quote stats::gaussian

Author: topepo

R/linear_reg_data.R

@@ -36,8 +36,8 @@ linear_reg_lm_data <-
       func = c(fun = "predict"),
       args =
         list(
-          object = quote(object$fit),
-          newdata = quote(new_data),
+          object = expr(object$fit),
+          newdata = expr(new_data),
           type = "response"
         )
     ),
@@ -51,10 +51,10 @@ linear_reg_lm_data <-
       func = c(fun = "predict"),
       args =
         list(
-          object = quote(object$fit),
-          newdata = quote(new_data),
+          object = expr(object$fit),
+          newdata = expr(new_data),
           interval = "confidence",
-          level = quote(level),
+          level = expr(level),
           type = "response"
         )
     ),
@@ -68,10 +68,10 @@ linear_reg_lm_data <-
       func = c(fun = "predict"),
       args =
         list(
-          object = quote(object$fit),
-          newdata = quote(new_data),
+          object = expr(object$fit),
+          newdata = expr(new_data),
           interval = "prediction",
-          level = quote(level),
+          level = expr(level),
           type = "response"
         )
     ),
@@ -80,8 +80,8 @@ linear_reg_lm_data <-
       func = c(fun = "predict"),
       args =
         list(
-          object = quote(object$fit),
-          newdata = quote(new_data)
+          object = expr(object$fit),
+          newdata = expr(new_data)
         )
     )
   )
@@ -104,19 +104,19 @@ linear_reg_glmnet_data <-
       func = c(fun = "predict"),
       args =
         list(
-          object = quote(object$fit),
-          newx = quote(as.matrix(new_data)),
+          object = expr(object$fit),
+          newx = expr(as.matrix(new_data)),
           type = "response",
-          s = quote(object$spec$args$penalty)
+          s = expr(object$spec$args$penalty)
         )
     ),
     raw = list(
       pre = NULL,
       func = c(fun = "predict"),
       args =
         list(
-          object = quote(object$fit),
-          newx = quote(as.matrix(new_data))
+          object = expr(object$fit),
+          newx = expr(as.matrix(new_data))
         )
     )
   )
@@ -130,7 +130,7 @@ linear_reg_stan_data <-
       func = c(pkg = "rstanarm", fun = "stan_glm"),
       defaults =
         list(
-          family = "gaussian"
+          family = expr(stats::gaussian)
         )
     ),
     pred = list(
@@ -139,8 +139,8 @@ linear_reg_stan_data <-
       func = c(fun = "predict"),
       args =
         list(
-          object = quote(object$fit),
-          newdata = quote(new_data)
+          object = expr(object$fit),
+          newdata = expr(new_data)
         )
     ),
     confint = list(
@@ -167,8 +167,8 @@ linear_reg_stan_data <-
       func = c(pkg = "rstanarm", fun = "posterior_linpred"),
       args =
         list(
-          object = quote(object$fit),
-          newdata = quote(new_data),
+          object = expr(object$fit),
+          newdata = expr(new_data),
           transform = TRUE,
           seed = expr(sample.int(10^5, 1))
         )
@@ -197,8 +197,8 @@ linear_reg_stan_data <-
       func = c(pkg = "rstanarm", fun = "posterior_predict"),
       args =
         list(
-          object = quote(object$fit),
-          newdata = quote(new_data),
+          object = expr(object$fit),
+          newdata = expr(new_data),
           seed = expr(sample.int(10^5, 1))
         )
     ),
@@ -207,8 +207,8 @@ linear_reg_stan_data <-
       func = c(fun = "predict"),
       args =
         list(
-          object = quote(object$fit),
-          newdata = quote(new_data)
+          object = expr(object$fit),
+          newdata = expr(new_data)
         )
     )
   )
@@ -232,8 +232,8 @@ linear_reg_spark_data <-
       func = c(pkg = "sparklyr", fun = "ml_predict"),
       args =
         list(
-          x = quote(object$fit),
-          dataset = quote(new_data)
+          x = expr(object$fit),
+          dataset = expr(new_data)
         )
     )
   )