# lm

0th

Percentile

##### Fitting Linear Models

lm is used to fit linear models. It can be used to carry out regression, single stratum analysis of variance and analysis of covariance (although aov may provide a more convenient interface for these).

Keywords
regression
##### Usage
lm(formula, data, subset, weights, na.action,
method = "qr", model = TRUE, x = FALSE, y = FALSE, qr = TRUE,
singular.ok = TRUE, contrasts = NULL, offset, …)
##### Arguments
formula

an object of class "formula" (or one that can be coerced to that class): a symbolic description of the model to be fitted. The details of model specification are given under ‘Details’.

data

an optional data frame, list or environment (or object coercible by as.data.frame to a data frame) containing the variables in the model. If not found in data, the variables are taken from environment(formula), typically the environment from which lm is called.

subset

an optional vector specifying a subset of observations to be used in the fitting process.

weights

an optional vector of weights to be used in the fitting process. Should be NULL or a numeric vector. If non-NULL, weighted least squares is used with weights weights (that is, minimizing sum(w*e^2)); otherwise ordinary least squares is used. See also ‘Details’,

na.action

a function which indicates what should happen when the data contain NAs. The default is set by the na.action setting of options, and is na.fail if that is unset. The ‘factory-fresh’ default is na.omit. Another possible value is NULL, no action. Value na.exclude can be useful.

method

the method to be used; for fitting, currently only method = "qr" is supported; method = "model.frame" returns the model frame (the same as with model = TRUE, see below).

model, x, y, qr

logicals. If TRUE the corresponding components of the fit (the model frame, the model matrix, the response, the QR decomposition) are returned.

singular.ok

logical. If FALSE (the default in S but not in R) a singular fit is an error.

contrasts

an optional list. See the contrasts.arg of model.matrix.default.

offset

this can be used to specify an a priori known component to be included in the linear predictor during fitting. This should be NULL or a numeric vector or matrix of extents matching those of the response. One or more offset terms can be included in the formula instead or as well, and if more than one are specified their sum is used. See model.offset.

additional arguments to be passed to the low level regression fitting functions (see below).

##### Details

Models for lm are specified symbolically. A typical model has the form response ~ terms where response is the (numeric) response vector and terms is a series of terms which specifies a linear predictor for response. A terms specification of the form first + second indicates all the terms in first together with all the terms in second with duplicates removed. A specification of the form first:second indicates the set of terms obtained by taking the interactions of all terms in first with all terms in second. The specification first*second indicates the cross of first and second. This is the same as first + second + first:second.

If the formula includes an offset, this is evaluated and subtracted from the response.

If response is a matrix a linear model is fitted separately by least-squares to each column of the matrix.

See model.matrix for some further details. The terms in the formula will be re-ordered so that main effects come first, followed by the interactions, all second-order, all third-order and so on: to avoid this pass a terms object as the formula (see aov and demo(glm.vr) for an example).

A formula has an implied intercept term. To remove this use either y ~ x - 1 or y ~ 0 + x. See formula for more details of allowed formulae.

Non-NULL weights can be used to indicate that different observations have different variances (with the values in weights being inversely proportional to the variances); or equivalently, when the elements of weights are positive integers $$w_i$$, that each response $$y_i$$ is the mean of $$w_i$$ unit-weight observations (including the case that there are $$w_i$$ observations equal to $$y_i$$ and the data have been summarized). However, in the latter case, notice that within-group variation is not used. Therefore, the sigma estimate and residual degrees of freedom may be suboptimal; in the case of replication weights, even wrong. Hence, standard errors and analysis of variance tables should be treated with care.

lm calls the lower level functions lm.fit, etc, see below, for the actual numerical computations. For programming only, you may consider doing likewise.

All of weights, subset and offset are evaluated in the same way as variables in formula, that is first in data and then in the environment of formula.

##### Value

lm returns an object of class "lm" or for multiple responses of class c("mlm", "lm").

The functions summary and anova are used to obtain and print a summary and analysis of variance table of the results. The generic accessor functions coefficients, effects, fitted.values and residuals extract various useful features of the value returned by lm.

An object of class "lm" is a list containing at least the following components:

coefficients

a named vector of coefficients

residuals

the residuals, that is response minus fitted values.

fitted.values

the fitted mean values.

rank

the numeric rank of the fitted linear model.

weights

(only for weighted fits) the specified weights.

df.residual

the residual degrees of freedom.

call

the matched call.

terms

the terms object used.

contrasts

(only where relevant) the contrasts used.

xlevels

(only where relevant) a record of the levels of the factors used in fitting.

offset

the offset used (missing if none were used).

y

if requested, the response used.

x

if requested, the model matrix used.

model

if requested (the default), the model frame used.

na.action

(where relevant) information returned by model.frame on the special handling of NAs.

In addition, non-null fits will have components assign, effects and (unless not requested) qr relating to the linear fit, for use by extractor functions such as summary and effects.

##### Note

Offsets specified by offset will not be included in predictions by predict.lm, whereas those specified by an offset term in the formula will be.

##### Using time series

Considerable care is needed when using lm with time series.

Unless na.action = NULL, the time series attributes are stripped from the variables before the regression is done. (This is necessary as omitting NAs would invalidate the time series attributes, and if NAs are omitted in the middle of the series the result would no longer be a regular time series.)

Even if the time series attributes are retained, they are not used to line up series, so that the time shift of a lagged or differenced regressor would be ignored. It is good practice to prepare a data argument by ts.intersect(…, dframe = TRUE), then apply a suitable na.action to that data frame and call lm with na.action = NULL so that residuals and fitted values are time series.

##### References

Chambers, J. M. (1992) Linear models. Chapter 4 of Statistical Models in S eds J. M. Chambers and T. J. Hastie, Wadsworth & Brooks/Cole.

Wilkinson, G. N. and Rogers, C. E. (1973). Symbolic descriptions of factorial models for analysis of variance. Applied Statistics, 22, 392--399. 10.2307/2346786.

summary.lm for summaries and anova.lm for the ANOVA table; aov for a different interface.

The generic functions coef, effects, residuals, fitted, vcov.

predict.lm (via predict) for prediction, including confidence and prediction intervals; confint for confidence intervals of parameters.

lm.influence for regression diagnostics, and glm for generalized linear models.

The underlying low level functions, lm.fit for plain, and lm.wfit for weighted regression fitting.

More lm() examples are available e.g., in anscombe, attitude, freeny, LifeCycleSavings, longley, stackloss, swiss.

biglm in package biglm for an alternative way to fit linear models to large datasets (especially those with many cases).

• lm
##### Examples
library(stats) # NOT RUN { require(graphics) ## Annette Dobson (1990) "An Introduction to Generalized Linear Models". ## Page 9: Plant Weight Data. ctl <- c(4.17,5.58,5.18,6.11,4.50,4.61,5.17,4.53,5.33,5.14) trt <- c(4.81,4.17,4.41,3.59,5.87,3.83,6.03,4.89,4.32,4.69) group <- gl(2, 10, 20, labels = c("Ctl","Trt")) weight <- c(ctl, trt) lm.D9 <- lm(weight ~ group) lm.D90 <- lm(weight ~ group - 1) # omitting intercept # } # NOT RUN { anova(lm.D9) summary(lm.D90) # } # NOT RUN { opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0)) plot(lm.D9, las = 1) # Residuals, Fitted, ... par(opar) # } # NOT RUN { ### less simple examples in "See Also" above # }
Documentation reproduced from package stats, version 3.6.2, License: Part of R 3.6.2

### Community examples

Luis Alberto at Sep 4, 2019 stats v3.6.1

linearmod1 <- lm(iq~read_ab, data= basedata1 ) summary(linearmod1)

richie@datacamp.com at Jan 17, 2017 stats v3.3.1

lm() takes a formula and a data frame. See [formula()](https://www.rdocumentation.org/packages/stats/topics/formula) for how to contruct the first argument. {r} (model_with_intercept <- lm(weight ~ group, PlantGrowth)) (model_without_intercept <- lm(weight ~ group - 1, PlantGrowth))  You get more information about the model using [summary()](https://www.rdocumentation.org/packages/stats/topics/summary.lm) {r} (model_without_intercept <- lm(weight ~ group - 1, PlantGrowth)) summary(model_without_intercept)  Diagnostic plots are available; see [plot.lm()](https://www.rdocumentation.org/packages/stats/topics/plot.lm) for more examples. {r} (model_without_intercept <- lm(weight ~ group - 1, PlantGrowth)) layout(matrix(1:6, nrow = 2)) plot(model_without_intercept, which = 1:6)  You can predict new values; see [predict()](https://www.rdocumentation.org/packages/stats/topics/predict) and [predict.lm()](https://www.rdocumentation.org/packages/stats/topics/predict.lm) . {r} (model_without_intercept <- lm(weight ~ group - 1, PlantGrowth)) predictions <- data.frame(group = levels(PlantGrowth$group)) predictions$weight <- predict(model_without_intercept, predictions) predictions # Plot predictions against the data boxplot(weight ~ group, PlantGrowth, ylab = "weight") points(weight ~ group, predictions, col = "red")  There are many methods available for inspecting lm objects. {r} (model_without_intercept <- lm(weight ~ group - 1, PlantGrowth)) confint(model_without_intercept) anova(model_without_intercept) residuals(model_without_intercept) fitted(model_without_intercept) influence(model_without_intercept) methods(class = "lm")