waba
(Package: multilevel) :
Covariance Theoreom Decomposition of Bivariate Two-Level Correlation
This routine performs the covariance theorem decomposition discussed by Robinson (1950) and Dansereau, Alutto and Yammarino (1984). Dansereau et al. have labeled the variance decomposition Within-And-Between-Analysis II or WABA II. The program decomposes a raw correlation from a two-level nested design into 6 components. These components are (1) eta-between value for X, (2) eta-between value for Y, (3) the group-size weighted group-mean correlation, (4) the within-eta value for X, (5) the within-eta value for Y, and (6) the within-group correlation between X and Y. The last value represents the correlation between X and Y after each variable has been group-mean centered.
● Data Source:
CranContrib
● Keywords: attribute
● Alias: waba
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This function provides a concise summary of objects created using the function rgr.waba.
● Data Source:
CranContrib
● Keywords: programming
● Alias: summary.rgr.waba
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This function provides a concise summary of objects created using the function rgr.agree.
● Data Source:
CranContrib
● Keywords: programming
● Alias: summary.rgr.agree
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This function provides a concise summary of objects created using the function ad.m.sim.
● Data Source:
CranContrib
● Keywords: programming
● Alias: summary.disagree.sim
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This function provides a concise summary of objects created using the functions rwg.sim and rwg.j.sim.
● Data Source:
CranContrib
● Keywords: programming
● Alias: summary.agree.sim
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sobel
(Package: multilevel) :
Estimate Sobel's (1982) Test for Mediation
Estimate Sobel's (1982) indirect test for mediation. The function provides an estimate of the magnitude of the indirect effect, Sobel's first-order estimate of the standard error associated with the indirect effect, and the corresponding z-value. The estimates are based upon three models as detailed on page 84 of MacKinnon, Lockwood, Hoffman, West and Sheets (2002).
● Data Source:
CranContrib
● Keywords: htest
● Alias: sobel
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simple.predict
(Package: multilevel) :
A simple variant of the generic predict command useful for S4 lmer models
Statistical models can be used to predict levels of an outcome variable given specific values of predictors. R has a number of predict functions linked to specific models (e.g., predict.lm, predict.glm). At their core, predict commands multiple the estimated coefficients and the corresponding predictors, and then sum across the intercept and predictors. Given the existing functions, there is little value in using simple.predict in most cases. As of 2013, however, models produced with lme4 functions (lmer and glmer) had no specific predict commands, so the simple.predict function was primarily designed to provide predicted values from lme4 models.
● Data Source:
CranContrib
● Keywords: predict
● Alias: simple.predict
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simbias
(Package: multilevel) :
Simulate Standard Error Bias in Non-Independent Data
Non-independence due to groups is a common characteristic of applied data. In non-independent data, responses from members of the same group are more similar to each other than would be expected by chance. Non-independence is typically measured using the Intraclass Correlation Coefficient 1 or ICC(1). When non-independent data is treated as though it is independent, standard errors will be biased and power can decrease. This simulation allows one to estimate the bias and loss of statistical power that occurs when non-independent data is treated as though it is independent. The simulation contrasts a simple Ordinary Least Squares (OLS) model that fails to account for non-independence with a random coefficient model that accounts for non-independence. The simulation assumes that both the outcome (y) and the predictor (x) vary among individuals in the same group.
● Data Source:
CranContrib
● Keywords: datagen
● Alias: simbias
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sam.cor
(Package: multilevel) :
Generate a Sample that Correlates with a Fixed Set of Observations
This function will generate a vector (y) with a known correlation to a given vector (x). The degree of correlation between x and y is determined by the parameter rho (the population correlation). Observed sample correlations between x and y will vary around rho, but this variation will decrease as the size of x increases.
● Data Source:
CranContrib
● Keywords: programming
● Alias: sam.cor
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rwg.sim
(Package: multilevel) :
Simulate rwg values from a random null distribution
This function is based on the work of Dunlap, Burke & Smith-Crowe (2003). The function draws data from a random uniform null distribution, and calculates the within group agreement measure rwg for single item measures as described in James, Demaree & Wolf (1984). By repeatedly drawing random samples, a distribution of the rwg is generated. The sampling distribution can be used to calculate confidence intervals for different combinations of group sizes and number of response options (A).
● Data Source:
CranContrib
● Keywords: attribute
● Alias: rwg.sim
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