build the manual

Author: davraam

R/ds.glmerSLMA.R

@@ -18,23 +18,23 @@
 #' In \code{formula} most shortcut notation allowed by \code{glmer()} function is
 #' also allowed by \code{ds.glmerSLMA}. 
 #' Many GLMEs can be fitted very simply using a formula like:
-#' \deqn{y~a+b+(1|c)} 
+#' \code{y~a+b+(1|c)} 
 #' which simply means fit an GLME  with \code{y} as the outcome variable (e.g. 
 #' a binary case-control using a logistic regression model or a count or a survival
 #' time using a Poisson regression model), \code{a} and \code{b}
 #' as fixed effects, and \code{c} as a random effect or grouping factor. 
 #' 
 #' It is also possible to fit models with random slopes by specifying a model such as 
-#' \deqn{y~a+b+(1+b|c)} 
+#' \code{y~a+b+(1+b|c)} 
 #' where the effect of \code{b} can vary randomly between groups defined by \code{c}.
-#' Implicit nesting can be specified with formulas such as: \eqn{y~a+b+(1|c/d)}
-#' or \eqn{y~a+b+(1|c)+(1|c:d)}. 
+#' Implicit nesting can be specified with formulas such as: \code{y~a+b+(1|c/d)}
+#' or \code{y~a+b+(1|c)+(1|c:d)}. 
 #' 
 #' 
 #' The \code{dataName} argument avoids you having to specify the name of the
 #' data frame in front of each covariate in the formula. 
 #' For example, if the data frame is called \code{DataFrame} you avoid having to write: 
-#' \eqn{DataFrame$y~DataFrame$a+DataFrame$b+(1|DataFrame$c)}.
+#' \code{DataFrame$y~DataFrame$a+DataFrame$b+(1|DataFrame$c)}.
 #' 
 #' The \code{checks} argument verifies that the variables in the model are all defined (exist) 
 #' on the server-site at every study
@@ -228,9 +228,9 @@
 #'                 
 #'      # Fit a Logistic regression model
 #'   
-#'   ds.glmerSLMA(formula = "Male ~  incid_rate +diabetes + (1 | age)",
+#'   ds.glmerSLMA(formula = "Male ~ incid_rate +diabetes + (1 | age)",
 #'                dataName = "D",
-#'                datasources = connections[2],#only the second server is used (study2)
+#'                datasources = connections[2], # only the second server is used (study2)
 #'                family = "binomial")
 #'   
 #'   

R/ds.kurtosis.R

@@ -4,10 +4,18 @@
 #' @details The function calculates the kurtosis of an input variable x with three different methods. 
 #' The method is specified by the argument \code{method}. If x contains any missings, the function removes those before
 #' the calculation of the kurtosis. If \code{method} is set to 1 the following formula is used
-#' \eqn{ kurtosis= \frac{\sum_{i=1}^{N} (x_i - \bar(x))^4 /N}{(\sum_{i=1}^{N} ((x_i - \bar(x))^2) /N)^(2) } - 3},
+#' 
+#' \eqn{ kurtosis= \frac{\sum_{i=1}^{N} (x_i - \bar{x})^4 /N}{(\sum_{i=1}^{N} ((x_i - \bar{x})^2) /N)^2 } - 3},
+#' 
 #' where \eqn{ \bar{x} } is the mean of x and \eqn{N} is the number of observations. If \code{method} is set to 2
-#' the following formula is used \eqn{ kurtosis= ((N+1)*(\frac{\sum_{i=1}^{N} (x_i - \bar(x))^4 /N}{(\sum_{i=1}^{N} ((x_i - \bar(x))^2) /N)^(2) } - 3) + 6)*((N-1)/((N-2)*(N-3)))}.
-#' If \code{method} is set to 3 the following formula is used \eqn{ kurtosis= (\frac{\sum_{i=1}^{N} (x_i - \bar(x))^4 /N}{(\sum_{i=1}^{N} ((x_i - \bar(x))^2) /N)^(2) })*(1-1/N)^2 - 3}.
+#' the following formula is used 
+#' 
+#' \eqn{ kurtosis= ((N+1)*(\frac{\sum_{i=1}^{N} (x_i - \bar{x})^4 /N}{(\sum_{i=1}^{N} ((x_i - \bar{x})^2) /N)^2 } - 3) + 6)*((N-1)/((N-2)*(N-3)))}.
+#' 
+#' If \code{method} is set to 3 the following formula is used 
+#' 
+#' \eqn{ kurtosis= (\frac{\sum_{i=1}^{N} (x_i - \bar{x})^4 /N}{(\sum_{i=1}^{N} ((x_i - \bar{x})^2) /N)^2 })*(1-1/N)^2 - 3}.
+#' 
 #' This function is similar to the function \code{kurtosis} in R package \code{e1071}.
 #' @param x a string character, the name of a numeric variable.
 #' @param method an integer between 1 and 3 selecting one of the algorithms for computing kurtosis