proc_glm: Calculates a General Linear Model
In procs: Recreates Some 'SAS®' Procedures in 'R'
proc_glm R Documentation
Calculates a General Linear Model
Description
The proc_glm function performs a general linear model
analysis for one or more models. Unlike proc_reg, the
proc_glm function accepts categorical predictors through the
class parameter, and produces Type I, Type II, and Type III sums
of squares. The model(s) are passed on the model parameter, and the
input dataset is passed on the data parameter. The by
parameter allows you to subset the data into groups and run the model on
each group. The weight parameter lets you assign a weight to each
observation. The output and options parameters provide
additional customization of the results. The proc_glm function currently
does not generate plots.
Usage
proc_glm(
data,
model,
class = NULL,
order = NULL,
by = NULL,
stats = NULL,
output = NULL,
weight = NULL,
lsmeans = NULL,
contrast = NULL,
estimate = NULL,
random = NULL,
options = NULL,
titles = NULL,
where = NULL
)
Arguments
data
The input data frame for which to perform the analysis.
This parameter is required.
model
A model for the analysis. The model can be specified using
either R syntax (y ~ a + b + a:b) or SAS syntax ("y = a b a*b").
To pass multiple models, use a list (R syntax) or a vector of strings
(SAS syntax). By default, models are named "MODEL1", "MODEL2", etc.
class
An optional vector of variable names to treat as categorical
(factor) predictors. These variables will be converted to factors prior to
fitting the model. Pass quoted, or unquoted using the
v function.
order
Controls the sort order of the class variable levels. Valid
values are "internal", "formatted", "data", and "freq", matching the
order parameter on proc_freq and proc_ttest.
The default "internal" uses the natural order of the values: character values
sort alphabetically, numeric values sort by value, and an existing factor
keeps its level order. "formatted" applies the variable's format and uses
the order defined by the format. "data" uses order of appearance, and "freq"
sorts by descending frequency. Pass one keyword per class variable, or
a single keyword to apply to all of them.
Note that for proc_glm the level order is not merely cosmetic. The
last level of a class variable is the reference level, so order
changes the Type III and "solution" results. It also determines how
contrast and estimate coefficients are mapped onto levels: the
coefficients are read in level order, so the same coefficient vector means
different things under different order values.
The "formatted" keyword goes further still. If the format maps several
values onto one label, those values become a single level, and the model
loses the degrees of freedom that separated them. This matches the SAS
practice of using a format to group a class variable, but it means
order = "formatted" can change the model and not just its
presentation. Only a user-defined format created with
value carries a level order, so that is the only kind of
format "formatted" accepts.
by
An optional by group. If specified, the input data will be subset
on the by variable(s) prior to performing the analysis.
stats
Optional statistics keywords, corresponding to the options on
the "model" statement in SAS. Valid values are "ss1", "ss2", "ss3",
"solution", "est", "clparm", "p", "clm", "cli", "noint", "xpx", "inverse", and
"e1", "e2", and "e3". See the "Statistics Keywords" section for an explanation
of these options.
output
Whether or not to return datasets from the function. Valid
values are "out", "none", and "report", plus the data shaping keywords
"long", "stacked", and "wide". Default is "out".
weight
The name of a variable to use as a weight for each observation.
lsmeans
The name of one or more class variables for which to compute
least-squares means. Each requested effect produces a least-squares means
table on the interactive report. The effect(s) must also appear on the
class parameter.
contrast
A named list of contrast specifications. Each element name
is the contrast label, and each value is itself a named list mapping a model
effect to its coefficients over that effect's levels, in level order. For
example, contrast = list("F vs M" = list(Sex = c(1, -1))) mirrors the
SAS statement contrast 'F vs M' Sex 1 -1. Each contrast produces an
F-test row on the interactive report.
Give an effect a matrix instead of a vector to reproduce the comma syntax of
a SAS CONTRAST statement: each row of the matrix is one row of the
contrast, and the result is a single test whose degrees of freedom equal the
number of linearly independent rows. So
list("Any diff" = list(AgeGroup = rbind(c(-1, 0, 1), c(0, -1, 1))))
is the SAS statement
contrast 'Any diff' AgeGroup -1 0 1, AgeGroup 0 -1 1;.
Interactions may be spelled either way: the SAS operator "a*b" is
accepted and normalized to the R operator "a:b". Any effect not named
gets a coefficient of zero, as does the intercept. A contrast that is not
estimable is dropped with a warning rather than reported.
estimate
A named list of estimate specifications, using the same
structure as the contrast parameter. Each estimate produces a row on
the interactive report with the estimate, standard error, t value, p value,
and confidence limits. Unlike contrast, estimate accepts only
single-row specifications, since it estimates one linear combination.
random
The name of one or more class variables to treat as random
effects. Produces a table of Type III expected mean squares on the
interactive report. The effect(s) must also appear on the class
parameter.
options
A vector of optional keywords. Valid values are "alpha =",
"noprint", "outstat". The
"outstat" option requests an output dataset of the model sums of squares.
This is the default output dataset, so the option does not normally need to
be passed.
titles
A vector of one or more titles to use for the report output.
where
An expression to filter the rows before statistics are
calculated. Use the expression function.
Details
The proc_glm function is a general-purpose linear modeling function
built on top of the GLM function from the sasLM
package. It produces a dataset output by default, and, when working in
RStudio, also produces an interactive report. Statistical output is
designed to match SAS.
A model may be specified using R model syntax or SAS model syntax. To use
SAS syntax, the model statement must be quoted. To pass multiple models using
R syntax, pass them to the model parameter in a list. To pass multiple
models using SAS syntax, pass them to the model parameter as a vector
of strings.
Value
Normally the requested statistics are shown interactively in the
viewer, and output results are returned as a data frame. If "report"
datasets are requested they are returned as a list.
Interactive Output
By default, proc_glm results will
be sent to the viewer as an HTML report. This functionality
makes it easy to get a quick analysis of your data. To turn off the
interactive report, pass the "noprint" keyword
to the options parameter.
The titles parameter allows you to set one or more titles for your
report. Pass these titles as a vector of strings.
The exact datasets used for the interactive report can be returned as a list.
To return these datasets, pass
the "report" keyword on the output parameter. This list may in
turn be passed to proc_print to write the report to a file.
Dataset Output
Dataset results are also returned from the function by default.
proc_glm typically returns a single dataset.
The default output dataset is optimized for data manipulation.
The column names have been standardized, and additional variables may
be present to help with data manipulation. The data values in the
output dataset are intentionally not rounded or formatted
to give you the most accurate numeric results.
You may also request
to return the datasets used in the interactive report. To request these
datasets, pass the "report" option to the output parameter. Each report
dataset will be named according to the category of statistical
results. There are six standard categories: "NObs",
"ANOVA", "FitStatistics", "TypeI", "TypeIII", and "ParameterEstimates".
Statistics options may generate additional tables in the interactive
report.
If you don't want any datasets returned, pass the "none" option on the
output parameter.
Statistics Keywords
The following statistics keywords can be passed on the stats
parameter. You may pass statistic keywords as a
quoted vector of strings, or an unquoted vector using the v() function.
An individual statistics keyword can be passed without quoting.
solution: Adds a parameter estimates table to the interactive
report. "est" is an accepted alias.
clparm: Adds confidence limits for the parameter estimates,
using the alpha value from the options parameter.
p: Adds predicted values and residuals to the interactive
report.
clm: Adds the standard error and confidence limits of the
mean predicted value to the predicted values table. Implies "p".
cli: Adds confidence limits for an individual predicted value
to the predicted values table. These are wider than the "clm" limits
because they include the error variance. Implies "p".
noint: Fits the model without an intercept. Every downstream
statistic reflects this, and the total sum of squares is no longer corrected
for the mean.
xpx: Adds the augmented X'X crossproducts matrix, which
carries the dependent variable as an extra row and column.
inverse: Adds the generalized inverse of the augmented X'X
matrix. Its bottom right cell is the error sum of squares.
e1, e2, e3: Add the Type I, Type II and
Type III estimable function tables.
ss1, ss2, ss3: Select which sums of squares
to produce. The default is Type I and Type III, as in SAS.
Options
The proc_glm function recognizes the following options. Options may
be passed as a quoted vector of strings, or an unquoted vector using the
v() function.
alpha = : The "alpha = " option will set the alpha
value for confidence limit statistics. Set the alpha as a decimal value
between 0 and 1. For example, you can set a 90% confidence limit as
alpha = 0.1.
noprint: Whether to print the interactive report to the
viewer. By default, the report is printed to the viewer. The "noprint"
option will inhibit printing. You may inhibit printing globally by
setting the package print option to false:
options("procs.print" = FALSE).
outstat: The "outstat" option is used to request the
TypeI and TypeIII model sum of squares, F, and P values be sent to the
output dataset.
The "outstat" dataset is the default output dataset, and this option
does not normally need to be passed.
Data Shaping
The output datasets produced by the function can be shaped
in different ways. These shaping options allow you to decide whether the
data should be returned long and skinny, or short and wide. The shaping
options can reduce the amount of data manipulation necessary to get the
data into the desired form. The
shaping options are as follows:
long: Transposes the output datasets
so that statistics are in rows and variables are in columns.
stacked: Requests that output datasets
be returned in "stacked" form, such that both statistics and
variables are in rows.
wide: Requests that output datasets
be returned in "wide" form, such that statistics are across the top in
columns, and variables are in rows. This shaping option is the default.
These shaping options are passed on the output parameter. For example,
to return the data in "long" form, use output = "long".
See Also
proc_reg()
Examples
# Turn off printing for CRAN checks
options("procs.print" = FALSE)
# Example 1: Basic model
res1 <- proc_glm(mtcars, mpg ~ hp + wt)
# View results
res1
# MODEL DEPVAR SOURCE TYPE DF SS MEANSQ FVAL PROBF
# 1 MODEL1 mpg ERROR ERROR 29 195.04775 6.725785 NA NA
# 2 MODEL1 mpg hp SS1 1 678.37287 678.372874 100.86152 5.987344e-11
# 3 MODEL1 mpg wt SS1 1 252.62656 252.626559 37.56091 1.119647e-06
# 4 MODEL1 mpg hp SS3 1 83.27418 83.274183 12.38133 1.451229e-03
# 5 MODEL1 mpg wt SS3 1 252.62656 252.626559 37.56091 1.119647e-06
# Example 2: Report output
res2 <- proc_glm(mtcars, mpg ~ hp + wt,
output = "report")
# View results
res2
# $NObs
# LABEL NOBS
# 1 Number of Observations Read 32
# 2 Number of Observations Used 32
#
# $ANOVA
# SOURCE DF SUMSQ MEANSQ FVAL PROBF
# 1 Model 2 930.9994 465.499716 69.21121 9.109047e-12
# 2 Error 29 195.0478 6.725785 NA NA
# 3 Corrected Total 31 1126.0472 NA NA NA
#
# $FitStatistics
# RSQ COEFVAR RMSE DEPMEAN
# 1 0.8267855 12.90857 2.593412 20.09062
#
# $TypeI
# SOURCE DF SUMSQ MEANSQ FVAL PROBF
# 1 hp 1 678.3729 678.3729 100.86152 5.987344e-11
# 2 wt 1 252.6266 252.6266 37.56091 1.119647e-06
#
# $TypeIII
# SOURCE DF SUMSQ MEANSQ FVAL PROBF
# 1 hp 1 83.27418 83.27418 12.38133 1.451229e-03
# 2 wt 1 252.62656 252.62656 37.56091 1.119647e-06
#
# $ParameterEstimates
# PARM EST STDERR T PROBT
# 1 Intercept 37.22727012 1.59878754 23.284689 0.000000e+00
# 2 hp -0.03177295 0.00902971 -3.518712 1.451229e-03
# 3 wt -3.87783074 0.63273349 -6.128695 1.119647e-06
#'
# Example 3: By variable
res3 <- proc_glm(mtcars, mpg ~ hp + wt,
by = cyl)
# View results
res3
# BY MODEL DEPVAR SOURCE TYPE DF SS MEANSQ FVAL PROBF
# 1 4 MODEL1 mpg ERROR ERROR 8 64.9396474 8.1174559 NA NA
# 2 4 MODEL1 mpg hp SS1 1 55.7389699 55.7389699 6.8665565 0.03063088
# 3 4 MODEL1 mpg wt SS1 1 82.7068372 82.7068372 10.1887633 0.01276524
# 4 4 MODEL1 mpg hp SS3 1 34.9973354 34.9973354 4.3113675 0.07151610
# 5 4 MODEL1 mpg wt SS3 1 82.7068372 82.7068372 10.1887633 0.01276524
# 6 6 MODEL1 mpg ERROR ERROR 4 5.2112707 1.3028177 NA NA
# 7 6 MODEL1 mpg hp SS1 1 0.2046882 0.2046882 0.1571119 0.71206529
# 8 6 MODEL1 mpg wt SS1 1 7.2611839 7.2611839 5.5734459 0.07759393
# 9 6 MODEL1 mpg hp SS3 1 1.5772106 1.5772106 1.2106150 0.33297540
# 10 6 MODEL1 mpg wt SS3 1 7.2611839 7.2611839 5.5734459 0.07759393
# 11 8 MODEL1 mpg ERROR ERROR 11 42.8497010 3.8954274 NA NA
# 12 8 MODEL1 mpg hp SS1 1 6.8542713 6.8542713 1.7595685 0.21155901
# 13 8 MODEL1 mpg wt SS1 1 35.4960277 35.4960277 9.1122294 0.01168393
# 14 8 MODEL1 mpg hp SS3 1 6.3136353 6.3136353 1.6207812 0.22923028
# 15 8 MODEL1 mpg wt SS3 1 35.4960277 35.4960277 9.1122294 0.01168393
# Example 4: Long output
res4 <- proc_glm(mtcars, mpg ~ hp + wt,
output = "long")
# View results
res4
# MODEL DEPVAR SOURCE TYPE STAT COL1
# 1 MODEL1 mpg ERROR ERROR DF 2.900000e+01
# 2 MODEL1 mpg ERROR ERROR SS 1.950478e+02
# 3 MODEL1 mpg ERROR ERROR MEANSQ 6.725785e+00
# 4 MODEL1 mpg ERROR ERROR FVAL NA
# 5 MODEL1 mpg ERROR ERROR PROBF NA
# 6 MODEL1 mpg hp SS1 DF 1.000000e+00
# 7 MODEL1 mpg hp SS1 SS 6.783729e+02
# 8 MODEL1 mpg hp SS1 MEANSQ 6.783729e+02
# 9 MODEL1 mpg hp SS1 FVAL 1.008615e+02
# 10 MODEL1 mpg hp SS1 PROBF 5.987344e-11
# 11 MODEL1 mpg wt SS1 DF 1.000000e+00
# 12 MODEL1 mpg wt SS1 SS 2.526266e+02
# 13 MODEL1 mpg wt SS1 MEANSQ 2.526266e+02
# 14 MODEL1 mpg wt SS1 FVAL 3.756091e+01
# 15 MODEL1 mpg wt SS1 PROBF 1.119647e-06
# 16 MODEL1 mpg hp SS3 DF 1.000000e+00
# 17 MODEL1 mpg hp SS3 SS 8.327418e+01
# 18 MODEL1 mpg hp SS3 MEANSQ 8.327418e+01
# 19 MODEL1 mpg hp SS3 FVAL 1.238133e+01
# 20 MODEL1 mpg hp SS3 PROBF 1.451229e-03
# 21 MODEL1 mpg wt SS3 DF 1.000000e+00
# 22 MODEL1 mpg wt SS3 SS 2.526266e+02
# 23 MODEL1 mpg wt SS3 MEANSQ 2.526266e+02
# 24 MODEL1 mpg wt SS3 FVAL 3.756091e+01
# 25 MODEL1 mpg wt SS3 PROBF 1.119647e-06
procs documentation built on Nov. 2, 2026, 5:06 p.m.
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| proc_glm | R Documentation |
Calculates a General Linear Model
Description
The proc_glm function performs a general linear model
analysis for one or more models. Unlike proc_reg, the
proc_glm function accepts categorical predictors through the
class parameter, and produces Type I, Type II, and Type III sums
of squares. The model(s) are passed on the model parameter, and the
input dataset is passed on the data parameter. The by
parameter allows you to subset the data into groups and run the model on
each group. The weight parameter lets you assign a weight to each
observation. The output and options parameters provide
additional customization of the results. The proc_glm function currently
does not generate plots.
Usage
proc_glm(
data,
model,
class = NULL,
order = NULL,
by = NULL,
stats = NULL,
output = NULL,
weight = NULL,
lsmeans = NULL,
contrast = NULL,
estimate = NULL,
random = NULL,
options = NULL,
titles = NULL,
where = NULL
)
Arguments
data |
The input data frame for which to perform the analysis. This parameter is required. |
model |
A model for the analysis. The model can be specified using
either R syntax ( |
class |
An optional vector of variable names to treat as categorical
(factor) predictors. These variables will be converted to factors prior to
fitting the model. Pass quoted, or unquoted using the
|
order |
Controls the sort order of the class variable levels. Valid
values are "internal", "formatted", "data", and "freq", matching the
Note that for The "formatted" keyword goes further still. If the format maps several
values onto one label, those values become a single level, and the model
loses the degrees of freedom that separated them. This matches the SAS
practice of using a format to group a class variable, but it means
|
by |
An optional by group. If specified, the input data will be subset on the by variable(s) prior to performing the analysis. |
stats |
Optional statistics keywords, corresponding to the options on the "model" statement in SAS. Valid values are "ss1", "ss2", "ss3", "solution", "est", "clparm", "p", "clm", "cli", "noint", "xpx", "inverse", and "e1", "e2", and "e3". See the "Statistics Keywords" section for an explanation of these options. |
output |
Whether or not to return datasets from the function. Valid values are "out", "none", and "report", plus the data shaping keywords "long", "stacked", and "wide". Default is "out". |
weight |
The name of a variable to use as a weight for each observation. |
lsmeans |
The name of one or more class variables for which to compute
least-squares means. Each requested effect produces a least-squares means
table on the interactive report. The effect(s) must also appear on the
|
contrast |
A named list of contrast specifications. Each element name
is the contrast label, and each value is itself a named list mapping a model
effect to its coefficients over that effect's levels, in level order. For
example, Give an effect a matrix instead of a vector to reproduce the comma syntax of
a SAS Interactions may be spelled either way: the SAS operator |
estimate |
A named list of estimate specifications, using the same
structure as the |
random |
The name of one or more class variables to treat as random
effects. Produces a table of Type III expected mean squares on the
interactive report. The effect(s) must also appear on the |
options |
A vector of optional keywords. Valid values are "alpha =", "noprint", "outstat". The "outstat" option requests an output dataset of the model sums of squares. This is the default output dataset, so the option does not normally need to be passed. |
titles |
A vector of one or more titles to use for the report output. |
where |
An expression to filter the rows before statistics are
calculated. Use the |
Details
The proc_glm function is a general-purpose linear modeling function
built on top of the GLM function from the sasLM
package. It produces a dataset output by default, and, when working in
RStudio, also produces an interactive report. Statistical output is
designed to match SAS.
A model may be specified using R model syntax or SAS model syntax. To use
SAS syntax, the model statement must be quoted. To pass multiple models using
R syntax, pass them to the model parameter in a list. To pass multiple
models using SAS syntax, pass them to the model parameter as a vector
of strings.
Value
Normally the requested statistics are shown interactively in the viewer, and output results are returned as a data frame. If "report" datasets are requested they are returned as a list.
Interactive Output
By default, proc_glm results will
be sent to the viewer as an HTML report. This functionality
makes it easy to get a quick analysis of your data. To turn off the
interactive report, pass the "noprint" keyword
to the options parameter.
The titles parameter allows you to set one or more titles for your
report. Pass these titles as a vector of strings.
The exact datasets used for the interactive report can be returned as a list.
To return these datasets, pass
the "report" keyword on the output parameter. This list may in
turn be passed to proc_print to write the report to a file.
Dataset Output
Dataset results are also returned from the function by default.
proc_glm typically returns a single dataset.
The default output dataset is optimized for data manipulation. The column names have been standardized, and additional variables may be present to help with data manipulation. The data values in the output dataset are intentionally not rounded or formatted to give you the most accurate numeric results.
You may also request
to return the datasets used in the interactive report. To request these
datasets, pass the "report" option to the output parameter. Each report
dataset will be named according to the category of statistical
results. There are six standard categories: "NObs",
"ANOVA", "FitStatistics", "TypeI", "TypeIII", and "ParameterEstimates".
Statistics options may generate additional tables in the interactive
report.
If you don't want any datasets returned, pass the "none" option on the
output parameter.
Statistics Keywords
The following statistics keywords can be passed on the stats
parameter. You may pass statistic keywords as a
quoted vector of strings, or an unquoted vector using the v() function.
An individual statistics keyword can be passed without quoting.
solution: Adds a parameter estimates table to the interactive report. "est" is an accepted alias.
clparm: Adds confidence limits for the parameter estimates, using the alpha value from the
optionsparameter.p: Adds predicted values and residuals to the interactive report.
clm: Adds the standard error and confidence limits of the mean predicted value to the predicted values table. Implies "p".
cli: Adds confidence limits for an individual predicted value to the predicted values table. These are wider than the "clm" limits because they include the error variance. Implies "p".
noint: Fits the model without an intercept. Every downstream statistic reflects this, and the total sum of squares is no longer corrected for the mean.
xpx: Adds the augmented X'X crossproducts matrix, which carries the dependent variable as an extra row and column.
inverse: Adds the generalized inverse of the augmented X'X matrix. Its bottom right cell is the error sum of squares.
e1, e2, e3: Add the Type I, Type II and Type III estimable function tables.
ss1, ss2, ss3: Select which sums of squares to produce. The default is Type I and Type III, as in SAS.
Options
The proc_glm function recognizes the following options. Options may
be passed as a quoted vector of strings, or an unquoted vector using the
v() function.
alpha = : The "alpha = " option will set the alpha value for confidence limit statistics. Set the alpha as a decimal value between 0 and 1. For example, you can set a 90% confidence limit as
alpha = 0.1.noprint: Whether to print the interactive report to the viewer. By default, the report is printed to the viewer. The "noprint" option will inhibit printing. You may inhibit printing globally by setting the package print option to false:
options("procs.print" = FALSE).outstat: The "outstat" option is used to request the TypeI and TypeIII model sum of squares, F, and P values be sent to the output dataset. The "outstat" dataset is the default output dataset, and this option does not normally need to be passed.
Data Shaping
The output datasets produced by the function can be shaped in different ways. These shaping options allow you to decide whether the data should be returned long and skinny, or short and wide. The shaping options can reduce the amount of data manipulation necessary to get the data into the desired form. The shaping options are as follows:
long: Transposes the output datasets so that statistics are in rows and variables are in columns.
stacked: Requests that output datasets be returned in "stacked" form, such that both statistics and variables are in rows.
wide: Requests that output datasets be returned in "wide" form, such that statistics are across the top in columns, and variables are in rows. This shaping option is the default.
These shaping options are passed on the output parameter. For example,
to return the data in "long" form, use output = "long".
See Also
proc_reg()
Examples
# Turn off printing for CRAN checks
options("procs.print" = FALSE)
# Example 1: Basic model
res1 <- proc_glm(mtcars, mpg ~ hp + wt)
# View results
res1
# MODEL DEPVAR SOURCE TYPE DF SS MEANSQ FVAL PROBF
# 1 MODEL1 mpg ERROR ERROR 29 195.04775 6.725785 NA NA
# 2 MODEL1 mpg hp SS1 1 678.37287 678.372874 100.86152 5.987344e-11
# 3 MODEL1 mpg wt SS1 1 252.62656 252.626559 37.56091 1.119647e-06
# 4 MODEL1 mpg hp SS3 1 83.27418 83.274183 12.38133 1.451229e-03
# 5 MODEL1 mpg wt SS3 1 252.62656 252.626559 37.56091 1.119647e-06
# Example 2: Report output
res2 <- proc_glm(mtcars, mpg ~ hp + wt,
output = "report")
# View results
res2
# $NObs
# LABEL NOBS
# 1 Number of Observations Read 32
# 2 Number of Observations Used 32
#
# $ANOVA
# SOURCE DF SUMSQ MEANSQ FVAL PROBF
# 1 Model 2 930.9994 465.499716 69.21121 9.109047e-12
# 2 Error 29 195.0478 6.725785 NA NA
# 3 Corrected Total 31 1126.0472 NA NA NA
#
# $FitStatistics
# RSQ COEFVAR RMSE DEPMEAN
# 1 0.8267855 12.90857 2.593412 20.09062
#
# $TypeI
# SOURCE DF SUMSQ MEANSQ FVAL PROBF
# 1 hp 1 678.3729 678.3729 100.86152 5.987344e-11
# 2 wt 1 252.6266 252.6266 37.56091 1.119647e-06
#
# $TypeIII
# SOURCE DF SUMSQ MEANSQ FVAL PROBF
# 1 hp 1 83.27418 83.27418 12.38133 1.451229e-03
# 2 wt 1 252.62656 252.62656 37.56091 1.119647e-06
#
# $ParameterEstimates
# PARM EST STDERR T PROBT
# 1 Intercept 37.22727012 1.59878754 23.284689 0.000000e+00
# 2 hp -0.03177295 0.00902971 -3.518712 1.451229e-03
# 3 wt -3.87783074 0.63273349 -6.128695 1.119647e-06
#'
# Example 3: By variable
res3 <- proc_glm(mtcars, mpg ~ hp + wt,
by = cyl)
# View results
res3
# BY MODEL DEPVAR SOURCE TYPE DF SS MEANSQ FVAL PROBF
# 1 4 MODEL1 mpg ERROR ERROR 8 64.9396474 8.1174559 NA NA
# 2 4 MODEL1 mpg hp SS1 1 55.7389699 55.7389699 6.8665565 0.03063088
# 3 4 MODEL1 mpg wt SS1 1 82.7068372 82.7068372 10.1887633 0.01276524
# 4 4 MODEL1 mpg hp SS3 1 34.9973354 34.9973354 4.3113675 0.07151610
# 5 4 MODEL1 mpg wt SS3 1 82.7068372 82.7068372 10.1887633 0.01276524
# 6 6 MODEL1 mpg ERROR ERROR 4 5.2112707 1.3028177 NA NA
# 7 6 MODEL1 mpg hp SS1 1 0.2046882 0.2046882 0.1571119 0.71206529
# 8 6 MODEL1 mpg wt SS1 1 7.2611839 7.2611839 5.5734459 0.07759393
# 9 6 MODEL1 mpg hp SS3 1 1.5772106 1.5772106 1.2106150 0.33297540
# 10 6 MODEL1 mpg wt SS3 1 7.2611839 7.2611839 5.5734459 0.07759393
# 11 8 MODEL1 mpg ERROR ERROR 11 42.8497010 3.8954274 NA NA
# 12 8 MODEL1 mpg hp SS1 1 6.8542713 6.8542713 1.7595685 0.21155901
# 13 8 MODEL1 mpg wt SS1 1 35.4960277 35.4960277 9.1122294 0.01168393
# 14 8 MODEL1 mpg hp SS3 1 6.3136353 6.3136353 1.6207812 0.22923028
# 15 8 MODEL1 mpg wt SS3 1 35.4960277 35.4960277 9.1122294 0.01168393
# Example 4: Long output
res4 <- proc_glm(mtcars, mpg ~ hp + wt,
output = "long")
# View results
res4
# MODEL DEPVAR SOURCE TYPE STAT COL1
# 1 MODEL1 mpg ERROR ERROR DF 2.900000e+01
# 2 MODEL1 mpg ERROR ERROR SS 1.950478e+02
# 3 MODEL1 mpg ERROR ERROR MEANSQ 6.725785e+00
# 4 MODEL1 mpg ERROR ERROR FVAL NA
# 5 MODEL1 mpg ERROR ERROR PROBF NA
# 6 MODEL1 mpg hp SS1 DF 1.000000e+00
# 7 MODEL1 mpg hp SS1 SS 6.783729e+02
# 8 MODEL1 mpg hp SS1 MEANSQ 6.783729e+02
# 9 MODEL1 mpg hp SS1 FVAL 1.008615e+02
# 10 MODEL1 mpg hp SS1 PROBF 5.987344e-11
# 11 MODEL1 mpg wt SS1 DF 1.000000e+00
# 12 MODEL1 mpg wt SS1 SS 2.526266e+02
# 13 MODEL1 mpg wt SS1 MEANSQ 2.526266e+02
# 14 MODEL1 mpg wt SS1 FVAL 3.756091e+01
# 15 MODEL1 mpg wt SS1 PROBF 1.119647e-06
# 16 MODEL1 mpg hp SS3 DF 1.000000e+00
# 17 MODEL1 mpg hp SS3 SS 8.327418e+01
# 18 MODEL1 mpg hp SS3 MEANSQ 8.327418e+01
# 19 MODEL1 mpg hp SS3 FVAL 1.238133e+01
# 20 MODEL1 mpg hp SS3 PROBF 1.451229e-03
# 21 MODEL1 mpg wt SS3 DF 1.000000e+00
# 22 MODEL1 mpg wt SS3 SS 2.526266e+02
# 23 MODEL1 mpg wt SS3 MEANSQ 2.526266e+02
# 24 MODEL1 mpg wt SS3 FVAL 3.756091e+01
# 25 MODEL1 mpg wt SS3 PROBF 1.119647e-06
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