diff --git a/toolbox/helpfiles/FSDA/grpstatsFS.html b/toolbox/helpfiles/FSDA/grpstatsFS.html index 60438eb5e..0545f302f 100644 --- a/toolbox/helpfiles/FSDA/grpstatsFS.html +++ b/toolbox/helpfiles/FSDA/grpstatsFS.html @@ -1,27 +1,32 @@ - grpstatsFS

grpstatsFS

grpstatsFS calls grpstats and reshapes the output in a much better way

expand all in page

Syntax

  • statTable=grpstatsFS(X, groupvars, whichstats)example
  • statTable=grpstatsFS(X, groupvars, whichstats,Name,Value)example

Description

grpstatsFS calls grpstats but shows the output in much better way. The + grpstatsFS

grpstatsFS

grpstatsFS calls grpstats and reshapes the output in a much better way

expand all in page

Syntax

  • statTable=grpstatsFS(TBL, groupvars, whichstats)example
  • statTable=grpstatsFS(TBL, groupvars, whichstats,Name,Value)example

Description

grpstatsFS calls grpstats, but shows the output in much better way. The output of grpstatsFS is a table with a number of rows equal to the number of variables for which statistics are computed. The number of columns of this table is equal to the number of statistics which are computed. In presence of a grouping variable the number of rows of the - output table remains the same but the number of columns is equal to the - number of statistics times the number of groups.

example

statTable =grpstatsFS(X, groupvars, whichstats) grpstats with just one input argument.

example

statTable =grpstatsFS(X, groupvars, whichstats, Name, Value) grpstats with second input the grouping variable.

Examples

expand all

  • grpstats with just one input argument.

    • + output table remains the same, but the number of columns is equal to the + number of statistics multiplied by the number of groups. The statistics which + are computed by the default are the two (non robust and robust) indexes + of location, (mean and median) the two (non robust and robust) indexes + of spread (standard deviation and scaled MAD) and the two (non robust + and robust) indexes of skewness. The robust index of skewness is the + medcouple. The scaled MAD is defined as 1.4826(med|x-med(x)|).

    example

    statTable =grpstatsFS(TBL, groupvars, whichstats) grpstatsFS with just one input argument.

    example

    statTable =grpstatsFS(TBL, groupvars, whichstats, Name, Value) grpstatsFS with second input the grouping variable.

    Examples

    expand all

  • grpstatsFS with just one input argument.

    • Load a table 

    load citiesItaly.mat
-% Compute mean, median,  std, MAD, skewness, kurtosis
+% Compute mean, median,  std, MAD, skewness and medcouple
 % for the 7 variables of the input table citiesItaly
 TBL=grpstatsFS(citiesItaly);
-disp(TBL)
                       mean     median     std       MAD      skewness    kurtosis
-                  ______    ______    ______    ______    ________    ________
-
-    addedval       18096     18469    4941.6    6001.8     0.1079      2.2193 
-    depos         7769.1    7661.1    2841.4    3150.5     1.0734      6.1128 
-    pensions       10044    9975.2    1230.8    1170.6    0.31692      2.9485 
-    unemploy      10.173      6.42    7.8789    4.8778     1.0687      2.9546 
-    export         23.11     21.53    15.642    16.457     0.5797      2.7386 
-    bankrup       30.467     29.62     12.11     11.49     1.0067      4.7287 
-    billsoverd    44.614      40.6    22.783    19.496    0.98031      3.7585 
-
-

  • grpstats with second input the grouping variable.

    • load citiesItaly.mat
+disp(TBL)
                       mean     median     std       MAD      skewness    medcouple
+                  ______    ______    ______    ______    ________    _________
+
+    addedval       18096     18469    4941.6    6001.8     0.1079      -0.12451
+    depos         7769.1    7661.1    2841.4    3150.5     1.0734      -0.03612
+    pensions       10044    9975.2    1230.8    1170.6    0.31692      0.089163
+    unemploy      10.173      6.42    7.8789    4.8778     1.0687       0.61846
+    export         23.11     21.53    15.642    16.457     0.5797      0.066667
+    bankrup       30.467     29.62     12.11     11.49     1.0067     -0.010969
+    billsoverd    44.614      40.6    22.783    19.496    0.98031        0.1586
+
+

  • grpstatsFS with second input the grouping variable.

    • load citiesItaly.mat
 % The first 46 rows are referred to provinces located in northern Italy and
 % the remaining in centre-south Italy.
 zone=[repelem("N",46) repelem("CS",57)]';
@@ -30,18 +35,18 @@
 TBL=grpstatsFS(citiesItaly,"zone");
 % First 6 columns are referred to province of the north.
 % The remaning columns to the other provinces.
-disp(TBL)
                      meanN     medianN     stdN      MADN     skewnessN    kurtosisN    meanCS    medianCS    stdCS     MADCS     skewnessCS    kurtosisCS
-                  ______    _______    ______    ______    _________    _________    ______    ________    ______    ______    __________    __________
-
-    addedval       22071     21936     2926.4    2136.5     0.78292      4.2884       14888      14049     3760.7    3500.3     0.83573        3.1482  
-    depos         9614.6    9407.5     2214.3    1052.6      2.9736      15.912      6279.7     5506.5     2389.6    1810.8      1.5434        5.6777  
-    pensions       10798     10658     891.71    716.05      0.8251      3.1664      9434.9     9465.1     1129.5    1103.6     0.95725         4.462  
-    unemploy      4.4713      4.34     1.6834    1.7643     0.81049      3.7212      14.774      15.25     7.9086    10.126     0.35304        1.9998  
-    export        30.366    29.025     13.042     13.84     0.48928      2.7313      17.255      12.94     15.193    14.366      1.1879        4.0334  
-    bankrup       27.855    27.745     10.451    10.215     0.73877      3.6387      32.575      31.64     13.009    12.691      1.0051        4.6087  
-    billsoverd    31.775      30.2     14.368    10.156      1.2639      5.3863      54.975      52.34     23.127    19.422     0.68542        3.2312  
-
-

    Related Examples

    expand all

                      meanN     medianN     stdN      MADN     skewnessN    medcoupleN    meanCS    medianCS    stdCS     MADCS     skewnessCS    medcoupleCS
+                  ______    _______    ______    ______    _________    __________    ______    ________    ______    ______    __________    ___________
+
+    addedval       22071     21936     2926.4    2136.5     0.78292     -0.024878      14888      14049     3760.7    3500.3     0.83573        0.23097  
+    depos         9614.6    9407.5     2214.3    1052.6      2.9736     -0.054686     6279.7     5506.5     2389.6    1810.8      1.5434        0.37286  
+    pensions       10798     10658     891.71    716.05      0.8251       0.14446     9434.9     9465.1     1129.5    1103.6     0.95725       -0.15654  
+    unemploy      4.4713      4.34     1.6834    1.7643     0.81049     0.0022075     14.774      15.25     7.9086    10.126     0.35304       -0.10805  
+    export        30.366    29.025     13.042     13.84     0.48928      0.088586     17.255      12.94     15.193    14.366      1.1879        0.29376  
+    bankrup       27.855    27.745     10.451    10.215     0.73877      -0.10754     32.575      31.64     13.009    12.691      1.0051       0.010132  
+    billsoverd    31.775      30.2     14.368    10.156      1.2639      0.056604     54.975      52.34     23.127    19.422     0.68542       0.069632  
+
+

    Related Examples

    expand all

  • Example of call to grpstatsFS with personalized statistics.

    • The second element is empty, that is there is no grouping variable. 

    load citiesItaly.mat
 % Just compute mean and median
 stats=["mean" "median"];
@@ -89,7 +94,23 @@
     bankrup        28.101       32.834      30.467
     billsoverd     40.161       49.066      44.614
 
-

  • Example of call to grpstatsFS to create conf int for the mean with groups.

    • load citiesItaly.mat
+

  • Example of call to grpstatsFS to create conf int for the mean.

    • load citiesItaly.mat
+% Note that in this case meanci has in output two columns
+stats={"meanci" 'mean'};
+% Confidence interval for the sample means
+TBL=grpstatsFS(citiesItaly,[],stats);
+disp(TBL)
                      meanCIinf    meanCIsup     mean 
+                  _________    _________    ______
+
+    addedval        17131        19062       18096
+    depos          7213.7       8324.4      7769.1
+    pensions       9803.2        10284       10044
+    unemploy       8.6327       11.712      10.173
+    export         20.053       26.167       23.11
+    bankrup        28.101       32.834      30.467
+    billsoverd     40.161       49.066      44.614
+
+

  • Example of call to grpstatsFS to create conf int for the mean with groups.

    • load citiesItaly.mat
 % Note that in this case meanci has in output two columns
 stats={"meanci" 'mean'};
 % The first 46 rows are referred to provinces located in northern Italy and
@@ -110,7 +131,7 @@
     bankrup         24.752        30.959      27.855      29.124         36.027       32.575
     billsoverd      27.508        36.042      31.775      48.839         61.111       54.975
 
-

  • Example of use of option Alpha.

    • load citiesItaly.mat
+

  • Example of use of option Alpha.

    • load citiesItaly.mat
 % Note that in this case meanci has in output two columns
 stats={"meanci" 'mean'};
 % The first 46 rows are referred to provinces located in northern Italy and
@@ -131,7 +152,7 @@
     bankrup         23.711            32      27.855      27.981          37.17       32.575
     billsoverd      26.077        37.473      31.775      46.807         63.143       54.975
 
-

  • Example of the use of option DataVars.

    • load citiesItaly.mat
+

  • Example of the use of option DataVars.

    • load citiesItaly.mat
 % Note that in this case meanci has in output two columns
 stats={"meanci" 'mean'};
 % The first 46 rows are referred to provinces located in northern Italy and
@@ -141,27 +162,77 @@
 citiesItaly.zone=zone;
 % Confidence interval for the sample means separated for the 2 groups
 TBL=grpstatsFS(citiesItaly,"zone",stats,'DataVars',["addedval" "unemploy"]);
-disp(TBL)

    Input Arguments

    expand all

    X — Input data. Table.

    Table containing n observations on v variables.

    - Rows of Y represent observations, and columns - represent variables.

    Data Types: single| double

    groupvars — grouping variable. Identifiers for the grouping variables in input X.

    If groupvars is [] than the output refers to the overall +disp(TBL)

                    meanCIinfN    meanCIsupN    meanN     meanCIinfCS    meanCIsupCS    meanCS
+                __________    __________    ______    ___________    ___________    ______
+
+    addedval       21202         22940       22071       13891          15886        14888
+    unemploy      3.9714        4.9712      4.4713      12.675         16.872       14.774
+
+

  • Example of the use of option DataVars with VarNames.

    • load citiesItaly.mat
+% Note that in this case meanci has in output two columns
+stats={"median" 'mean'};
+TBL=grpstatsFS(citiesItaly,[],stats, ...
+'DataVars',[1 2 5],'VarNames', ...
+["Robust location" "Non robust location"]);
+disp(TBL)
                    Robust location    Non robust location
+                _______________    ___________________
+
+    addedval         18469                18096       
+    depos           7661.1               7769.1       
+    export           21.53                23.11       
+
+

  • Example of the use of option DataVars with VarNames and grouping variable.

    • load citiesItaly.mat
+% Note that in this case meanci has in output two columns
+stats={"meanci" 'mean'};
+% The first 46 rows are referred to provinces located in northern Italy and
+% the remaining in centre-south Italy.
+zone=[repelem("N",46) repelem("CS",57)]';
+% Add zone to citiesItaly
+citiesItaly.zone=zone;
+% Confidence interval for the sample means separated for the 2 groups
+TBL=grpstatsFS(citiesItaly,"zone",stats, ...
+'DataVars',["addedval" "unemploy"],'VarNames', ...
+["Mean: lower confidence interval" "Mean: upper confidence interval" "Sample mean"]);
+disp(TBL)
                    Mean: lower confidence intervalN    Mean: upper confidence intervalN    Sample meanN    Mean: lower confidence intervalCS    Mean: upper confidence intervalCS    Sample meanCS
+                ________________________________    ________________________________    ____________    _________________________________    _________________________________    _____________
+
+    addedval                  21202                               22940                     22071                     13891                                15886                      14888    
+    unemploy                 3.9714                              4.9712                    4.4713                    12.675                               16.872                     14.774    
+
+

    Input Arguments

    expand all

    TBL — Input data. Table.

    Table containing n observations on p variables.

    + Rows of TBL represent observations, and columns + represent variables. If it necessary to compute the + statistics for subgroups, TBL must include at least one + grouping variable, which you specify using groupvars.

    Data Types: single| double

    groupvars — grouping variable. Identifiers for the grouping variables in input TBL.

    If groupvars is [] then the output refers to the overall sample. For additional information on groupvars see the - help of grpstats.

    - Example - 'groupvars',[ones(50,1); 2*ones(50,1)] -

    Data Types: character vector | string array | cell array of character vectors | vector of positive integers | logical vector | []

    whichstats — Types of summary statistics. Name of the statistics which have to be computed - For additional information on whichstats see the help of - grpstats.

    If whichstats is empty of is not specifies the + help of grpstats. For example + Example - 'groupvars',2 +

    Data Types: character vector | string array | cell array of character vectors | vector of positive integers | logical vector | []

    whichstats — Types of summary statistics. Name of the statistics which have to be computed.

    For additional information on whichstats see the help of + grpstats. If whichstats is empty or it is not specified, the summary statistics which are computed are ["mean" "median" - "std" "MAD" "skewness" "kurtosis"] - Example - 'groupvars',"group" -

    Data Types: character vector | string array | function handle | cell array of character vectors or function handles.

    Name-Value Pair Arguments

    Specify optional comma-separated pairs of Name,Value arguments. Name is the argument name and Value is the corresponding value. Name must appear inside single quotes (' '). You can specify several name and value pair arguments in any order as Name1,Value1,...,NameN,ValueN.

    Example: 'Alpha',0.01 + "std" "MAD" "skewness" "medcouple"];

    + Example - ["mean" "std"] +

    Data Types: character vector | string array | function handle | cell array of character vectors or function handles.

    Name-Value Pair Arguments

    Specify optional comma-separated pairs of Name,Value arguments. Name is the argument name and Value is the corresponding value. Name must appear inside single quotes (' '). You can specify several name and value pair arguments in any order as Name1,Value1,...,NameN,ValueN.

    Example: 'Alpha',0.01 , 'DataVars',[2 4] +, 'VarNames',["location" "robust location"];

    Alpha —Significance level.scalar in [0 1).

    Significance level for confidence and prediction intervals.

    For additional information on Alpha see the help of grpstats.

    Example: 'Alpha',0.01

    Data Types: double

    DataVars —Table variables for which to compute summary statistics.for additional information on Alpha see the help of grpstats.

    Example: 'DataVars',[2 4] -

    Data Types: character vector | string array | cell array of character vectors | vector of positive integers | logical vector

    Output Arguments

    expand all

    statTable —table with p rows. The rows are referred to the variables of the input table - and the columns to the requested statistics

    +

    Data Types: character vector | string array | cell array of character vectors | vector of positive integers | logical vector

    VarNames —Variable names for output table.cell array | characters | string array.

    Note that the length of VarNames must be equal to the + number of statistics which are computed. Variable (column) + names for the output table statTable, specified as a string + array or a cell array of character vectors. By default, + grpstatsFS removes the @ if it is present in the name of + the statistic. In presence of a grouping variable + grpstatsFS appends the name corresponding to each + category of the groups.

    +

    Example: 'VarNames',["location" "robust location"]; +

    Data Types: character vector | string array | cell array of character vectors | vector of positive integers | logical vector

    Output Arguments

    expand all

    statTable —Summary statistics for the table input TBL. table with p rows. + Table

    + The rows are referred to the variables of the input table + and the columns to the requested statistics.

    The number of columns of statTable is equal to - the number of required statistcs times - the number of groups.

    References

    See Also

    This page has been automatically generated by our routine publishFS
    \ No newline at end of file + the number of requested statistics multiplied by + the number of groups.

    References

    See Also

    |

    This page has been automatically generated by our routine publishFS
    \ No newline at end of file diff --git a/toolbox/utilities_stat/grpstatsFS.m b/toolbox/utilities_stat/grpstatsFS.m index 8a5d4b027..e2a0ad079 100644 --- a/toolbox/utilities_stat/grpstatsFS.m +++ b/toolbox/utilities_stat/grpstatsFS.m @@ -1,37 +1,44 @@ -function [statTable]=grpstatsFS(X, groupvars, whichstats, varargin) +function [statTable]=grpstatsFS(TBL, groupvars, whichstats, varargin) %grpstatsFS calls grpstats and reshapes the output in a much better way % %Link to the help function % -% grpstatsFS calls grpstats but shows the output in much better way. The +% grpstatsFS calls grpstats, but shows the output in much better way. The % output of grpstatsFS is a table with a number of rows equal to the % number of variables for which statistics are computed. The number of % columns of this table is equal to the number of statistics which are % computed. In presence of a grouping variable the number of rows of the -% output table remains the same but the number of columns is equal to the -% number of statistics times the number of groups. +% output table remains the same, but the number of columns is equal to the +% number of statistics multiplied by the number of groups. The statistics which +% are computed by the default are the two (non robust and robust) indexes +% of location, (mean and median) the two (non robust and robust) indexes +% of spread (standard deviation and scaled MAD) and the two (non robust +% and robust) indexes of skewness. The robust index of skewness is the +% medcouple. The scaled MAD is defined as 1.4826(med|x-med(x)|). % % Required input arguments: % -% X: Input data. Table. Table containing n observations on v variables. -% Rows of Y represent observations, and columns -% represent variables. +% TBL: Input data. Table. Table containing n observations on p variables. +% Rows of TBL represent observations, and columns +% represent variables. If it necessary to compute the +% statistics for subgroups, TBL must include at least one +% grouping variable, which you specify using groupvars. % % groupvars: grouping variable. -% Identifiers for the grouping variables in input X. -% If groupvars is [] than the output refers to the overall +% Identifiers for the grouping variables in input TBL. +% If groupvars is [] then the output refers to the overall % sample. For additional information on groupvars see the -% help of grpstats. -% Example - 'groupvars',[ones(50,1); 2*ones(50,1)] +% help of grpstats. For example +% Example - 'groupvars',2 % Data Types - character vector | string array | cell array of character vectors | vector of positive integers | logical vector | [] % % whichstats: Types of summary statistics. -% Name of the statistics which have to be computed +% Name of the statistics which have to be computed. % For additional information on whichstats see the help of -% grpstats. If whichstats is empty of is not specifies the +% grpstats. If whichstats is empty or it is not specified, the % summary statistics which are computed are ["mean" "median" -% "std" "MAD" "skewness" "kurtosis"] -% Example - 'groupvars',"group" +% "std" "MAD" "skewness" "medcouple"]; +% Example - ["mean" "std"] % Data Types - character vector | string array | function handle | cell array of character vectors or function handles. % % Optional input arguments: @@ -49,16 +56,32 @@ % Example - 'DataVars',[2 4] % Data Types - character vector | string array | cell array of character vectors | vector of positive integers | logical vector % +% VarNames : Variable names for output table. cell array or characters +% or string array. +% Note that the length of VarNames must be equal to the +% number of statistics which are computed. Variable (column) +% names for the output table statTable, specified as a string +% array or a cell array of character vectors. By default, +% grpstatsFS removes the @ if it is present in the name of +% the statistic. In presence of a grouping variable +% grpstatsFS appends the name corresponding to each +% category of the groups. +% Example - 'VarNames',["location" "robust location"]; +% Data Types - character vector | string array | cell array of character vectors | vector of positive integers | logical vector +% +% % Output: % -% statTable : table with p rows. The rows are referred to the variables of the input table +% statTable : table with p rows. +% Table containing summary statistics for the table input TBL. +% The rows are referred to the variables of the input table % and the columns to the requested statistics. % The number of columns of statTable is equal to -% the number of required statistcs times +% the number of requested statistics multiplied by % the number of groups. % % -% See also: grpstats +% See also: grpstats, medcouple % % References: % @@ -74,17 +97,17 @@ %$LastChangedDate:: $: Date of the last commit %{ - %% grpstats with just one input argument. + %% grpstatsFS with just one input argument. % Load a table load citiesItaly.mat - % Compute mean, median, std, MAD, skewness, kurtosis + % Compute mean, median, std, MAD, skewness and medcouple % for the 7 variables of the input table citiesItaly TBL=grpstatsFS(citiesItaly); disp(TBL) %} %{ - %% grpstats with second input the grouping variable. + %% grpstatsFS with second input the grouping variable. load citiesItaly.mat % The first 46 rows are referred to provinces located in northern Italy and % the remaining in centre-south Italy. @@ -98,7 +121,7 @@ %} %{ - %% Example of call to grpstats with personalized statistics. + %% Example of call to grpstatsFS with personalized statistics. % The second element is empty, that is there is no grouping variable. load citiesItaly.mat % Just compute mean and median @@ -126,6 +149,16 @@ disp(TBL) %} +%{ + %% Example of call to grpstatsFS to create conf int for the mean. + load citiesItaly.mat + % Note that in this case meanci has in output two columns + stats={"meanci" 'mean'}; + % Confidence interval for the sample means + TBL=grpstatsFS(citiesItaly,[],stats); + disp(TBL) +%} + %{ %% Example of call to grpstatsFS to create conf int for the mean with groups. load citiesItaly.mat @@ -157,7 +190,7 @@ %} %{ - % Example of the use of option DataVars. + %% Example of the use of option DataVars. load citiesItaly.mat % Note that in this case meanci has in output two columns stats={"meanci" 'mean'}; @@ -171,6 +204,34 @@ disp(TBL) %} +%{ + %% Example of the use of option DataVars with VarNames. + load citiesItaly.mat + % Note that in this case meanci has in output two columns + stats={"median" 'mean'}; + TBL=grpstatsFS(citiesItaly,[],stats, ... + 'DataVars',[1 2 5],'VarNames', ... + ["Robust location" "Non robust location"]); + disp(TBL) +%} + +%{ + %% Example of the use of option DataVars with VarNames and grouping variable. + load citiesItaly.mat + % Note that in this case meanci has in output two columns + stats={"meanci" 'mean'}; + % The first 46 rows are referred to provinces located in northern Italy and + % the remaining in centre-south Italy. + zone=[repelem("N",46) repelem("CS",57)]'; + % Add zone to citiesItaly + citiesItaly.zone=zone; + % Confidence interval for the sample means separated for the 2 groups + TBL=grpstatsFS(citiesItaly,"zone",stats, ... + 'DataVars',["addedval" "unemploy"],'VarNames', ... + ["Mean: lower confidence interval" "Mean: upper confidence interval" "Sample mean"]); + disp(TBL) +%} + %% Beginning of code if nargin<2 groupvars=[]; @@ -180,8 +241,8 @@ mads=@(x)median(abs(x-median(x)))/norminv(0.75); if nargin<3 || isempty(whichstats) - whichstats={"@mean" "@median" "@std" mads "@skewness" "@kurtosis"}; - nomiStat=["mean" "median" "std" "MAD" "skewness" "kurtosis"]; + whichstats={"@mean" "@median" "@std" mads "@skewness" "@medcouple"}; + nomiStat=["mean" "median" "std" "MAD" "skewness" "medcouple"]; else if iscell(whichstats) @@ -198,29 +259,39 @@ end if ~isempty(varargin) - UserOptions=varargin{1:2:length(varargin)}; + UserOptions=varargin(1:2:length(varargin)); % Check if DataVars is present inside varargin checkDataVars = strcmp(UserOptions,'DataVars')>0; - if checkDataVars==true + if any(checkDataVars==true) lmsval = varargin{2*find(checkDataVars)}; - vnames=X.Properties.VariableNames(lmsval); + vnames=TBL.Properties.VariableNames(lmsval); else - vnames=X.Properties.VariableNames; + vnames=TBL.Properties.VariableNames; + end + + % Check if VarNames is present inside varargin + checkVarNames = strcmp(UserOptions,'VarNames')>0; + if any(checkVarNames)==true + fvarNames=2*find(checkVarNames); + lmsval = varargin{fvarNames}; + nomiStat=lmsval; + varargin([fvarNames-1 fvarNames])=[]; end + else - if istable(X) - vnames=X.Properties.VariableNames; + if istable(TBL) + vnames=TBL.Properties.VariableNames; else error('FSDA:grpstatsFS:WrongInp','grpstatsFS just supports a table in input') - % vnames="X"+string(1:size(X,2)); + % vnames="TBL"+string(1:size(TBL,2)); end end if ~isempty(groupvars) vnames=setdiff(vnames,groupvars,'stable'); end p=length(vnames); -tabTutti=grpstats(X,groupvars,whichstats,varargin{:}); +tabTutti=grpstats(TBL,groupvars,whichstats,varargin{:}); ngroups=size(tabTutti,1); lstats=length(nomiStat); if ngroups>1