Rework docs source

Author: StuartWheater

R/ds.glm.R

@@ -63,7 +63,7 @@
 #' The \code{data} 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$b+DataFrame$c+DataFrame$d}
+#' avoid having to write: \eqn{DataFrame$y ~ DataFrame$a + DataFrame$b + DataFrame$c + DataFrame$d}
 #' 
 #' The \code{checks} argument verifies that the variables in the model are all defined (exist) 
 #' on the server-side at every study

R/ds.glmSLMA.R

@@ -134,7 +134,7 @@
 #' 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$b+DataFrame$c+DataFrame$d}
+#' avoid having to write: \eqn{DataFrame$y ~ DataFrame$a + DataFrame$b + DataFrame$c + DataFrame$d}
 #' 
 #' The \code{checks} argument verifies that the variables in the model are all defined (exist) 
 #' on the server-site at every study

R/ds.glmerSLMA.R

@@ -34,7 +34,7 @@
 #' 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)}.
+#' \eqn{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

R/ds.lmerSLMA.R

@@ -18,20 +18,20 @@
 #' 
 #' In \code{formula} most shortcut notation allowed by  \code{lmer()} function is
 #' also allowed by \code{ds.lmerSLMA}. Many LMEs can be fitted very simply using a formula like:
-#' \deqn{y~a+b+(1|c)}
+#' \deqn{y ~ a + b + (1 | c)}
 #' which simply means fit an LME with \code{y} as the outcome variable with \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)} 
+#' \deqn{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 formulae 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 formulae such as \eqn{y ~ a + b + (1 | c / d)}
+#' or \eqn{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)}.
+#' \eqn{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
@@ -192,7 +192,7 @@
 #'  
 #'   # Fit the lmer
 #'   
-#'   ds.lmerSLMA(formula = "BMI ~  incid_rate + diabetes + (1 | Male)",
+#'   ds.lmerSLMA(formula = "BMI ~ incid_rate + diabetes + (1 | Male)",
 #'                dataName = "D.comp",
 #'                datasources = connections)
 #'