R/mrinfo.R
In bcdudek/bcdstats: A collection of functions to support B. Dudek's APSY510/511 classes
#' Regression Information for lm class models
#'
#' The \code{mrinfo} function produces a wealth of additional information
#' on multiple regression models above and beyond what summary(), anova(),
#' and Anova() produce.
#'
#' @param lm.fit A \code{lm} class model object.
#' @param minimal specifies volume of returned information (see details). Default=T.
#' @param cilevel Confidence interval level. Default is .95.
#'
#' @details The function takes an \code{lm} fit object. Supplemental information is
#' calculated for \code{lm} fit objects. When \code{minimal=T} is specified,
#' a data frame of several indices (see values) is returned. When \code{minimal=F},
#' a list of information, including the supplemental values is returned:
#' zero-order correlations, the coefficients table, confidence intervals for the
#' regression coefficients, type I SS anova table and type III SS Anova table.
#'
#' @section Values of the Supplemental Information:
#' \tabular{ll}{
#' \code{beta wt} \tab Standardized Regression Coefficients\cr
#' \code{structure r} \tab Structure Coefficients\cr
#' \code{partial r} \tab Partial correlations of IVs with the DV\cr
#' \code{semi-partial r} \tab Semi-partial correlations of IVs with the DV\cr
#' \code{tolerances} \tab Tolerance for each IV\cr
#' \code{unique} \tab Unique proportion of variance in DV accounted for
#' by each IV\cr
#' \code{common} \tab Common proportion of variance in DV accounted for
#' by each IV shared with other IVs\cr
#' \code{total} \tab Total proportion of variance in DV accounted for
#' by each IV\cr
#' \code{pearsons} \tab The zero-order pearson correlation matrix among
#' all variables\cr
#' }
#'
#'@details Structure coefficients can be very helpful in interpretation of
#'\code{lm} models. The are defined as the pearson correlation of each IV in
#'the model with the yhat vector. See Thompson and Borello (1985), Cooley and Lohnes
#'(1971), Cohen and Cohen (2003) or Nimon et. al., (2008).
#'
#'@section Warnings:
#'
#'The \code{mrinfo} function is designed to work with multiple regression objects
#'where an intercept is estimated. Models where the intercept is forced through
#'the origin ('no intercept' models) are problematic for interpretation of the
#'supplemental information listed above and it is not returned. The specification of
#' \code{minimal=FALSE} will still provide the list of items described above.
#'
#'Simple regression models do not require the supplemental information, but the
#'user can specify \code{minimal=F} to obtain the longer list of detailed information.
#'
#'Models with factor IV's may create situations where interpretation of the
#'supplemental indices is problematic. This can easily happen with
#'coding schemes such dummy coding (indicator coding or \code{contr.treatment()}).
#'Often, suppressor effect can occur,
#'rendering interpretation of beta weights, partial and semi-partial correlations, and
#'particularly the unique and common variance proportions challenging. The unique
#'proportion of variance index is calculated as the square of the semi-partial correlation
#'(appropriately). But the common proportion is calculated as the difference between
#'this unique fraction and the squared zero order pearson correlation for that IV. When
#'suppressor effects arise because of patterns related to contrast coding schemes this
#'common proportion can sometimes be found as a nonsensical negative quantity. Careful understanding
#'of one's model is required in these circumstances.
#'
#' @author Bruce Dudek <bruce.dudek@@albany.edu>
#'
#' @references This function is a modeled on a function originally coded
#' in the \code{regr} function in the \bold{yhat} package. It uses the
#' \code{effect.size} function from that package.
#'
#' Cohen, J., & Cohen, J. (2003). *Applied multiple regression/correlation
#' analysis for the behavioral sciences* (3rd ed.). L. Erlbaum Associates.
#'
#' Cooley, W. W., & Lohnes, P. R. (1971). *Multivariate data analysis*. Wiley.
#'
#' Nimon, K., Lewis, M., Kane, R., & Haynes, R. M. (2008). An R package to
#' compute commonality coefficients in the multiple regression case:
#' An introduction to the package and a practical example. *Behavior Research Methods*,
#' 40(2), 457-466.
#'
#' Pedhazur, E. *Multiple regression in behavioral research*. 1997.
#' Orlando, FL: Harcourt.
#'
#' Ray‐Mukherjee, J., Nimon, K., Mukherjee, S., Morris, D. W., Slotow, R.,
#' & Hamer, M. (2014). Using commonality analysis in multiple regressions:
#' a tool to decompose regression effects in the face of multicollinearity.
#' *Methods in Ecology and Evolution*, 5(4), 320-328.
#'
#' Thompson, B., & Borrello, G. M. (1985). The importance of structure coefficients
#' in regression research. *Educational and psychological measurement*, 45(2), 203-209.
#'
#' @examples
#' data(attitude)
#' fit1 <- lm(rating ~ complaints + learning + privileges, data=attitude)
#' #summary(fit1)
#' mrinfo(fit1, minimal=TRUE, cilevel=.99)
#' mrinfo(fit1, minimal=FALSE, cilevel=.95)
#' data(mtcars)
#' mtcars$cyl <- as.factor(mtcars$cyl)
#' contrasts(mtcars$cyl) <- contr.helmert(3)
#' fit2 <- lm (mpg ~ cyl + hp, data= mtcars)
#' mrinfo(fit2, minimal=TRUE)
#'
#' @export mrinfo
#' @importFrom yhat commonalityCoefficients
#' @importFrom yhat effect.size
#' @importFrom stats coef
#' @importFrom stats anova
#' @importFrom stats confint
#' @importFrom stats cor
#' @importFrom stats fitted.values
#' @importFrom stats model.frame
#' @importFrom stats model.matrix
#' @importFrom stats na.omit
#' @importFrom HH vif
#' @importFrom car Anova
#'
mrinfo <- function (lm.fit,
cilevel = .95,
minimal = TRUE)
{
if (names(lm.fit$coefficients)[1] == "(Intercept)") {
test1 = TRUE
} else {
test1 = FALSE
}
terms <- labels(terms(lm.fit))
n.terms <- length(terms)
if (n.terms < 2) {
test2 = FALSE
} else {
test2 = TRUE
}
termlist <- attr(lm.fit$terms, "dataClasses")[-1]
ifelse ("factor" %in% termlist,
test3 <- TRUE,
test3 <- FALSE)
# section1 intercept, MR fac/nofac. Or int, one IV factor
####################################################################################
if (test1 == TRUE & test2 == TRUE | test1 == TRUE & test2 == FALSE & test3 == TRUE ) {
new.scale <- model.matrix(lm.fit)
row.names(new.scale) <- NULL
IV <- attr(lm.fit$coefficients, "names")
DV <- dimnames(attr(lm.fit$terms, "factors"))[[1]][1]
IVx <- IV[-1]
new.data2 <- cbind.data.frame(lm.fit$model[[1]], new.scale[,-1])
colnames(new.data2)[1] <- DV # working
beta.out <- coef(lm.fit)[-1] * sapply(new.data2[IVx],
"sd") / sapply(new.data2[DV], "sd")
structure.coef <- as.vector(
cor(na.omit(fitted.values(lm.fit)), new.scale[, IVx]))
hhvif <- HH::vif(lm.fit)
tolerances <- 1 / HH::vif(lm.fit)
sqrttol <- tolerances ^.5
sr.out <- beta.out * sqrttol
zeros.out <- cor(new.data2)
ci <- confint(lm.fit, level = cilevel)
tvals <- summary(lm.fit)$coefficients[, 3][-1]
tvals.sq <- tvals ^ 2
pr_denom <- (tvals.sq + lm.fit$df.residual) ^ .5
pr.out <- tvals / pr_denom
unique <- sr.out^2
common <- zeros.out[DV,IVx]^2 - unique
total <- unique + common
es = yhat::effect.size(lm.fit)
corrs <-
as.data.frame(cbind(
round(beta.out, 5),
round(structure.coef, 5),
round(pr.out, 5),
round(sr.out, 5),
round(tolerances, 5),
round(unique, 5),
round(common, 5),
round(total, 5)))
names(corrs) <-
c("beta wt",
"structure r",
"partial r",
"semipartial r",
"tolerances",
"unique",
"common",
"total")
ifelse (minimal,
return(list(
if (test3 ==TRUE){"In models with factors (categorical IVs), interpretation of supplemental information requires caution"},
"supplemental information" = corrs,
"var infl factors from HH:vif"= hhvif,
if (test3){
Comment = list(
paste0(
"For models with factors (categorical IVs), car::vif may be more appropriate"))}
)),
return(
list(
"Pearson zero-order correlations" = zeros.out,
"lm summary" = summary(lm.fit),
"confidence intervals" = ci,
if (test3 == TRUE){"In models with factors (categorical IVs) interpretation of supplemental information requires caution"},
"supplemental information" = corrs,
"var infl factors from HH:vif"= hhvif,
if (test3 == TRUE){"For models with factors, car::vif may be more appropriate"},
"Anova Type III SS" = Anova(lm.fit, type = 3),
"Anova Type I SS" = anova(lm.fit),
"Effect Size" = es,
Comment = list(
paste0(
"Effect sizes from yhat::effect.size(); recommendations based on Yin and Fan (2001)."
),
paste0("Choose these with care based on your covariance structure")
)
)
))
}
####################################################################################
## section 2 DONE
# intercept, simple regression, no factor
if (test1 == TRUE & test2 == FALSE & test3 == FALSE) {
new.scale <- model.matrix(lm.fit)
row.names(new.scale) <- NULL
IV <- attr(lm.fit$coefficients, "names")
DV <- dimnames(attr(lm.fit$terms, "factors"))[[1]][1]
IVx <- IV[-1]
new.data2 <- cbind.data.frame(lm.fit$model[[1]], new.scale[,-1])
colnames(new.data2) <- c(DV,IVx) # working
zeros.out <- cor(new.data2)
if (minimal == T) {
return("Supplemental information is not needed for a simple regression model")
} else
if (minimal == F) {
return(
list(
"Pearson correlationx" = zeros.out,
"lm summary" = summary(lm.fit),
"confidence intervals" = confint(lm.fit, level = cilevel),
"Anova Type III SS" = Anova(lm.fit, type = 3),
"Anova Type I SS" = anova(lm.fit)
)
)
}
}
####################################################################################
## section3
## zero intercept, MR, without factor
if (test1 == FALSE & test2 == TRUE & test3 == FALSE & minimal == TRUE) {
return("mrinfo will not process supplemental information for 'no intercept' models")
}
if (test1 == FALSE & test2 == TRUE & test3 == FALSE & minimal == FALSE) {
new.scale <- model.matrix(lm.fit)
row.names(new.scale) <- NULL
IV <- attr(lm.fit$coefficients, "names")
DV <- dimnames(attr(lm.fit$terms, "factors"))[[1]][1]
#IVx <- IV[-1]
new.data2 <- cbind.data.frame(lm.fit$model[[1]], new.scale)
colnames(new.data2) <- c(DV,IV) # working
zeros.out <- cor(new.data2)
ci <- confint(lm.fit, level = cilevel)
return(
list(
"CAUTION: interpret a 'no-intercept' model carefully",
"mrinfo will not process supplemental information for 'no intercept' models",
"Pearson zero-order correlations" = zeros.out,
"lm summary" = summary(lm.fit),
"confidence intervals" = ci,
"Anova Type III SS" = Anova(lm.fit, type = 3),
"Anova Type I SS" = anova(lm.fit)
)
)
}
####################################################################################
## section4
## zero intercept, MR, with factor
if (test1 == FALSE & test2 == TRUE & test3 == TRUE & minimal == TRUE) {
return("mrinfo will not process supplemental information for 'no intercept' models")
}
if (test1 == FALSE & test2 == TRUE & test3 == TRUE & minimal == FALSE) {
new.scale <- model.matrix(lm.fit)
row.names(new.scale) <- NULL
IV <- attr(lm.fit$coefficients, "names")
DV <- dimnames(attr(lm.fit$terms, "factors"))[[1]][1]
#IVx <- IV[-1]
new.data2 <- cbind.data.frame(lm.fit$model[[1]], new.scale)
colnames(new.data2) <- c(DV,IV) # working
#zeros.out <- cor(new.data2)
ci <- confint(lm.fit, level = cilevel)
return(
list(
"CAUTION: A 'no-intercept' model with a factor IV may be inappropriate",
"mrinfo will not process supplemental information for 'no intercept' models",
#"Pearson zero-order correlations" = zeros.out,
"lm summary" = summary(lm.fit),
"confidence intervals" = ci,
"Anova Type III SS" = Anova(lm.fit, type = 3),
"Anova Type I SS" = anova(lm.fit)
)
)
}
####################################################################################
## section 5
# zero intercept, simple regression, no factor
if (test1 == FALSE & test2 == FALSE & test3 == FALSE & minimal == TRUE) {
return("supplemental info for zero-intercept models with factors (categorical IVs) is not provided")
}
if (test1 == FALSE & test2 == FALSE & test3 == FALSE & minimal == F) {
new.scale <- model.matrix(lm.fit)
row.names(new.scale) <- NULL
IV <- attr(lm.fit$coefficients, "names")
DV <- dimnames(attr(lm.fit$terms, "factors"))[[1]][1]
IVx <- IV[-1]
new.data2 <- cbind.data.frame(lm.fit$model[[1]], new.scale[,1])
colnames(new.data2) <- c(DV,IV) # working
zeros.out <- cor(new.data2)
ci <- confint(lm.fit, level = cilevel)
return(
list(
"CAUTION: interpret a 'no-intercept' model carefully",
"Pearson correlation" = zeros.out,
"lm summary" = summary(lm.fit),
"confidence intervals" = ci,
"Anova Type III SS" = Anova(lm.fit, type = 3),
"Anova Type I SS" = anova(lm.fit)
)
)
}
####################################################################################
## section 6
# zero intercept, simple regression, factor IV
if (test1 == FALSE & test2 == FALSE & test3 == TRUE & minimal == TRUE) {
return("Supplemental information for no-intercept models with Factor IVs is not provided")
}
if (test1 == FALSE & test2 == FALSE & test3 == TRUE & minimal == F) {
new.scale <- model.matrix(lm.fit)
row.names(new.scale) <- NULL
IV <- attr(lm.fit$coefficients, "names")
DV <- dimnames(attr(lm.fit$terms, "factors"))[[1]][1]
IVx <- IV[-1]
new.data2 <- cbind.data.frame(lm.fit$model[[1]], new.scale[,IVx])
colnames(new.data2) <- c(DV,IVx)
zeros.out <- cor(new.data2)
ci <- confint(lm.fit, level = cilevel)
return(
list(
"CAUTION: A 'no-intercept' model with a factor IV may be inappropriate",
#"Pearson correlation" = zeros.out,
"lm summary" = summary(lm.fit),
"confidence intervals" = ci,
"Anova Type III SS" = Anova(lm.fit, type = 3),
"Anova Type I SS" = anova(lm.fit)
)
)
}
}
bcdudek/bcdstats documentation built on Jan. 3, 2024, 10:09 p.m.
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#' Regression Information for lm class models
#'
#' The \code{mrinfo} function produces a wealth of additional information
#' on multiple regression models above and beyond what summary(), anova(),
#' and Anova() produce.
#'
#' @param lm.fit A \code{lm} class model object.
#' @param minimal specifies volume of returned information (see details). Default=T.
#' @param cilevel Confidence interval level. Default is .95.
#'
#' @details The function takes an \code{lm} fit object. Supplemental information is
#' calculated for \code{lm} fit objects. When \code{minimal=T} is specified,
#' a data frame of several indices (see values) is returned. When \code{minimal=F},
#' a list of information, including the supplemental values is returned:
#' zero-order correlations, the coefficients table, confidence intervals for the
#' regression coefficients, type I SS anova table and type III SS Anova table.
#'
#' @section Values of the Supplemental Information:
#' \tabular{ll}{
#' \code{beta wt} \tab Standardized Regression Coefficients\cr
#' \code{structure r} \tab Structure Coefficients\cr
#' \code{partial r} \tab Partial correlations of IVs with the DV\cr
#' \code{semi-partial r} \tab Semi-partial correlations of IVs with the DV\cr
#' \code{tolerances} \tab Tolerance for each IV\cr
#' \code{unique} \tab Unique proportion of variance in DV accounted for
#' by each IV\cr
#' \code{common} \tab Common proportion of variance in DV accounted for
#' by each IV shared with other IVs\cr
#' \code{total} \tab Total proportion of variance in DV accounted for
#' by each IV\cr
#' \code{pearsons} \tab The zero-order pearson correlation matrix among
#' all variables\cr
#' }
#'
#'@details Structure coefficients can be very helpful in interpretation of
#'\code{lm} models. The are defined as the pearson correlation of each IV in
#'the model with the yhat vector. See Thompson and Borello (1985), Cooley and Lohnes
#'(1971), Cohen and Cohen (2003) or Nimon et. al., (2008).
#'
#'@section Warnings:
#'
#'The \code{mrinfo} function is designed to work with multiple regression objects
#'where an intercept is estimated. Models where the intercept is forced through
#'the origin ('no intercept' models) are problematic for interpretation of the
#'supplemental information listed above and it is not returned. The specification of
#' \code{minimal=FALSE} will still provide the list of items described above.
#'
#'Simple regression models do not require the supplemental information, but the
#'user can specify \code{minimal=F} to obtain the longer list of detailed information.
#'
#'Models with factor IV's may create situations where interpretation of the
#'supplemental indices is problematic. This can easily happen with
#'coding schemes such dummy coding (indicator coding or \code{contr.treatment()}).
#'Often, suppressor effect can occur,
#'rendering interpretation of beta weights, partial and semi-partial correlations, and
#'particularly the unique and common variance proportions challenging. The unique
#'proportion of variance index is calculated as the square of the semi-partial correlation
#'(appropriately). But the common proportion is calculated as the difference between
#'this unique fraction and the squared zero order pearson correlation for that IV. When
#'suppressor effects arise because of patterns related to contrast coding schemes this
#'common proportion can sometimes be found as a nonsensical negative quantity. Careful understanding
#'of one's model is required in these circumstances.
#'
#' @author Bruce Dudek <bruce.dudek@@albany.edu>
#'
#' @references This function is a modeled on a function originally coded
#' in the \code{regr} function in the \bold{yhat} package. It uses the
#' \code{effect.size} function from that package.
#'
#' Cohen, J., & Cohen, J. (2003). *Applied multiple regression/correlation
#' analysis for the behavioral sciences* (3rd ed.). L. Erlbaum Associates.
#'
#' Cooley, W. W., & Lohnes, P. R. (1971). *Multivariate data analysis*. Wiley.
#'
#' Nimon, K., Lewis, M., Kane, R., & Haynes, R. M. (2008). An R package to
#' compute commonality coefficients in the multiple regression case:
#' An introduction to the package and a practical example. *Behavior Research Methods*,
#' 40(2), 457-466.
#'
#' Pedhazur, E. *Multiple regression in behavioral research*. 1997.
#' Orlando, FL: Harcourt.
#'
#' Ray‐Mukherjee, J., Nimon, K., Mukherjee, S., Morris, D. W., Slotow, R.,
#' & Hamer, M. (2014). Using commonality analysis in multiple regressions:
#' a tool to decompose regression effects in the face of multicollinearity.
#' *Methods in Ecology and Evolution*, 5(4), 320-328.
#'
#' Thompson, B., & Borrello, G. M. (1985). The importance of structure coefficients
#' in regression research. *Educational and psychological measurement*, 45(2), 203-209.
#'
#' @examples
#' data(attitude)
#' fit1 <- lm(rating ~ complaints + learning + privileges, data=attitude)
#' #summary(fit1)
#' mrinfo(fit1, minimal=TRUE, cilevel=.99)
#' mrinfo(fit1, minimal=FALSE, cilevel=.95)
#' data(mtcars)
#' mtcars$cyl <- as.factor(mtcars$cyl)
#' contrasts(mtcars$cyl) <- contr.helmert(3)
#' fit2 <- lm (mpg ~ cyl + hp, data= mtcars)
#' mrinfo(fit2, minimal=TRUE)
#'
#' @export mrinfo
#' @importFrom yhat commonalityCoefficients
#' @importFrom yhat effect.size
#' @importFrom stats coef
#' @importFrom stats anova
#' @importFrom stats confint
#' @importFrom stats cor
#' @importFrom stats fitted.values
#' @importFrom stats model.frame
#' @importFrom stats model.matrix
#' @importFrom stats na.omit
#' @importFrom HH vif
#' @importFrom car Anova
#'
mrinfo <- function (lm.fit,
cilevel = .95,
minimal = TRUE)
{
if (names(lm.fit$coefficients)[1] == "(Intercept)") {
test1 = TRUE
} else {
test1 = FALSE
}
terms <- labels(terms(lm.fit))
n.terms <- length(terms)
if (n.terms < 2) {
test2 = FALSE
} else {
test2 = TRUE
}
termlist <- attr(lm.fit$terms, "dataClasses")[-1]
ifelse ("factor" %in% termlist,
test3 <- TRUE,
test3 <- FALSE)
# section1 intercept, MR fac/nofac. Or int, one IV factor
####################################################################################
if (test1 == TRUE & test2 == TRUE | test1 == TRUE & test2 == FALSE & test3 == TRUE ) {
new.scale <- model.matrix(lm.fit)
row.names(new.scale) <- NULL
IV <- attr(lm.fit$coefficients, "names")
DV <- dimnames(attr(lm.fit$terms, "factors"))[[1]][1]
IVx <- IV[-1]
new.data2 <- cbind.data.frame(lm.fit$model[[1]], new.scale[,-1])
colnames(new.data2)[1] <- DV # working
beta.out <- coef(lm.fit)[-1] * sapply(new.data2[IVx],
"sd") / sapply(new.data2[DV], "sd")
structure.coef <- as.vector(
cor(na.omit(fitted.values(lm.fit)), new.scale[, IVx]))
hhvif <- HH::vif(lm.fit)
tolerances <- 1 / HH::vif(lm.fit)
sqrttol <- tolerances ^.5
sr.out <- beta.out * sqrttol
zeros.out <- cor(new.data2)
ci <- confint(lm.fit, level = cilevel)
tvals <- summary(lm.fit)$coefficients[, 3][-1]
tvals.sq <- tvals ^ 2
pr_denom <- (tvals.sq + lm.fit$df.residual) ^ .5
pr.out <- tvals / pr_denom
unique <- sr.out^2
common <- zeros.out[DV,IVx]^2 - unique
total <- unique + common
es = yhat::effect.size(lm.fit)
corrs <-
as.data.frame(cbind(
round(beta.out, 5),
round(structure.coef, 5),
round(pr.out, 5),
round(sr.out, 5),
round(tolerances, 5),
round(unique, 5),
round(common, 5),
round(total, 5)))
names(corrs) <-
c("beta wt",
"structure r",
"partial r",
"semipartial r",
"tolerances",
"unique",
"common",
"total")
ifelse (minimal,
return(list(
if (test3 ==TRUE){"In models with factors (categorical IVs), interpretation of supplemental information requires caution"},
"supplemental information" = corrs,
"var infl factors from HH:vif"= hhvif,
if (test3){
Comment = list(
paste0(
"For models with factors (categorical IVs), car::vif may be more appropriate"))}
)),
return(
list(
"Pearson zero-order correlations" = zeros.out,
"lm summary" = summary(lm.fit),
"confidence intervals" = ci,
if (test3 == TRUE){"In models with factors (categorical IVs) interpretation of supplemental information requires caution"},
"supplemental information" = corrs,
"var infl factors from HH:vif"= hhvif,
if (test3 == TRUE){"For models with factors, car::vif may be more appropriate"},
"Anova Type III SS" = Anova(lm.fit, type = 3),
"Anova Type I SS" = anova(lm.fit),
"Effect Size" = es,
Comment = list(
paste0(
"Effect sizes from yhat::effect.size(); recommendations based on Yin and Fan (2001)."
),
paste0("Choose these with care based on your covariance structure")
)
)
))
}
####################################################################################
## section 2 DONE
# intercept, simple regression, no factor
if (test1 == TRUE & test2 == FALSE & test3 == FALSE) {
new.scale <- model.matrix(lm.fit)
row.names(new.scale) <- NULL
IV <- attr(lm.fit$coefficients, "names")
DV <- dimnames(attr(lm.fit$terms, "factors"))[[1]][1]
IVx <- IV[-1]
new.data2 <- cbind.data.frame(lm.fit$model[[1]], new.scale[,-1])
colnames(new.data2) <- c(DV,IVx) # working
zeros.out <- cor(new.data2)
if (minimal == T) {
return("Supplemental information is not needed for a simple regression model")
} else
if (minimal == F) {
return(
list(
"Pearson correlationx" = zeros.out,
"lm summary" = summary(lm.fit),
"confidence intervals" = confint(lm.fit, level = cilevel),
"Anova Type III SS" = Anova(lm.fit, type = 3),
"Anova Type I SS" = anova(lm.fit)
)
)
}
}
####################################################################################
## section3
## zero intercept, MR, without factor
if (test1 == FALSE & test2 == TRUE & test3 == FALSE & minimal == TRUE) {
return("mrinfo will not process supplemental information for 'no intercept' models")
}
if (test1 == FALSE & test2 == TRUE & test3 == FALSE & minimal == FALSE) {
new.scale <- model.matrix(lm.fit)
row.names(new.scale) <- NULL
IV <- attr(lm.fit$coefficients, "names")
DV <- dimnames(attr(lm.fit$terms, "factors"))[[1]][1]
#IVx <- IV[-1]
new.data2 <- cbind.data.frame(lm.fit$model[[1]], new.scale)
colnames(new.data2) <- c(DV,IV) # working
zeros.out <- cor(new.data2)
ci <- confint(lm.fit, level = cilevel)
return(
list(
"CAUTION: interpret a 'no-intercept' model carefully",
"mrinfo will not process supplemental information for 'no intercept' models",
"Pearson zero-order correlations" = zeros.out,
"lm summary" = summary(lm.fit),
"confidence intervals" = ci,
"Anova Type III SS" = Anova(lm.fit, type = 3),
"Anova Type I SS" = anova(lm.fit)
)
)
}
####################################################################################
## section4
## zero intercept, MR, with factor
if (test1 == FALSE & test2 == TRUE & test3 == TRUE & minimal == TRUE) {
return("mrinfo will not process supplemental information for 'no intercept' models")
}
if (test1 == FALSE & test2 == TRUE & test3 == TRUE & minimal == FALSE) {
new.scale <- model.matrix(lm.fit)
row.names(new.scale) <- NULL
IV <- attr(lm.fit$coefficients, "names")
DV <- dimnames(attr(lm.fit$terms, "factors"))[[1]][1]
#IVx <- IV[-1]
new.data2 <- cbind.data.frame(lm.fit$model[[1]], new.scale)
colnames(new.data2) <- c(DV,IV) # working
#zeros.out <- cor(new.data2)
ci <- confint(lm.fit, level = cilevel)
return(
list(
"CAUTION: A 'no-intercept' model with a factor IV may be inappropriate",
"mrinfo will not process supplemental information for 'no intercept' models",
#"Pearson zero-order correlations" = zeros.out,
"lm summary" = summary(lm.fit),
"confidence intervals" = ci,
"Anova Type III SS" = Anova(lm.fit, type = 3),
"Anova Type I SS" = anova(lm.fit)
)
)
}
####################################################################################
## section 5
# zero intercept, simple regression, no factor
if (test1 == FALSE & test2 == FALSE & test3 == FALSE & minimal == TRUE) {
return("supplemental info for zero-intercept models with factors (categorical IVs) is not provided")
}
if (test1 == FALSE & test2 == FALSE & test3 == FALSE & minimal == F) {
new.scale <- model.matrix(lm.fit)
row.names(new.scale) <- NULL
IV <- attr(lm.fit$coefficients, "names")
DV <- dimnames(attr(lm.fit$terms, "factors"))[[1]][1]
IVx <- IV[-1]
new.data2 <- cbind.data.frame(lm.fit$model[[1]], new.scale[,1])
colnames(new.data2) <- c(DV,IV) # working
zeros.out <- cor(new.data2)
ci <- confint(lm.fit, level = cilevel)
return(
list(
"CAUTION: interpret a 'no-intercept' model carefully",
"Pearson correlation" = zeros.out,
"lm summary" = summary(lm.fit),
"confidence intervals" = ci,
"Anova Type III SS" = Anova(lm.fit, type = 3),
"Anova Type I SS" = anova(lm.fit)
)
)
}
####################################################################################
## section 6
# zero intercept, simple regression, factor IV
if (test1 == FALSE & test2 == FALSE & test3 == TRUE & minimal == TRUE) {
return("Supplemental information for no-intercept models with Factor IVs is not provided")
}
if (test1 == FALSE & test2 == FALSE & test3 == TRUE & minimal == F) {
new.scale <- model.matrix(lm.fit)
row.names(new.scale) <- NULL
IV <- attr(lm.fit$coefficients, "names")
DV <- dimnames(attr(lm.fit$terms, "factors"))[[1]][1]
IVx <- IV[-1]
new.data2 <- cbind.data.frame(lm.fit$model[[1]], new.scale[,IVx])
colnames(new.data2) <- c(DV,IVx)
zeros.out <- cor(new.data2)
ci <- confint(lm.fit, level = cilevel)
return(
list(
"CAUTION: A 'no-intercept' model with a factor IV may be inappropriate",
#"Pearson correlation" = zeros.out,
"lm summary" = summary(lm.fit),
"confidence intervals" = ci,
"Anova Type III SS" = Anova(lm.fit, type = 3),
"Anova Type I SS" = anova(lm.fit)
)
)
}
}
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