Before',1);

T3 = varfun(@(x)reshape(x,[],1),T2);

T3.Properties.VariableNames = T.Properties.VariableNames;

Tout = T3;

end

Cris LaPierre 2023-5-9

That sort of comparison does not make sense to me. ANOVA is the analysis of variance. You only can have variance if you have more than one element in your group.

My code takes all the values for m1 and puts them in one column, and then takes all the values from m2 and puts them in a second column, and all values from m3 into the 3rd column (72x3), and then compares samples from m1 to the samples from m2 to the samples from m3 and tests if they are from the same population.

Laura 2023-5-10

@Cris LaPierre Okay, I will use this then!! Thanks!!!!!!!

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error: X and GROUP must have the same length.but the lengths are the same. This must be a malfunctin how can i fix please help

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error: X and GROUP must have the same length.but the lengths are the same. This must be a malfunctin how can i fix please help

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