Predict method for the regress function

# S3 method for regress
predict(
  object,
  pred_data = NULL,
  pred_cmd = "",
  conf_lev = 0.95,
  se = TRUE,
  interval = "confidence",
  dec = 3,
  envir = parent.frame(),
  ...
)

Arguments

object	Return value from `regress`
pred_data	Provide the dataframe to generate predictions (e.g., diamonds). The dataset must contain all columns used in the estimation
pred_cmd	Command used to generate data for prediction
conf_lev	Confidence level used to estimate confidence intervals (.95 is the default)
se	Logical that indicates if prediction standard errors should be calculated (default = FALSE)
interval	Type of interval calculation ("confidence" or "prediction"). Set to "none" if se is FALSE
dec	Number of decimals to show
envir	Environment to extract data from
...	further arguments passed to or from other methods

Details

See https://radiant-rstats.github.io/docs/model/regress.html for an example in Radiant

Examples

result <- regress(diamonds, "price", c("carat", "clarity"))
predict(result, pred_cmd = "carat = 1:10")
#> Linear regression (OLS)
#> Data                 : diamonds 
#> Response variable    : price 
#> Explanatory variables: carat, clarity 
#> Interval             : confidence 
#> Prediction command   : carat = 1:10 
#> 
#>  clarity carat Prediction      2.5%     97.5%     +/-
#>      SI1     1   5265.569  5174.776  5356.362  90.793
#>      SI1     2  13703.599 13557.662 13849.536 145.937
#>      SI1     3  22141.629 21908.326 22374.933 233.303
#>      SI1     4  30579.660 30251.571 30907.748 328.088
#>      SI1     5  39017.690 38592.329 39443.051 425.361
#>      SI1     6  47455.720 46931.983 47979.458 523.738
#>      SI1     7  55893.751 55271.056 56516.445 622.695
#>      SI1     8  64331.781 63609.787 65053.775 721.994
#>      SI1     9  72769.811 71948.301 73591.322 821.511
#>      SI1    10  81207.842 80286.667 82129.017 921.175
predict(result, pred_cmd = "clarity = levels(clarity)")
#> Linear regression (OLS)
#> Data                 : diamonds 
#> Response variable    : price 
#> Explanatory variables: carat, clarity 
#> Interval             : confidence 
#> Prediction command   : clarity = levels(clarity) 
#> 
#>  carat clarity Prediction     2.5%    97.5%     +/-
#>  0.794      I1    -78.806 -462.319  304.707 383.513
#>  0.794     SI2   2711.953 2603.644 2820.263 108.310
#>  0.794     SI1   3529.725 3440.015 3619.436  89.711
#>  0.794     VS2   4171.100 4077.495 4264.704  93.605
#>  0.794     VS1   4383.150 4268.576 4497.725 114.574
#>  0.794    VVS2   5030.670 4886.596 5174.743 144.074
#>  0.794    VVS1   4948.863 4785.838 5111.888 163.025
#>  0.794      IF   5186.364 4942.495 5430.234 243.869
result <- regress(diamonds, "price", c("carat", "clarity"), int = "carat:clarity")
predict(result, pred_data = diamonds) %>% head()
#> Linear regression (OLS)
#> Data                 : diamonds 
#> Response variable    : price 
#> Explanatory variables: carat, clarity 
#> Interval             : confidence 
#> Prediction dataset   : diamonds 
#> 
#>  carat clarity Prediction     2.5%    97.5%     +/-
#>  0.320     VS1    240.946   88.847  393.045 152.099
#>  0.340     SI1   -119.580 -249.112    9.952 129.532
#>  0.300     VS2     -1.681 -129.827  126.465 128.146
#>  0.350    VVS2    854.443  690.259 1018.626 164.184
#>  0.400     VS2    842.564  727.982  957.146 114.582
#>  0.600    VVS1   3450.421 3290.503 3610.339 159.918

Predict method for the regress function

Arguments

Details

See also

Examples