unified response curves

.Rbuildignore

@@ -1 +1,3 @@
 .gitignore
+ToDo
+COPYING

NEWS

@@ -5,7 +5,8 @@ mirt 0.4.0
 * Added itemfit() and personfit() functions for uni and multidimensional models. Within itemfit
   empirical response curves can also be plotted for unidimensional models
 
-* Wrapped itemplot() and fscores() into S3 function for better documentation   
+* Wrapped itemplot() and fscores() into S3 function for better documentation. Also response curve
+  now are all contained in individual plots
 
 * Added free.start list option for all estimation functions. Allows a quicker way to 
   specify free and fixed parameters

R/itemplot.R

@@ -87,7 +87,7 @@ itemplot.main <- function(x, item, type, degrees = 45, ...){
                  ylab = expression(I(theta)), xlab = expression(theta), las = 1)
         }
         if(type == 'infocontour') stop('Cannot draw contours for 1 factor models')        
-    } else {        
+    } else {                
         plt <- data.frame(info = info, Theta1 = Theta[,1], Theta2 = Theta[,2])
         plt2 <- data.frame(P = P, Theta1 = Theta[,1], Theta2 = Theta[,2])
         colnames(plt2) <- c(paste("P", 1:ncol(P), sep=''), "Theta1", "Theta2")
@@ -103,7 +103,7 @@ itemplot.main <- function(x, item, type, degrees = 45, ...){
                              zlab=expression(I(theta)), xlab=expression(theta[1]), ylab=expression(theta[2]), 
                              scales = list(arrows = FALSE), colorkey = TRUE, drape = TRUE, ...))
         if(type == 'trace'){
-            return(lattice::wireframe(P ~ Theta1 + Theta2|time, data = plt2, main = paste("Item", item, "Trace"), 
+            return(lattice::wireframe(P ~ Theta1 + Theta2, data = plt2, group = time, main = paste("Item", item, "Trace"), 
                              zlab=expression(P(theta)), xlab=expression(theta[1]), ylab=expression(theta[2]), 
                              scales = list(arrows = FALSE), colorkey = TRUE, drape = TRUE, ...))            
         }        

R/mirt.R

@@ -191,7 +191,7 @@
 #' the predicted category. 
 #' \deqn{P(x = k | \theta, \psi) = P(x \ge k | \theta, \phi) - P(x \ge k + 1 | \theta, \phi)}
 #' }
-#' \item{gpcm/nominal}{For the gpcm the \eqn{d_k} values are treated as fixed and orded values 
+#' \item{gpcm/nominal}{For the gpcm the \eqn{d_k} values are treated as fixed and orderd values 
 #' from 0:(k-1) (in the nominal model \eqn{d_0} is also set to 0). Additionally, for identification 
 #' in the nominal model \eqn{ak_0 = 1}, \eqn{ak_k = (k - 1)}.
 #' \deqn{P(x = k | \theta, \psi) = \frac{exp(-1.702 * ak_k * (a_1 * \theta_1 + a_2 * \theta_2) + d_k)}

man/mirt.Rd

@@ -322,9 +322,9 @@ Theory)}
   the predicted category. \deqn{P(x = k | \theta, \psi) =
   P(x \ge k | \theta, \phi) - P(x \ge k + 1 | \theta,
   \phi)} } \item{gpcm/nominal}{For the gpcm the \eqn{d_k}
-  values are treated as fixed and orded values from 0:(k-1)
-  (in the nominal model \eqn{d_0} is also set to 0).
-  Additionally, for identification in the nominal model
+  values are treated as fixed and orderd values from
+  0:(k-1) (in the nominal model \eqn{d_0} is also set to
+  0). Additionally, for identification in the nominal model
   \eqn{ak_0 = 1}, \eqn{ak_k = (k - 1)}. \deqn{P(x = k |
   \theta, \psi) = \frac{exp(-1.702 * ak_k * (a_1 * \theta_1
   + a_2 * \theta_2) + d_k)} {\sum_i^k exp(-1.702 * ak_k *