cor.sdf: Bivariate Correlation

Description Usage Arguments Details Value Author(s) See Also Examples

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

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cor.sdf(x, y, data, method = c("Pearson", "Spearman", "Polychoric",
  "Polyserial"), weightVar = "default", reorder = NULL,
  omittedLevels = TRUE, defaultConditions = TRUE, recode = NULL,
  condenseLevels = TRUE)

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.

weightVar

character indicating the weight variable to use. See Details.

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.

omittedLevels

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.

Details

Note that 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").

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 so 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

Author(s)

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

See Also

cor and weightedCorr

Examples

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## 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 May 2, 2019, 7:30 a.m.