9/15/2023 0 Comments Rstudio summary statistics![]() ![]() Converting Frequency Tables to an "Original" Flat fileįinally, there may be times that you wil need the original "flat file" data frame rather than the frequency table. To practice making these charts, try the data visualization course at DataCamp. Use the ca package for correspondence analysis (visually exploring relationships between rows and columns in contingency tables). Use the vcd package for visualizing relationships among categorical data (e.g. Use bar and pie charts for visualizing frequencies in one dimension. See Richard Darlington's article on Measures of Association in Crosstab Tables for an excellent review of these statistics. The kappa( mytable) function in the vcd package calculates Cohen's kappa and weighted kappa for a confusion matrix. The assocstats( mytable ) function in the vcd package calculates the phi coefficient, contingency coefficient, and Cramer's V for an rxc table. Martin Theus and Stephan Lauer have written an excellent article on Visualizing Loglinear Models, using mosaic plots. No Three-Way Interaction loglm(~A+B+C+A*B+A*C+B*C, mytable) Partial Independence: A is partially independent of B and C (i.e., A is independent of the composite variable BC).Ĭonditional Independence: A is independent of B, given C. Mutual Independence: A, B, and C are pairwise independent. For example, let's assume we have a 3-way contingency table based on variables A, B, and C. You can use the loglm( ) function in the MASS package to produce log-linear models. x is a 3 dimensional contingency table, where the last dimension refers to the strata. Use the mantelhaen.test( x ) function to perform a Cochran-Mantel-Haenszel chi-squared test of the null hypothesis that two nominal variables are conditionally independent in each stratum, assuming that there is no three-way interaction. ![]() x is a two dimensional contingency table in matrix form. Fisher Exact Testįisher.test( x ) provides an exact test of independence. Optionally, the p-value can be derived via Monte Carlo simultation. By default, the p-value is calculated from the asymptotic chi-squared distribution of the test statistic. Tests of Independence Chi-Square Testįor 2-way tables you can use chisq.test( mytable ) to test independence of the row and column variable. There are options to report percentages (row, column, cell), specify decimal places, produce Chi-square, Fisher, and McNemar tests of independence, report expected and residual values (pearson, standardized, adjusted standardized), include missing values as valid, annotate with row and column titles, and format as SAS or SPSS style output! It has a wealth of options.ĬrossTable(mydata$myrowvar, mydata$mycolvar) The CrossTable( ) function in the gmodels package produces crosstabulations modeled after PROC FREQ in SAS or CROSSTABS in SPSS. If a variable is included on the left side of the formula, it is assumed to be a vector of frequencies (useful if the data have already been tabulated). ![]() Summary(mytable) # chi-square test of indepedence The xtabs( ) function allows you to create crosstabulations using formula style input. If the variable is a factor you have to create a new factor using newfactor <- factor(oldfactor, exclude=NULL). To include NA as a category in counts, include the table option exclude=NULL if the variable is a vector. In this case, use the ftable( ) function to print the results more attractively. Table( ) can also generate multidimensional tables based on 3 or more categorical variables. Prop.table(mytable, 2) # column percentages Margin.table(mytable, 2) # B frequencies (summed over A) Margin.table(mytable, 1) # A frequencies (summed over B) Mytable <- table(A,B) # A will be rows, B will be columns You can generate frequency tables using the table( ) function, tables of proportions using the prop.table( ) function, and marginal frequencies using margin.table( ). In the following examples, assume that A, B, and C represent categorical variables. R provides many methods for creating frequency and contingency tables. This section describes the creation of frequency and contingency tables from categorical variables, along with tests of independence, measures of association, and methods for graphically displaying results. ![]()
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