diff --git a/dev/CODE_OF_CONDUCT.html b/dev/CODE_OF_CONDUCT.html
index e1c4a810..a8766246 100644
--- a/dev/CODE_OF_CONDUCT.html
+++ b/dev/CODE_OF_CONDUCT.html
@@ -46,6 +46,9 @@
Dynamic Data Definition
+
+ Simulating data from logistic model with specified characterstics
+
Longitudinal Data
diff --git a/dev/CONTRIBUTING.html b/dev/CONTRIBUTING.html
index 323b8b0f..49bf85a7 100644
--- a/dev/CONTRIBUTING.html
+++ b/dev/CONTRIBUTING.html
@@ -46,6 +46,9 @@
Dynamic Data Definition
+
+ Simulating data from logistic model with specified characterstics
+
Longitudinal Data
diff --git a/dev/LICENSE-text.html b/dev/LICENSE-text.html
index adc7ba03..42c3ddf7 100644
--- a/dev/LICENSE-text.html
+++ b/dev/LICENSE-text.html
@@ -46,6 +46,9 @@
Dynamic Data Definition
+
+ Simulating data from logistic model with specified characterstics
+
Longitudinal Data
diff --git a/dev/SUPPORT.html b/dev/SUPPORT.html
index dcc6f135..5f6012c0 100644
--- a/dev/SUPPORT.html
+++ b/dev/SUPPORT.html
@@ -46,6 +46,9 @@
Dynamic Data Definition
+
+ Simulating data from logistic model with specified characterstics
+
Longitudinal Data
diff --git a/dev/articles/clustered.html b/dev/articles/clustered.html
index 02bb651f..0d8591d7 100644
--- a/dev/articles/clustered.html
+++ b/dev/articles/clustered.html
@@ -65,6 +65,9 @@
Dynamic Data Definition
+
+ Simulating data from logistic model with specified characterstics
+
Longitudinal Data
diff --git a/dev/articles/corelationmat.html b/dev/articles/corelationmat.html
index 0da4b905..6717698f 100644
--- a/dev/articles/corelationmat.html
+++ b/dev/articles/corelationmat.html
@@ -65,6 +65,9 @@
Dynamic Data Definition
+
+ Simulating data from logistic model with specified characterstics
+
Longitudinal Data
@@ -1095,18 +1098,18 @@ Varying correlation matrices by
## site period n ## 1: 1 0 4 ## 2: 1 1 2 ## 3: 1 2 2 ## 3: 1 2 3 ## 4: 3 0 3 ## 5: 3 1 2 ## 5: 3 1 3 ## 6: 3 2 3 ## 7: 7 0 4 ## 8: 7 1 4 ## 9: 7 2 4 ## 7: 7 0 3 ## 8: 7 1 2 ## 9: 7 2 3 The sites will also have unique decay rates:
-## [1] 0.76 0.94 0.87 ## [1] 0.66 0.85 0.81 Here are the correlation matrices for these three sites:
N <- dd [ , .N , keyby = . ( site , period ) ] [ , N ] Varying correlation matrices by
lapply ( R , function ( x ) round ( x ,2 ) ) [ c ( 1 , 3 , 7 ) ] ## [[1]] ## [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] ## [1,] 1.00 0.60 0.60 0.60 0.46 0.46 0.35 0.35 ## [2,] 0.60 1.00 0.60 0.60 0.46 0.46 0.35 0.35 ## [3,] 0.60 0.60 1.00 0.60 0.46 0.46 0.35 0.35 ## [4,] 0.60 0.60 0.60 1.00 0.46 0.46 0.35 0.35 ## [5,] 0.46 0.46 0.46 0.46 1.00 0.60 0.46 0.46 ## [6,] 0.46 0.46 0.46 0.46 0.60 1.00 0.46 0.46 ## [7,] 0.35 0.35 0.35 0.35 0.46 0.46 1.00 0.60 ## [8,] 0.35 0.35 0.35 0.35 0.46 0.46 0.60 1.00 ## [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] ## [1,] 1.00 0.60 0.60 0.60 0.4 0.4 0.26 0.26 0.26 ## [2,] 0.60 1.00 0.60 0.60 0.4 0.4 0.26 0.26 0.26 ## [3,] 0.60 0.60 1.00 0.60 0.4 0.4 0.26 0.26 0.26 ## [4,] 0.60 0.60 0.60 1.00 0.4 0.4 0.26 0.26 0.26 ## [5,] 0.40 0.40 0.40 0.40 1.0 0.6 0.40 0.40 0.40 ## [6,] 0.40 0.40 0.40 0.40 0.6 1.0 0.40 0.40 0.40 ## [7,] 0.26 0.26 0.26 0.26 0.4 0.4 1.00 0.60 0.60 ## [8,] 0.26 0.26 0.26 0.26 0.4 0.4 0.60 1.00 0.60 ## [9,] 0.26 0.26 0.26 0.26 0.4 0.4 0.60 0.60 1.00 ## ## [[2]] ## [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] ## [1,] 1.00 0.60 0.60 0.56 0.56 0.53 0.53 0.53 ## [2,] 0.60 1.00 0.60 0.56 0.56 0.53 0.53 0.53 ## [3,] 0.60 0.60 1.00 0.56 0.56 0.53 0.53 0.53 ## [4,] 0.56 0.56 0.56 1.00 0.60 0.56 0.56 0.56 ## [5,] 0.56 0.56 0.56 0.60 1.00 0.56 0.56 0.56 ## [6,] 0.53 0.53 0.53 0.56 0.56 1.00 0.60 0.60 ## [7,] 0.53 0.53 0.53 0.56 0.56 0.60 1.00 0.60 ## [8,] 0.53 0.53 0.53 0.56 0.56 0.60 0.60 1.00 ## [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] ## [1,] 1.00 0.60 0.60 0.51 0.51 0.51 0.43 0.43 0.43 ## [2,] 0.60 1.00 0.60 0.51 0.51 0.51 0.43 0.43 0.43 ## [3,] 0.60 0.60 1.00 0.51 0.51 0.51 0.43 0.43 0.43 ## [4,] 0.51 0.51 0.51 1.00 0.60 0.60 0.51 0.51 0.51 ## [5,] 0.51 0.51 0.51 0.60 1.00 0.60 0.51 0.51 0.51 ## [6,] 0.51 0.51 0.51 0.60 0.60 1.00 0.51 0.51 0.51 ## [7,] 0.43 0.43 0.43 0.51 0.51 0.51 1.00 0.60 0.60 ## [8,] 0.43 0.43 0.43 0.51 0.51 0.51 0.60 1.00 0.60 ## [9,] 0.43 0.43 0.43 0.51 0.51 0.51 0.60 0.60 1.00 ## ## [[3]] ## [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10] [,11] [,12] ## [1,] 1.00 0.60 0.60 0.60 0.52 0.52 0.52 0.52 0.45 0.45 0.45 0.45 ## [2,] 0.60 1.00 0.60 0.60 0.52 0.52 0.52 0.52 0.45 0.45 0.45 0.45 ## [3,] 0.60 0.60 1.00 0.60 0.52 0.52 0.52 0.52 0.45 0.45 0.45 0.45 ## [4,] 0.60 0.60 0.60 1.00 0.52 0.52 0.52 0.52 0.45 0.45 0.45 0.45 ## [5,] 0.52 0.52 0.52 0.52 1.00 0.60 0.60 0.60 0.52 0.52 0.52 0.52 ## [6,] 0.52 0.52 0.52 0.52 0.60 1.00 0.60 0.60 0.52 0.52 0.52 0.52 ## [7,] 0.52 0.52 0.52 0.52 0.60 0.60 1.00 0.60 0.52 0.52 0.52 0.52 ## [8,] 0.52 0.52 0.52 0.52 0.60 0.60 0.60 1.00 0.52 0.52 0.52 0.52 ## [9,] 0.45 0.45 0.45 0.45 0.52 0.52 0.52 0.52 1.00 0.60 0.60 0.60 ## [10,] 0.45 0.45 0.45 0.45 0.52 0.52 0.52 0.52 0.60 1.00 0.60 0.60 ## [11,] 0.45 0.45 0.45 0.45 0.52 0.52 0.52 0.52 0.60 0.60 1.00 0.60 ## [12,] 0.45 0.45 0.45 0.45 0.52 0.52 0.52 0.52 0.60 0.60 0.60 1.00 ## [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] ## [1,] 1.00 0.60 0.60 0.49 0.49 0.39 0.39 0.39 ## [2,] 0.60 1.00 0.60 0.49 0.49 0.39 0.39 0.39 ## [3,] 0.60 0.60 1.00 0.49 0.49 0.39 0.39 0.39 ## [4,] 0.49 0.49 0.49 1.00 0.60 0.49 0.49 0.49 ## [5,] 0.49 0.49 0.49 0.60 1.00 0.49 0.49 0.49 ## [6,] 0.39 0.39 0.39 0.49 0.49 1.00 0.60 0.60 ## [7,] 0.39 0.39 0.39 0.49 0.49 0.60 1.00 0.60 ## [8,] 0.39 0.39 0.39 0.49 0.49 0.60 0.60 1.00 And here is code to generate the empirical correlation matrices for
the three sites, based on 5000 replications of the data:
diff --git a/dev/articles/correlated.html b/dev/articles/correlated.html
index 70d5d127..e5541fcc 100644
--- a/dev/articles/correlated.html
+++ b/dev/articles/correlated.html
@@ -65,6 +65,9 @@
Dynamic Data Definition
+
+ Simulating data from logistic model with specified characterstics
+
Longitudinal Data
diff --git a/dev/articles/double_dot_extension.html b/dev/articles/double_dot_extension.html
index 595d30f3..d3a5f755 100644
--- a/dev/articles/double_dot_extension.html
+++ b/dev/articles/double_dot_extension.html
@@ -65,6 +65,9 @@
Dynamic Data Definition
+
+ Simulating data from logistic model with specified characterstics
+
Longitudinal Data
@@ -340,17 +343,20 @@
Using non-scalar double-
d1 <- defData ( d1 , varname = "b" , formula = ".5;.5" , variance = "1;2" , dist = "categorical" ) d1 <- defData ( d1 , varname = "outcome" , formula = "..effect[a, b]" , dist= "nonrandom" ) ## id a b outcome ## 1: 1 2 2 7 ## 2: 2 2 2 7 ## 3: 3 2 1 4 ## 4: 4 2 1 4 ## 5: 5 1 1 0 ## 6: 6 2 2 7 ## 7: 7 2 1 4 ## 8: 8 1 2 5 ## id a b outcome ## 1: 1 2 2 7 ## 2: 2 2 1 4 ## 3: 3 2 1 4 ## 4: 4 2 2 7 ## 5: 5 1 2 5 ## --- ## 996: 996 2 2 7 ## 997: 997 2 2 7 ## 998: 998 1 1 0 ## 999: 999 2 2 7 ## 1000: 1000 1 2 5 It is possible to generate normally distributed data based on these
means:
@@ -358,11 +364,6 @@ Using non-scalar double-
dx <- genData ( 1000 , d1 ) The plot shows the individual values as well as the mean values by
intervention arm:
-## Warning: Using `size` aesthetic for lines was deprecated in ggplot2 3.4.0. ## ℹ Please use `linewidth` instead. ## This warning is displayed once every 8 hours. ## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was ## generated.
diff --git a/dev/articles/double_dot_extension_files/figure-html/unnamed-chunk-18-1.png b/dev/articles/double_dot_extension_files/figure-html/unnamed-chunk-18-1.png
index eadfee20..e62c7cf1 100644
Binary files a/dev/articles/double_dot_extension_files/figure-html/unnamed-chunk-18-1.png and b/dev/articles/double_dot_extension_files/figure-html/unnamed-chunk-18-1.png differ
diff --git a/dev/articles/index.html b/dev/articles/index.html
index 7ecff4fa..92866f60 100644
--- a/dev/articles/index.html
+++ b/dev/articles/index.html
@@ -46,6 +46,9 @@
Dynamic Data Definition
+
+ Simulating data from logistic model with specified characterstics
+
Longitudinal Data
@@ -98,6 +101,8 @@ All vignettes
Dynamic Data Definition
+ Simulating data from logistic model with specified characterstics
Longitudinal Data
Missing Data Simulating data from logistic model with specified characterstics • simstudy
+
+
+
+
+
+
+
+
In simstudy, there are at least two ways to define a
+binary data generating process. The first is to operate on the scale of
+the proportion or probability using the identity link. This
+allows users to characterize a data generating process that reflects
+assumptions about risk ratios and risk differences when comparing two
+groups defined by an exposure or treatment. However, this process can
+become challenging when introducing other covariates, because it can be
+difficult to constrain the probabilities so that they fall between 0 and
+1.
+
Alternatively, the second approach works on the log-odds scale and
+uses the logit link, and is much more amenable to accomodating
+covariates. Unfortunately, this comes at the price of being able to
+easily generate specific risk ratios and risk differences, because all
+parameters are log-odds ratios. If we are interested in the controlling
+overall probability in a population with a set of covariates that each
+have a certain distribution and particular association with an outcome,
+the overall (marginal) proportion will vary across different
+distributions and associations.
+
Here we start with the simplest case where we have a target marginal
+proportion or prevalence, and illustrate data generation with three
+other target statistics: risk ratios , risk
+differences , and AUCs .
+
+
Prevalence
+
In this first example, we start with one set of assumptions for four
+covariates \(x_1 \sim N(0, 1)\) , \(x_2 \sim N(0, 1)\) , \(b_1 \sim Bin(0.3)\) , and \(b_2 \sim Bin(0.7)\) , and generate the
+outcome y with the following data generating process:
+
\[ \text{logit}(y) = 0.15*x1 + 0.25*x2 +
+0.10*b1 + 0.30*b2\]
+
+coefs1 <- c ( 0.15 , 0.25 , 0.10 , 0.30 ) d1 <- defData ( varname = "x1" , formula = 0 , variance = 1 ) d1 <- defData ( d1 , varname = "x2" , formula = 0 , variance = 1 ) d1 <- defData ( d1 , varname = "b1" , formula = 0.3 , dist = "binary" ) d1 <- defData ( d1 , varname = "b2" , formula = 0.7 , dist = "binary" ) d1a <- defData ( d1 , varname = "y" , formula = "t(..coefs1) %*% c(x1, x2, b1, b2)" ,
+ dist = "binary" , link = "logit" )
+set.seed ( 48392 ) dd <- genData ( 500000 , d1a ) dd
+
## id x1 x2 b1 b2 y ## 1: 1 0.29 0.390 0 1 1 ## 2: 2 0.76 -0.925 0 0 0 ## 3: 3 -1.47 0.939 0 0 1 ## 4: 4 1.92 0.560 0 1 1 ## 5: 5 1.40 -0.238 0 1 0 ## --- ## 499996: 499996 -0.32 0.367 0 0 0 ## 499997: 499997 -1.08 2.152 0 0 0 ## 499998: 499998 -1.10 0.380 1 0 0 ## 499999: 499999 0.56 -1.042 0 1 0 ## 500000: 500000 0.52 0.076 0 1 1
+
The overall proportion of \(y=1\) in
+this case is
+
+
## [1] 0.56
+
If we have a desired marginal proportion of 0.40, then we can add an
+intercept of -0.66 to the data generating process:
+
\[ \text{logit}(y) = -0.66 + 0.15*x1 +
+0.25 *x2 + 0.10*b1 + 0.30*b2\]
+
The simulation now gives us the desired target:
+
+d1a <- defData ( d1 , varname = "y" , formula = "t(c(-0.66,..coefs1)) %*% c(1, x1, x2, b1, b2)" ,
+ dist = "binary" , link = "logit" )
+genData ( 500000 , d1a ) [ , mean ( y ) ]
+
## [1] 0.4
+
If we change the distribution of the covariates, so that \(x_1 \sim N(1, 1)\) , \(x_2 \sim N(2, 1)\) , \(b_1 \sim Bin(0.5)\) , and \(b_2 \sim Bin(0.8)\) , and the strength of
+the association of these covariates with the outcome so that
+
\[ \text{logit}(y) = 0.20*x1 + 0.35 *x2 +
+0.20*b1 + 0.45*b2,\]
+
the marginal proportion/prevalence (assuming no intercept term) also
+changes, going from 0.56 to 0.84:
+
+coefs2 <- c ( 0.20 , 0.35 , 0.20 , 0.45 ) d2 <- defData ( varname = "x1" , formula = 1 , variance = 1 ) d2 <- defData ( d2 , varname = "x2" , formula = 3 , variance = 1 ) d2 <- defData ( d2 , varname = "b1" , formula = 0.5 , dist = "binary" ) d2 <- defData ( d2 , varname = "b2" , formula = 0.8 , dist = "binary" ) d2a <- defData ( d2 , varname = "y" , formula = "t(..coefs2) %*% c(x1, x2, b1, b2)" ,
+ dist = "binary" , link = "logit" )
+genData ( 500000 , d2a ) [ , mean ( y ) ]
+
## [1] 0.84
+
But under this new distribution, adding an intercept of -2.14 yields
+the desired target.
+
\[ \text{logit}(y) = -2.14 + 0.20*x1 +
+0.35 *x2 + 0.20*b1 + 0.45*b2 \]
+
+d2a <- defData ( d2 , varname = "y" , formula = "t(c(-2.14,..coefs2)) %*% c(1, x1, x2, b1, b2)" ,
+ dist = "binary" , link = "logit" )
+genData ( 500000 , d1a ) [ , mean ( y ) ]
+
## [1] 0.4
+
+
Finding the intercept
+
Where did those two intercepts come from? A paper by Peter Austin describes an iterative
+bisection procedure that takes a distribution of covariates and a set of
+coefficients to identify the intercept coefficient that yields the
+target marginal proportion or prevalence. This algorithm has been
+implemented in simstudy through the function
+logisticCoefs. (If you want the details for the algorithm,
+take a look at the paper. Here, I will demonstrate the application.)
+
The intercept is provided by a simple call to
+logisticCoefs. Here are the calls for the two sets of
+definitions in definition tables d1 and d2 .
+
+
## B0 x1 x2 b1 b2 ## -0.66 0.15 0.25 0.10 0.30
+
+
## B0 x1 x2 b1 b2 ## -2.14 0.20 0.35 0.20 0.45
+
+
+
+
Risk ratios
+
Just as the prevalance depends on the distribution of covariates and
+their association with the outcome, risk ratios comparing the outcome
+probabilities for two groups also depend on the additional covariates.
+The risk ratio comparing treatment (\(A
+=1\) to control (\(A=0\) ) is
+
\[RR = \frac{P(y=1 | A = 1)}{P(y=1 | A =
+0)}\] In the data generation process we use a log-odds ratio of
+-0.40 (odds ratio of approximately 0.67) in both cases, but we get
+different risk ratios (0.82 vs. 0.90).
+
+d1a <- defData ( d1 , varname = "rx" , formula = "1;1" , dist = "trtAssign" ) d1a <- defData ( d1a , varname = "y" , formula = "t(c(-0.40, ..coefs1)) %*% c(rx, x1, x2, b1, b2)" ,
+ dist = "binary" , link = "logit"
+) dd <- genData ( 500000 , d1a ) dd [ rx == 1 , mean ( y ) ] / dd [ rx == 0 , mean ( y ) ]
+
## [1] 0.83
+
+d2a <- defData ( d2 , varname = "rx" , formula = "1;1" , dist = "trtAssign" ) d2a <- defData ( d2a , varname = "y" , formula = "t(c(-0.40, ..coefs2)) %*% c(rx, x1, x2, b1, b2)" ,
+ dist = "binary" , link = "logit"
+) dd <- genData ( 500000 , d2a ) dd [ rx == 1 , mean ( y ) ] / dd [ rx == 0 , mean ( y ) ]
+
## [1] 0.93
+
By specifying both a population prevalence and a target risk ratio in
+the call to logisticCoefs, we can get the necessary
+parameters. When specifying the target risk ratio, it is required to be
+between 0 and 1/popPrev. A risk ratio cannot be negative, and the
+probability of the outcome under treatment cannot exceed 1 (which will
+happen if the risk ratio is greater than 1/popPrev).
+
+
## B0 A x1 x2 b1 b2 ## -0.66 -0.26 0.15 0.25 0.10 0.30
+
If we use \(C_1\) in the data
+generation process, we will get a data set with the desired target
+prevalence and risk ratio:
+
+d1a <- defData ( d1 , varname = "rx" , formula = "1;1" , dist = "trtAssign" ) d1a <- defData ( d1a , varname = "y" , formula = "t(..C1) %*% c(1, rx, x1, x2, b1, b2)" ,
+ dist = "binary" , link = "logit"
+) dd <- genData ( 500000 , d1a )
+
Here are the prevalence and risk ratio:
+
+
## [1] 0.4
+
+
## [1] 0.85
+
You can do the same for the second set of assumptions.
+
+
+
Risk differences
+
Risk differences have the same set of issues, and are handled in the
+same way. The risk difference is defined as
+
\[ RD = P(y=1 | A = 1) - P(y=1 | A =
+0)\]
+
To get the coefficients related to a population prevalence of 0.40
+and risk difference of -0.15 (so that the proportion in the exposure arm
+is 0.25), we use the rd argument:
+
+
## B0 A x1 x2 b1 b2 ## -0.66 -0.71 0.15 0.25 0.10 0.30
+
Again, using \(C_1\) in the data
+generation process, we will get a data set with the desired target
+prevalence and risk difference:
+
+d1a <- defData ( d1 , varname = "rx" , formula = "1;1" , dist = "trtAssign" ) d1a <- defData ( d1a , varname = "y" , formula = "t(..C1) %*% c(1, rx, x1, x2, b1, b2)" ,
+ dist = "binary" , link = "logit"
+) dd <- genData ( 500000 , d1a ) dd [ rx == 0 , mean ( y ) ]
+
## [1] 0.4
+
+dd [ rx == 1 , mean ( y ) ] - dd [ rx == 0 , mean ( y ) ]
+
## [1] -0.15
+
+
+
AUC
+
The AUC is another commonly used statistic to evaulate a logistic
+model. We can use logisticCoefs to find the parameters that
+will allow us to generate data from a model with a specific AUC. To get
+the coefficients related to a population prevalence of 0.40 and an AUC
+of 0.85, we use the auc argument (the auc must be
+between 0.5 and 1):
+
+
## B0 x1 x2 b1 b2 ## -2.09 0.89 1.48 0.59 1.78
+
Again, using \(C_1\) in the data
+generation process, we will get a data set with the desired target
+prevalence and the AUC (calculated here using the lrm
+function in the rms package:
+
+d1a <- defData ( d1 , varname = "y" , formula = "t(..C1) %*% c(1, x1, x2, b1, b2)" ,
+ dist = "binary" , link = "logit"
+) dd <- genData ( 500000 , d1a ) dd [ , mean ( y ) ]
+
## [1] 0.4
+
+fit <- rms :: lrm ( y ~ x1 + x2 + b1 + b2 , data = dd ) fit $ stats [ "C" ]
+
## C ## 0.86
+
+
+
References:
+
Austin, Peter C. “The iterative bisection procedure: a useful tool
+for determining parameter values in data-generating processes in Monte
+Carlo simulations.” BMC Medical Research Methodology 23, no. 1 (2023):
+1-10.
+
+
+
+
+
+
+
+
+
+
+
+
+
Dynamic Data Definition
+
+ Simulating data from logistic model with specified characterstics
+
Longitudinal Data
diff --git a/dev/articles/missing.html b/dev/articles/missing.html
index b918fc12..9122d48d 100644
--- a/dev/articles/missing.html
+++ b/dev/articles/missing.html
@@ -65,6 +65,9 @@
Dynamic Data Definition
+
+ Simulating data from logistic model with specified characterstics
+
Longitudinal Data
@@ -178,24 +181,24 @@ Missing Data
## 5: 5 1 1 0 1 0 ## --- ## 996: 996 0 0 0 1 0 ## 997: 997 1 0 0 1 0 ## 998: 998 0 1 0 1 0 ## 997: 997 1 0 1 1 0 ## 998: 998 0 0 0 1 0 ## 999: 999 0 0 0 1 0 ## 1000: 1000 0 0 0 1 0 ## id m u x1 x2 x3 ## 1: 1 0 NA 21.57022 17.8409320 19.3776659 ## 2: 2 1 NA 22.45516 19.7657768 18.9571816 ## 3: 3 1 NA NA 20.0228080 20.4701953 ## 4: 4 1 NA NA 20.0519908 23.0250625 ## 5: 5 1 NA NA NA 20.3956892 ## id m u x1 x2 x3 ## 1: 1 1 NA 18.9361265 17.885697 19.254580 ## 2: 2 0 NA -0.3032213 -1.359335 2.313597 ## 3: 3 1 NA NA 39.748202 39.829287 ## 4: 4 1 NA NA 18.835516 19.857468 ## 5: 5 1 NA NA NA 41.212529 ## --- ## 996: 996 1 NA 20.32037 20.3843003 17.9600725 ## 997: 997 0 NA NA -0.6202465 0.3317878 ## 998: 998 1 NA 21.19199 NA 16.6785657 ## 999: 999 1 NA 19.82385 19.6601089 22.0630420 ## 1000: 1000 1 NA 38.92186 42.9372445 37.4961738 ## 996: 996 0 NA 21.2893997 19.816068 21.048983 ## 997: 997 0 NA NA 19.105992 NA ## 998: 998 0 NA 1.2360994 -2.947338 1.037609 ## 999: 999 0 NA 17.3002879 21.239352 18.597798 ## 1000: 1000 1 NA 40.5050171 40.186471 39.383540 The impacts of the various data mechanisms on estimation can be seen
with a simple calculation of means using both the “true” data set
without missing data as a comparison for the “observed” data set. Since
@@ -223,9 +226,9 @@
Missing Data
showDif ( meanAct , meanObs ) ## x1 x2 x3 ## Actual 20.6 20.5 20.5 ## Observed 20.5 19.1 18.9 ## Difference 0.1 1.4 1.6 ## Actual 20.5 20.4 20.5 ## Observed 20.4 18.9 18.9 ## Difference 0.1 1.5 1.6 After adjusting for the measured covariate m, the bias
for the estimate of the mean of x2 is mitigated, but not
for x3, since u is not observed:
@@ -237,17 +240,17 @@ Missing Data
showDif ( meanActm [ m == 0 , . ( x1 , x2 , x3 ) ] , meanObsm [ m == 0 , . ( x1 , x2 , x3 ) ] ) ## x1 x2 x3 ## Actual 10.3 10.2 10.2 ## Observed 10.2 10.4 8.6 ## Difference 0.1 -0.2 1.6 ## Actual 10.2 10.2 10.2 ## Observed 10.3 10.1 9.0 ## Difference -0.1 0.1 1.2
# compare observed and actual when m = 1 showDif ( meanActm [ m == 1 , . ( x1 , x2 , x3 ) ] , meanObsm [ m == 1 , . ( x1 , x2 , x3 ) ] ) ## x1 x2 x3 ## Actual 30.6 30.4 30.6 ## Observed 30.7 30.5 29.0 ## Difference -0.1 -0.1 1.6 ## Actual 30.6 30.4 30.7 ## Observed 30.3 30.7 29.0 ## Difference 0.3 -0.3 1.7
Longitudinal data with missingness
diff --git a/dev/articles/ordinal.html b/dev/articles/ordinal.html
index 00074653..66e961c2 100644
--- a/dev/articles/ordinal.html
+++ b/dev/articles/ordinal.html
@@ -65,6 +65,9 @@
Dynamic Data Definition
+
+ Simulating data from logistic model with specified characterstics
+
Longitudinal Data
diff --git a/dev/articles/simstudy.html b/dev/articles/simstudy.html
index 605f7757..42fc52c3 100644
--- a/dev/articles/simstudy.html
+++ b/dev/articles/simstudy.html
@@ -65,6 +65,9 @@
Dynamic Data Definition
+
+ Simulating data from logistic model with specified characterstics
+
Longitudinal Data
diff --git a/dev/articles/spline.html b/dev/articles/spline.html
index 10e056e2..0d826a7f 100644
--- a/dev/articles/spline.html
+++ b/dev/articles/spline.html
@@ -65,6 +65,9 @@
Dynamic Data Definition
+
+ Simulating data from logistic model with specified characterstics
+
Longitudinal Data
diff --git a/dev/articles/survival.html b/dev/articles/survival.html
index 7b5be45a..7f8b3fe8 100644
--- a/dev/articles/survival.html
+++ b/dev/articles/survival.html
@@ -65,6 +65,9 @@
Dynamic Data Definition
+
+ Simulating data from logistic model with specified characterstics
+
Longitudinal Data
diff --git a/dev/articles/treat_and_exposure.html b/dev/articles/treat_and_exposure.html
index f561154d..d0ae3912 100644
--- a/dev/articles/treat_and_exposure.html
+++ b/dev/articles/treat_and_exposure.html
@@ -65,6 +65,9 @@
Dynamic Data Definition
+
+ Simulating data from logistic model with specified characterstics
+
Longitudinal Data
@@ -152,17 +155,17 @@
Assigned treatmentstudy1
## cid male over65 baseDBP rxGrp ## 1: 1 0 0 66.72311 1 ## 2: 2 0 0 70.24825 3 ## 3: 3 0 0 64.82704 3 ## 4: 4 1 0 54.48131 1 ## 5: 5 1 0 81.27965 3 ## 1: 1 1 0 65.68068 1 ## 2: 2 0 0 73.27155 2 ## 3: 3 0 0 76.77130 2 ## 4: 4 1 0 72.58889 3 ## 5: 5 0 0 69.46232 2 ## --- ## 326: 326 1 1 69.74081 2 ## 327: 327 1 0 84.62739 3 ## 328: 328 1 0 72.14023 3 ## 329: 329 1 0 72.85896 3 ## 330: 330 1 0 70.80602 3 ## 326: 326 1 0 75.90014 1 ## 327: 327 0 0 73.85968 3 ## 328: 328 0 0 70.80436 2 ## 329: 329 0 0 53.63268 1 ## 330: 330 0 0 65.40682 3 Balanced treatment assignment (without stratification)
study2 <- trtAssign ( dtstudy , n = 3 , balanced = TRUE , grpName = "rxGrp" ) Assigned tre
dtstudy <- genData ( 330 , def ) dtstudy ## cid male over65 rx y ## 1: 1 0 1 0 31.62968 ## 2: 2 1 1 1 39.46643 ## 3: 3 1 0 0 31.54437 ## 4: 4 0 0 1 38.13068 ## 5: 5 0 0 1 22.90007 ## 1: 1 1 0 0 26.27656 ## 2: 2 1 1 1 40.39218 ## 3: 3 1 0 0 23.84090 ## 4: 4 1 1 0 34.63347 ## 5: 5 0 0 0 20.67332 ## --- ## 326: 326 1 0 1 32.72379 ## 327: 327 1 0 1 29.96777 ## 328: 328 0 0 0 11.67538 ## 329: 329 1 0 1 30.46859 ## 330: 330 0 0 1 32.55906 ## 326: 326 0 0 1 40.02070 ## 327: 327 1 1 0 42.10206 ## 328: 328 1 1 0 42.69091 ## 329: 329 0 0 1 19.85681 ## 330: 330 1 0 1 29.69225 Here are the counts and average outcomes for each gender ,
age , and treatment combination:
dtstudy [ , . ( n = .N , avg = round ( mean ( y ) , 1 ) ) , keyby = . ( male , over65 , rx ) ] ## male over65 rx n avg ## 1: 0 0 0 69 19.9 ## 2: 0 0 1 69 29.5 ## 3: 0 1 0 12 30.8 ## 4: 0 1 1 12 38.4 ## 5: 1 0 0 59 24.8 ## 6: 1 0 1 59 36.0 ## 7: 1 1 0 25 34.9 ## 8: 1 1 1 25 45.1 ## 1: 0 0 0 72 18.3 ## 2: 0 0 1 71 30.6 ## 3: 0 1 0 15 32.9 ## 4: 0 1 1 16 41.8 ## 5: 1 0 0 56 25.1 ## 6: 1 0 1 56 35.5 ## 7: 1 1 0 22 33.4 ## 8: 1 1 1 22 45.8
Observed treatment
Dynamic Data Definition
+
+ Simulating data from logistic model with specified characterstics
+
Longitudinal Data
diff --git a/dev/index.html b/dev/index.html
index eae92815..5f5add18 100644
--- a/dev/index.html
+++ b/dev/index.html
@@ -65,6 +65,9 @@
Dynamic Data Definition
+
+ Simulating data from logistic model with specified characterstics
+
Longitudinal Data
diff --git a/dev/news/index.html b/dev/news/index.html
index b4ca431e..31096a7a 100644
--- a/dev/news/index.html
+++ b/dev/news/index.html
@@ -46,6 +46,9 @@
Dynamic Data Definition
+
+ Simulating data from logistic model with specified characterstics
+
Longitudinal Data
@@ -89,6 +92,14 @@
Changelog
+
+
New features
+
Function logisticCoefs determines the intercept and treatmen/exposure parameter for a data generating process (based on a logistic regression model) that has a specific target population prevalence of a binary outcome, and an option to target a risk ratio, risk difference, or AUC.
+
+
+
Major fix
+
Data generation speed has been improved for very large data sets with many variables.
+
diff --git a/dev/pkgdown.yml b/dev/pkgdown.yml
index 07d26222..c7333ed4 100644
--- a/dev/pkgdown.yml
+++ b/dev/pkgdown.yml
@@ -6,6 +6,7 @@ articles:
corelationmat: corelationmat.html
correlated: correlated.html
double_dot_extension: double_dot_extension.html
+ logisticCoefs: logisticCoefs.html
longitudinal: longitudinal.html
missing: missing.html
ordinal: ordinal.html
@@ -13,5 +14,5 @@ articles:
spline: spline.html
survival: survival.html
treat_and_exposure: treat_and_exposure.html
-last_built: 2023-06-06T16:29Z
+last_built: 2023-06-16T14:47Z
diff --git a/dev/reference/addColumns.html b/dev/reference/addColumns.html
index 2dead909..038b8b3d 100644
--- a/dev/reference/addColumns.html
+++ b/dev/reference/addColumns.html
@@ -46,6 +46,9 @@
Dynamic Data Definition
+
+ Simulating data from logistic model with specified characterstics
+
Longitudinal Data
@@ -139,16 +142,16 @@
Examples
dt <- addColumns ( def2 , dt ) dt #> idnum xNr xUni y1 y2
-
#> 1: 1 7 19.77519 12.127445 0
-
#> 2: 2 7 10.25355 9.585016 0
-
#> 3: 3 7 11.50730 9.902337 1
-
#> 4: 4 7 16.11763 8.013333 1
-
#> 5: 5 7 13.24324 11.138120 0
-
#> 6: 6 7 14.17467 9.050218 0
-
#> 7: 7 7 14.22460 11.077808 0
-
#> 8: 8 7 15.68954 8.934533 1
-
#> 9: 9 7 16.98865 10.114972 1
-
#> 10: 10 7 16.56864 8.763078 1
+
#> 1: 1 7 19.86661 9.809267 0
+
#> 2: 2 7 11.49860 12.245914 0
+
#> 3: 3 7 15.70662 11.937422 0
+
#> 4: 4 7 12.06378 8.553814 0
+
#> 5: 5 7 16.76965 10.411291 1
+
#> 6: 6 7 11.07966 10.508007 1
+
#> 7: 7 7 13.74720 9.993135 1
+
#> 8: 8 7 12.50105 10.577383 0
+
#> 9: 9 7 15.85960 11.470825 0
+
#> 10: 10 7 16.67648 9.133191 0
diff --git a/dev/reference/addCompRisk.html b/dev/reference/addCompRisk.html
index 7a8363ec..fd58fe08 100644
--- a/dev/reference/addCompRisk.html
+++ b/dev/reference/addCompRisk.html
@@ -1,5 +1,5 @@
-Generating single competing risk survival varible — addCompRisk • simstudy Generating single competing risk survival variable — addCompRisk • simstudy
Dynamic Data Definition
+
+ Simulating data from logistic model with specified characterstics
+
Longitudinal Data
@@ -83,13 +86,13 @@
-
Generating single competing risk survival varible
+
Generating single competing risk survival variable
@@ -170,17 +173,17 @@
Examples
addCompRisk ( dd , c ( "reinc" ,"death" , "censor" ) , timeName = "time" , censorName = "censor" , keepEvents = FALSE )
-
#> id x1 x2 time event type
-
#> 1: 1 1 1 11.059 1 death
-
#> 2: 2 0 0 13.400 0 reinc
-
#> 3: 3 0 0 5.789 1 death
-
#> 4: 4 1 1 24.062 0 reinc
-
#> 5: 5 0 1 12.414 1 death
-
#> 6: 6 1 1 25.269 1 death
-
#> 7: 7 1 0 20.788 1 death
-
#> 8: 8 0 1 16.256 1 death
-
#> 9: 9 0 1 12.555 1 death
-
#> 10: 10 0 1 17.968 0 reinc
+
#> id x1 x2 time event type
+
#> 1: 1 1 0 21.091 1 reinc
+
#> 2: 2 1 1 7.145 1 reinc
+
#> 3: 3 0 0 20.549 2 death
+
#> 4: 4 1 0 20.919 1 reinc
+
#> 5: 5 1 1 17.112 2 death
+
#> 6: 6 1 0 20.722 2 death
+
#> 7: 7 1 1 4.044 2 death
+
#> 8: 8 1 1 7.642 0 censor
+
#> 9: 9 0 0 13.386 0 censor
+
#> 10: 10 1 1 4.955 2 death
diff --git a/dev/reference/addCondition-1.png b/dev/reference/addCondition-1.png
index bd5c9275..523daa7e 100644
Binary files a/dev/reference/addCondition-1.png and b/dev/reference/addCondition-1.png differ
diff --git a/dev/reference/addCondition.html b/dev/reference/addCondition.html
index 2109fba4..127f6ef1 100644
--- a/dev/reference/addCondition.html
+++ b/dev/reference/addCondition.html
@@ -46,6 +46,9 @@
Dynamic Data Definition
+
+ Simulating data from logistic model with specified characterstics
+
Longitudinal Data
diff --git a/dev/reference/addCorData.html b/dev/reference/addCorData.html
index fc7b8d7e..7e503e75 100644
--- a/dev/reference/addCorData.html
+++ b/dev/reference/addCorData.html
@@ -46,6 +46,9 @@
Dynamic Data Definition
+
+ Simulating data from logistic model with specified characterstics
+
Longitudinal Data
@@ -176,29 +179,29 @@
Examples
rho = .7 , corstr = "cs" ) dtAdd
-
#> myID xUni xNorm V1 V2 V3
-
#> 1: 1 16.76895 32.59501 4.085658 8.711057 17.90749
-
#> 2: 2 19.49947 34.61665 2.283458 8.946570 13.02386
-
#> 3: 3 19.04379 32.85605 1.612831 3.229573 11.47516
-
#> 4: 4 18.92928 35.31883 2.522701 6.802303 12.02146
-
#> 5: 5 18.55993 39.02851 1.909222 7.228421 14.03043
-
#> ---
-
#> 246: 246 12.28024 23.35406 3.108757 8.160088 16.30125
-
#> 247: 247 11.72010 24.93940 3.681857 6.551933 15.35067
-
#> 248: 248 18.40408 33.99754 1.990739 8.636358 13.71233
-
#> 249: 249 11.96329 25.42146 4.247991 9.144226 14.46889
-
#> 250: 250 15.21991 29.86339 4.115358 9.214027 17.52878
+
#> myID xUni xNorm V1 V2 V3
+
#> 1: 1 17.95661 32.54301 3.313414 7.212918 15.01587
+
#> 2: 2 12.00886 23.92937 3.882947 8.471799 14.35276
+
#> 3: 3 14.04347 28.34766 2.432766 8.396424 12.44341
+
#> 4: 4 14.77774 29.28248 1.945070 7.403755 13.94964
+
#> 5: 5 13.52062 26.88314 3.607590 6.923766 15.87514
+
#> ---
+
#> 246: 246 15.93900 32.47159 3.015867 8.673155 13.65394
+
#> 247: 247 14.69141 30.34699 3.856043 10.518124 16.29388
+
#> 248: 248 17.56281 29.77698 4.279660 11.374829 17.30741
+
#> 249: 249 14.37553 27.77826 2.823699 6.358117 12.11985
+
#> 250: 250 11.95165 28.71428 5.216236 9.706980 21.01525round ( var ( dtAdd [ , . ( V1 , V2 , V3 ) ] ) , 3 ) #> V1 V2 V3
-
#> V1 0.955 1.227 2.096
-
#> V2 1.227 3.623 3.885
-
#> V3 2.096 3.885 8.888
+
#> V1 0.921 1.123 1.914
+
#> V2 1.123 3.463 3.592
+
#> V3 1.914 3.592 8.482round ( cor ( dtAdd [ , . ( V1 , V2 , V3 ) ] ) , 2 ) #> V1 V2 V3
-
#> V1 1.00 0.66 0.72
-
#> V2 0.66 1.00 0.68
-
#> V3 0.72 0.68 1.00
+
#> V1 1.00 0.63 0.68
+
#> V2 0.63 1.00 0.66
+
#> V3 0.68 0.66 1.00dtAdd <- addCorData ( dt , "myID" , mu = mu , sigma = sigma ,
@@ -206,9 +209,9 @@
Examples
) round ( cor ( dtAdd [ , . ( V1 , V2 , V3 ) ] ) , 2 ) #> V1 V2 V3
-
#> V1 1.00 0.70 0.47
-
#> V2 0.70 1.00 0.68
-
#> V3 0.47 0.68 1.00
+
#> V1 1.00 0.68 0.47
+
#> V2 0.68 1.00 0.69
+
#> V3 0.47 0.69 1.00corMat <- matrix ( c ( 1 , .2 , .8 , .2 , 1 , .6 , .8 , .6 , 1 ) , nrow = 3 )
@@ -218,9 +221,9 @@
Examples
) round ( cor ( dtAdd [ , . ( V1 , V2 , V3 ) ] ) , 2 ) #> V1 V2 V3
-
#> V1 1.00 0.13 0.80
-
#> V2 0.13 1.00 0.54
-
#> V3 0.80 0.54 1.00
+
#> V1 1.00 0.17 0.79
+
#> V2 0.17 1.00 0.58
+
#> V3 0.79 0.58 1.00
diff --git a/dev/reference/addCorFlex.html b/dev/reference/addCorFlex.html
index b56ded84..b2ff7f40 100644
--- a/dev/reference/addCorFlex.html
+++ b/dev/reference/addCorFlex.html
@@ -46,6 +46,9 @@
Dynamic Data Definition
+
+ Simulating data from logistic model with specified characterstics
+
Longitudinal Data
@@ -195,63 +198,63 @@ Examples
print ( cor ( di [ idClust == i , list ( A , B , C , D ) ] ) ) } #> A B C D#> A 1.0000000 0.3406562 0.2495626 0.2554243#> B 0.3406562 1.0000000 0.3398297 0.4784836#> C 0.2495626 0.3398297 1.0000000 0.3167690#> D 0.2554243 0.4784836 0.3167690 1.0000000#> A B C D#> A 1.00000000 0.2485535 0.3780640 0.05100379#> B 0.24855346 1.0000000 0.3311455 0.22076053#> C 0.37806401 0.3311455 1.0000000 0.26893525#> D 0.05100379 0.2207605 0.2689352 1.00000000#> A B C D#> A 1.0000000 0.3835578 0.3884835 0.3927248#> B 0.3835578 1.0000000 0.4349660 0.5778788#> C 0.3884835 0.4349660 1.0000000 0.4943901#> D 0.3927248 0.5778788 0.4943901 1.0000000#> A 1.0000000 0.4286875 0.3297979 0.2347647#> B 0.4286875 1.0000000 0.2004366 0.3897232#> C 0.3297979 0.2004366 1.0000000 0.2569380#> D 0.2347647 0.3897232 0.2569380 1.0000000#> A B C D#> A 1.000000000 0.2469078 0.4215374 0.008593497#> B 0.246907764 1.0000000 0.6061375 0.435897490#> C 0.421537408 0.6061375 1.0000000 0.414279116#> D 0.008593497 0.4358975 0.4142791 1.000000000#> A B C D#> A 1.0000000 0.2971884 0.4203225 0.5474606#> B 0.2971884 1.0000000 0.3336864 0.2578099#> C 0.4203225 0.3336864 1.0000000 0.4372210#> D 0.5474606 0.2578099 0.4372210 1.0000000#> A 1.0000000 0.5197699 0.3874457 0.4339078#> B 0.5197699 1.0000000 0.5296502 0.4542076#> C 0.3874457 0.5296502 1.0000000 0.5224635#> D 0.4339078 0.4542076 0.5224635 1.0000000#> A B C D#> A 1.0000000 0.2356170 0.4640510 0.5503669#> B 0.2356170 1.0000000 0.4896065 0.4483039#> C 0.4640510 0.4896065 1.0000000 0.5575444#> D 0.5503669 0.4483039 0.5575444 1.0000000#> A 1.0000000 0.4333561 0.6089613 0.4664374#> B 0.4333561 1.0000000 0.4916776 0.5531181#> C 0.6089613 0.4916776 1.0000000 0.3630633#> D 0.4664374 0.5531181 0.3630633 1.0000000#> A B C D#> A 1.0000000 0.3750797 0.4733852 0.3695352#> B 0.3750797 1.0000000 0.3815429 0.4148739#> C 0.4733852 0.3815429 1.0000000 0.4355969#> D 0.3695352 0.4148739 0.4355969 1.0000000#> A 1.0000000 0.4389587 0.3305654 0.3407185#> B 0.4389587 1.0000000 0.1882755 0.3591182#> C 0.3305654 0.1882755 1.0000000 0.5175346#> D 0.3407185 0.3591182 0.5175346 1.0000000#> A B C D#> A 1.00000000 -0.02356498 0.2878693 0.3795695#> B -0.02356498 1.00000000 0.2002829 0.2880845#> C 0.28786926 0.20028289 1.0000000 0.1596746#> D 0.37956948 0.28808447 0.1596746 1.0000000#> A B C D#> A 1.0000000 0.2672245 0.3068318 0.4097004#> B 0.2672245 1.0000000 0.1792928 0.5399817#> C 0.3068318 0.1792928 1.0000000 0.3704618#> D 0.4097004 0.5399817 0.3704618 1.0000000#> A 1.0000000 0.3245598 0.4249850 0.4635660#> B 0.3245598 1.0000000 0.4420268 0.3517828#> C 0.4249850 0.4420268 1.0000000 0.4934050#> D 0.4635660 0.3517828 0.4934050 1.0000000#> A B C D#> A 1.0000000 0.3981507 0.4721447 0.4164991#> B 0.3981507 1.0000000 0.4990947 0.4643299#> C 0.4721447 0.4990947 1.0000000 0.4055073#> D 0.4164991 0.4643299 0.4055073 1.0000000#> A 1.0000000 0.5406002 0.1722469 0.3500954#> B 0.5406002 1.0000000 0.4066897 0.3756615#> C 0.1722469 0.4066897 1.0000000 0.2934906#> D 0.3500954 0.3756615 0.2934906 1.0000000#> A B C D#> A 1.0000000 0.5660632 0.5780797 0.2497885#> B 0.5660632 1.0000000 0.4944815 0.4466131#> C 0.5780797 0.4944815 1.0000000 0.2114349#> D 0.2497885 0.4466131 0.2114349 1.0000000#> A 1.0000000 0.2209653 0.4439960 0.4594370#> B 0.2209653 1.0000000 0.3999685 0.3989203#> C 0.4439960 0.3999685 1.0000000 0.3344698#> D 0.4594370 0.3989203 0.3344698 1.0000000#> A B C D#> A 1.0000000 0.3140088 0.3248843 0.4577602#> B 0.3140088 1.0000000 0.2616308 0.3146415#> C 0.3248843 0.2616308 1.0000000 0.1257582#> D 0.4577602 0.3146415 0.1257582 1.0000000#> A 1.0000000 0.3149325 0.4666405 0.3883964#> B 0.3149325 1.0000000 0.2010666 0.3356168#> C 0.4666405 0.2010666 1.0000000 0.5085276#> D 0.3883964 0.3356168 0.5085276 1.0000000# Check global correlations - should not be as correlated cor ( di [ , list ( A , B , C , D ) ] ) #> A B C D#> A 1.00000000 0.05756453 -0.07253847 0.3187693#> B 0.05756453 1.00000000 0.10629546 0.4970023#> C -0.07253847 0.10629546 1.00000000 0.0437867#> D 0.31876935 0.49700229 0.04378670 1.0000000#> A B C D#> A 1.00000000 -0.04198754 0.2574572 0.4056920#> B -0.04198754 1.00000000 0.3198121 -0.2539433#> C 0.25745718 0.31981207 1.0000000 0.5076336#> D 0.40569198 -0.25394326 0.5076336 1.0000000
Dynamic Data Definition
+
+ Simulating data from logistic model with specified characterstics
+
Longitudinal Data
@@ -204,108 +207,108 @@ Examples
dtOld = dt , idvar = "cid" , nvars = 3 , rho = .7 , corstr = "cs" , dist = "poisson" , param1 = "lambda" ) #> cid xbase lambda V1 V2 V3#> 1: 1 3.4094338 2.318553 2 3 4#> 2: 2 4.3205701 2.539728 4 4 4#> 3: 3 1.5115581 1.917756 1 3 4#> 4: 4 3.3610479 2.307362 3 2 3#> 5: 5 4.3143978 2.538161 3 3 2#> 6: 6 9.5840047 4.299078 6 5 5#> 7: 7 5.4586679 2.845864 3 5 4#> 8: 8 0.8735171 1.799217 0 1 0#> 9: 9 3.6513524 2.375327 4 3 4#> 10: 10 4.4338858 2.568671 1 1 1#> 11: 11 2.8321725 2.188502 0 3 1#> 12: 12 2.0704586 2.027991 1 1 2#> 13: 13 5.4199592 2.834870 2 3 5#> 14: 14 2.5085289 2.118806 1 3 2#> 15: 15 5.3271270 2.808675 4 5 3#> 16: 16 9.3886520 4.215909 4 6 4#> 17: 17 5.8162043 2.949455 4 5 4#> 18: 18 3.8117082 2.413724 1 0 0#> 19: 19 4.4753317 2.579339 2 1 3#> 20: 20 9.7653371 4.377745 5 3 4#> 21: 21 1.0583979 1.832791 3 4 3#> 22: 22 14.2134379 6.830131 9 7 7#> 23: 23 4.8661637 2.682144 6 6 4#> 24: 24 9.6228939 4.315829 5 5 8#> 25: 25 1.1853512 1.856207 1 0 0#> 26: 26 2.8891579 2.201009 5 4 5#> 27: 27 4.6121543 2.614873 2 4 2#> 28: 28 2.4641674 2.109428 1 1 1#> 29: 29 1.3611947 1.889136 2 1 1#> 30: 30 0.3610781 1.709341 2 2 3#> 31: 31 5.0892229 2.742644 1 2 1#> 32: 32 2.7761360 2.176273 4 3 3#> 33: 33 1.2756937 1.873052 1 0 0#> 34: 34 1.0834498 1.837388 2 1 0#> 35: 35 2.6209688 2.142765 4 3 3#> 36: 36 0.9234207 1.808218 1 1 0#> 37: 37 3.1722282 2.264203 0 0 1#> 38: 38 11.3312510 5.119850 7 8 7#> 39: 39 4.7175394 2.642575 2 4 2#> 40: 40 1.6187080 1.938415 0 0 2#> 41: 41 1.3986995 1.896234 2 1 1#> 42: 42 9.6774368 4.339433 0 0 0#> 43: 43 6.8831493 3.281547 7 3 3#> 44: 44 4.9060027 2.692850 7 6 6#> 45: 45 5.6907223 2.912676 5 5 5#> 46: 46 0.7885944 1.784003 0 0 0#> 47: 47 2.2880766 2.072608 2 1 1#> 48: 48 9.0446404 4.073343 2 2 3#> 49: 49 5.7377532 2.926407 2 2 3#> 50: 50 1.6000527 1.934803 2 2 2#> 51: 51 4.5675434 2.603234 2 4 4#> 52: 52 4.5902866 2.609161 2 1 1#> 53: 53 2.7012180 2.160029 5 7 4#> 54: 54 4.8222140 2.670382 3 3 4#> 55: 55 2.8847482 2.200038 4 2 5#> 56: 56 4.7863225 2.660814 2 2 1#> 57: 57 1.8060185 1.975066 1 3 1#> 58: 58 2.6095997 2.140330 3 2 3#> 59: 59 2.4707688 2.110821 5 6 3#> 60: 60 1.5330876 1.921889 3 4 1#> 61: 61 5.2935561 2.799261 5 4 4#> 62: 62 8.4051509 3.821011 3 4 2#> 63: 63 1.3597355 1.888860 1 1 1#> 64: 64 1.7225548 1.958650 2 1 1#> 65: 65 4.8931382 2.689388 1 2 2#> 66: 66 4.0627835 2.475094 3 1 1#> 67: 67 2.8022831 2.181970 2 2 4#> 68: 68 7.8142636 3.601773 3 3 4#> 69: 69 1.7765191 1.969248 2 3 4#> 70: 70 2.4438905 2.105155 0 1 1#> 71: 71 1.1230543 1.844679 2 3 1#> 72: 72 7.5323149 3.501640 6 3 3#> 73: 73 7.2219261 3.394623 3 1 3#> 74: 74 7.0877792 3.349389 1 3 1#> 75: 75 9.8732858 4.425258 7 4 5#> 76: 76 7.0358355 3.332036 2 4 3#> 77: 77 8.3124283 3.785745 3 5 4#> 78: 78 6.1794470 3.058561 1 2 4#> 79: 79 3.0947057 2.246718 1 1 1#> 80: 80 6.4046239 3.128214 3 2 2#> 81: 81 6.0149109 3.008649 3 3 2#> 82: 82 1.6337468 1.941333 7 2 4#> 83: 83 5.2561957 2.788823 2 2 3#> 84: 84 2.1189323 2.037846 1 1 2#> 85: 85 0.5597910 1.743647 3 4 3#> 86: 86 2.0641053 2.026703 1 1 2#> 87: 87 6.8990315 3.286763 1 2 2#> 88: 88 3.5857042 2.359785 0 2 1#> 89: 89 5.9644946 2.993519 2 2 3#> 90: 90 8.8304086 3.987007 4 4 2#> 91: 91 4.1456056 2.495678 2 3 3#> 92: 92 5.8402484 2.956555 3 6 3#> 93: 93 4.0341027 2.468005 4 5 5#> 94: 94 6.0262396 3.012059 1 2 1#> 95: 95 6.4167423 3.132008 5 5 4#> 96: 96 6.1817612 3.059269 3 1 2#> 97: 97 3.9864354 2.456269 6 4 6#> 98: 98 3.7262284 2.393180 2 1 1#> 99: 99 10.7648633 4.837927 4 7 2#> 100: 100 8.2873786 3.776274 5 5 5#> cid xbase lambda V1 V2 V3#> cid xbase lambda V1 V2 V3#> 1: 1 4.011436 2.462417 1 2 5#> 2: 2 8.428646 3.829999 3 3 4#> 3: 3 2.489232 2.114722 3 4 4#> 4: 4 8.572591 3.885529 2 2 1#> 5: 5 8.324969 3.790496 3 2 3#> 6: 6 1.171242 1.853590 2 2 3#> 7: 7 2.080005 2.029928 1 1 2#> 8: 8 4.706431 2.639642 0 1 0#> 9: 9 8.450644 3.838434 4 4 4#> 10: 10 4.283009 2.530207 1 2 1#> 11: 11 8.368432 3.807007 4 2 4#> 12: 12 3.366053 2.308517 2 1 3#> 13: 13 11.157589 5.031705 4 5 6#> 14: 14 4.305168 2.535819 2 1 3#> 15: 15 3.585776 2.359802 3 3 3#> 16: 16 2.388765 2.093582 0 0 0#> 17: 17 7.350617 3.438591 2 2 1#> 18: 18 5.046456 2.730939 2 2 4#> 19: 19 2.822952 2.186485 5 5 3#> 20: 20 5.655354 2.902393 2 2 2#> 21: 21 11.535734 5.225620 7 6 7#> 22: 22 5.077100 2.739321 2 4 2#> 23: 23 2.316007 2.078405 2 3 2#> 24: 24 3.075632 2.242437 2 2 3#> 25: 25 6.387841 3.122969 2 3 2#> 26: 26 13.274401 6.217949 7 9 10#> 27: 27 3.807493 2.412707 4 2 3#> 28: 28 4.923015 2.697435 5 2 4#> 29: 29 3.328250 2.299807 2 2 3#> 30: 30 5.837247 2.955668 3 3 2#> 31: 31 7.055706 3.338664 2 2 1#> 32: 32 1.566527 1.928327 3 6 5#> 33: 33 4.017819 2.463990 5 6 3#> 34: 34 1.515077 1.918431 2 0 1#> 35: 35 1.874329 1.988604 1 1 0#> 36: 36 3.111651 2.250529 0 2 3#> 37: 37 5.198627 2.772814 3 2 4#> 38: 38 2.375636 2.090835 2 4 3#> 39: 39 3.722932 2.392391 2 2 1#> 40: 40 3.977413 2.454054 1 2 2#> 41: 41 4.686227 2.634314 1 0 2#> 42: 42 3.205793 2.271816 3 1 1#> 43: 43 2.642529 2.147389 1 1 1#> 44: 44 3.437511 2.325072 2 3 4#> 45: 45 2.841980 2.190649 3 3 4#> 46: 46 6.481946 3.152496 3 3 4#> 47: 47 4.150008 2.496777 4 4 4#> 48: 48 11.225877 5.066183 7 5 7#> 49: 49 3.590104 2.360823 2 1 2#> 50: 50 3.042534 2.235027 2 2 3#> 51: 51 3.294647 2.292091 2 2 3#> 52: 52 1.624699 1.939577 3 2 4#> 53: 53 2.348286 2.085124 4 3 3#> 54: 54 4.954290 2.705885 2 4 3#> 55: 55 5.538630 2.868711 1 0 1#> 56: 56 5.606321 2.888196 0 0 1#> 57: 57 2.605689 2.139493 1 0 1#> 58: 58 4.346589 2.546345 1 3 2#> 59: 59 3.350912 2.305024 5 4 3#> 60: 60 1.168177 1.853022 2 0 1#> 61: 61 4.275685 2.528354 3 4 3#> 62: 62 4.281689 2.529873 5 5 3#> 63: 63 5.090669 2.743040 5 5 6#> 64: 64 8.076270 3.697389 6 5 4#> 65: 65 5.832956 2.954400 3 1 1#> 66: 66 3.672025 2.380243 4 5 5#> 67: 67 7.694308 3.558826 3 2 3#> 68: 68 2.560804 2.129911 0 0 0#> 69: 69 4.983781 2.713877 7 5 4#> 70: 70 4.802971 2.665248 2 2 2#> 71: 71 2.006444 2.015051 1 1 0#> 72: 72 5.902628 2.975056 9 8 7#> 73: 73 8.086293 3.701097 6 7 5#> 74: 74 5.557074 2.874007 3 3 3#> 75: 75 2.205741 2.055613 1 2 1#> 76: 76 9.564178 4.290562 6 3 6#> 77: 77 4.626451 2.618614 2 1 2#> 78: 78 1.940035 2.001713 2 2 1#> 79: 79 4.956950 2.706605 2 1 3#> 80: 80 11.007787 4.956891 8 5 5#> 81: 81 4.430759 2.567868 2 1 1#> 82: 82 3.076396 2.242608 4 3 3#> 83: 83 5.882472 2.969065 2 2 1#> 84: 84 6.660641 3.209336 3 3 3#> 85: 85 3.602634 2.363783 2 3 2#> 86: 86 6.671896 3.212950 6 7 7#> 87: 87 5.289981 2.798261 0 0 1#> 88: 88 7.783629 3.590756 2 2 1#> 89: 89 2.756380 2.171977 2 3 4#> 90: 90 9.232222 4.150473 3 4 2#> 91: 91 6.294883 3.094073 2 2 3#> 92: 92 1.910178 1.995746 4 3 4#> 93: 93 1.371105 1.891009 2 1 2#> 94: 94 2.690735 2.157766 2 0 1#> 95: 95 10.543166 4.731851 1 1 3#> 96: 96 9.742460 4.367741 2 1 2#> 97: 97 3.119730 2.252347 2 2 5#> 98: 98 4.327010 2.541364 5 4 4#> 99: 99 10.393026 4.661338 5 4 6#> 100: 100 7.789054 3.592705 4 2 2#> cid xbase lambda V1 V2 V3# Long example
Dynamic Data Definition
+
+ Simulating data from logistic model with specified characterstics
+
Longitudinal Data
diff --git a/dev/reference/addMultiFac.html b/dev/reference/addMultiFac.html
index a664a278..2962f7d3 100644
--- a/dev/reference/addMultiFac.html
+++ b/dev/reference/addMultiFac.html
@@ -46,6 +46,9 @@
Dynamic Data Definition
+
+ Simulating data from logistic model with specified characterstics
+
Longitudinal Data
@@ -139,18 +142,18 @@ Examples
DT <- genData ( 360 , defD ) DT <- addMultiFac ( DT , nFactors = 3 , levels = c ( 2 , 3 , 3 ) , colNames = c ( "A" , "B" , "C" ) ) DT #> id x A B C#> 1: 1 -0.31215079 1 2 3#> 2: 2 -0.21354424 1 3 3#> 3: 3 1.04041592 2 1 2#> 4: 4 -0.46483693 1 3 3#> 5: 5 0.17092930 2 1 2#> --- #> 356: 356 -0.06047427 2 1 2#> 357: 357 -0.91227685 1 1 1#> 358: 358 -0.38668095 1 1 3#> 359: 359 0.91642033 2 2 3#> 360: 360 1.13539478 1 3 1#> id x A B C#> 1: 1 0.7463632 2 2 1#> 2: 2 -1.4032972 1 1 2#> 3: 3 -0.4359190 2 3 2#> 4: 4 -1.2726915 1 2 2#> 5: 5 0.2410753 1 1 3#> --- #> 356: 356 -1.8271667 1 2 1#> 357: 357 -0.4022704 2 3 2#> 358: 358 -0.4346730 1 3 3#> 359: 359 -1.0089664 2 1 3#> 360: 360 0.7636342 2 1 2DT [ , .N , keyby = . ( A , B , C ) ] #> A B C N#> 1: 1 1 1 20Examples
DT [ , .N , keyby = . ( Var1 , Var2 , Var3 ) ] #> Var1 Var2 Var3 N#> 1: 0 0 0 37#> 2: 0 0 1 37#> 3: 0 1 0 38#> 2: 0 0 1 38#> 3: 0 1 0 37#> 4: 0 1 1 38#> 5: 1 0 0 37#> 5: 1 0 0 38#> 6: 1 0 1 37#> 7: 1 1 0 38#> 8: 1 1 1 38#> 8: 1 1 1 37
Dynamic Data Definition
+
+ Simulating data from logistic model with specified characterstics
+
Longitudinal Data
@@ -153,11 +156,11 @@ Examples
dtTrial <- genData ( 5 , tdef ) dtTrial #> id T Y0 Y1 Y2#> 1: 1 0 10.602532 16.01595 20.26540#> 2: 2 0 8.761621 12.84098 17.36761#> 3: 3 1 10.632761 21.20437 24.29927#> 4: 4 1 9.760414 20.53569 25.07561#> 5: 5 1 10.539279 19.68567 25.80316#> 1: 1 1 10.099804 19.95882 25.30015#> 2: 2 1 9.114251 20.01162 22.55616#> 3: 3 0 11.080168 16.07542 18.82528#> 4: 4 0 7.941049 11.65252 17.73128#> 5: 5 0 10.360406 13.25476 22.03741dtTime <- addPeriods ( dtTrial , nPeriods = 3 , idvars = "id" , Examples
) dtTime #> id period T Y timeID#> 1: 1 0 0 10.602532 1#> 2: 1 1 0 16.015946 2#> 3: 1 2 0 20.265401 3#> 4: 2 0 0 8.761621 4#> 5: 2 1 0 12.840982 5#> 6: 2 2 0 17.367610 6#> 7: 3 0 1 10.632761 7#> 8: 3 1 1 21.204366 8#> 9: 3 2 1 24.299274 9#> 10: 4 0 1 9.760414 10#> 11: 4 1 1 20.535685 11#> 12: 4 2 1 25.075607 12#> 13: 5 0 1 10.539279 13#> 14: 5 1 1 19.685672 14#> 15: 5 2 1 25.803162 15#> 1: 1 0 1 10.099804 1#> 2: 1 1 1 19.958817 2#> 3: 1 2 1 25.300149 3#> 4: 2 0 1 9.114251 4#> 5: 2 1 1 20.011619 5#> 6: 2 2 1 22.556164 6#> 7: 3 0 0 11.080168 7#> 8: 3 1 0 16.075421 8#> 9: 3 2 0 18.825278 9#> 10: 4 0 0 7.941049 10#> 11: 4 1 0 11.652521 11#> 12: 4 2 0 17.731278 12#> 13: 5 0 0 10.360406 13#> 14: 5 1 0 13.254757 14#> 15: 5 2 0 22.037411 15# Varying # of periods and intervals - need to have variables # called nCount and mInterval Examples
dt <- genData ( 200 , def ) dt [ id %in% c ( 8 , 121 ) ] #> id xbase nCount mInterval vInterval#> 1: 8 21.07535 6 34.37963 0.07#> 2: 121 19.22680 4 29.45772 0.07#> 1: 8 21.36395 4 28.78482 0.07#> 2: 121 18.48159 10 30.47625 0.07dtPeriod <- addPeriods ( dt ) dtPeriod [ id %in% c ( 8 , 121 ) ] # View individuals 8 and 121 only #> id period xbase time timeID#> 1: 8 0 21.07535 0 48#> 2: 8 1 21.07535 36 49#> 3: 8 2 21.07535 71 50#> 4: 8 3 21.07535 100 51#> 5: 8 4 21.07535 128 52#> 6: 8 5 21.07535 162 53#> 7: 121 0 19.22680 0 747#> 8: 121 1 19.22680 27 748#> 9: 121 2 19.22680 58 749#> 10: 121 3 19.22680 81 750#> 1: 8 0 21.36395 0 44#> 2: 8 1 21.36395 24 45#> 3: 8 2 21.36395 48 46#> 4: 8 3 21.36395 73 47#> 5: 121 0 18.48159 0 748#> 6: 121 1 18.48159 23 749#> 7: 121 2 18.48159 55 750#> 8: 121 3 18.48159 101 751#> 9: 121 4 18.48159 124 752#> 10: 121 5 18.48159 157 753#> 11: 121 6 18.48159 187 754#> 12: 121 7 18.48159 210 755#> 13: 121 8 18.48159 233 756#> 14: 121 9 18.48159 255 757
Dynamic Data Definition
+
+ Simulating data from logistic model with specified characterstics
+
Longitudinal Data
diff --git a/dev/reference/betaGetShapes.html b/dev/reference/betaGetShapes.html
index 7f93f4ce..0aebf82a 100644
--- a/dev/reference/betaGetShapes.html
+++ b/dev/reference/betaGetShapes.html
@@ -46,6 +46,9 @@
Dynamic Data Definition
+
+ Simulating data from logistic model with specified characterstics
+
Longitudinal Data
diff --git a/dev/reference/blockDecayMat.html b/dev/reference/blockDecayMat.html
index 7762fba3..0b9c2036 100644
--- a/dev/reference/blockDecayMat.html
+++ b/dev/reference/blockDecayMat.html
@@ -50,6 +50,9 @@
Dynamic Data Definition
+
+ Simulating data from logistic model with specified characterstics
+
Longitudinal Data
diff --git a/dev/reference/blockExchangeMat.html b/dev/reference/blockExchangeMat.html
index 6a37135e..5f9bb7f4 100644
--- a/dev/reference/blockExchangeMat.html
+++ b/dev/reference/blockExchangeMat.html
@@ -50,6 +50,9 @@
Dynamic Data Definition
+
+ Simulating data from logistic model with specified characterstics
+
Longitudinal Data
diff --git a/dev/reference/catProbs.html b/dev/reference/catProbs.html
index 90184c32..a26ab14f 100644
--- a/dev/reference/catProbs.html
+++ b/dev/reference/catProbs.html
@@ -46,6 +46,9 @@
Dynamic Data Definition
+
+ Simulating data from logistic model with specified characterstics
+
Longitudinal Data
diff --git a/dev/reference/defCondition.html b/dev/reference/defCondition.html
index eea6e419..5b4f06f4 100644
--- a/dev/reference/defCondition.html
+++ b/dev/reference/defCondition.html
@@ -47,6 +47,9 @@
Dynamic Data Definition
+
+ Simulating data from logistic model with specified characterstics
+
Longitudinal Data
diff --git a/dev/reference/defData.html b/dev/reference/defData.html
index da7bce1c..341fbc5b 100644
--- a/dev/reference/defData.html
+++ b/dev/reference/defData.html
@@ -46,6 +46,9 @@
Dynamic Data Definition
+
+ Simulating data from logistic model with specified characterstics
+
Longitudinal Data
diff --git a/dev/reference/defDataAdd.html b/dev/reference/defDataAdd.html
index 61242d90..429664ca 100644
--- a/dev/reference/defDataAdd.html
+++ b/dev/reference/defDataAdd.html
@@ -47,6 +47,9 @@
Dynamic Data Definition
+
+ Simulating data from logistic model with specified characterstics
+
Longitudinal Data
diff --git a/dev/reference/defMiss.html b/dev/reference/defMiss.html
index c0ebc3d0..976ddd10 100644
--- a/dev/reference/defMiss.html
+++ b/dev/reference/defMiss.html
@@ -46,6 +46,9 @@
Dynamic Data Definition
+
+ Simulating data from logistic model with specified characterstics
+
Longitudinal Data
diff --git a/dev/reference/defRead.html b/dev/reference/defRead.html
index a5a21f4c..d4b3b2f9 100644
--- a/dev/reference/defRead.html
+++ b/dev/reference/defRead.html
@@ -46,6 +46,9 @@
Dynamic Data Definition
+
+ Simulating data from logistic model with specified characterstics
+
Longitudinal Data
diff --git a/dev/reference/defReadAdd.html b/dev/reference/defReadAdd.html
index 682075a8..e67c6aea 100644
--- a/dev/reference/defReadAdd.html
+++ b/dev/reference/defReadAdd.html
@@ -46,6 +46,9 @@
Dynamic Data Definition
+
+ Simulating data from logistic model with specified characterstics
+
Longitudinal Data
diff --git a/dev/reference/defReadCond.html b/dev/reference/defReadCond.html
index aedb7c6d..398cb1f6 100644
--- a/dev/reference/defReadCond.html
+++ b/dev/reference/defReadCond.html
@@ -46,6 +46,9 @@
Dynamic Data Definition
+
+ Simulating data from logistic model with specified characterstics
+
Longitudinal Data
diff --git a/dev/reference/defRepeat.html b/dev/reference/defRepeat.html
index 84a881c4..0bfa4145 100644
--- a/dev/reference/defRepeat.html
+++ b/dev/reference/defRepeat.html
@@ -46,6 +46,9 @@
Dynamic Data Definition
+
+ Simulating data from logistic model with specified characterstics
+
Longitudinal Data
diff --git a/dev/reference/defRepeatAdd.html b/dev/reference/defRepeatAdd.html
index 121e81e3..23a96bd2 100644
--- a/dev/reference/defRepeatAdd.html
+++ b/dev/reference/defRepeatAdd.html
@@ -47,6 +47,9 @@
Dynamic Data Definition
+
+ Simulating data from logistic model with specified characterstics
+
Longitudinal Data
diff --git a/dev/reference/defSurv.html b/dev/reference/defSurv.html
index 33cd9b01..ff2184eb 100644
--- a/dev/reference/defSurv.html
+++ b/dev/reference/defSurv.html
@@ -46,6 +46,9 @@
Dynamic Data Definition
+
+ Simulating data from logistic model with specified characterstics
+
Longitudinal Data
diff --git a/dev/reference/delColumns.html b/dev/reference/delColumns.html
index 980c80bc..bf98eecf 100644
--- a/dev/reference/delColumns.html
+++ b/dev/reference/delColumns.html
@@ -46,6 +46,9 @@
Dynamic Data Definition
+
+ Simulating data from logistic model with specified characterstics
+
Longitudinal Data
diff --git a/dev/reference/distributions.html b/dev/reference/distributions.html
index db454fe7..105c0910 100644
--- a/dev/reference/distributions.html
+++ b/dev/reference/distributions.html
@@ -47,6 +47,9 @@
Dynamic Data Definition
+
+ Simulating data from logistic model with specified characterstics
+
Longitudinal Data
diff --git a/dev/reference/gammaGetShapeRate.html b/dev/reference/gammaGetShapeRate.html
index 958c87a5..3f13a30c 100644
--- a/dev/reference/gammaGetShapeRate.html
+++ b/dev/reference/gammaGetShapeRate.html
@@ -46,6 +46,9 @@
Dynamic Data Definition
+
+ Simulating data from logistic model with specified characterstics
+
Longitudinal Data
diff --git a/dev/reference/genCatFormula.html b/dev/reference/genCatFormula.html
index 75a03edf..ef3288ce 100644
--- a/dev/reference/genCatFormula.html
+++ b/dev/reference/genCatFormula.html
@@ -47,6 +47,9 @@
Dynamic Data Definition
+
+ Simulating data from logistic model with specified characterstics
+
Longitudinal Data
diff --git a/dev/reference/genCluster.html b/dev/reference/genCluster.html
index 61d48dc4..6e396d77 100644
--- a/dev/reference/genCluster.html
+++ b/dev/reference/genCluster.html
@@ -48,6 +48,9 @@
Dynamic Data Definition
+
+ Simulating data from logistic model with specified characterstics
+
Longitudinal Data
diff --git a/dev/reference/genCorData.html b/dev/reference/genCorData.html
index 48d44324..ce134545 100644
--- a/dev/reference/genCorData.html
+++ b/dev/reference/genCorData.html
@@ -46,6 +46,9 @@
Dynamic Data Definition
+
+ Simulating data from logistic model with specified characterstics
+
Longitudinal Data
diff --git a/dev/reference/genCorFlex.html b/dev/reference/genCorFlex.html
index 7e87bd65..e3057e9f 100644
--- a/dev/reference/genCorFlex.html
+++ b/dev/reference/genCorFlex.html
@@ -46,6 +46,9 @@
Dynamic Data Definition
+
+ Simulating data from logistic model with specified characterstics
+
Longitudinal Data
diff --git a/dev/reference/genCorGen.html b/dev/reference/genCorGen.html
index 5c8cefb1..6979ed36 100644
--- a/dev/reference/genCorGen.html
+++ b/dev/reference/genCorGen.html
@@ -46,6 +46,9 @@
Dynamic Data Definition
+
+ Simulating data from logistic model with specified characterstics
+
Longitudinal Data
diff --git a/dev/reference/genCorMat.html b/dev/reference/genCorMat.html
index 802a59c0..a55ff4c2 100644
--- a/dev/reference/genCorMat.html
+++ b/dev/reference/genCorMat.html
@@ -46,6 +46,9 @@
Dynamic Data Definition
+
+ Simulating data from logistic model with specified characterstics
+
Longitudinal Data
diff --git a/dev/reference/genCorOrdCat.html b/dev/reference/genCorOrdCat.html
index 982281d5..572608aa 100644
--- a/dev/reference/genCorOrdCat.html
+++ b/dev/reference/genCorOrdCat.html
@@ -46,6 +46,9 @@
Dynamic Data Definition
+
+ Simulating data from logistic model with specified characterstics
+
Longitudinal Data
diff --git a/dev/reference/genData.html b/dev/reference/genData.html
index 223ce95a..4d206446 100644
--- a/dev/reference/genData.html
+++ b/dev/reference/genData.html
@@ -46,6 +46,9 @@
Dynamic Data Definition
+
+ Simulating data from logistic model with specified characterstics
+
Longitudinal Data
diff --git a/dev/reference/genDummy.html b/dev/reference/genDummy.html
index c1abbf97..d7f9dc78 100644
--- a/dev/reference/genDummy.html
+++ b/dev/reference/genDummy.html
@@ -46,6 +46,9 @@
Dynamic Data Definition
+
+ Simulating data from logistic model with specified characterstics
+
Longitudinal Data
diff --git a/dev/reference/genFactor.html b/dev/reference/genFactor.html
index 39804390..e33d93c7 100644
--- a/dev/reference/genFactor.html
+++ b/dev/reference/genFactor.html
@@ -46,6 +46,9 @@
Dynamic Data Definition
+
+ Simulating data from logistic model with specified characterstics
+
Longitudinal Data
diff --git a/dev/reference/genFormula.html b/dev/reference/genFormula.html
index 76cacb8a..2a1d2c89 100644
--- a/dev/reference/genFormula.html
+++ b/dev/reference/genFormula.html
@@ -47,6 +47,9 @@
Dynamic Data Definition
+
+ Simulating data from logistic model with specified characterstics
+
Longitudinal Data
diff --git a/dev/reference/genMarkov.html b/dev/reference/genMarkov.html
index 44665baa..cedd34af 100644
--- a/dev/reference/genMarkov.html
+++ b/dev/reference/genMarkov.html
@@ -47,6 +47,9 @@
Dynamic Data Definition
+
+ Simulating data from logistic model with specified characterstics
+
Longitudinal Data
diff --git a/dev/reference/genMiss.html b/dev/reference/genMiss.html
index 913ab95c..444dc4a4 100644
--- a/dev/reference/genMiss.html
+++ b/dev/reference/genMiss.html
@@ -46,6 +46,9 @@
Dynamic Data Definition
+
+ Simulating data from logistic model with specified characterstics
+
Longitudinal Data
diff --git a/dev/reference/genMixFormula.html b/dev/reference/genMixFormula.html
index b095bc42..6ec0d5d3 100644
--- a/dev/reference/genMixFormula.html
+++ b/dev/reference/genMixFormula.html
@@ -47,6 +47,9 @@
Dynamic Data Definition
+
+ Simulating data from logistic model with specified characterstics
+
Longitudinal Data
diff --git a/dev/reference/genMultiFac.html b/dev/reference/genMultiFac.html
index 9506d6d3..e26f407b 100644
--- a/dev/reference/genMultiFac.html
+++ b/dev/reference/genMultiFac.html
@@ -46,6 +46,9 @@
Dynamic Data Definition
+
+ Simulating data from logistic model with specified characterstics
+
Longitudinal Data
diff --git a/dev/reference/genNthEvent.html b/dev/reference/genNthEvent.html
index d8d64c6d..5cdb6206 100644
--- a/dev/reference/genNthEvent.html
+++ b/dev/reference/genNthEvent.html
@@ -47,6 +47,9 @@
Dynamic Data Definition
+
+ Simulating data from logistic model with specified characterstics
+
Longitudinal Data
diff --git a/dev/reference/genObs.html b/dev/reference/genObs.html
index 293914e2..61e75e09 100644
--- a/dev/reference/genObs.html
+++ b/dev/reference/genObs.html
@@ -46,6 +46,9 @@
Dynamic Data Definition
+
+ Simulating data from logistic model with specified characterstics
+
Longitudinal Data
diff --git a/dev/reference/genOrdCat.html b/dev/reference/genOrdCat.html
index be806f05..0e23a4bd 100644
--- a/dev/reference/genOrdCat.html
+++ b/dev/reference/genOrdCat.html
@@ -47,6 +47,9 @@
Dynamic Data Definition
+
+ Simulating data from logistic model with specified characterstics
+
Longitudinal Data
diff --git a/dev/reference/genSpline.html b/dev/reference/genSpline.html
index 45488b09..0414f312 100644
--- a/dev/reference/genSpline.html
+++ b/dev/reference/genSpline.html
@@ -46,6 +46,9 @@
Dynamic Data Definition
+
+ Simulating data from logistic model with specified characterstics
+
Longitudinal Data
diff --git a/dev/reference/genSurv.html b/dev/reference/genSurv.html
index f99ef37b..9b8de4a2 100644
--- a/dev/reference/genSurv.html
+++ b/dev/reference/genSurv.html
@@ -46,6 +46,9 @@
Dynamic Data Definition
+
+ Simulating data from logistic model with specified characterstics
+
Longitudinal Data
diff --git a/dev/reference/genSynthetic.html b/dev/reference/genSynthetic.html
index 1d9d23dd..224aa004 100644
--- a/dev/reference/genSynthetic.html
+++ b/dev/reference/genSynthetic.html
@@ -46,6 +46,9 @@
Dynamic Data Definition
+
+ Simulating data from logistic model with specified characterstics
+
Longitudinal Data
diff --git a/dev/reference/iccRE.html b/dev/reference/iccRE.html
index cdeffaba..87d6e8eb 100644
--- a/dev/reference/iccRE.html
+++ b/dev/reference/iccRE.html
@@ -47,6 +47,9 @@
Dynamic Data Definition
+
+ Simulating data from logistic model with specified characterstics
+
Longitudinal Data
diff --git a/dev/reference/index.html b/dev/reference/index.html
index b20484ca..c4a4d70b 100644
--- a/dev/reference/index.html
+++ b/dev/reference/index.html
@@ -46,6 +46,9 @@
Dynamic Data Definition
+
+ Simulating data from logistic model with specified characterstics
+
Longitudinal Data
@@ -313,7 +316,7 @@ 