cor.sdf: Bivariate Correlation

In EdSurvey: Analysis of NCES Education Survey and Assessment Data

Bivariate Correlation

Description

Computes the correlation of two variables on an edsurvey.data.frame, a light.edsurvey.data.frame, or an edsurvey.data.frame.list. The correlation accounts for plausible values and the survey design.

Usage

cor.sdf(
  x,
  y,
  data,
  method = c("Pearson", "Spearman", "Polychoric", "Polyserial"),
  weightVar = "default",
  reorder = NULL,
  dropOmittedLevels = TRUE,
  defaultConditions = TRUE,
  recode = NULL,
  condenseLevels = TRUE,
  fisherZ = if (match.arg(method) %in% "Pearson") {
     TRUE
 } else {
     FALSE
 },
  jrrIMax = Inf,
  verbose = TRUE,
  omittedLevels = deprecated()
)

Arguments

x	a character variable name from the data to be correlated with y
y	a character variable name from the data to be correlated with x
data	an edsurvey.data.frame, a light.edsurvey.data.frame, or an edsurvey.data.frame.list
method	a character string indicating which correlation coefficient (or covariance) is to be computed. One of Pearson (default), Spearman, Polychoric, or Polyserial. For Polyserial, the continuous argument must be x.
weightVar	character indicating the weight variable to use. See Details section in lm.sdf.
reorder	a list of variables to reorder. Defaults to NULL (no variables are reordered). Can be set as reorder = list(var1 = c("a","b","c"), var2 = c("4", "3", "2", "1")). See Examples.
dropOmittedLevels	a logical value. When set to the default value of TRUE, drops those levels of all factor variables that are specified in an edsurvey.data.frame. Use print on an edsurvey.data.frame to see the omitted levels.
defaultConditions	a logical value. When set to the default value of TRUE, uses the default conditions stored in an edsurvey.data.frame to subset the data. Use print on an edsurvey.data.frame to see the default conditions.
recode	a list of lists to recode variables. Defaults to NULL. Can be set as recode = list(var1 = list(from = c("a","b","c"), to = "d")). See Examples.
condenseLevels	a logical value. When set to the default value of TRUE and either x or y is a categorical variable, the function will drop all unused levels and rank the levels of the variable before calculating the correlation. When set to FALSE, the numeric levels of the variable remain the same as in the codebook. See Examples.
fisherZ	for standard error and mean calculations, set to TRUE to use the Fisher Z-transformation (see details), or FALSE to use no transformation of the data. The fisherZ argument defaults to Fisher Z-transformation for Pearson and no transformation for other correlation types.
jrrIMax	a numeric value; when using the jackknife variance estimation method, the default estimation option, jrrIMax=Inf, uses the sampling variance from all plausible values as the component for sampling variance estimation. The Vjrr term (see Statistical Methods Used in EdSurvey) can be estimated with any number of plausible values, and values larger than the number of plausible values on the survey (including Inf) will result in all plausible values being used. Higher values of jrrIMax lead to longer computing times and more accurate variance estimates.
verbose	a logical value. Set to FALSE to avoid messages about variable conversion.
omittedLevels	this argument is deprecated. Use dropOmittedLevels.

Details

The getData arguments and recode.sdf may be useful. (See Examples.) The correlation methods are calculated as described in the documentation for the wCorr package—see browseVignettes(package="wCorr").

When method is set to polyserial, all x arguments are assumed to be continuous and all y assumed discrete. Therefore, be mindful of variable selection as this may result in calculations taking a very long time to complete.

The Fisher Z-transformation is both a variance stabilizing and normalizing transformation for the Pearson correlation coefficient (Fisher, 1915). The transformation takes the inverse hyperbolic tangent of the correlation coefficients and then calculates all variances and confidence intervals. These are then transformed back to the correlation space (values between -1 and 1, inclusive) using the hyperbolic tangent function. The Taylor series approximation (or delta method) is applied for the standard errors.

Value

An edsurvey.cor that has print and summary methods.

The class includes the following elements:

correlation	numeric estimated correlation coefficient
Zse	standard error of the correlation (Vimp + Vjrr). In the case of Pearson, this is calculated in the linear atanh space and is not a standard error in the usual sense.
correlates	a vector of length two showing the columns for which the correlation coefficient was calculated
variables	correlates that are discrete
order	a list that shows the order of each variable
method	the type of correlation estimated
Vjrr	the jackknife component of the variance estimate. For Pearson, in the atanh space.
Vimp	the imputation component of the variance estimate. For Pearson, in the atanh space.
weight	the weight variable used
npv	the number of plausible values used
njk	the number of the jackknife replicates used
n0	the original number of observations
nUsed	the number of observations used in the analysis—after any conditions and any listwise deletion of missings is applied
se	the standard error of the correlation, in the correlation ([-1,1]) space
ZconfidenceInterval	the confidence interval of the correlation in the transformation space
confidenceInterval	the confidence interval of the correlation in the correlation ([-1,1]) space
transformation	the name of the transformation used when calculating standard errors

Author(s)

Paul Bailey; relies heavily on the wCorr package, written by Ahmad Emad and Paul Bailey

References

Fisher, R. A. (1915). Frequency distribution of the values of the correlation coefficient in samples from an indefinitely large population. Biometrika, 10(4), 507–521.

See Also

cor and weightedCorr

Examples

## Not run: 
# read in the example data (generated, not real student data)
sdf <- readNAEP(system.file("extdata/data", "M36NT2PM.dat", package = "NAEPprimer"))

# for two categorical variables any of the following work
c1_pears <- cor.sdf(x="b017451", y="b003501", data=sdf, method="Pearson",
                    weightVar="origwt")
c1_spear <- cor.sdf(x="b017451", y="b003501", data=sdf, method="Spearman",
                    weightVar="origwt")
c1_polyc <- cor.sdf(x="b017451", y="b003501", data=sdf, method="Polychoric",
                    weightVar="origwt")

c1_pears
c1_spear
c1_polyc

# for categorical variables, users can either keep the original numeric levels of the variables
# or condense the levels (default)
# the following call condenses the levels of the variable 'c046501'
cor.sdf(x="c046501", y="c044006", data=sdf)

# the following call keeps the original levels of the variable 'c046501'
cor.sdf(x="c046501", y="c044006", data=sdf, condenseLevels = FALSE)

# these take awhile to calculate for large datasets, so limit to a subset
sdf_dnf <- subset(sdf, b003601 == 1)

# for a categorical variable and a scale score any of the following work
c2_pears <- cor.sdf(x="composite", y="b017451", data=sdf_dnf, method="Pearson",
                    weightVar="origwt")
c2_spear <- cor.sdf(x="composite", y="b017451", data=sdf_dnf, method="Spearman",
                    weightVar="origwt")
c2_polys <- cor.sdf(x="composite", y="b017451", data=sdf_dnf, method="Polyserial",
                    weightVar="origwt")

c2_pears
c2_spear
c2_polys

# recode two variables
cor.sdf(x="c046501", y="c044006", data=sdf, method="Spearman", weightVar="origwt",
        recode=list(c046501=list(from="0%",to="None"),
                    c046501=list(from=c("1-5%", "6-10%", "11-25%", "26-50%",
                                        "51-75%", "76-90%", "Over 90%"),
                                 to="Between 0% and 100%"),
                    c044006=list(from=c("1-5%", "6-10%", "11-25%", "26-50%",
                                        "51-75%", "76-90%", "Over 90%"),
                                 to="Between 0% and 100%")))

# reorder two variables
cor.sdf(x="b017451", y="sdracem", data=sdf, method="Spearman", weightVar="origwt", 
        reorder=list(sdracem=c("White", "Hispanic", "Black", "Asian/Pacific Island",
                               "Amer Ind/Alaska Natv", "Other"),
                     b017451=c("Every day", "2 or 3 times a week", "About once a week",
                               "Once every few weeks", "Never or hardly ever")))

# recode two variables and reorder
cor.sdf(x="pared", y="b013801", data=subset(sdf, !pared %in% "I Don\'t Know"),
        method="Spearman", weightVar = "origwt",
        recode=list(pared=list(from="Some ed after H.S.", to="Graduated H.S."), 
                    pared=list(from="Graduated college", to="Graduated H.S."),
                    b013801=list(from="0-10", to="Less than 100"), 
                    b013801=list(from="11-25", to="Less than 100"),
                    b013801=list(from="26-100", to="Less than 100")),
        reorder=list(b013801=c("Less than 100", ">100")))

## End(Not run)

EdSurvey documentation built on Nov. 2, 2023, 6:25 p.m.