Na\u010dten\u00ed dat<\/h4>\nCarData: LOAD RowNo() as \"car_ID\", symboling, \"normalized-losses\" as \"normalizedlosses\", make, \"fuel-type\" as \"fueltype\", aspiration, \"num-of-doors\" as \"numofdoors\", \"body-style\" as \"bodystyle\", \"drive-wheels\" as \"drivewheels\", \"engine-location\" as \"enginelocation\", \"wheel-base\" as \"wheelbase\", \"length\", width, height, \"curb-weight\" as \"curbweight\", \"engine-type\" as \"enginetype\", \"num-of-cylinders\" as \"numofcylinders\", \"engine-size\" as \"enginesize\", \"fuel-system\" as fuelsystem, bore, stroke, \"compression-ratio\" as \"compressionratio\", horsepower, \"peak-rpm\" as \"peakrpm\", \"city-mpg\" as \"citympg\", \"highway-mpg\" as \"highwaympg\", price FROM [lib:\/\/Data\/Correlation matrix\/Automobile_data.csv] (txt, codepage is 28591, embedded labels, delimiter is ';', msq); <\/pre>\nSpu\u0161t\u011bn\u00ed R skriptu<\/h4>\n
Nyn\u00ed mus\u00edme pomoc\u00ed R vypo\u010d\u00edtat korelace a p\u0159\u00edslu\u0161n\u00e9 P-hodnoty. Nev\u00e1hejte pou\u017e\u00edt n\u00e1sleduj\u00edc\u00ed skript. <\/p>\n
Tmp_Correlation: Load * Extension R.ScriptEval( '# install.packages(Hmisc, repos=\"http:\/\/cran.us.r-project.org\"); \/\/ponechte # pokud u\u017e m\u00e1te tento bal\u00ed\u010dek nainstalovan\u00fd library(Hmisc); df <- as.data.frame.list(q, strings.as.factors = FALSE); df <- Filter(is.numeric, df); cor <- as.data.frame.list(rcorr(as.matrix(df), type=\"pearson\")); cor$varname <- rownames(cor); cor;', CarData{wheelbase,length,width,height,curbweight,enginesize,bore,stroke,compressionratio,horsepower,peakrpm,citympg,highwaympg,price});<\/pre>\nTransformace v\u00fdsledku z R skriptu<\/h4>\n
R skript vrac\u00ed kompaktn\u00ed tabulku obsahuj\u00edc\u00ed v\u0161e. Preferuji ji trochu vy\u010distit, abychom skon\u010dili s p\u011bkn\u00fdm datov\u00fdm modelem. <\/p>\n
Tmp_Correlation_Crosstable: CROSSTABLE (Variable2, Value) LOAD varname as Variable1, * RESIDENT Tmp_Correlation ;\r\n\r\nTab_Correlation:\r\nLOAD\r\n,\r\nVariable1&'|'&Variable2 as %KeyCor;\r\nLOAD\r\nVariable1,\r\nReplace(Variable2, 'r.', '') as Variable2,\r\nValue as Correlation\r\nRESIDENT Tmp_Correlation_Crosstable\r\nWhere Variable2 like 'r.';\r\n\r\nTab_PValues:\r\nLOAD\r\n[P-Value],\r\nVariable1&'|'&Variable2 as %KeyCor;\r\nLOAD\r\nVariable1,\r\nReplace(Variable2, 'P.', '') as Variable2,\r\nValue as [P-Value]\r\nRESIDENT Tmp_Correlation_Crosstable\r\nWhere Variable2 like 'P.*';\r\n\r\nDrop Tables Tmp_Correlation, Tmp_Correlation_Crosstable;<\/pre>\nM\u011bli byste skon\u010dit s dvoutabulkov\u00fdm datov\u00fdm modelem, kter\u00fd vypad\u00e1 takto:<\/p>\n
![\"Datov\u00fd](\"https:\/\/emarkanalytics.com\/wp-content\/uploads\/2026\/04\/Datamodel.png\")
<\/p>\n
Vytvo\u0159en\u00ed korela\u010dn\u00ed matice v Qlik Sense<\/h2>\n
V\u00fdborn\u00e1 pr\u00e1ce! Jsme p\u0159ipraveni nastavit vizualizaci nad datov\u00fdm modelem, kter\u00fd m\u00e1me. V\u00fdsledkem bude korela\u010dn\u00ed matice zobrazuj\u00edc\u00ed aktu\u00e1ln\u00ed hodnoty Pearsonova korela\u010dn\u00edho koeficientu spolu s barvami (tmav\u011b modr\u00e1 pro negativn\u00ed korelace a \u010derven\u00e1 pro pozitivn\u00ed). Krom\u011b toho budeme m\u00edt tak\u00e9 slider pro v\u00fdb\u011br \u00farovn\u00ed P-hodnot, kter\u00e9 se budou zobrazovat. T\u00edmto zp\u016fsobem m\u016f\u017eete zobrazovat a analyzovat pouze statisticky v\u00fdznamn\u00e9 korelace a z\u00e1rove\u0148 si vybrat libovolnou \u00farove\u0148 v\u00fdznamnosti. <\/p>\n
Vytvo\u0159en\u00ed korela\u010dn\u00ed matice pomoc\u00ed heatmap grafu<\/h4>\n
Vyberte Heatmap chart z Qlik Visualization bundle a nastavte: <\/p>\n
\n- Data > Dimensions: <\/strong>nastavte 2 dimenze Variable1 a Variable2<\/li>\n
- Vytvo\u0159te novou prom\u011bnnou vSign a ponechte Definition<\/strong> pr\u00e1zdn\u00e9<\/li>\n
- Data > Measures<\/strong>:
=Avg({<[P-Value] = {\"<=$(vSign)\"}>} Correlation)<\/code> <\/strong>; nastavte label na Pearson\u016fv korela\u010dn\u00ed koeficient<\/li>\n- Appearance > Options:<\/strong> Nepou\u017e\u00edvejte mean in scale a m\u00edsto toho pou\u017eijte fixed scale. Nastavte Min Scale Value<\/strong> na -1; Max Scale Value<\/strong> na 1 a Minimum Horizontal Size<\/strong> na 0<\/li>\n
- Appearance > Design:<\/strong> Vyberte barevn\u00e9 sch\u00e9ma Qlik Sense Diverging<\/li>\n
- Appearance > General: <\/strong>nastavte n\u00e1zev na Korela\u010dn\u00ed matice<\/li>\n<\/ol>\n
Ud\u011blejte heatmap responzivn\u00ed na \u00farove\u0148 v\u00fdznamnosti<\/h4>\n
Heatmap je nastaven, ale st\u00e1le mus\u00edme p\u0159idat n\u011bjak\u00fd vstup pro p\u0159i\u0159azen\u00ed hodnot do prom\u011bnn\u00e9 vSign. Pou\u017eijme k tomu slider.<\/p>\n
Spu\u0161t\u011bn\u00ed R skriptu<\/h4>\n
Nyn\u00ed mus\u00edme pomoc\u00ed R vypo\u010d\u00edtat korelace a p\u0159\u00edslu\u0161n\u00e9 P-hodnoty. Nev\u00e1hejte pou\u017e\u00edt n\u00e1sleduj\u00edc\u00ed skript. <\/p>\n
Tmp_Correlation: Load * Extension R.ScriptEval( '# install.packages(Hmisc, repos=\"http:\/\/cran.us.r-project.org\"); \/\/ponechte # pokud u\u017e m\u00e1te tento bal\u00ed\u010dek nainstalovan\u00fd library(Hmisc); df <- as.data.frame.list(q, strings.as.factors = FALSE); df <- Filter(is.numeric, df); cor <- as.data.frame.list(rcorr(as.matrix(df), type=\"pearson\")); cor$varname <- rownames(cor); cor;', CarData{wheelbase,length,width,height,curbweight,enginesize,bore,stroke,compressionratio,horsepower,peakrpm,citympg,highwaympg,price});<\/pre>\nTransformace v\u00fdsledku z R skriptu<\/h4>\n
R skript vrac\u00ed kompaktn\u00ed tabulku obsahuj\u00edc\u00ed v\u0161e. Preferuji ji trochu vy\u010distit, abychom skon\u010dili s p\u011bkn\u00fdm datov\u00fdm modelem. <\/p>\n
Tmp_Correlation_Crosstable: CROSSTABLE (Variable2, Value) LOAD varname as Variable1, * RESIDENT Tmp_Correlation ;\r\n\r\nTab_Correlation:\r\nLOAD\r\n,\r\nVariable1&'|'&Variable2 as %KeyCor;\r\nLOAD\r\nVariable1,\r\nReplace(Variable2, 'r.', '') as Variable2,\r\nValue as Correlation\r\nRESIDENT Tmp_Correlation_Crosstable\r\nWhere Variable2 like 'r.';\r\n\r\nTab_PValues:\r\nLOAD\r\n[P-Value],\r\nVariable1&'|'&Variable2 as %KeyCor;\r\nLOAD\r\nVariable1,\r\nReplace(Variable2, 'P.', '') as Variable2,\r\nValue as [P-Value]\r\nRESIDENT Tmp_Correlation_Crosstable\r\nWhere Variable2 like 'P.*';\r\n\r\nDrop Tables Tmp_Correlation, Tmp_Correlation_Crosstable;<\/pre>\nM\u011bli byste skon\u010dit s dvoutabulkov\u00fdm datov\u00fdm modelem, kter\u00fd vypad\u00e1 takto:<\/p>\n
<\/p>\n
Vytvo\u0159en\u00ed korela\u010dn\u00ed matice v Qlik Sense<\/h2>\n
V\u00fdborn\u00e1 pr\u00e1ce! Jsme p\u0159ipraveni nastavit vizualizaci nad datov\u00fdm modelem, kter\u00fd m\u00e1me. V\u00fdsledkem bude korela\u010dn\u00ed matice zobrazuj\u00edc\u00ed aktu\u00e1ln\u00ed hodnoty Pearsonova korela\u010dn\u00edho koeficientu spolu s barvami (tmav\u011b modr\u00e1 pro negativn\u00ed korelace a \u010derven\u00e1 pro pozitivn\u00ed). Krom\u011b toho budeme m\u00edt tak\u00e9 slider pro v\u00fdb\u011br \u00farovn\u00ed P-hodnot, kter\u00e9 se budou zobrazovat. T\u00edmto zp\u016fsobem m\u016f\u017eete zobrazovat a analyzovat pouze statisticky v\u00fdznamn\u00e9 korelace a z\u00e1rove\u0148 si vybrat libovolnou \u00farove\u0148 v\u00fdznamnosti. <\/p>\n
Vytvo\u0159en\u00ed korela\u010dn\u00ed matice pomoc\u00ed heatmap grafu<\/h4>\n
Vyberte Heatmap chart z Qlik Visualization bundle a nastavte: <\/p>\n
\n- Data > Dimensions: <\/strong>nastavte 2 dimenze Variable1 a Variable2<\/li>\n
- Vytvo\u0159te novou prom\u011bnnou vSign a ponechte Definition<\/strong> pr\u00e1zdn\u00e9<\/li>\n
- Data > Measures<\/strong>:
=Avg({<[P-Value] = {\"<=$(vSign)\"}>} Correlation)<\/code> <\/strong>; nastavte label na Pearson\u016fv korela\u010dn\u00ed koeficient<\/li>\n- Appearance > Options:<\/strong> Nepou\u017e\u00edvejte mean in scale a m\u00edsto toho pou\u017eijte fixed scale. Nastavte Min Scale Value<\/strong> na -1; Max Scale Value<\/strong> na 1 a Minimum Horizontal Size<\/strong> na 0<\/li>\n
- Appearance > Design:<\/strong> Vyberte barevn\u00e9 sch\u00e9ma Qlik Sense Diverging<\/li>\n
- Appearance > General: <\/strong>nastavte n\u00e1zev na Korela\u010dn\u00ed matice<\/li>\n<\/ol>\n
Ud\u011blejte heatmap responzivn\u00ed na \u00farove\u0148 v\u00fdznamnosti<\/h4>\n
Heatmap je nastaven, ale st\u00e1le mus\u00edme p\u0159idat n\u011bjak\u00fd vstup pro p\u0159i\u0159azen\u00ed hodnot do prom\u011bnn\u00e9 vSign. Pou\u017eijme k tomu slider.<\/p>\n
![\"Korela\u010dn\u00ed](\"https:\/\/emarkanalytics.com\/wp-content\/uploads\/2026\/04\/CorrelationMatrix.gif\")
![\"Model](\"https:\/\/emarkanalytics.com\/wp-content\/uploads\/2026\/04\/modelPrice.png\")
<\/p>\n