readme edits

mcconvil · Dec 13, 2023 · 18bcf96 · 18bcf96
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diff --git a/README.Rmd b/README.Rmd
@@ -41,7 +41,7 @@ The available variance estimation techniques are:
   - LinHB
   - LinHH
   - LinHTSRS 
-  - LinHT,
+  - LinHT
   - bootstrapSRS
 
 See `mase/inst/REFERENCES.bib` for sources related to each variance estimator.
@@ -127,19 +127,19 @@ greg_est["coefficients"]
 All of the mase regression estimators can also perform variable selection internally using the parameter `modelselect`
 
 ```{r, message = FALSE}
-greg_est <- greg(y = samp$BA_TPA_ADJ,
-     N = pop$npixels,
-     xsample = xsample,
-     xpop = xpop,
-     modelselect = TRUE,
-     var_est = TRUE,
-     var_method = "LinHB",
-     datatype = "means")
+greg_select <- greg(y = samp$BA_TPA_ADJ,
+                    N = pop$npixels,
+                    xsample = xsample,
+                    xpop = xpop,
+                    modelselect = TRUE,
+                    var_est = TRUE,
+                    var_method = "LinHB",
+                    datatype = "means")
 ```
 
 And we can examine which predictors were chosen:
 
 ```{r}
-greg_est["coefficients"]
+greg_select["coefficients"]
 ```
 
diff --git a/README.md b/README.md
@@ -34,7 +34,7 @@ The available variance estimation techniques are:
 - LinHB
 - LinHH
 - LinHTSRS
-- LinHT,
+- LinHT
 - bootstrapSRS
 
 See `mase/inst/REFERENCES.bib` for sources related to each variance
@@ -165,20 +165,20 @@ All of the mase regression estimators can also perform variable
 selection internally using the parameter `modelselect`
 
 ``` r
-greg_est <- greg(y = samp$BA_TPA_ADJ,
-     N = pop$npixels,
-     xsample = xsample,
-     xpop = xpop,
-     modelselect = TRUE,
-     var_est = TRUE,
-     var_method = "LinHB",
-     datatype = "means")
+greg_select <- greg(y = samp$BA_TPA_ADJ,
+                    N = pop$npixels,
+                    xsample = xsample,
+                    xpop = xpop,
+                    modelselect = TRUE,
+                    var_est = TRUE,
+                    var_method = "LinHB",
+                    datatype = "means")
 ```
 
 And we can examine which predictors were chosen:
 
 ``` r
-greg_est["coefficients"]
+greg_select["coefficients"]
 #> $coefficients
 #>  (Intercept)          tcc         elev          ppt 
 #> -33.24787647   0.65151379   0.04209371   0.06643125