change in description

Cowplot move to suggestions

DESCRIPTION

@@ -1,7 +1,7 @@
 Package: sicegar
 Type: Package
 Title: Analysis of Single-Cell Viral Growth Curves
-Version: 0.2
+Version: 0.2.1
 Date: 2017-07-10
 Authors@R: c( person("M. Umut", "Caglar", role = c("aut", "cre"), email =
     "umut.caglar@gmail.com"), person("Claus O.", "Wilke", role = c("aut"), email =
@@ -13,12 +13,12 @@ Imports:
     minpack.lm,
     fBasics,
     ggplot2,
-    cowplot,
     stats
 License: GPL-2 | GPL-3
 LazyData: true
 Suggests:
-    knitr
+    knitr,
+    cowplot
 VignetteBuilder: knitr
 BugReports: https://github.com/wilkelab/sicegar/issues
 Collate:

R/mainFunctions.R

@@ -1,10 +1,10 @@
 #' @title Fit and categorize.
 #'
 #' @param dataInput Un_normalized input data that will be fitted transferred into related functions
-#' @param n_runs_max_sm This number indicates the upper limit of the fitting attempts for sigmoidal model.
-#' @param n_runs_min_sm This number indicates the lower limit of the successful fitting attempts for sigmoidal model. It should be smaller than the upper limit of the fitting attempts (n_runs_max_sm).
-#' @param n_runs_max_dsm This number indicates the upper limit of the fitting attempts for sigmoidal model for double sigmoidal model.
-#' @param n_runs_min_dsm This number indicates the lower limit of the successful fitting attempts for double sigmoidal model. It should be smaller than the upper limit of the fitting attempts (n_runs_max_dsm).
+#' @param n_runs_max_sm This number indicates the upper limit of the fitting attempts for sigmoidal model. Default is 500
+#' @param n_runs_min_sm This number indicates the lower limit of the successful fitting attempts for sigmoidal model. It should be smaller than the upper limit of the fitting attempts (n_runs_max_sm). Default is 20
+#' @param n_runs_max_dsm This number indicates the upper limit of the fitting attempts for sigmoidal model for double sigmoidal model. Default is 500
+#' @param n_runs_min_dsm This number indicates the lower limit of the successful fitting attempts for double sigmoidal model. It should be smaller than the upper limit of the fitting attempts (n_runs_max_dsm). Default is 20
 #' @param startList_sm The initial set of parameters vector that sigmoidal fit algorithm tries for the first fit attempt for the relevant parameters. The vector composes of three elements; 'maximum', 'slopeParam' and, 'midPoint'.  Detailed explanations of those parameters can be found in vignettes. Defaults are maximum = 1, slopeParam = 1 and, midPoint = 0.33. The numbers are in normalized time intensity scale.
 #' @param lowerBounds_sm The lower bounds for the randomly generated start parameters for the sigmoidal fit. The vector composes of three elements; 'maximum', 'slopeParam' and, 'midPoint'. Detailed explanations of those parameters can be found in vignettes. Defaults are maximum = 0.3, slopeParam = 0.01, and midPoint = -0.52. The numbers are in normalized time intensity scale.
 #' @param upperBounds_sm The upper bounds for the randomly generated start parameters for the sigmoidal fit. The vector composes of three elements; 'maximum', 'slopeParam' and, 'midPoint'. Detailed explanations of those parameters can be found in vignettes. Defaults are maximum = 1.5, slopeParam = 180,  midPoint = 1.15. The numbers are in normalized time intensity scale.

vignettes/categorizing_fits.Rmd

@@ -141,10 +141,10 @@ Next the algorithm checks if the sigmoidal and double sigmoidal models make sens
 * Test 4b: The value `startPoint_x` for the double-sigmoidal model must be a positive number; otherwise, the data cannot be labeled with `"double_sigmoidal"`.
 * Test 5a: The value `start_intensity` for the sigmoidal model must be smaller than `threshold_t0_max_int`; otherwise, the data cannot be labeled with `"sigmoidal"`.
 * Test 5b: The value `start_intensity` for the double-sigmoidal model must be smaller than `threshold_t0_max_int`; otherwise, the data cannot be labeled with `"double_sigmoidal"`.
-* Test 6: For the double-sigmoidal model, the ratio of _/the model's intensity prediction at the last observation time/_ to _/the model's maximum intesity prediction/_ must be smaller than `threshold_dsm_tmax_IntensityRatio`; otherwise, the data cannot be labeled with `"double_sigmoidal"`.
-* Test 7: For the sigmoidal model; the ratio of _/the model's intensity prediction at the last observation time/_ to _/the model's maximum intesity prediction/_ must be smaller than `threshold_sm_tmax_IntensityRatio`; otherwise, the data cannot be labeled with `"sigmoidal"`.
+* Test 6: For the double-sigmoidal model, the ratio of _/the model's intensity prediction at the last observation time/_ to _/the model's maximum intensity prediction/_ must be smaller than `threshold_dsm_tmax_IntensityRatio`; otherwise, the data cannot be labeled with `"double_sigmoidal"`.
+* Test 7: For the sigmoidal model; the ratio of _/the model's intensity prediction at the last observation time/_ to _/the model's maximum intensity prediction/_ must be smaller than `threshold_sm_tmax_IntensityRatio`; otherwise, the data cannot be labeled with `"sigmoidal"`.
 
-In step eight, the algorithm checks wheather the data should be labelled as `"ambiguous"` or not.
+In step eight, the algorithm checks whether the data should be labelled as `"ambiguous"` or not.
 
 * Test 8: If at this point we still have at least one of the two options `"sigmoidal"` or `"double_sigmoidal"`, then the data cannot be labeled with `"ambiguous"`.