tetrachoric) works through a tedious calculation. shannon finds the Shannon index (H) of diversity or of information. test.all tests all the examples in a package. best.items sorts a factor matrix for absolute values and displays the expanded items namespsych.misc()
lowerCor(x,digits=2,use="pairwise",method="pearson")
lowerMat(R, digits = 2)
tableF(x,y)
reflect(f,flip=NULL)
progressBar(value,max,label=NULL)
shannon(x,correct=FALSE,base=2)
test.all(p)
best.items(x,c1=1,cut=.3, abs=TRUE, contents=NULL,digits=2)
lookup(x,y,c1=1)lowerCor prints the lower diagonal correlation matrix but returns (invisibly) the full correlation matrix found with the use and method parameters. The default values are for pairwise deletion of variables, and to print to 2 decimal places.
tableF (for tableFast) is a cut down version of table that does no error checking, nor returns pretty output, but is significantly faster than table. It will just work on two integer vectors. This is used in polychoric an tetrachoric for about a 50% speed improvement for large problems.
shannon finds Shannon's H index of information. Used for estimating the complexity or diversity of the distribution of responses in a vector or matrix. $$H = -\sum{p_i log(p_i) }$$
link{test.all} allows one to test all the examples in specified package. This allows us to make sure that those examples work when other packages are also loaded.
best.items returns a sorted list of factor loadings or correlations with the labels as provided in the contents field.
lookup is a very simple implementation of the match function.
corFiml is a very short wrapper function that uses the power of the lavaan package. corFiml calls three lavaan functions: getMissingPatterns, getMissingPatternStats and estimate.moments.fiml. corFiml is included here to allow for Full Information Maximum Likelihood (FIML) correlation and covariance matrices without digging into the lavaan package. lavaan in turn requires a number of other packages, including boot, MASS, mnormt, pbivnorm, and quadprog. lowerCor prints out the lower off diagonal matrix rounded to digits with column names abbreviated to digits + 3 characters, but also returns the full and unrounded matrix. By default, it uses pairwise deletion of variables. It in turn calls
lowerMat which does the pretty printing.
best.items and lookup are simple helper functions to summarize correlation matrices or factor loading matrices. best.items will sort the specified column (c1) of x on the basis of the (absolute) value of the column. The return as a default is just the rowname of the variable with those absolute values > cut. If there is a dictionary of item content and item names, then include the contents as a two column matrix with item name and item content.
lookup is used by best.items and will find values in c1 of y that match those in x. It returns those rows of y of that match x.
Suppose that you have a "dictionary" of the many variables in a study but you want to consider a small subset of them in a data set x. Then, you can find the entries in the dictionary corresponding to x by lookup(rownames(x),y)
Similarly, given a correlation matrix, r, of the x variables, if you want to find the items that most correlate with another item or scale, and then show the contents of that item from the dictionary, best.items(r,c1=column number or name of x, contents = y)
corr.test to find correlations, count the pairwise occurrences, and to give significance tests for each correlation. r.test for a number of tests of correlations, including tests of the difference between correlations. lowerUpper will display the differences between two matrices.lowerMat(Thurstone)
lb <- lowerCor(bfi[1:10]) #finds and prints the he lower correlation matrix,
# returns the square matrix.
#fiml <- corFiml(bfi[1:10]) #FIML correlations require lavaan package
#lowerMat(fiml) #to get pretty output
f3 <- fa(Thurstone,3)
f3r <- reflect(f3,2) #reflect the second factor
#find the complexity of the response patterns of the iqitems.
round(shannon(iqitems),2)
#test.all('BinNor') #Does the BinNor package work when we are using other packages
best.items(lb,3,cut=.1)
#to make this a latex table
#df2latex(best.items(lb,2,cut=.2))
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