| Title: | Profile Analysis of Multivariate Data in R |
|---|---|
| Description: | A suite of multivariate methods and data visualization tools to implement profile analysis and cross-validation techniques described in Davison & Davenport (2002) <DOI: 10.1037/1082-989X.7.4.468>, Bulut (2013), and other published and unpublished resources. The package includes routines to perform criterion-related profile analysis, profile analysis via multidimensional scaling, moderated profile analysis, generalizability theory, profile analysis by group, and a within-person factor model to derive score profiles. |
| Authors: | Okan Bulut [aut], Christopher David Desjardins [aut, cre] |
| Maintainer: | Christopher David Desjardins <[email protected]> |
| License: | GPL-3 |
| Version: | 0.3-6 |
| Built: | 2024-11-05 03:57:12 UTC |
| Source: | https://github.com/cddesja/profiler |
The package profileR provides a set of multivariate methods and data visualization tools to implement profile analysis and cross-validation techniques described in Davison & Davenport (2002), Bulut (2013), and other resources.This package includes routines to perform criterion-related profile analysis, profile analysis via multidimensional scaling, moderated profile analysis, profile analysis by group, and a within-person factor model to derive score profiles.
Okan Bulut [email protected]
Christopher David Desjardins [email protected]
Bulut, O. (2013). Between-person and within-person subscore reliability: Comparison of unidimensional and multidimensional IRT models. (Doctoral dissertation). University of Minnesota. University of Minnesota, Minneapolis, MN. (AAT 3589000).
Davison, M. L., & Davenport, E. C. (2002). Identifying criterion-related patterns of predictor scores using multiple regression. Psychological Methods, 7(4), 468-484.
Davison, M. L., Kim, S-K., & Close, C. W. (2009). Factor analytic modeling of within person variation in score profiles. Multivariate Behavioral Research, 44, 668-87.
Computes an analysis of variance table for a criterion-related profile analysis
## S3 method for class 'critpat' anova(object, ...)## S3 method for class 'critpat' anova(object, ...)
object |
an object containing the results returned by a model fitting |
... |
additional objects of the same type. |
Simulated data based on the Baccalaureate and Beyond Longitudinal Study 2000/2001 based on the values presented in Tables 1 and 2 in Davison & Davenport (unpublished).
bacc2001bacc2001
A data frame with 1080 rows and 4 variables:
Are you a STEM major? 1: yes; 0: no
College major
GPA
SAT quantitative
SAT verbal
https://nces.ed.gov/pubsearch/pubsinfo.asp?pubid=2003174
Implements the criterion-related profile analysis described in Davison & Davenport (2002).
cpa( formula, data, k = 100, na.action = "na.fail", family = "gaussian", weights = NULL )cpa( formula, data, k = 100, na.action = "na.fail", family = "gaussian", weights = NULL )
formula |
An object of class |
data |
An optional data frame, list or environment containing the variables in the model. |
k |
Corresponds to the scalar constant and must be greater than 0. Defaults to 100. |
na.action |
How should missing data be handled? Function defaults to failing if missing data are present. |
family |
A description of the error distribution and link function to be used in the model. See |
weights |
An option vector of weights to be used in the fitting process. |
The cpa function requires two arguments: criterion and predictors. The function returns the criterion-related
profile analysis described in Davison & Davenport (2002). Missing data are presently handled by specifying
na.action = "na.omit", which performs listwise deletion and na.action = "na.fail", the default,
which causes the function to fail. The following S3 generic functions are available: summary(),anova(),
print(), and plot(). These functions provide a summary of the analysis (namely, R2 and the level a
nd pattern components); perform ANOVA of the R2 for the pattern, the level, and the overall model; provide
output similar to lm(), and plots the pattern effect.
An object of class critpat is returned, listing the following components:
lvl.comp - the level component
pat.comp - the pattern component
b - the unstandardized regression weights
bstar - the mean centered regression weights
xc - the scalar constant times bstar
k - the scale constant
Covpc - the pattern effect
Ypred - the predicted values
r2 - the proportion of variability attributed to the different components
F.table - the associated F-statistic table
F.statistic - the F-statistics
df - the df used in the test
pvalue - the p-values for the test
Davison, M., & Davenport, E. (2002). Identifying criterion-related patterns of predictor scores using multiple regression. Psychological Methods, 7(4), 468-484. DOI: 10.1037/1082-989X.7.4.468.
## Not run: data(IPMMc) mod <- cpa(R ~ A + H + S + B, data = IPMMc) print(mod) summary(mod) plot(mod) anova(mod) ## End(Not run)## Not run: data(IPMMc) mod <- cpa(R ~ A + H + S + B, data = IPMMc) print(mod) summary(mod) plot(mod) anova(mod) ## End(Not run)
The EEGS is a subset of the Entrance Examination
for Graduate Studies. There are three subscores
in EEGS: Quantitative 1, Quantitative 2, and Verbal. In
order to show the utility of subscore reliability method
in this package, each subtest was separated into two
parallel forms.
First form of Quantitative 1
Second form of Quantitative 1
First form of Quantitative 2
Second form of Quantitative 2
First form of Verbal
Second form of Verbal
The data come from a fabricated cognitive, personality, and vocational interested inventory. This data set can be used to demonstrate regression and structural equation modeling.
interestinterest
A data frame with 250 rows and 33 variables:
1 is female and 2 is male
Years of education
Age, in years
Vocabulary test
Reading comprehension
Sentence completion
Mathematics
Geometry
Analytical reasoning
Social dominance
Sociability
Stress reaction
Worry scale
Impulsivity
Thrill-seeking
Carpentry
Forest ranger
Mortician
Police
Fireman
Sales representative
Teacher
Business executive
Stock broker
Artist
Social worker
Truck driver
Doctor
Clergyman
Lawyer
Actor
Architect
Landscaper
http://psych.colorado.edu/~carey/Courses/PSYC7291/ClassDataSets.htm
The IPMMc data frame has 6 rows and 5 columns. See
Davison and Davenport (2002) for more information.
This data frame contains the following columns:
Anxiety
Hypochondriasis
Schizophrenia
Bipolar Disorder
The Neurotic versus Psychotic Criterion Variable, where Neurotic (= 1) or Psychotic (= 0)
Davison, M. L., & Davenport, E. C. (2002). Identifying criterion-related patterns of predictor scores using multiple regression. Psychological Methods, 7(4), 468-484.
Davison, M. L., & Davenport, E. C. (2002). Identifying criterion-related patterns of predictor scores using multiple regression. Psychological Methods, 7(4), 468-484.
The leisure dataset includes leisure activity
rankings for three different groups: politicians,
administrators, and belly-dancers. Rankings are provided
in four categories: Reading, Dancing, Watching TV, and Skiing.
See Tabachnik and Fidell (1996) for more details.
Tabachnick, B. G., & Fidell, L. S. (1996). Using multivariate statistics (3rd ed.). New York: Harper Collins.
## Not run: data(leisure) ## End(Not run)## Not run: data(leisure) ## End(Not run)
Randomly generated data to test the mpa function.
This data frame contains the following columns:
Dependent variable
Predictor variable 1
Predictor variable 2
The moderator variable
This data set was randomly generated to demonstrate how to use the mpa function.
Implements the moderated profile analysis approach developed by Davison & Davenport (unpublished)
mpa(formula, data, moderator, k = 100, na.action = "na.fail")mpa(formula, data, moderator, k = 100, na.action = "na.fail")
formula |
An object of class |
data |
An optional data frame, list or environment containing the variables in the model. |
moderator |
Name of the moderator variable. |
k |
Corresponds to the scalar constant and must be greater than 0. Defaults to 100. |
na.action |
How should missing data be handled? Function defaults to failing if missing data are present. |
The function returns the criterion-related moderated profile analysis described in Davison & Davenport (unpublished). Missing data are presently handled by specifying na.action = "na.omit", which performs listwise deletion and na.action = "na.fail", the default, which causes the function to fail. The following S3 generic functions are not yet available but will be in future implementations. summary(),anova(), print(), and plot(). These functions provide a summary of the analysis (namely, R2 and the level and pattern components); perform ANOVA of the R2 for the pattern, the level, and the overall model; provide output similar to lm(), and plots the pattern effect. WORKS ONLY WITH TWO GROUPS!
A list containing the following components:
call - The model call
output - The output from the moderated criterion-related profile analysis
f.table - The corrected F-table for assessing differences in patterns.
moder.model - The standard moderated regression model
Davison, M., & Davenport, E. (unpublished). Comparing Criterion-Related Patterns of Predictor Variables across Populations Using Moderated Regression.
## Not run: data(mod_data) mod <- mpa(gpa ~ satv * major + satq * major, moderator = "major", data = bacc2001) summary(mod$output) mod$f.table summary(mod$moder.model) ## End(Not run)## Not run: data(mod_data) mod <- mpa(gpa ~ satv * major + satq * major, moderator = "major", data = bacc2001) summary(mod$output) mod$f.table summary(mod$moder.model) ## End(Not run)
In 1985, the United States Department of Agriculture (USDA) commissioned a study of women's nutrition. Nutrient intake was measured for a random sample of 737 women aged 25-50 years. Five nutritional components were measured: calcium, iron, protein, vitamin A and vitamin C.
Calcium amount
Iron amount
Protein amount
Vitamin A amount
Vitamin C amount
The pams function implements profile analysis via multidimensional scaling as described by Davison, Davenport, and Bielinski (1995) and Davenport, Ding, and Davison (1995).
pams(data, dim)pams(data, dim)
data |
A data matrix or data frame; rows represent individuals, columns represent scores; missing scores are not allowed. |
dim |
Number of dimensions to be extracted from the data. |
The pams function computes similarity/dissimilarity indices based on Euclidean distances between the scores provided in the data, and then extracts dimensional coordinates for each score using multidimensional scaling. A weight matrix, level parameters, and fit measures are computed for each subject in the data.
dimensional.configuration - A matrix that provides prototypical profiles of dimensions extracted from the data.
weights.matrix - A matrix that includes the subject correspondence weights for all dimensions, level parameters, and the subject fit measure which is the proportion of variance in the subject's actual profiles accounted for by the prototypical profiles.
Davenport, E. C., Ding, S., & Davison, M. L. (1995). PAMS: SAS Template.
Davison, M. L., Davenport, E. C., & Bielinski, J. (1995). PAMS: SPSS Template.
## Not run: data(PS) result <- pams(PS[,2:4], dim=2) result ## End(Not run)## Not run: data(PS) result <- pams(PS[,2:4], dim=2) result ## End(Not run)
The paos function implements profile analysis for one sample using Hotelling's T-square.
paos(data, scale = TRUE)paos(data, scale = TRUE)
data |
A data matrix or data frame; rows represent individuals, columns represent variables. |
scale |
If TRUE (default), variables are standardized by dividing their standard deviations. |
The paos function runs profile analysis for one sample based on the Hotelling's T-square test and
tests the two htypothesis. First, the null hypothesis that all the ratios of the variables in the data are
equal to 1. After rejecting the first hypothesis, a secondary null hypothesis that all of the ratios of the
variables in the data equal to one another (not necessarily equal to 1) is tested.
A summary table is returned, listing the following two hypothesis:
Hypothesis 1 - Ratios of the means of the variables over the hypothesized mean are equal to 1.
Hypothesis 2 - All of the ratios are equal to each other.
## Not run: data(nutrient) paos(nutrient, scale=TRUE) ## End(Not run)## Not run: data(nutrient) paos(nutrient, scale=TRUE) ## End(Not run)
The pbg function implements three hypothesis tests. These tests are whether the profiles are parallel, have equal levels, and are flat across groups defined by the grouping variable. If parallelism is rejected, the other two tests are not necessary. In that case, flatness may be assessed within each group, and various within- and between-group contrasts may be analyzed.
pbg(data, group, original.names = FALSE, profile.plot = FALSE)pbg(data, group, original.names = FALSE, profile.plot = FALSE)
data |
A matrix or data frame with multiple scores; rows represent individuals, columns represent subscores. Missing subscores have to be inserted as NA. |
group |
A vector or data frame that indicates a grouping variable. It can be either numeric or character (e.g., male-female, A-B-C, 0-1-2). The grouping variable must have the same length of x. Missing values are not allowed in y. |
original.names |
Use original column names in x. If FALSE, variables are renamed using v1, v2, ..., vn for subscores and "group" for the grouping variable. Default is FALSE. |
profile.plot |
Print a profile plot of scores for the groups. Default is FALSE. |
An object of class profg is returned, listing the following components:
data.summary - Means of observed variables by the grouping variable
corr.table - A matrix of correlations among observed variables splitted by the grouping variable
profile.test - Results of F-tests for testing parallel, coincidential, and level profiles across two groups.
## Not run: data(spouse) mod <- pbg(data=spouse[,1:4], group=spouse[,5], original.names=TRUE, profile.plot=TRUE) print(mod) #prints average scores in the profile across two groups summary(mod) #prints the results of three profile by group hypothesis tests ## End(Not run)## Not run: data(spouse) mod <- pbg(data=spouse[,1:4], group=spouse[,5], original.names=TRUE, profile.plot=TRUE) print(mod) #prints average scores in the profile across two groups summary(mod) #prints the results of three profile by group hypothesis tests ## End(Not run)
Implements the cross-validation described in Davison & Davenport (2002).
pcv( formula, data, seed = NULL, na.action = "na.fail", family = "gaussian", weights = NULL )pcv( formula, data, seed = NULL, na.action = "na.fail", family = "gaussian", weights = NULL )
formula |
An object of class |
data |
An optional data frame, list or environment containing the variables in the model. |
seed |
Should a seed be set? Function defaults to a random seed. |
na.action |
How should missing data be handled? Function defaults to failing if missing data are present. |
family |
A description of the error distribution and link function to be used in the model. See |
weights |
An option vector of weights to be used in the fitting process. |
The pcv function requires two arguments: criterion and predictor. The criterion corresonds to the dependent variable and the predictor corresponds to the matrix of predictor variables. The function performs the cross-validation technique described in Davison & Davenport (2002) and an object of class critpat is returned. There the following s3 generic functions are available: summary(),anova(), print(), and plot(). These functions provide a summary of the cross-validation (namely, R2); performs ANOVA of the R2 based on the split for the level, pattern, and overall; provide output similar to lm(); and plot the estimated parameters for the random split. Missing data are presently handled by specifying na.action = "na.omit", which performs listwise deletion and na.action = "na.fail", the default, which causes the function to fail. A seed may also be set for reproducibility by setting the seed.
An object of class critpat is returned, listing the f ollowing components:
R2.full, test of the null hypothesis that R2 = 0
R2.pat, test that the R2_pattern = 0
R2.level, test that the R2_level = 0
R2.full.lvl, test that the R2_full = R2_level = 0
R2.full.pat, test that the R2_full = R2_pattern = 0
Davison, M., & Davenport, E. (2002). Identifying criterion-related patterns of predictor scores using multiple regression. Psychological Methods, 7(4), 468-484. DOI: 10.1037/1082-989X.7.4.468.
cpa,print.critpat,summary.critpat,anova.critpat,plot.critpat
Plots the criterion-related level and pattern profiles for each observation
## S3 method for class 'critpat' plot(x, ...)## S3 method for class 'critpat' plot(x, ...)
x |
|
... |
additional arguments affecting the plot produced. |
Plots the pattern vs. level reliability returned from the pr function of class prof.
## S3 method for class 'prof' plot(x, ...)## S3 method for class 'prof' plot(x, ...)
x |
an object returned from the |
... |
additional objects of the same type. |
The pr function uses subscores from two parallel test forms and computes profile reliability coefficients as described in Bulut (2013).
pr(form1, form2)pr(form1, form2)
form1, form2
|
Two data matrices or data frames; rows represent individuals, columns represent subscores. Both forms should have the same individuals and subscores in the same order. Missing subscores have to be inserted as NA. |
Profile pattern and level reliability coefficients are based on the profile analysis approach described in Davison and Davenport (2002) and Bulut (2013). Using the parallel test forms or multiple administration of the same test form, pattern and level reliability coefficients are computed. Pattern reliability is an indicator of variability between the subscores of an examinee and the level reliability is an indicator of the average subscore variation among all examinees. For details, see Bulut(2013)
An object of class prof is returned, listing the following components:
reliability - Within-in person, between-person, and overall subscore reliability
pattern.level - A matrix of all pattern and level values obtained from the subscores
Bulut, O. (2013). Between-person and within-person subscore reliability: Comparison of unidimensional and multidimensional IRT models. (Doctoral dissertation). University of Minnesota. University of Minnesota, Minneapolis, MN. (AAT 3589000).
Davison, M. L., & Davenport, E. C. (2002). Identifying criterion-related patterns of predictor scores using multiple regression. Psychological Methods, 7(4), 468-484. DOI: 10.1037/1082-989X.7.4.468
## Not run: data(EEGS) result <- pr(EEGS[,c(1,3,5)],EEGS[,c(2,4,6)]) print(result) plot(result) ## End(Not run)## Not run: data(EEGS) result <- pr(EEGS[,c(1,3,5)],EEGS[,c(2,4,6)]) print(result) plot(result) ## End(Not run)
Prints the default output from fitting the cpa function.
## S3 method for class 'critpat' print(x, ...)## S3 method for class 'critpat' print(x, ...)
x |
object of class |
... |
additional objects of the same type. |
The profileplot function creates a profile plot for a matrix or dataframe with multiple scores or subscores using ggplot function in ggplot2 package.
profileplot( form, person.id, standardize = TRUE, interval = 10, by.pattern = TRUE, original.names = TRUE )profileplot( form, person.id, standardize = TRUE, interval = 10, by.pattern = TRUE, original.names = TRUE )
form |
A matrix or dataframe including two or more subscores. |
person.id |
A vector that includes person ID values (Optional). |
standardize |
If not FALSE, all scores are rescaled with a mean of 0 and standard deviation of 1. Default is TRUE. |
interval |
The number of equal intervals from the mimimum score to the meximum score. Default is 10. Ignored when by.pattern=FALSE. |
by.pattern |
If TRUE, the function creates a profile plot with level and pattern values using ggplot2. Otherwise, the function creates a profile plot showing profile scores of persons using the base graphics in R. Default is TRUE. |
original.names |
Use the original column names in the data. Otherwise, columns are renamed as v1,v2,.... Default is TRUE. |
The profileplot functions returns a score profile plot from either ggplot or the base graphics in R.
## Not run: data(PS) myplot <- profileplot(PS[,2:4], person.id = PS$Person,by.pattern = TRUE, original.names = TRUE) myplot data(leisure) leis.plot <- profileplot(leisure[,2:4],standardize=TRUE,by.pattern=FALSE) leis.plot ## End(Not run)## Not run: data(PS) myplot <- profileplot(PS[,2:4], person.id = PS$Person,by.pattern = TRUE, original.names = TRUE) myplot data(leisure) leis.plot <- profileplot(leisure[,2:4],standardize=TRUE,by.pattern=FALSE) leis.plot ## End(Not run)
The PS shows score profiles of six respondents to
a hypothetical personality scale. It includes three types
of profile patterns: Linearly increasing, inverted V, and
linearly decreasing.
Person ID
Neurotic scale score
Psychotic scale score
Character disorder scale score
Davison, M. L., Kim, S-K., & Close, C. W. (2009). Factor analytic modeling of within person variation in score profiles. Multivariate Behavioral Research, 44, 668-87.
Davison, M. L., Kim, S-K., & Close, C. W. (2009). Factor analytic modeling of within person variation in score profiles. Multivariate Behavioral Research, 44, 668-87.
The spouse data come from a study of love and marriage. A sample of 30 husbands and their wives were asked to respond to the following questions:
Question 1: What is the level of passionate love you feel for your partner?
Question 2: What is the level of passionate love that your partner feels for you?
Question 3: What is the level of companionate love that you feel for your partner?
Question 4: What is the level of companionate love that your partner feels for you?
The responses to all four questions are on a five-point Likert scale where 1 indicates "none at all" and 5 indicates "tremendous amount".
Question 1 with a score ranging from 1 to 5.
Question 2 with a score ranging from 1 to 5.
Question 3 with a score ranging from 1 to 5.
Question 4 with a score ranging from 1 to 5.
Spouse type. It is either "Husband" or "Wife"
## Not run: data(spouse) ## End(Not run)## Not run: data(spouse) ## End(Not run)
Provides a summary of the criterion-related profile analysis
## S3 method for class 'critpat' summary(object, ...)## S3 method for class 'critpat' summary(object, ...)
object |
object of class |
... |
additional arguments affecting the summary produced. |
Within-Person Random Intercept Factor Model
wprifm(data, scale = FALSE, save_model = FALSE)wprifm(data, scale = FALSE, save_model = FALSE)
data |
Data.frame containing the manifest variables. |
scale |
Should the data be scaled? Default = FALSE |
save_model |
Should the temporary lavaan model syntax be saved. Default = FALSE |
This function performs the within-person random intercept factor model described in Davison, Kim, and Close (2009). For information about this model, please see this reference. This function returns an object of lavaan class and thus any generics defined for lavaan will work on this object. This function provides a simple wrapper for lavaan.
an object of class lavaan
Davison, M., Kim, S.-K., Close, C. (2009). Factor analytic modeling of within person variation in score profiles. Multivariate Behavioral Research, 44(5), 668 - 687. DOI: 10.1080/00273170903187665
data <- HolzingerSwineford1939[,7:ncol(HolzingerSwineford1939)] wprifm(data, scale = TRUE)data <- HolzingerSwineford1939[,7:ncol(HolzingerSwineford1939)] wprifm(data, scale = TRUE)