Glance accepts a model object and returns a tibble::tibble()
with exactly one row of model summaries. The summaries are typically
goodness of fit measures, p-values for hypothesis tests on residuals,
or model convergence information.
Glance never returns information from the original call to the modeling function. This includes the name of the modeling function or any arguments passed to the modeling function.
Glance does not calculate summary measures. Rather, it farms out these
computations to appropriate methods and gathers the results together.
Sometimes a goodness of fit measure will be undefined. In these cases
the measure will be reported as NA.
Glance returns the same number of columns regardless of whether the
model matrix is rank-deficient or not. If so, entries in columns
that no longer have a well-defined value are filled in with an NA
of the appropriate type.
# S3 method for geeglm glance(x, ...)
| x | A |
|---|---|
| ... | Additional arguments. Not used. Needed to match generic
signature only. Cautionary note: Misspelled arguments will be
absorbed in |
A tibble::tibble() with exactly one row and columns:
Estimated correlation parameter for geepack::geeglm.
Residual degrees of freedom.
Estimated scale parameter for geepack::geeglm.
Max number of elements in clusters.
Number of clusters.
if (requireNamespace("geepack", quietly = TRUE)) { library(geepack) data(state) ds <- data.frame(state.region, state.x77) geefit <- geeglm(Income ~ Frost + Murder, id = state.region, data = ds, family = gaussian, corstr = "exchangeable" ) tidy(geefit) tidy(geefit, conf.int = TRUE) } #> $fct #> function(dose, parm) #> { #> parmMat <- matrix(parmVec, nrow(parm), numParm, byrow = TRUE) #> parmMat[, notFixed] <- parm #> fd(dose, parmMat[, 1], parmMat[, 2], parmMat[, 3], parmMat[, 4], parmMat[, 5]) #> } #> <bytecode: 0x55bef4906c20> #> <environment: 0x55bef9849428> #> #> $ssfct #> function(dframe) #> { #> x <- dframe[, 1] #> y <- dframe[, 2] #> #> ## Finding initial values for c and d parameters #> cdVal <- findcd(x, y) #> if (useFixed) {} # not implemented at the moment #> #> ## Finding initial values for b, e, and f parameters #> if (logg) #> { #> bVal <- 0.75 * sd(log(x[y > quantile(y, .75)])) #> } else { #> bVal <- 0.75 * sd(x[y > quantile(y, .75)]) #> } #> befVal <- c(bVal, x[which.max(y)], 1) #> # befVal <- c(sd(x), mean(x), 1) #> #> return(c(befVal[1], cdVal, befVal[2:3])[is.na(fixed)]) #> } #> <bytecode: 0x55befcb76b18> #> <environment: 0x55beff845550> #> #> $names #> [1] "b" "c" "d" "e" "f" #> #> $deriv1 #> function(dose, parm) #> { #> parmMat <- matrix(parmVec, nrow(parm), numParm, byrow = TRUE) #> parmMat[, notFixed] <- parm #> attr(fd(dose, parmMat[, 1], parmMat[, 2], parmMat[, 3], parmMat[, 4], parmMat[, 5]), "gradient")[, notFixed] #> } #> <bytecode: 0x55bef4910b78> #> <environment: 0x55bef9849428> #> #> $deriv2 #> NULL #> #> $derivx #> function(dose, parm) #> { #> parmMat <- matrix(parmVec, nrow(parm), numParm, byrow = TRUE) #> parmMat[, notFixed] <- parm #> #> dFct <- function (dose, b, c, d, e, f) #> { #> .expr1 <- d - c #> .expr4 <- (dose - e)/b #> .expr5 <- .expr4^2 #> .expr6 <- sqrt(.expr5) #> .expr9 <- exp(-0.5 * .expr6^f) #> .value <- c + .expr1 * .expr9 #> .grad <- array(0, c(length(.value), 1L), list(NULL, c("dose"))) #> .grad[, "dose"] <- -(.expr1 * (.expr9 * (0.5 * (.expr6^(f - 1) * (f * (0.5 * (2 * (1/b * .expr4) * .expr5^-0.5))))))) #> attr(.value, "gradient") <- .grad #> .value #> } #> attr(dFct(dose, parmMat[, 1], parmMat[, 2], parmMat[, 3], parmMat[, 4], parmMat[, 5]), "gradient") #> } #> <bytecode: 0x55bef491c6d0> #> <environment: 0x55bef9849428> #> #> $edfct #> function(parm, respl, reference, type, ...) #> { #> parmVec[notFixed] <- parm #> # if (type == "absolute") #> # { #> # p <- 100*((parmVec[3] - respl)/(parmVec[3] - parmVec[2])) #> # } else { #> # p <- respl #> # } #> # if ( (parmVec[1] < 0) && (reference == "control") ) #> # { #> # p <- 100 - p #> # } #> p <- absToRel(parmVec, abs(respl), type) #> #> ## Reversing p #> if (identical(type, "absolute")) #> { #> p <- 100 - p #> } #> if (identical(type, "relative") && (parmVec[1] < 0) && (reference == "control")) #> { #> p <- 100 - p #> } #> #> pProp <- 1 - (100-p) / 100 #> #> ## deriv(~b*(-2*22)^(1 / f)+e, c("b", "c", "d", "e", "f"), function(b,c,d,e,f){}) #> ## using "22" insted of log(pProp) #> EDfct <- function (b, c, d, e, f) #> { #> # .expr2 <- -2 * 22 #> .expr2 <- -2 * log(pProp) #> .expr4 <- sign(respl) * .expr2^(1/f) #> .value <- b * .expr4 + e #> .grad <- array(0, c(length(.value), 5L), list(NULL, c("b", "c", "d", "e", "f"))) #> .grad[, "b"] <- .expr4 #> .grad[, "c"] <- 0 #> .grad[, "d"] <- 0 #> .grad[, "e"] <- 1 #> .grad[, "f"] <- -(b * (.expr4 * (log(.expr2) * (1/f^2)))) #> attr(.value, "gradient") <- .grad #> .value #> } #> EDp <- EDfct(parmVec[1], parmVec[2], parmVec[3], parmVec[4], parmVec[5]) #> EDder <- attr(EDfct(parmVec[1], parmVec[2], parmVec[3], parmVec[4], parmVec[5]), "gradient") #> return(list(EDp, EDder[notFixed])) #> } #> <bytecode: 0x55bef493be70> #> <environment: 0x55bef9849428> #> #> $name #> [1] "family" #> #> $text #> [1] "Gaussian" #> #> $noParm #> [1] 5 #> #> $lowerAs #> function(parm) #> { #> parmVec[indexVec] <- parm #> parmVec[parmNo] #> } #> <bytecode: 0x55bef4dd4870> #> <environment: 0x55befa086d18> #> #> $upperAs #> function(parm) #> { #> parmVec[indexVec] <- parm #> parmVec[parmNo] #> } #> <bytecode: 0x55bef4dd4870> #> <environment: 0x55befa087098> #> #> $monoton #> [1] NA #> #> $fixed #> [1] NA NA NA NA NA #> #> attr(,"class") #> [1] "gaussian" #> Error in glm(formula = Income ~ Frost + Murder, family = gaussian, data = ds): 'family' not recognized