source:`R/kor_sort.R`

`cor_sort.Rd`

Sort the correlation matrix based on`group()`

.

## use

`cor_black(x, the distance= "Link", hclust_method= "completely",...)`

## parameter

- x
correlation matrix.

- distance
How to calculate the distance between each variable. If

`Link`

(default; suitable for correlation matrix), the matrix will be scaled to 0-1 (`distance = 0`

Show relevance`1`

;`distance = 1`

demonstrate relevance`-1`

).I`Strict`

, then this matrix will be used as the distance matrix. could be someone else (`Euclid`

,`Manhattan`

, ...), in which case it will be forwarded to`distance()`

(see parameters).- hclust_method
parameters passed

`method`

argument`group()`

.- ...
Additional arguments to pass to or from other functions.

## example

`x <- Link(car)cor_black(as. matrix(x))#>carb wt hp cyl disp qsec#>Carbohydrates 1.00000000 0.4276059 0.7498125 0.5269883 0.3949769 -0.65624923#>Weight 0.42760594 1.0000000 0.6587479 0.7824958 0.8879799 -0.17471588#>Moc 0,74981247 0,6587479 1,0000000 0,8324475 0,7909486 -0,70822339#>Cylindrical 0.52698829 0.7824958 0.8324475 1.0000000 0.9020329 -0.59124207#>Display 0.39497686 0.8879799 0.7909486 0.9020329 1.0000000 -0.43369788#>qsec -0,65624923 -0,1747159 -0,7082234 -0,5912421 -0,4336979 1,00000000#>Compare -0.56960714 -0.5549157 -0.7230967 -0.8108118 -0.7104159 0.74453544#>mpg -0,55092507 -0,8676594 -0,7761684 -0,8521620 -0,8475514 0,41868403#>Gwinty -0,09078980 -0,7124406 -0,4487591 -0,6999381 -0,7102139 0,09120476#>i am 0.05753435 -0.6924953 -0.2432043 -0.5226070 -0.5912270 -0.22986086#>Gear 0.27407284 -0.5832870 -0.1257043 -0.4926866 -0.5555692 -0.21268223#>vs mpg drat am gear#>Carbohydrates -0.5696071 -0.5509251 -0.09078980 0.05753435 0.2740728#>Weight -0.5549157 -0.8676594 -0.71244065 -0.69249526 -0.5832870#>HP -0,7230967 -0,7761684 -0,44875912 -0,24320426 -0,1257043#>Cylinder -0.8108118 -0.8521620 -0.69993811 -0.52260705 -0.4926866#>Screen -0.7104159 -0.8475514 -0.71021393 -0.59122704 -0.5555692#>qsec 0,7445354 0,4186840 0,09120476 -0,22986086 -0,2126822#>Compare 1.0000000 0.6640389 0.44027846 0.16834512 0.2060233#>MPG 0,6640389 1,0000000 0,68117191 0,59983243 0,4802848#>Thread 0.4402785 0.6811719 1.00000000 0.71271113 0.6996101#>mama 0,1683451 0,5998324 0,71271113 1,00000000 0,7940588#>Przekładnia 0.2060233 0.4802848 0.69961013 0.79405876 1.0000000cor_black(x, hclust_method= "Odred.D2") # Can also change the order of long format printing#> # correlation matrix (Pearson method)#> #>parameter1 | parameter2 | r| 95% confidence interval | t(30) | p#>---------------------------------------------- ---- --------------#>Weight | Carbohydrates | 0.43| [0.09, 0.68] | 2.59 | 0.205#>weight | sec | -0.17| [-0.49, 0.19] | -0.97| > 0.999#>Weight | Contrast | -0.55| [-0.76, -0.26] | -3.65| 0.023*#>weight |i| -0.69| [-0.84, -0.45] | -5.26| < 0.001***#>Masa |Przekładnia | -0.58 | [-0.77, -0.29] | -3.93 | 0.012*#>Cylinder | Time | 0.78 | [0.60, 0.89] | 6.88 | < 0.001***#>Cylindrical | Display | 0.90| [0.81, 0.95] | 11.45 | < 0.001***#>Cylindrical | HP | 0.83| [0.68, 0.92] | 8.23 | < 0.001***#>Cylindrical | Carbohydrates | 0.53| [0.22, 0.74] | 3.40 | 0.041*#>cilindar | sec | -0.59 | [-0.78, -0.31] | -4.02 | 0.010*#>Cylindrical | Contrast | -0.81| [-0.90, -0.64] | -7.59 | < 0.001***#>Cylinder | Nonsense | -0.70| [-0.84, -0.46] | -5.37 | < 0.001***#>cylinder |i | -0.52 | [-0.74, -0.21] | -3.36 | 0.043*#>Cylindrical |Gear | -0.49| [-0.72, -0.17] | -3.10| 0.079#>show | weight | 0.89| [0.78, 0.94] | 10.58 | < 0.001***#>Show | HP | 0.79| [0.61, 0.89] | 7.08 | < 0.001***#>show | carbohydrates | 0.39| [0.05, 0.65] | 2.35| 0.303#>show |sec | -0.43| [-0.68, -0.10] | -2.64| 0.197#>Show | Contrast | -0.71| [-0.85, -0.48] | -5.53| < 0.001***#>show |nil|-0.71|[-0.85, -0.48]|-5.53|<.001***#>show | and | -0.59| [-0.78, -0.31] | -4.02| 0.010*#>show |run | -0.56| [-0.76, -0.26] | -3.66| 0.023*#>HP | Waga | 0.66 | [0.40, 0.82] | 4.80 | 0.001**#>HP | Carbohydrates | 0.75| [0.54, 0.87] | 6.21 | < 0.001***#>KM | sek | -0,71 | [-0,85, -0,48] | -5,49 | < 0,001***#>HP | Contrast | -0.72 | [-0.86, -0.50] | -5.73 | < 0.001***#>KM | niti | -0.45| [-0.69, -0.12] | -2.75| 0.170#>KM |i | -0.24 | [-0.55, 0.12] | -1.37 | > 0.999#>KM | to run | -0.13| [-0.45, 0.23] | -0.69| > 0.999#>with | carbohydrate | -0.66| [-0.82, -0.40] | -4.76| 0.001**#>with | vs | 0.74 | [0.53, 0.87] | 6.11 | < 0.001***#>s |i | -0,23 | [-0,54, 0,13] | -1,29 | > 0,999#>seconds | to run | -0.21| [-0.52, 0.15] | -1.19| > 0.999#>Contrast | Carbohydrates | -0.57| [-0.77, -0.28] | -3.80 | 0.017*#>against | and | 0.17 | [-0.19, 0.49] | 0.94 | > 0.999#>Contrast | Przekładnia | 0.21 | [-0.15, 0.52] | 1.15 | > 0.999#>Miles | Weight | -0.87| [-0.93, -0.74] | -9.56| < 0.001***#>Miles | Cylindrical | -0.85| [-0.93, -0.72] | -8.92| < 0.001***#>mile | display | -0.85| [-0.92, -0.71] | -8.75 | < 0.001***#>Mile | KM | -0.78 | [-0.89, -0.59] | -6.74 | < 0.001***#>Miles | Carbohydrates | -0.55| [-0.75, -0.25] | -3.62| 0.024*#>mile | seconds | 0.42 | [0.08, 0.67] | 2.53 | 0.222#>Mile | Contrast | 0.66 | [0.41, 0.82] | 4.86 | 0.001**#>mile | nor | 0.68| [0.44, 0.83] | 5.10 | < 0.001***#>miles | and | 0.60 | [0.32, 0.78] | 4.11 | 0.008**#>mile | to run | 0.48| [0.16, 0.71] | 3.00 | 0.097#>Nonsense | Weight | -0.71| [-0.85, -0.48] | -5.56| < 0.001***#>nonsense | carbohydrates | -0.09| [-0.43, 0.27] | -0.50 | > 0.999#>nonsense | sec | 0.09 | [-0.27, 0.43] | 0.50 | > 0.999#>Thread | Contrast | 0.44| [0.11, 0.68] | 2.69 | 0.187#>Nonsense | and | 0.71| [0.48, 0.85] | 5.57 | < 0.001***#>Thread | Gear | 0.70| [0.46, 0.84] | 5.36 | < 0.001***#>i|carbohydrate|0.06|[-0.30, 0.40]|0.32|>.999#>and | to run | 0.79| [0.62, 0.89] | 7.16 | < 0.001***#>to run | carbohydrates | 0.27| [-0.08, 0.57] | 1.56| > 0.999#> #>p-value adjustment method: Holm (1979)#>Observations: 32cor_black(summarize(x, extra= he says)) # and from the summary#> # correlation matrix (Pearson method)#> #>The parameter | Carbohydrates | Weight | HP | Cylindrical | Display | Seconds | Contrast | Miles | Nonsense | and | Jogging#>---------------------------------------------- ---- ------------------------------------------------ -------- ----------------------#>Carbohydrates | | 0.43*** | 0.75*** | 0.53*** | 0.39*** | -0.66*** | -0.57| -0.55** | -0.09** |#>Weight | 0.43*** | | 0.66*** | 0.78*** | 0.89*** | -0.17*** | -0.55* |#>HP | 0,75*** | 0,66*** | | 0,83*** | 0,79*** | -0,71*** | -0,72 |#>Cylindrical | 0.53*** | 0.78*** | 0.83*** | | 0.90| -0.59** | -0.81*** | -0.85*** | -0.70| -0.52| -0.49***#>Display | 0.39*** | 0.89*** | 0.79*** | 0.90| | -0.43*** | -0.71| -0.85| -0.71*** | -0.59*** | -0.56#>Second | -0,66*** | -0,17*** | -0,71*** | -0,59** | -0,43*** | | 0.74 | 0,42* | 0,09*** | -0,23* |#>Contrast | -0.57 | -0.55* | -0.72 | -0.81*** | -0.71 | 0.74 | | 0.66*** | 0.44 | 0.17 | 0.21**#>-0,55** | -0,87*** | -0,78*** | -0,85*** | -0,85 | 0,42* | 0,66*** | | 0,68 | 0,60 | 0,48*#>Nit | -0,09** | -0,71* | -0,45* | -0,70| -0,71*** | 0,09*** | 0,44| 0,68| | 0,71*** | 0,70#>and | 0.06 | -0.69 | -0.24* | -0.52 | -0.59*** | -0.23* | 0.17 | 0.60 | 0.71*** | | 0.79#>Start | 0.27* | -0.58* | -0.13| -0.49*** | -0.56| -0.21| 0.21** | 0.48* | 0.70| 0.79|#> #>p-value adjustment method: Holm (1979)`

## FAQs

### What are the benefits of correlation matrix? ›

We can use a correlation matrix to **summarize a large data set and to identify patterns and make a decision according to it**. We can also see which variable is more correlated to which variable, and we can visualize our results. A correlation matrix involves a rows and columns table that shows the variables.

**What is the difference between correlation matrix and covariance matrix? ›**

Covariance Matrix vs Correlation Matrix

Though people use both terms in statistics to help study patterns, covariance and correlation matrix are two opposite terms. While **the former indicates the extent to which two or more variables differ, the latter shows the extent to which they are related**.

**How do you Analyse a correlation matrix? ›**

**How to read a correlation matrix?**

- Look at the number in each cell to see the strength and direction of the correlation.
- Positive numbers indicate positive correlations, while negative numbers indicate negative correlations.
- The closer the number is to 1 (or -1), the stronger the correlation.

**What does the correlation matrix for a multiple regression analysis contain? ›**

Simple correlation coefficients Explanation : The correlation matrix for a multiple regression analysis contain simple correlation coefficients.

**What are advantages and disadvantages of correlation analysis? ›**

Correlational Study Advantages | Correlational Study Disadvantages |
---|---|

May predict human behaviors | No inferences can be found by results |

Can be more cost-effective | Possibility of the third variable problem / confounding factor |

**What is the benefit of correlation matrix diversification? ›**

A correlation matrix **makes the task of choosing different assets easier by presenting their correlation with each other in a tabular form**. Once you have the matrix, you can use it for choosing a wide variety of assets having different correlations with each other.

**What is an advantage of the correlation coefficient over the covariance? ›**

Both correlation and covariance measures are also unaffected by the change in location. However, when it comes to making a choice between covariance vs correlation to measure relationship between variables, correlation is preferred over covariance because **it does not get affected by the change in scale**.

**Why covariance matrix is important? ›**

The most important feature of covariance matrix is that **it is positive semi-definite, which brings about Cholesky decomposition** . In a nutshell, Cholesky decomposition is to decompose a positive definite matrix into the product of a lower triangular matrix and its transpose.

**What is the significance of covariance matrix? ›**

Although the covariance matrix seems just a matrix that shows the variance between variables, it contains essential information that can explain the trends of your data. Properties like direction, slope, and magnitude of the variation can be found through eigenvectors of the covariance matrix.

**What should a correlation matrix look like? ›**

Typically, a correlation matrix is **“square”, with the same variables shown in the rows and columns**.

### What are the different types of correlation matrix? ›

Usually, in statistics, we measure four types of correlations: **Pearson correlation, Kendall rank correlation, Spearman correlation, and the Point-Biserial correlation**.

**What is correlation analysis techniques? ›**

Correlation analysis in research is **a statistical method used to measure the strength of the linear relationship between two variables and compute their association**. Simply put - correlation analysis calculates the level of change in one variable due to the change in the other.

**How do you interpret multiple correlation coefficients? ›**

**Higher values indicate higher predictability of the dependent variable from the independent variables**, with a value of 1 indicating that the predictions are exactly correct and a value of 0 indicating that no linear combination of the independent variables is a better predictor than is the fixed mean of the dependent ...

**Is correlation matrix the same as linear regression? ›**

The most commonly used techniques for investigating the relationship between two quantitative variables are correlation and linear regression. **Correlation quantifies the strength of the linear relationship between a pair of variables, whereas regression expresses the relationship in the form of an equation**.

**What information does the correlation coefficient provide about a regression model? ›**

A correlation analysis provides information on **the strength and direction of the linear relationship between two variables**, while a simple linear regression analysis estimates parameters in a linear equation that can be used to predict values of one variable based on the other.

**What are two major limitations for a correlation? ›**

**The two main limitations of Pearson's R are;**

- It cannot determine the nonlinear relationships between variables.
- It does not distinguish between dependent and independent variables.

**What are the strengths of correlation techniques? ›**

Correlational research can **help us understand the complex relationships between a lot of different variables**. If we measure these variables in realistic settings, then we can learn more about how the world really works.

**What are the major limitations of correlation analysis? ›**

An important limitation of the correlation coefficient is that **it assumes a linear association**. This also means that any linear transformation and any scale transformation of either variable X or Y, or both, will not affect the correlation coefficient.

**What are the benefits of correlation regression? ›**

**Benefits of Correlation and Regression**

- Improve Operations. Improves business performance by impacting operational efficiency, such as discovering innovative material substitutions to reduce manufacturing costs.
- Sales Forecasting. ...
- Analyzing Results. ...
- Improve Employee Efficiency. ...
- Develop New Strategies. ...
- Correct Mistakes.

**What is the importance of correlation method? ›**

You want to find out if there is an association between two variables, but you don't expect to find a causal relationship between them. Correlational research can **provide insights into complex real-world relationships, helping researchers develop theories and make predictions**.

### Which correlation value would provide the highest level of diversification benefit? ›

Diversification benefits: risk reduction through a diversification of investments. Investments that are negatively correlated or that have low positive correlation provide the best diversification benefits.

**Why is it important to understand correlation and covariance? ›**

Covariance and Correlation are very helpful in understanding the relationship between two continuous variables. Covariance tells whether both variables vary in the same direction (positive covariance) or in the opposite direction (negative covariance).

**What is the conclusion of correlation and covariance? ›**

Conclusion. **Both measures only linear relationship between two variables, i.e. when the correlation coefficient is zero, covariance is also zero**. Further, the two measures are unaffected by the change in location. Correlation is a special case of covariance which can be obtained when the data is standardized.

**What are the benefits of covariance? ›**

Covariance can be used to **maximize diversification in a portfolio of assets**. By adding assets with a negative covariance to a portfolio, the overall risk is quickly reduced. Covariance provides a statistical measurement of the risk for a mix of assets.

**Why covariance is better than variance? ›**

Variance tells us how single variables vary whereas **Covariance tells us how two variables vary together**. Variance measures the volatility of variables whereas Covariance measure to indicate the extent to which two random variables change.

**Why is covariance not a good measure? ›**

One of the reasons covariance is not a good way to measure the strength of a linear relationship is because **it is not invariant to deterministic linear transformations**.

**What does covariance tell us about a set of data? ›**

Covariance provides insight into **how two variables are related to one another**. More precisely, covariance refers to the measure of how two random variables in a data set will change together. A positive covariance means that the two variables at hand are positively related, and they move in the same direction.

**What is the significance of co variance? ›**

Covariance is **an important statistical metric for comparing the relationships between multiple variables**. In investing, covariance is used to identify assets that can help diversify a portfolio.

**What is the impact of covariance? ›**

Covariance is a statistical measure of how two assets move in relation to each other. It **provides diversification and reduces the overall volatility for a portfolio**. A positive covariance indicates that two assets move in tandem. A negative covariance indicates that two assets move in opposite directions.

**What is considered strong in a correlation matrix? ›**

The relationship between two variables is generally considered strong **when their r value is larger than 0.7**. The correlation r measures the strength of the linear relationship between two quantitative variables. Pearson r: r is always a number between -1 and 1.

### What is the difference between correlation and correlation matrix? ›

The correlation coefficient measures the extent to which two pairs of variables are related to each other. Scatter plots are used to get a visual understanding of correlation. **Correlation Matrix can be used to get a snapshot of the relationship between more than two variables in a tabular format**.

**What is the strongest correlation in the matrix? ›**

The strongest linear relationship is indicated by a correlation coefficient of **-1 or 1**.

**What are the two types of correlation analysis? ›**

**Linear and non-linear correlation**.

**What are the three types of correlation in research? ›**

There are three basic types of correlational studies that are used in eHealth evaluation: **cohort, cross-sectional, and case-control studies** (Vandenbroucke et al., 2014).

**What is the best correlation method? ›**

The **Pearson product-moment correlation** is one of the most commonly used correlations in statistics. It's a measure of the strength and the direction of a linear relationship between two variables.

**What is the most widely used correlation technique? ›**

**The Pearson correlation method** is the most common method to use for numerical variables; it assigns a value between − 1 and 1, where 0 is no correlation, 1 is total positive correlation, and − 1 is total negative correlation.

**Which tool is best for correlation analysis? ›**

**The Pearson method** is the most commonly used correlation method in market research. It's a way to measure the degree of a relationship between two linearly related variables.

**What are the three interpretations of a correlation coefficient? ›**

A linear correlation coefficient that is greater than zero indicates a positive relationship. A value that is less than zero signifies a negative relationship. Finally, a value of zero indicates no relationship between the two variables x and y.

**What are the three ways in which we can interpret a correlation coefficient? ›**

Correlation Coefficient = +1: A perfect positive relationship. Correlation Coefficient = 0.8: A fairly strong positive relationship. Correlation Coefficient = 0.6: A moderate positive relationship. Correlation Coefficient = 0: No relationship.

**When interpreting a correlation coefficient it is important to look at? ›**

In interpreting correlation coefficients, two properties are important. Magnitude. Correlations range in magnitude from -1.00 to 1.00. **The larger the absolute value of the coefficient (the size of the number without regard to the sign) the greater the magnitude of the relationship**.

### What is the correlation matrix of a dataset? ›

A correlation matrix is simply **a table showing the correlation coefficients between variables**. The table above has used data from the full health data set. Observations: We observe that Duration and Calorie_Burnage are closely related, with a correlation coefficient of 0.89.

**What affects correlation coefficient? ›**

The authors describe and illustrate 6 factors that affect the size of a Pearson correlation: (a) the amount of variability in the data, (b) differences in the shapes of the 2 distributions, (c) lack of linearity, (d) the presence of 1 or more "outliers," (e) characteristics of the sample, and (f) measurement error.

**What type of variables are analyzed when analyzing the correlation and regression between the two? ›**

Correlation and linear regression analysis are statistical techniques to quantify associations between **an independent, sometimes called a predictor, variable (X) and a continuous dependent outcome variable (Y)**.

**What is correlation and why is it useful? ›**

Correlation is **a statistical method used to assess a possible linear association between two continuous variables**. It is simple both to calculate and to interpret. However, misuse of correlation is so common among researchers that some statisticians have wished that the method had never been devised at all.

**What is the importance and uses of correlation? ›**

Correlation **facilitates the decision-making in the business world**. It reduces the range of uncertainty as predictions based on correlation are likely to be more reliable and near to reality.

**What are the three uses of correlation in statistics? ›**

Correlations have three important characterstics. They can **tell us about the direction of the relationship, the form (shape) of the relationship, and the degree (strength) of the relationship between two variables**.

**What do correlations reveal to a researcher? ›**

Correlational studies allow researchers to detect **the presence and strength of a relationship between variables**, while experimental studies allow researchers to look for cause and effect relationships.

**What is the advantage of knowing the correlation between two variables? ›**

The variables that get studied with correlational research help us to find the direction and strength of each relationship. This advantage makes it possible to narrow the findings in future studies as needed to determine causation experimentally as needed.

**What is the biggest advantage of correlational research? ›**

It allows researchers to determine the strength and direction of a relationship so that later studies can narrow the findings down and, if possible, determine causation experimentally. Correlation research only uncovers a relationship; it cannot provide a conclusive reason for why there's a relationship.