added more notes in engine docs

Author: topepo

man/rmd/discrim_linear_MASS.Rmd

@@ -27,6 +27,11 @@ discrim_linear() %>%
 ```{r child = "template-zv.Rmd"}
 ```
 
+## Case weights
+
+```{r child = "template-no-case-weights.Rmd"}
+```
+
 ## References
 
  - Kuhn, M, and K Johnson. 2013. _Applied Predictive Modeling_. Springer.

man/rmd/discrim_linear_MASS.md

@@ -39,6 +39,11 @@ Variance calculations are used in these computations so _zero-variance_ predicto
 
 
 
+## Case weights
+
+
+The underlying model implementation does not allow for case weights. 
+
 ## References
 
  - Kuhn, M, and K Johnson. 2013. _Applied Predictive Modeling_. Springer.

man/rmd/discrim_linear_sda.Rmd

@@ -37,6 +37,11 @@ discrim_linear() %>%
 ```{r child = "template-zv.Rmd"}
 ```
 
+## Case weights
+
+```{r child = "template-no-case-weights.Rmd"}
+```
+
 ## References
 
  - Ahdesmaki, A., and K. Strimmer. 2010. Feature selection in omics prediction problems using cat scores and false non-discovery rate control. Ann. Appl. Stat. 4: 503-519. [Preprint](https://arxiv.org/abs/0903.2003).

man/rmd/discrim_linear_sda.md

@@ -49,6 +49,11 @@ Variance calculations are used in these computations so _zero-variance_ predicto
 
 
 
+## Case weights
+
+
+The underlying model implementation does not allow for case weights. 
+
 ## References
 
  - Ahdesmaki, A., and K. Strimmer. 2010. Feature selection in omics prediction problems using cat scores and false non-discovery rate control. Ann. Appl. Stat. 4: 503-519. [Preprint](https://arxiv.org/abs/0903.2003).

man/rmd/discrim_linear_sparsediscrim.Rmd

@@ -49,6 +49,11 @@ discrim_linear(regularization_method = character(0)) %>%
 ```{r child = "template-zv.Rmd"}
 ```
 
+## Case weights
+
+```{r child = "template-no-case-weights.Rmd"}
+```
+
 ## References
 
 

man/rmd/discrim_linear_sparsediscrim.md

@@ -54,6 +54,11 @@ Variance calculations are used in these computations so _zero-variance_ predicto
 
 
 
+## Case weights
+
+
+The underlying model implementation does not allow for case weights. 
+
 ## References
 
 

man/rmd/discrim_quad_MASS.Rmd

@@ -27,6 +27,11 @@ discrim_quad() %>%
 ```{r child = "template-zv-conditional.Rmd"}
 ```
 
+## Case weights
+
+```{r child = "template-no-case-weights.Rmd"}
+```
+
 ## References
 
  - Kuhn, M, and K Johnson. 2013. _Applied Predictive Modeling_. Springer.

man/rmd/discrim_quad_MASS.md

@@ -39,6 +39,11 @@ Variance calculations are used in these computations within each outcome class.
 
 
 
+## Case weights
+
+
+The underlying model implementation does not allow for case weights. 
+
 ## References
 
  - Kuhn, M, and K Johnson. 2013. _Applied Predictive Modeling_. Springer.

man/rmd/discrim_quad_sparsediscrim.Rmd

@@ -48,6 +48,11 @@ discrim_quad(regularization_method = character(0)) %>%
 ```{r child = "template-zv-conditional.Rmd"}
 ```
 
+## Case weights
+
+```{r child = "template-no-case-weights.Rmd"}
+```
+
 ## References
 
  - `qda_diag()`: Dudoit, Fridlyand and Speed (2002) Comparison of Discrimination Methods for the Classification of Tumors Using Gene Expression Data, _Journal of the American Statistical Association_, 97:457, 77-87. 

man/rmd/discrim_quad_sparsediscrim.md

@@ -53,6 +53,11 @@ Variance calculations are used in these computations within each outcome class.
 
 
 
+## Case weights
+
+
+The underlying model implementation does not allow for case weights. 
+
 ## References
 
  - `qda_diag()`: Dudoit, Fridlyand and Speed (2002) Comparison of Discrimination Methods for the Classification of Tumors Using Gene Expression Data, _Journal of the American Statistical Association_, 97:457, 77-87. 

man/rmd/discrim_regularized_klaR.Rmd

@@ -51,6 +51,11 @@ discrim_regularized(frac_identity = numeric(0), frac_common_cov = numeric(0)) %>
 ```{r child = "template-zv-conditional.Rmd"}
 ```
 
+## Case weights
+
+```{r child = "template-no-case-weights.Rmd"}
+```
+
 ## References
 
  - Friedman, J (1989). Regularized Discriminant Analysis. _Journal of the American Statistical Association_, 84, 165-175.

man/rmd/discrim_regularized_klaR.md

@@ -59,6 +59,11 @@ Variance calculations are used in these computations within each outcome class.
 
 
 
+## Case weights
+
+
+The underlying model implementation does not allow for case weights. 
+
 ## References
 
  - Friedman, J (1989). Regularized Discriminant Analysis. _Journal of the American Statistical Association_, 84, 165-175.

man/rmd/linear_reg_gls.Rmd

@@ -119,7 +119,10 @@ lme_fit %>% tidy() %>%
 gls_fit %>% tidy()
 ```
 
+## Case weights
 
+```{r child = "template-no-case-weights.Rmd"}
+```
 
 ## References
 

man/rmd/linear_reg_gls.md

@@ -197,8 +197,11 @@ gls_fit %>% tidy()
 ## 2 week           -2.12     0.224     -9.47 2.26e-18
 ```
 
+## Case weights
 
 
+The underlying model implementation does not allow for case weights. 
+
 ## References
 
 - J Pinheiro, and D Bates. 2000. _Mixed-effects models in S and S-PLUS_. Springer, New York, NY

man/rmd/logistic_reg_gee.Rmd

@@ -69,6 +69,11 @@ fit(gee_wflow, data = toenail)
 
 Also, because of issues with the `gee()` function, a supplementary call to `glm()` is needed to get the rank and QR decomposition objects so that `predict()` can be used.
 
+## Case weights
+
+```{r child = "template-no-case-weights.Rmd"}
+```
+
 ## References
 
  - Liang, K.Y. and Zeger, S.L. (1986) Longitudinal data analysis using generalized linear models. _Biometrika_, 73 13–22.

man/rmd/logistic_reg_gee.md

@@ -80,6 +80,11 @@ The `gee::gee()` function always prints out warnings and output even when `silen
 
 Also, because of issues with the `gee()` function, a supplementary call to `glm()` is needed to get the rank and QR decomposition objects so that `predict()` can be used.
 
+## Case weights
+
+
+The underlying model implementation does not allow for case weights. 
+
 ## References
 
  - Liang, K.Y. and Zeger, S.L. (1986) Longitudinal data analysis using generalized linear models. _Biometrika_, 73 13–22.

man/rmd/multinom_reg_brulee.Rmd

@@ -50,6 +50,11 @@ multinom_reg(penalty = double(1)) %>%
 ```{r child = "template-same-scale.Rmd"}
 ```
 
+## Case weights
+
+```{r child = "template-no-case-weights.Rmd"}
+```
+
 ## References
 
  - Kuhn, M, and K Johnson. 2013. _Applied Predictive Modeling_. Springer.

man/rmd/multinom_reg_brulee.md

@@ -56,6 +56,11 @@ Factor/categorical predictors need to be converted to numeric values (e.g., dumm
 Predictors should have the same scale. One way to achieve this is to center and 
 scale each so that each predictor has mean zero and a variance of one.
 
+## Case weights
+
+
+The underlying model implementation does not allow for case weights. 
+
 ## References
 
  - Kuhn, M, and K Johnson. 2013. _Applied Predictive Modeling_. Springer.

man/rmd/multinom_reg_keras.Rmd

@@ -42,6 +42,11 @@ multinom_reg(penalty = double(1)) %>%
 ```{r child = "template-same-scale.Rmd"}
 ```
 
+## Case weights
+
+```{r child = "template-no-case-weights.Rmd"}
+```
+
 ## Examples 
 
 The "Fitting and Predicting with parsnip" article contains [examples](https://parsnip.tidymodels.org/articles/articles/Examples.html#multinom-reg-keras) for `multinom_reg()` with the `"keras"` engine.

man/rmd/multinom_reg_keras.md

@@ -46,6 +46,11 @@ Factor/categorical predictors need to be converted to numeric values (e.g., dumm
 Predictors should have the same scale. One way to achieve this is to center and 
 scale each so that each predictor has mean zero and a variance of one.
 
+## Case weights
+
+
+The underlying model implementation does not allow for case weights. 
+
 ## Examples 
 
 The "Fitting and Predicting with parsnip" article contains [examples](https://parsnip.tidymodels.org/articles/articles/Examples.html#multinom-reg-keras) for `multinom_reg()` with the `"keras"` engine.

man/rmd/naive_Bayes_naivebayes.Rmd

@@ -44,6 +44,11 @@ For count data, integers can be estimated using a Poisson distribution if the ar
 ```{r child = "template-zv.Rmd"}
 ```
 
+## Case weights
+
+```{r child = "template-no-case-weights.Rmd"}
+```
+
 ## References
 
  - Kuhn, M, and K Johnson. 2013. _Applied Predictive Modeling_. Springer.

man/rmd/naive_Bayes_naivebayes.md

@@ -52,6 +52,11 @@ Variance calculations are used in these computations so _zero-variance_ predicto
 
 
 
+## Case weights
+
+
+The underlying model implementation does not allow for case weights. 
+
 ## References
 
  - Kuhn, M, and K Johnson. 2013. _Applied Predictive Modeling_. Springer.

man/rmd/nearest-neighbor.Rmd

@@ -71,8 +71,6 @@ This package is available via the Bioconductor repository and is not accessible
   remotes::install_bioc("mixOmics")
 ```
 
-
-
 ## Preprocessing requirements
 
 ```{r child = "template-makes-dummies.Rmd"}
@@ -84,6 +82,11 @@ This package is available via the Bioconductor repository and is not accessible
 ```{r child = "template-same-scale.Rmd"}
 ```
 
+## Case weights
+
+```{r child = "template-no-case-weights.Rmd"}
+```
+
 ## References
 
  - Rohart F and Gautier B and Singh A and Le Cao K-A  (2017). "mixOmics: An R package for 'omics feature selection and multiple data integration." PLoS computational biology, 13(11), e1005752.

man/rmd/pls_mixOmics.Rmd

@@ -92,8 +92,6 @@ This package is available via the Bioconductor repository and is not accessible
   remotes::install_bioc("mixOmics")
 ```
 
-
-
 ## Preprocessing requirements
 
 
@@ -108,6 +106,11 @@ Variance calculations are used in these computations so _zero-variance_ predicto
 Predictors should have the same scale. One way to achieve this is to center and 
 scale each so that each predictor has mean zero and a variance of one.
 
+## Case weights
+
+
+The underlying model implementation does not allow for case weights. 
+
 ## References
 
  - Rohart F and Gautier B and Singh A and Le Cao K-A  (2017). "mixOmics: An R package for 'omics feature selection and multiple data integration." PLoS computational biology, 13(11), e1005752.

man/rmd/pls_mixOmics.md

@@ -26,9 +26,12 @@ poisson_reg(engine = "gee") %>%
 
 There are no specific preprocessing needs. However, it is helpful to keep the clustering/subject identifier column as factor or character (instead of making them into dummy variables). See the examples in the next section. 
 
-## Other details
+## Case weights
+
+```{r child = "template-no-case-weights.Rmd"}
+```
 
-The model cannot accept case weights. 
+## Other details
 
 Both `gee:gee()` and `gee:geepack()` specify the id/cluster variable using an argument `id` that requires a vector. parsnip doesn't work that way so we enable this model to be fit using a artificial function `id_var()` to be used in the formula. So, in the original package, the call would look like:
 

man/rmd/poisson_reg_gee.Rmd

@@ -37,9 +37,12 @@ poisson_reg(engine = "gee") %>%
 
 There are no specific preprocessing needs. However, it is helpful to keep the clustering/subject identifier column as factor or character (instead of making them into dummy variables). See the examples in the next section. 
 
-## Other details
+## Case weights
+
 
-The model cannot accept case weights. 
+The underlying model implementation does not allow for case weights. 
+
+## Other details
 
 Both `gee:gee()` and `gee:geepack()` specify the id/cluster variable using an argument `id` that requires a vector. parsnip doesn't work that way so we enable this model to be fit using a artificial function `id_var()` to be used in the formula. So, in the original package, the call would look like: