R notes (2) : predict.lm function

Reference

https://www.rdocumentation.org/packages/stats/versions/3.6.2/topics/predict.lm

Predict method for Linear Model Fits

Predicted values based on linear model object.

Usage

# S3 method for lm
predict(object, newdata, se.fit = FALSE, scale = NULL, df = Inf,
        interval = c("none", "confidence", "prediction"),
        level = 0.95, type = c("response", "terms"),
        terms = NULL, na.action = na.pass,
        pred.var = res.var/weights, weights = 1, …)

Arguments

Arguments	Instrument
object	Object of class inheriting from “lm”
newdata	An optional data frame in which to look for variables with which to predict. If omitted, the fitted values are used.
se.fit	A switch indicating if standard errors are required.
scale	Scale parameter for std.err. calculation.
df	Degrees of freedom for scale.
interval	Type of interval calculation. Can be abbreviated.
level	Tolerance/confidence level.
type	Type of prediction (response or model term). Can be abbreviated.
terms	If type = “terms”, which terms (default is all terms), a character vector.
na.action	function determining what should be done with missing values in newdata. The default is to predict NA.
pred.var	the variance(s) for future observations to be assumed for prediction intervals. See ‘Details’.
weights	variance weights for prediction. This can be a numeric vector or a one-sided model formula. In the latter case, it is interpreted as an expression evaluated in newdata.

Details

predict.lm produces predicted values, obtained by evaluating the regression function in the frame newdata (which defaults to model.frame(object)). If the logical se.fit is TRUE, standard errors of the predictions are calculated. If the numeric argument scale is set (with optional df), it is used as the residual standard deviation in the computation of the standard errors, otherwise this is extracted from the model fit. Setting intervals specifies computation of confidence or prediction (tolerance) intervals at the specified level, sometimes referred to as narrow vs. wide intervals.

If the fit is rank-deficient, some of the columns of the design matrix will have been dropped. Prediction from such a fit only makes sense if newdata is contained in the same subspace as the original data. That cannot be checked accurately, so a warning is issued.

If newdata is omitted the predictions are based on the data used for the fit. In that case how cases with missing values in the original fit are handled is determined by the na.action argument of that fit. If na.action = na.omit omitted cases will not appear in the predictions, whereas if na.action = na.exclude they will appear (in predictions, standard errors or interval limits), with value NA. See also napredict.

The prediction intervals are for a single observation at each case in newdata (or by default, the data used for the fit) with error variance(s) pred.var. This can be a multiple of res.var, the estimated value of σ2: the default is to assume that future observations have the same error variance as those used for fitting. If weights is supplied, the inverse of this is used as a scale factor. For a weighted fit, if the prediction is for the original data frame, weights defaults to the weights used for the model fit, with a warning since it might not be the intended result. If the fit was weighted and newdata is given, the default is to assume constant prediction variance, with a warning.

Example 1

data

Codes

> xy = read.table(file = 'C:/Users/DELL/Desktop/Data Mining/Lecture/sp2001A/data01A.dat', header = T)
> xy
      x1    x2    x3    x4    x5     y
1   1.29  0.40 -0.02  0.23  0.37  1.12
2  -0.54 -1.43  0.74 -0.27  0.45 -0.95
3   0.50 -0.85 -0.02 -0.39  1.55 -0.92
4   0.54 -1.39  1.29  1.03 -0.28 -3.14
5   0.34  0.24  0.65  1.95